CONTRIBUTORS TO VOLUME XLIII Joaquin Abidn Pesticide Residue Group, University of Almeria, Ctra Sacramento sin, 04120 La Canadade San Urbano,Almeria, Spain Ana Agiiera Pesticide Residues Group, University of Almeria, Ctra Sacramentos/n, 04120 La Canada de San Urbano, Almeria, Spain Lutz Alder FederalInstitute for Risk Assessment, Thieleallee 88-92, Berlin D-14195, Germany Michelangelo Anastassiades Stuttgart Regional Chemical and Veterinary Control Laboratory, Schaflandstrasse3/2, 70736 Fellbach, Germany Andre de Kok PesticidesAnalysis Group, VWA - Food and Consumer Product Safety Authority, Hoogte Kadijk 401, 1018 BKAmsterdam, The Netherlands Amadeo R. Ferndndez-Alba Pesticide Residue Group, University of Almeria, Ctra Sacramento s/n, 04120 La Canada de San Urbano, Almeria, Spain Amadeo R. FernAndez-Alba Pesticide Residue Research Group, University of Almeria, 04071 Almeria, Spain Imma Ferrer Pesticide Residue Group, University of Almeria, Ctra Sacramento s/n, 04120 La Caiada de San Urbano, Almeria, Spain Richard J. Fussell Central Science Laboratory, Departmentfor Food, Environment and Rural Affairs, Sand Hutton, York Y041 1LZ, UK Alan R.C. Hill Central Science Laboratory, Departmentfor Food, Environment and Rural Affairs, Sand Hutton, York Y041 1LZ, UK Silvia Lacorte Department of Environmental Chemistry, IIQAB-CSIC, Jordi Girona 18-26, 08034 Barcelona, Spain E. Michael Thurman Pesticide Residue Research Group, University of Almeria, Almeria, Spain Luis Martin Plaza EuropeanCommission. Health and ConsumerProtectionDirectionGeneral. Rue Froissart101, Bureau 6/86-1040 Bruxelles, Belgium vii
Contributors to Volume XLIII
Stewart L. Reynolds Central Science Laboratory, Sand Hutton, York YO41 ILZ, UK Ellen Scherbaum Stuttgart Regional Chemical and Veterinary Control Laboratory, Schaflandstrasse 3 / 2 , 70736 Fellbach, Germany Hans-Jiirgen Stan Institute of Food Chemistry, Technical University, Gustau-Meyer-Allee 25, 0-13355 Berlin, Germany James R. Startin Central Science Laboratory, Department for Food, Environment and Rural Affairs, Sand Hutton, York YO41 1LZ, U K Christoph von Holst European Commission, DG Joint Research Centre, Institute for Reference Materials and Measurements, B-2440 Geel, Belgium
WILSON AND WILSON'S COMPREHENSIVE ANALYTICAL CHEMISTRY VOLUMES IN THE SERIES Vol. IA
Vol. IB Vol. IC Vol. IIA
Vol. IIB
Vol. IIC
Vol. IID Vol. III
Vol. IV
Vol. V
Vol. VI Vol. VII Vol. VIII
Vol. IX
Vol. X
Analytical Processes Gas Analysis Inorganic Qualitative Analysis Organic Qualitative Analysis Inorganic Gravimetric Analysis Inorganic Titrimetric Analysis Organic Quantitative Analysis Analytical Chemistry of the Elements Electrochemical Analysis Electrodeposition Potentiometric Titrations Conductometric Titrations High-Frequency Titrations Liquid Chromatography in Columns Gas Chromatography Ion Exchangers Distillation Paper and Thin Layer Chromatography Radiochemical Methods Nuclear Magnetic Resonance and Electron Spin Resonance Methods X-Ray Spectrometry Coulometric Analysis Elemental Analysis with Minute Sample Standards and Standardization Separation by Liquid Amalgams Vacuum Fusion Analysis of Gases in Metals Electroanalysis in Molten Salts Instrumentation for Spectroscopy Atomic Absorption and Fluorescence Spectroscopy Diffuse Reflectance Spectroscopy Emission Spectroscopy Analytical Microwave Spectroscopy Analytical Applications of Electron Microscopy Analytical Infrared Spectroscopy Thermal Methods in Analytical Chemistry Substoichiometric Analytical Methods Enzyme Electrodes in Analytical Chemistry Molecular Fluorescence Spectroscopy Photometric Titrations Analytical Applications of Interferometry Ultraviolet Photoelectron and Photoion Spectroscopy Auger Electron Spectroscopy Plasma Excitation in Spectrochemical Analysis Organic Spot Tests Analysis The History of Analytical Chemistry
ix
Volumes in the series Vol. XI
Vol. XII
Vol. XIII
Vol. XIV Vol. XV Vol. XVI Vol. XVII Vol. XVIII Vol. Vol. Vol. Vol. Vol.
XIX XX XXI XXII XXIII
Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol. Vol.
XXIV XXV XXVI XXVII XXVIII XXIX XXX XXXI XXXII XXXIII XXXIV
Vol. Vol. Vol. Vol.
XXXV XXXVI XXXVII XXXVIII
Vol. Vol. Vol. Vol.
XXXIX XL XLI XLII
x
The Application of Mathematical Statistics in Analytical Chemistry Mass Spectrometry Ion Selective Electrodes Thermal Analysis Part A. Simultaneous Thermoanalytical Examination by Means of the Derivatograph Part B. Biochemical and Clinical Applications of Thermometric and Thermal Analysis Part C. Emanation Thermal Analysis and other Radiometric Emanation Methods Part D. Thermophysical Properties of Solids Part E. Pulse Method of Measuring Thermophysical Parameters Analysis of Complex Hydrocarbons Part A. Separation Methods Part B. Group Analysis and Detailed Analysis Ion-Exchangers in Analytical Chemistry Methods of Organic Analysis Chemical Microscopy Thermomicroscopy of Organic Compounds Gas and Liquid Analysers Kinetic Methods in Chemical Analysis Application of Computers in Analytical Chemistry Analytical Visible and Ultraviolet Spectrometry Photometric Methods in Inorganic Trace Analysis New Developments in Conductometric and Oscillometric Analysis Titrimetric Analysis in Organic Solvents Analytical and Biomedical Applications of Ion-Selective Field-Effect Transistors Energy Dispersive X-Ray Fluorescence Analysis Preconcentration of Trace Elements Radionuclide X-Ray Fluorescence Analysis Voltammetry Analysis of Substances in the Gaseous Phase Chemiluminescence Immunoassay Spectrochemical Trace Analysis for Metals and Metalloids Surfactants in Analytical Chemistry Environmental Analytical Chemistry Elemental Speciation - New Approaches for Trace Element Analysis Discrete Sample Introduction Techniques for Inductively Coupled Plasma Mass Spectrometry Modern Fourier Transform Infrared Spectroscopy Chemical Test Methods of Analysis Sampling and Sample Preparation for Field and Laboratory Countercurrent Chromatography: The Support-Free liquid Stationary Phase Integrated Analytical Systems Analysis and Fate of Surfactants in the Aquatic Environment Sample Preparation for Trace Element Analysis Non-destructive Microanalysis of Cultural Heritage Materials
Series Editor's Preface Pesticides play an important role in many areas of science and industrial activity, ranging in scope from the production of pesticides and their formulations to their wide range of applications in agriculture, especially in tropical countries, the environment and domestic applications. After pesticide application the target compound may degrade and residues will remain not only in the plant, leaves or fruits but also in various environmental matrices, like water, soil or sediments. Pesticide analysis requires a comprehensive approach and for this reason it is very difficult to compile in a single book all the analytical methods applied to the great variety and complexity of pesticides used and found nowadays in the environment. This book, edited by my old friend and colleague Amadeo R. Ferndndez-Alba, offers a focussed approach and presents analytical methods for the trace determination of pesticides in food. It is a useful addition to the Comprehensive Analytical Chemistry series since there is an urgent need for such a book due to the the large number of analytical chemists working in this emerging field. Its 10 chapters are devoted to sample preparation techniques, chromatographic-mass spectrometric methods, including GC-MS and LC-MS protocols, and quality control and proficiency testing schemes. The content of the book should enable the analyst to solve most of the problems encountered in pesticide analysis, and be useful both for newcomers as well as analysts in expert food laboratories looking either for a multiresidue analysis or for a tailor-made determination of a specific pesticide. The various chapters on mass spectrometry should also be useful to gain an insight into the techniques that are now routinely used in pesticide analysis, partly due to the lower costs of the MS instruments and also to the recently developed instruments like time-of-flight or hybrid instruments, based on triple quadrupoles followed by other mass analysers like ion traps. As well as being an applied book covering the increasingly growing field of pesticide residue analysis in food, it also contains some fundamental information on the techniques that are used. Food laboratories are well organised around the world since exports and imports of fruits and vegetables are a key issue in most economies. For this reason laboratories in the food area should be aware of new developments for ensuring quality control of pesticide xix
Series Editor's Preface residues in the various food matrices. Harmonisation of the methods is a key element to avoid any economic losses and to be able to sell any food product in any part of the world. This book will be of great help to those trading in this global economic market, being a useful tool-box that should help the analyst avoid pitfalls and assure method harmonisation in pesticide food control laboratories. Overall, this book covers most of the aspects of pesticide analysis in food and I expect it to become a key reference in the community of pesticide residue specialists. Finally I would like to thank not only the editor but also the various authors, some of whom have been my co-workers for several years, for their contributions in compiling this excellent book on pesticides in food. D. Barcel6 June 2004
xx
Preface
Far from being a mature analytical field, the trace determination of pesticides* continues to be a target topic for analytical chemists working in research centres, government and universities. This is a consequence of (i) new compounds, based on new chemical structures, continually being introduced into the market, (ii) new regulations, which are becoming ever more restrictive concerning the maximum residue limits legally permitted in food, and (iii) an increasing social, economic and academic interest in food safety, which has important trade implications. As a consequence of the specific characteristics of pesticides (i.e. high number of compounds, extremely diverse physical and chemical properties, analysis levels per day for effective control, system robustness, analytical performance, etc.) chromatography-based techniques are clearly the main choice for the practitioner. Traditionally, the introduction of mass spectrometric analysers/detectors coupled to gas chromatography (GC) or liquid chromatography (LC) has received less attention in this field compared to others, such as the environment. This is probably a consequence of special difficulties with these types of matrices/analytes, the high cost of these systems and the difficulties present in routine operation. However, during the last few years this situation has completely changed and chromatography-mass spectrometry (GC and LC) based techniques have become the core of pesticide food analysis. This change has been a result of important developments in and improvements of these techniques, making the great majority of pesticides/levels/commodities amenable to mass spectrometric detection with adequate analytical performance and robustness. In addition, we must not consider the detection step as separate from other stages of the analytical methodology, especially sample treatment and clean-up, which are closely-linked and together determine the quality and performance of the analyses as a whole. Therefore, developments in these *Note: The term "pesticide" covers a very diverse range of substances, not only single chemicals of natural or synthetic origin but additionally, among other things, micro-organisms. Throughout this text, the word "pesticide" is typically used in the restricted sense of a synthetic organic molecule and its degradation products.
xxi
Preface areas have also contributed to improvements in pesticide food analysis and, in many cases, to the MS-based method itself. As far as pesticide residues are concerned, consumer confidence, food trade decisions and regulatory controls depend heavily on the quality of analysis. Thus, laboratories analysing food samples for the determination of pesticide residues need to be assured of the quality of their data and whether they are appropriate. The "fit-for-purpose" quality requirements are obviously related to the analytical procedures applied, as well as the legislative driving forces. Therefore, these topics are always relevant to get an adequate and realistic perspective of the proposed food analytical methods. On account of all the points mentioned above, the core of this book contains four chapters (chapters 6-9) devoted to chromatography-mass spectrometry methods. This part draws a clear and concise pathway between the relevant analytical aspects, allowing the reader to understand the analytical basis, technical characteristics and possibilities to evaluate pesticides in food by GC-MS and LC-MS. Furthermore, the book also gives a well-defined and critical compilation of the sample treatment and clean-up procedures, as well as injection techniques applied in GC and LC food analysis (chapters 3-5). Finally the book deals with aspects related to analytical quality control requirements for pesticide residues, in addition to the pesticide regulation aspects, which allows laboratories involved in residue analyses to meet the requirements of a recognised accreditation scheme (chapters 1, 2 and 10). These issues are considered in order to give to the readers a "field" dimension with regard to the proposed analytical tools. I give my heartfelt thanks to the authors who have contributed their expertise here. I must especially thank those authors who prepared their manuscripts early on, for their patience, while they waited for us to tidy up the remainder. I am impressed by the energy and work expended by all the authors and I hope they feel wellrewarded when seeing the final product. I would also like to thank Dr. Damia Barcel6 (Series Editor) for his help and support throughout this time. Amadeo Ferndndez-Alba
xxii
Chapter 1
Quality control for pesticide residues analysis Alan R.C. Hill, James R. Startin and Richard J. Fussell
1.1 INTRODUCTION The determination of pesticide residues presents analysts with challenges ranging from moderately to extremely difficult. Some of the complexities and problems are sufficiently subtle, or lacking robust solutions, that it has always been uncomfortably easy to generate spurious results. The term "pesticide" has a very diverse range of meanings in terms of biological activity but it also encompasses many hundreds of chemicals, exhibiting extremely diverse physical and chemical properties. Consequently the analytical methods involved are also diverse, although the majority of pesticides are amenable to mass spectrometric detection. Amongst the most problematic for the analyst are those pesticides that are labile, or volatile, or have no chemical or physical features that differentiate them from co-extractives, or are zwitterionic, or are insoluble in anything, or are of incompletely-defined structure. Such analytes tend to require so-called single residues methods (SRMs) and therefore the cost per result of analysis tends to be very high. In contrast, certain large groups of pesticides share physico-chemical properties that render them amenable to the use of multiresidue methods (MRMs). Some MRMs are capable of detecting the presence of several hundred pesticides as a part of a single determination, whereas others are intended for a much smaller group, so that "typical" MRMs and SRMs represent the extremes of a continuum. Mass spectrometry (MS) coupled with gas or liquid chromatographic separation, and certain techniques based on detection of a common moiety, are particularly suited for use with MRMs. However, MRMs can provide special challenges for analytical quality control (AQC). Comprehensive Analytical Chemistry XLIII FernAndez-Alba (Ed.) © 2005 Elsevier B.V. All rights reserved
I
A.R.C. Hill, J.R. Startin and R.J. Fussell Almost by definition, pesticides are toxic to something and therefore present risks-to consumers, the environment or whatever-which require control. Because of their potential for biological impact and the consequential need for a precautionary approach, the limits at which residues must be controlled in food and other matrices are low-from sub-pg/kg to mg/kg. The matrices in which residues may occur is extremely diverse, ranging from reasonably homogeneous liquids (water, beverages, oils, emulsified fats, etc.) to highly heterogeneous solids (animal tissues, fruit, vegetables, etc.). Whatever techniques are used in residues analysis, a sound knowledge of the operating principles of the method and equipment will invariably help to resolve problems arising during the use of the method and help to select the most appropriate AQC procedures. During the initial development of an analytical method, the analyst will gain insights into its strengths and weaknesses, and the factors critical to producing acceptable results can be identified though ruggedness testing [1]. This chapter does not address method development but key indicators of method performance should be identified at that stage, with a view to defining AQC requirements. As far as pesticide residues are concerned, consumer confidence, food trade decisions and regulatory controls depend heavily on the quality of analyses. Analytical mistakes can be extremely costly in terms of lost trade, destruction of crops, fines for growers, litigation against the analyst, and so on. AQC must therefore be rigorous but the challenging nature of the analyses creates conflicting requirements. The cost of the residues analysis is generally rather high, few of the techniques are rapid, and AQC requirements can contribute substantially to costs and time requirements. High costs and lengthy analyses constrain the numbers of samples analysed but, residues being generally very variable in distribution, most clients would prefer more data in order to ensure satisfactory control of residues. Thus, there is an inevitable desire to limit the cost of AQC, because its benefits are not as immediately tangible as the results produced from the clients' samples. However, good AQC can avoid expensive, and potentially very damaging, mistakes and the analyst and client must recognise the risks associated with inadequate AQC. The AQC procedures adopted must balance the competing requirements for sufficient numbers of results, affordable costs and sufficient reliability, such that the information generated is fit for its purpose. Reliability has two aspects: identity and quantity (i.e., concentration) of the analyte. Identity is critical to all pesticide residues analysis. There can be no such thing as a determination of "pesticides" as a residue and, in those few cases where it is possible to integrate the response of groups of pesticides into a simultaneous determination, the data are unlikely to be fit for anything more than crude 2
Quality control for pesticide residues analysis screening purposes. Determination of identity ranges from straightforward to difficult, because of the varying nature of the analytes. Inevitably, the lower the concentration the more difficult and expensive the confirmation of identity becomes. Determination of quantity can also be divided into two aspects: determination of whether or not a specific concentration limit has been exceeded and determination of the absolute concentration. For legal and trading purposes, pesticide residues in foods are controlled by maximum residue limits (or levels) (MRLs) and the routine AQC requirements for determining compliance with MRLs can be less stringent than where the "exact" concentration is to be determined. Checking for compliance with MRLs is an important tool for post-registration control of pesticides, ensuring that users adhere to good agricultural practice (GAP), i.e., that they adhere to the label recommendations approved at the time the pesticide was registered. Determination of the exact concentration is much more important for calculation of consumer intake of pesticides and for the consequential risk assessments made in respect of pesticides. Except in some relatively homogeneous materials, such as liquids and finely divided manufactured products, a consistent characteristic of pesticide residues is their heterogeneity of distribution within treated or contaminated crops, animals, the environment, etc. The term concentration is therefore usually taken to mean the average concentration in the sample received at the laboratory (laboratory sample). In some cases the distribution of residues within the laboratory sample, and especially within the units (e.g., individual fruit, vegetables) of the sample, may also be extremely heterogeneous. The average result obtained for the sample may or may not be representative of the whole population of material from which the sample was taken. Sampling protocols, such as that of Codex [2], are intended to provide representative samples but, because it is virtually impossible to prove this in practice, MRLs usually apply to the laboratory sample. Analytical results are estimates of the true concentration (which cannot be known with complete certainty) and are inherently prone to measurement errors. The objective of AQC is to provide reassurance of fitness for purpose of, and appropriate data to support, the estimates (results) generated. Adherence to sound AQC procedures goes a long way towards ensuring mutual acceptance of laboratory results. This is of great importance in ensuring the free flow of trade between and within countries and indirectly supports the safe and efficient use of pesticides. The possible use of MRLs as non-tariff trade barriers is beyond the scope of this chapter but there is no doubt that confidence between trading partners in the residues data
3
A.R.C. Hill, J.R. Startin and R.J. Fussell they produce serves to remove what could otherwise be prejudicial barriers to free trade. General aspects of AQC in trace analysis have been considered by Sargent [31 and Wells [41 but they do not focus on issues of special relevance to the determination of pesticide residues, especially in fresh commodities. Validation of methods for pesticide residues analysis has been considered by Hill and Reynolds [5] and comprehensively by many authors in a recent book by Fajgelj and Ambrus [6]. AQC requirements for pesticide residues analysis have also been dealt with comprehensively by the European Union (EU) [7]. This chapter is based upon the EU requirements, because they have been adopted, in part or in whole, in well over 100 residues laboratories in some 20 European countries. They have also been adopted or adapted, in part, in some other parts of the world. Some examples are provided to show the practice and limitations of the quality control procedures described. We use the term "analyte" to denote the chemical species present at the start of the analysis and/or the species that enters the detector. The analyte present at the start may differ from the analyte detected but, in each case, these are expected to be qualitatively and quantitatively related to one another. The analyte may be the pesticide, its target degradation or derivatisation products, or the internal standard. The term "matrix" (plural matrices) is used to denote the sample type, or its extract at any stage of the analysis. Thus "apple matrix" may be anything from whole apples to an aliquot of a cleaned-up extract. We use the term "lot" in the sense of Codex [2], to mean the bulk of material from which the laboratory sample originated. We use the term "sample" to mean the laboratory sample (i.e., that received for analysis), the analytical sample (the laboratory sample after preparation and processing for sub-sampling), or the blank/reference samples used for quality control purposes. We use the term "test portion" to denote the sub-sample analysed and the term "extract" to denote extracted solutions, volatiles or residual solids from solutions produced from the test portion, irrespective of clean-up.
1.2 ACCREDITATION In many countries of the world, it is now mandatory that residue laboratory operations should meet the requirements of a recognised accreditation scheme, usually requiring compliance with the requirements of ISO 17025. Many countries have recognised the need for accreditation and the requirements of global trade are leading to others following suit. Whilst certain requirements of accreditation schemes may seem a little excessive or impractical for 4
Quality control for pesticide residues analysis the residues laboratory, accreditation has been a powerful driving force in the implementation of improved quality standards. The documentation required may also seem onerous in some cases, and there may be scope for improving efficiency in this respect, but even the best laboratories now possess better evidence of the quality of their data than they did in the past. An important aspect of accreditation documentation is the use of standard operating procedures (SOPs), which describe the principles of the work and how it is to be performed. In the early days of accreditation of residues analysis there was a strong emphasis on the accreditation of specific methods for specific tasks. The problem with such an approach is that every extension of scope of the method-to a new concentration, to a new sample matrix, or to a new analyte in the case of MRMs-requires extensive validation before any samples are analysed. In many cases, this is either too time-consuming or too costly to be practicable. For this reason, there is a growing emphasis on the use of so-called "generic" accreditation, where the use. of the technique is accredited and the supporting validation is produced by adherence to performance criteria adopted as part of the accreditation. For example, in our laboratory, generic accreditation to the ISO 17025 standard has been implemented for HPLC-MS or HPLC-MS/MS confirmation of the presence (identity), demonstration of absence (subject to a reporting limit), or determination of concentration of any amenable analyte in extracts. The SOP allows for variations in the calibration protocol and experimental conditions, but carefully specifies the minimum performance requirements for chromatography, MS, and quantitative determination. Whatever system of accreditation is adopted, sample data records, laboratory notebooks, chromatograms, tables of results, disks bearing chromatographic or spectral data, etc., must be stored in a safe place for subsequent scrutiny. The period of retention should be in accordance with national or accreditation requirements.
1.3 SAMPLING, TRANSPORT, PROCESSING AND STORAGE OF SAMPLES 1.3.1
Sampling
Here we refer to the practice of removing a sample from a bulk of some commodity, to be sent to the laboratory for analysis. We do not refer to the programme of sampling that may be devised to answer some specific question 5
A.R.C. Hill, J.R. Startin and R.J. Fussell or for general residue monitoring purposes. Sub-sampling to remove a representative analytical portion from the laboratory sample is dealt with in section 1.3.3. As indicated in section 1.1, pesticide residues are usually characterised by great variability in concentration within any population to be sampled. For example, Hill [8] and Hill and Reynolds [9] showed that the highest and lowest residues in the units of common fruit and vegetables usually differ considerably, in some cases by factors of several hundred. The situation is exacerbated by the common trading practice of mixing the produce from different growers, in order to produce larger and more uniform batches of product for large markets. Attempts to determine "typical" residue distributions in any particular commodity are probably doomed to failure, because of the almost endless range of scenarios that can affect the distribution. Although the distribution of residues in a bulk of a commodity (a lot) may be more or less random, it is impossible to be sure of this-with the possible exception of bulk liquids and manufactured products, which are usually well mixed. Most sampling recommendations for fruit, vegetable, cereal and animal primary products are based upon assembling samples incrementally from randomly chosen positions within the lot. Codex recommendations [2] are widely used throughout the world and recognise the possibility that the primary products can be sufficiently well mixed that, although a truly random distribution is not produced, the distribution may be such that a sample taken from a single position in the lot may be as representative as one taken from several positions. This is important because, in some cases, it may be physically or economically impracticable to increment samples from randomly chosen positions in the lot. For example, to take a truly random sample from a 1000-tonne standing lot of potatoes would not only take enormous time and effort, but it would also seriously jeopardise the quality of the remaining potatoes. The job is simplified if the potatoes form a moving stream on a conveyor belt but a truly random sample might still take far too much time, be too costly, or in some situations too hazardous to collect, to be practicable. Sampling is therefore a compromise between the aims, the cost and practicality. Cost and practicality are almost invariably the dominant considerations. 1.3.2
Laboratory sample transportation
Samples must be transported to the laboratory in clean containers and robust packaging. The costs of sampling and analysis can be wasted by poor practice at this stage. Polythene bags, ventilated if appropriate, are acceptable for 6
Quality control for pesticide residues analysis most samples but low-permeability bags (e.g., nylon-film) must be used for samples to be analysed for residues of fumigants. Generally, samples of commodities pre-packed for retail sale should not be removed from their packaging before transport. Very fragile or perishable products (e.g., ripe raspberries) may have to be frozen to avoid spoilage and then transported in "dry ice" or similar, to avoid thawing in transit. Samples that are frozen at the time of collection must be transported without thawing. Samples that may be damaged by chilling (e.g., bananas) must be protected from both high and low temperatures. Rapid transportation to the laboratory, preferably within a day or two, is essential for samples of most fresh products. In hot climates, refrigerated transport may be required, even for samples that are not frozen. The condition of samples delivered to the laboratory should approximate to that acceptable to a discerning purchaser, otherwise samples should normally be considered unfit for analysis. Samples must be identified clearly and indelibly, in a way that prevents inadvertent loss or confusion of labelling. The use of marker pens containing organic solvents should normally be avoided for labelling bags containing samples to be analysed for fumigant residues. 1.3.3
Sample preparation and processing prior to analysis
As in the case of sample transportation, the costs of sampling and, in some cases, the costs of analysis can be wasted by poor practice at this stage. On receipt, each laboratory sample must be allocated a unique reference code by the laboratory. Sample preparation should be in accordance with the definition of the commodity and the part(s) to be analysed, if MRL compliance is to be checked. Such definitions may be provided by national legislation or Codex [10] but these may vary according to the purpose of the work. For example, Codex recommendations are based on checking products in trade for compliance with MRLs (and hence GAP in the production of the products). Hence, the part(s) to be analysed may include inedible material, simply because residues on the sum total of edible and inedible parts were used to define the maximum residue that should result from GAP. In contrast, if the analysis is to estimate consumer exposure in, for example, a total diet study, the samples may be prepared for analysis by removing inedible parts, followed by cooking and mixing with other products. Sample preparation, sample processing and sub-sampling to obtain test portions must take place before the sample deteriorates visibly. Canned, dried 7
A.R.C. Hill, J.R. Startin and R.J. Fussell or similarly processed samples should normally be analysed within the stated shelf-life, unless stored in deep freeze. Sample processing and storage procedures should be demonstrated to have no significant effect on the residues present in the test sample [11,12]. Where labile residues could otherwise be lost, samples may be comminuted frozen (e.g., in the presence of "dry ice" [13]). Where comminution is known to affect residues (e.g., dithiocarbamates [14] or fumigants) and robust alternative procedures are not available, the test portion should consist of whole units of the commodity, or segments removed from large units. All analyses should be undertaken within the shortest time practicable, to minimise sample storage. Determination of very labile or volatile residues should be started, and procedures involving potential loss of the analyte completed, on the day of sample receipt. If a single test portion is unlikely to be representative of the sample, as may be the case where a segment is removed from a large fruit or vegetable, replicate portions should be analysed even if an initial determination appears to show the absence of measurable residues.
1.4 PESTICIDE STANDARDS, CALIBRATION SOLUTIONS AND SIMILAR 1.4.1
Identity and purity of standards
Standard materials of analytes ("pure", or reference, standards) should be of known purity. Such standards must be uniquely identified, the date of receipt recorded, and an expiry date allocated. After the expiry date, the "pure" standard may be retained until a newly allocated expiry date if its purity is shown to remain acceptable, otherwise it should be replaced. The relative purity of new and old "pure" standards may be determined by comparing the detector responses obtained from freshly-prepared dilutions. Inexplicable differences in apparent concentration or identity between old and new "pure" standards should be investigated. Ideally, the identity of "pure" standards should be checked if the analytes are new to the laboratory. At method development or during validation, the response detected must be shown to be due to the analyte, rather than to an impurity or artefact. A peculiar problem in the determination of residues of certain pesticides is that the analyte can degrade during extraction, clean-up or chromatography, to produce a product that normally occurs in residues but which is excluded from 8
Quality control for pesticide residues analysis the residue definition. In such cases, positive results must be confirmed using techniques that avoid this problem.l 1.4.2
Storage of reference standards
"Pure" standards should be stored according to the suppliers' instructions (where given), to minimise degradation. Generally, storage at low temperature (refrigerator or freezer) in the dark is satisfactory. The containers must be sealed to avoid entry of water, which is especially likely during equilibration to room temperature. If a "pure" standard changes visibly (for example, if the colour changes, if crystals change to a powder or liquify) during storage it must not be used without checking the purity, unless the change is simply due to freezing and melting. An example of a poor quality standard is presented in Fig. 1.1. A reference standard of bupirimate had originally been acceptable. However, it probably became contaminated by traces of condensed water during equilibration to room temperature, following storage in the freezer, and a high proportion of the analyte had degraded to products (probably including ethirimol) that were not transmitted by the gas chromatographic system. When compared with a fresh standard of high purity (A), the faulty standard (B) was shown to be < 10% purity. 1.4.3 Preparation, use and storage of stock and working standards Stock standards are the initial dilutions of the "pure" standard, whereas the working standards are further dilutions for use in calibration and for additions in measurement of recovery. Stock standards are commonly prepared for a single analyte, whereas working standards may contain more than one, particularly for use with MRMs. The preparation of stock and working standards (which may be solutions, dispersions or gaseous dilutions) from "pure" standards requires careful attention to detail. Any inaccuracy in their preparation may not be apparent from checks of calibration or recovery but will directly affect analytical bias. The identity and mass (or volume, for highly volatile compounds) of the 1 This requirement applies where the product of analytical degradation must be distinguished from the chemically identical metabolite in the sample, in order to determine the residue level according to the definition. For example, 4,4'-dichlorobenzophenone from dicofol, tetrahydrophthalimide from captan and captafol, phthalimide from folpet, 2-chlorobenzonitrile from clofentezine.
9
A.R.C. Hill, J.R. Startin and R.J. Fussell Abundance 280000 260000 240000 220000 200000 180000 160000 140000 120000 100000 80000 60000 40000 20000 0 9.00
9.20
9.40
9.60 9.80 Retention time, min
10.00
10.20
10.40
Fig. 1.1. Chromatograms obtained from solutions of nominally the same concentration, prepared from two reference standards of bupirimate. (A) New reference standard; (B) old reference standard presumed to have been degraded by condensation formed during warming from storage at - 18C. reference standard, the identity of the solvent (or other diluent), and the volumes and dilution steps employed, must be recorded. The potential cost of illegible labels is very high, so stock and working standards must be labelled indelibly. It is usually impossible to record all necessary information on the flasks, etc., that contain working standards, so careful record keeping in a paper-based system or a computerised laboratory information management system (LIMS) is essential. At some stage in the data recording and calculation procedures, concentrations must be corrected for the purity of the reference material. This should be easy to arrange with a LIMS but it is easily overlooked. The analyte must not react with, and should have adequate solubility in, the solvent(s) used to prepare solutions. Polar analytes that are likely to degrade in protic solvents, such as methanol or water, may be dissolved in acetonitrile. Hydrolysis or oxidation is an ever-present threat to the stability of many standards. Maintenance of an appropriate pH or the use of an antioxidant may be required. Solvents which are prone to oxidation are usually 10
Quality control for pesticide residues analysis sold with a stabiliser or anti-oxidant but care is required if redistilled before use. In general, methanol should be avoided for dissolution of esters, because of the potential for transesterification, particularly under acidic conditions. Methanol also induces a rearrangement of iprodione. Higher alcohols may be less of a problem in both cases. In various solvents, pesticides such as pyrethroids (prone to epimerisation) and dicofol (prone to loss of chloroform) must be maintained under mildly acidic conditions, even in non-aqueous solvents. Analytes which can form tightly bound complexes (such as paraquat, glyphosate or thiram) may require maintenance of particular pH conditions, the addition of "competing" complexing agents, and/or the use of plastics instead of glassware. The solvent(s) must be appropriate to the method of analysis and be compatible with the determination system used. Even small proportions or quantities of inappropriate solvents may be detrimental to peak shape in chromatography or to the response of some GC detectors. If the analyte is known to be prone to photolysis, solutions should be kept in the dark as much as possible and certainly out of sunlight. Solutions of photolytically unstable analytes may require the use of darkened or shielded flasks and vials. Analytes that possess no UV chromophore are unlikely to undergo direct photolysis but it is better practice to keep all solutions in the dark when not in use. Unless suitably accurate facilities are available, not less than 5-10 mg of the "pure" standard should be weighed. Volatile liquid analytes should be dispensed by weight or volume (if the density is known) directly into solvent. Gaseous (fumigant) analytes may be dispensed by bubbling into solvent and weighing the mass transferred, or by preparing gaseous dilutions (e.g., with a gas-tight syringe, avoiding contact with reactive metals). Analyte solutions (or other dilutions) should be allocated an expiry date, after which they should normally be discarded. If practicable, newly prepared stock standards should be diluted and compared with those that have just expired. This has the dual benefits of checking against the possibility of weighing or dilution errors and of checking whether the expiry date is either unduly optimistic or pessimistic. If the average measured value for the new solution differs by more than ±+5% from the old one,2 the new solution should be checked for accuracy against a further newly prepared one. If the number of replicate determinations required to distinguish a difference of 5% is unacceptably large for problematic analytes, the acceptable range may be increased to ± 10%. If the old standard produces - 95 (or 90% in the case of 2 Alternatively, a t-test of the means should not show a significant difference at the 5% level.
11
A.R.C. Hill, J.R. Startin and R.J. Fussell problematic analytes) of the response obtained from the new standard, the storage period for solutions must be shortened or the storage conditions improved. If the responses from old and new standards do not differ significantly, a longer storage period may be considered. Aqueous suspensions of insoluble dithiocarbamates and solutions (or gaseous dilutions) of highly volatile fumigants must be prepared freshly. The concentration of such a standard may be checked only by comparison with a further one prepared independently. Solutions should be stored at low temperature, in a refrigerator or freezer, sealed to avoid loss of solvent and entry of water which may condense during warming to room temperature. Unless they are internally standardised, solutions must be equilibrated to room temperature and re-mixed before use. If solubility at low temperatures is limited, great care must be taken to ensure that the analyte is completely re-dissolved after storage of solutions. Unless they are internally standardised, solvent losses by evaporation from stock and working standard solutions (and extracts) are unacceptable. Solvent losses from small volumes are difficult to monitor and, in the absence of an internal standard, great care is required to avoid evaporation. Septum closures on auto-sampler vials are particularly prone to evaporation losses (in addition to being a source of contamination) and, if a solution/extract is to be retained, the vial cap should be replaced as soon as practicable after piercing the septum. 1.5 EXTRACTION AND CONCENTRATION
1.5.1
Extraction conditions and efficiency
Test portions should be disintegrated thoroughly during extraction to maximise extraction efficiency, except where this is known to be unnecessary (e.g., some SFE extractions) or inappropriate (e.g., for determination of fumigants or surface residues, or for the analysis of liquids). Temperature, pH, etc., must be controlled if these parameters affect extraction efficiency, analyte stability or solvent losses.
1.5.2
Extract concentration and dilution to volume
Great care must be exercised when extracts are evaporated to dryness, as trace quantities of many non-ionic analytes can be lost in this way, particularly if the clean-up has been very effective in removing co-extractives such as fatty materials. A small volume of high boiling point solvent may be added as a "keeper" but the evaporation temperature should normally be as 12
Quality control for pesticide residues analysis low as practicable. Frothing and vigorous boiling of extracts, or dispersion of droplets, must be avoided. A stream of dry nitrogen or vacuum centrifugal evaporation is generally preferable to the use of an air stream for small-scale evaporation, as the air is more likely to lead to oxidation or to introduce water and other contaminants. Where extracts are diluted to a fixed volume for external standardisation, accurately calibrated vessels of not less than 1 ml capacity should be used and further evaporation should avoided. Alternatively, an internal standard may be used, particularly for small volumes. Analyte stability in extracts should be investigated during method development or validation. Storage of extracts in a refrigerator or freezer will minimise degradation but potential losses at the higher temperatures of an autosampler rack should not be ignored. 1.6
1.6.1
CONTAMINATION, INTERFERENCE, AND NATURAL SOURCES OF THE ANALYTE Contamination
Samples must be kept separate from each other, and from other sources of potential cross-contamination, during transit to, and storage at, the laboratory. This is particularly important with surface or dusty residues, or with volatile analytes. Samples that are known, or thought, to bear such residues should be doubly sealed in polythene or nylon bags and transported and processed separately. Pest control near, or especially in, the laboratory should be restricted to the use of pesticides that will not be sought as residues. This is critically important for sample reception and preparation facilities. The otherwise acceptable use of household insecticides in food preparation facilities has unwittingly led to the contamination of samples being prepared for total diet studies in the UK and USA. Volumetric equipment, such as flasks, pipettes and syringes, must be cleaned scrupulously, especially for re-use. As far as practicable, separate glassware, etc., should be allocated to standards and extracts, in order to avoid cross-contamination. Badly scratched or etched glassware should be avoided. Solvents used for fumigant residues analysis should be checked to ensure that they do not contain the analyte. Where an internal standard is used, unintended contamination of extracts or analyte solutions with the internal standard, or vice versa, must be avoided. Contamination of samples and extracts (and possibly even standards) with the analyte derived from non-pesticide sources can be insidious. The use of 13
A.R.C. Hill, J.R. Startin and R.J. Fussell rubber materials that have been manufactured using dithiocarbamate vulcanisation accelerators must be avoided otherwise dithiocarbamates and/or ethylenethiouruea [15] may be detected as "pesticide residues". Similarly, the use of rubber vial seals in which diphenylamine has been incorporated as an anti-oxidant [15] may give rise to the detection of spurious "residues". 1.6.2
Interference
Not all interference originates from the samples. Equipment, containers, solvents (including water), reagents, filter aids, etc., should be checked as sources of possible interference. Rubber and plastic items (e.g., seals, protective gloves, wash bottles), polishes and lubricants are frequent sources. The plasticisers, monomers, polymerisation and cross-linking accelerators, UV-stabilisers, anti-oxidants, slips, etc., which can occur in and on such products can create serious problems. Perhaps the most ubiquitous of interferents in pesticide residues analysis are phthalates, silicones and long-chain hydrocarbons. These species are so common in buildings, furniture and even parts of analytical equipment that their presence should come as no surprise but the analyst should try to minimise the level of contamination and interference. Vial seals made of rubber materials should be PTFE-lined. Extracts should be kept out of contact with seals, especially after piercing, by keeping vials upright. Vial seals must be replaced quickly after piercing, if re-analysis of the extracts is necessary. Silicone rubber materials generally contain rather fewer interfering chemicals than other rubbers but, inevitably, they present a high risk of contamination with low molecular weight silicones. Analysis of reagent blanks should help to identify sources of interference in the equipment or materials used. Interference from co-extractives (i.e., natural constituents extracted from samples) is frequent in pesticide residues analysis. The interference may be peculiar to the determination system used, it may be variable in occurrence and intensity, and may also be subtle in nature. If the interference takes the form of a response overlapping that of the analyte, a different clean-up, chromatography or detector system may be required. Interference in the form of suppression or enhancement of detector system response is dealt with in section 1.7.3. If it is not practicable to eliminate the interference, or to compensate for it by matrix-matched calibration (section 1.7.3), the overall accuracy (bias) and precision of analysis should nonetheless comply with the criteria in section 1.8. 14
Quality control for pesticide residues analysis Interference can also occur between analytes in multi-residue analysis, where the use of mixed calibration standards is often essential (section 1.7.4). Ironically, the problem might be overlooked in MS determinations if analytes co-elute and produce common ions. Figure 1.2 shows an example of the determination of parathion-methyl in oranges, using GC-MS and electron ionisation (EI). The mixed calibration standard contained tolclofos-methyl that, under the GC conditions employed, co-eluted with parathion-methyl. Both pesticides produce a fragment ion at m/z 125, although this is of relatively low abundance in the spectrum of tolclofos-methyl. In the spectrum of parathion-methyl, the ions at m/z 125, 109 and 263 are of rather similar (and higher) abundance. In the example, it is clear that if the m/z 125 ion of the mixed standard solution is used to quantify the residue, an erroneous result will be obtained. A separate standard of parathion-methyl was used for correct calibration. 1.6.3
Natural sources of the analyte
Where the analyte occurs naturally in, or is produced from, samples, residues from pesticide use cannot be distinguished from natural levels. Examples are: inorganic bromide in all commodities; sulfur in soil, or samples contaminated with soil; carbon disulfide (CS 2) produced from cruciferous crops (Brassicaceae) and certain distantly related plants such as capers (Capparisspinosa). The last example only afflicts dithiocarbamate residue determinations based upon degradation to CS 2 but, as this is the most robust and cost-effective approach to this determination, it is used almost universally for the purpose. The natural occurrence of these various analytes must be considered in the interpretation of results, because low levels arising from the use of pesticides may be impossible to differentiate from those arising "naturally". There is no clear dividing line between "natural" levels and those arising from pesticide use, as both can give rise to highly variable concentrations. Although analysts may apply a "cut-off' concentration, below which "residues" are considered likely to be of natural origin, a proportion of incorrect decisions, above or below the cut-off concentration, is almost inevitable. 1.7 CALIBRATION AND CHROMATOGRAPHIC INTEGRATION 1.7.1
Mass calibration of mass spectrometric detectors
The software used in modern mass spectrometric detector systems display acquired spectra in a digitised form, disguising the fact that they are derived 15
A.R.C. Hill, J.R. Startin and R.J. Fussell
O
0C
I
E
7N
-
E
.
.
rJ)
CC v
bba
o
o
o ON
aouepunqv
eouepunqv
aouepunqv
3
Cd
d
-
CZ
I ,
N O 11
,
-
-
11
"
0E-
cd
N
__,
X
0,:~C)
.2
IC CC
E I
4
NO
o
=
T. .
-.. 1
...........= =
=
-N O
C)
-4a
6.~ U1Cd a C C.
7I-
Pi
m
-
0
S
. n
s
.
C
0
. S
0
( 80 1¢88
O
0 80I
.
.
C
°
0
0 0
.
Bo
o 0
a8$ CCa
aouepunqv
aouepunqV
aouepunqv
0 o
8
8 a~8 88
~ $CoN8 $
8
a
o
0
mo
Co
. ,o
I7|E
6 0
. 'T'
Co
.
0
S
o
eouepunqv
16
O
O
O
0
0 o
N
aR0 0
8
R0 o
.
0 o
.
GauepunqV
R 0$
a
^--
---
-E
C
C*
mi
a-C
d]
o o
N
o
,
Co
N
0
IP
o
aouepunqv
o
t P
C
N
bf:4
Om
CC)
Quality control for pesticide residues analysis from an analogue signal. Good mass calibration is essential to ensure that the spectra acquired reflect the "true" mass spectra (and therefore identification against library spectra is acceptable) and to ensure that quantitative measurements are as reliable and sensitive as possible. 1.7.2
General requirements for quantitative calibration
Correct quantitative calibration is dependent upon correct identification of the analyte (see section 1.9). It is also dependent upon a good knowledge of the calibration function and dynamic range of the detection system. All detection systems can become saturated with high concentrations or quantities of analyte. It may be less obvious that all detection systems give a response similar to that of zero at some positive concentration or mass. Thus inclusion of a "zero" concentration or mass in the calibration curve should be treated with caution, because interpolation between the zero value and the next higher point in the curve may give a false impression of the lowest concentration or mass that can be detected. If it is essential to establish the lowest concentration or mass that can be detected, the system should be calibrated at and about the level at which it gives responses differing little from those produced by the zero level. At such concentrations, in addition to the inevitably decreased precision, calibration accuracy may also decline unless care is taken with the calculation of the calibration function. The concentration- or mass-response of all detection systems to an analyte tends to be variable, even over short periods of time, and the variation may be influenced by the material being analysed. In some cases, external standardisation may not be sufficient to reduce the impact of the variations or influences to an acceptably low level. In such cases, internal standardisation, particularly with stable isotope-labelled standards or the use of socalled standard addition, may be required. Standard addition is the addition of a known quantity of analyte to an extract (etc.) containing an unknown quantity of the same analyte. The difference in response produced is ascribed to the known quantity and the unknown quantity is calculated from its response. The term "internal standardisation" has different meanings and it is important to distinguish between them. (i) At its simplest, the internal standard is any suitable chemical, added to an extract prior to the final determination stage. Following detection, its function is to "correct" for uncontrolled changes in the volume of the extract, which is particularly useful where very small volumes of extracts are involved. The analyte: internal standard response ratio is calibrated routinely. (ii) An extension of 17
A.R.C. Hill, J.R. Startin and R.J. Fussell this procedure is to utilise an internal standard that shares most or all of the physico-chemical properties of the target analyte. The response ratio is then normally stable over time and need not be calibrated so frequently once this stability is demonstrated. Standard addition and isotopically labelled standards fall into this category and their response ratios can be expected to remain constant. (iii) Finally, the internal standard may be added to the test portion at the start of the analysis and the quantity of analyte is determined from the response ratio. Again, the response ratio is assumed to be constant but this approach to internal standardisation provides both calibration and an automatic correction for recovery (section 1.8). Bracketing calibration (i.e., quantitative calibration of the detector system immediately before and after the determination of residues in the samples) should be used unless the determination system has been shown to be free from significant drift in absolute response or response ratio, depending on the form of standardisation employed. In general, HPLC with UV-absorption detection shows slow and small drift, whereas some forms of liquid chromatography-mass spectroscopy (LC-MS) detection can give rapid and high drift. Detector responses used to quantify residues must be within the dynamic range of the system. Beyond either end of the dynamic range of the detector, analyte concentrations can only be quantified loosely. For example, less than or greater than x mg/kg. Certain detection systems, such as the flamephotometric detector operated in the sulphur mode and enzyme-linked immunosorbent assay are associated with non-linear responses, so that care is required to ensure that determinations are made within the dynamic range. LC-MS techniques also have a tendency to produce a response that is not wholly linear so, again, care is required to ensure operation within the dynamic range. In chromatographic analysis, especially GC, it is common to observe that the detector response, relative to the concentration or mass, declines more markedly as the level at which the analyte produces no signal is approached. This is another reason why "zero" points on the calibration curve should be treated cautiously. The detection system should be calibrated for every batch of analyses. If calibration for all analytes sought implies an unacceptably large number of calibration determinations, the system may be calibrated with representative analytes during each batch of analyses. A representative analyte is one that can be considered to represent a group of analytes by virtue of its physicochemical properties, its likelihood of occurrence in residues and/or, especially, its relatively extreme uncertainty of measurement. A suggested minimum 18
Quality control for pesticide residues analysis TABLE 1.1 Frequencies for calibration and recovery determination Representative analytes
Represented analytes
Frequency of calibration and recovery
Each batch
Detected response required Measurement required
Each batch Each batch
Either a rolling programme, to include all represented analytes intermittently, or all included in each batch Each batcha Only when residues are detected
"The result for the analyte is essentially qualitative, i.e., "present/not present lowest calibrated level" (LCL).
frequency for calibration of representative and represented analytes (i.e., the others in the group) is given in Table 1.1. Reliance on a rolling programme of representative analytes carries an increased risk of false negative results. Therefore representative analytes must be chosen very carefully and, if possible, it is better to institute a programme in which recovery of all analytes is assessed qualitatively (present/not present) in each batch. If a rolling programme (Table 1.1) of recovery and calibration of a represented analyte produces an unacceptable result, all results produced after the previous successful recovery or calibration of that analyte must be treated as potentially false negatives. The lowest calibrated level (LCL) is the lowest concentration with which the detection system is successfully calibrated for the batch. Residues detected below LCL should be considered poorly calibrated, and therefore normally reported as
A.R.C. Hill, J.R. Startin and R.J. Fussell Single-level calibration may provide more accurate results than multilevel calibration, if the detector response tends to drift. When single-level calibration is employed for quantitative results, the response from the extract should be within + 20% of the calibration standard response if the MRL (or other action limit) is exceeded. If the MRL is not exceeded, the response from the extract should be within + 50% of the calibration response, unless further extrapolation is supported by evidence of acceptable linearity of response. These limits may be disregarded if the project is intended for large-scale screening as described in the paragraph above. Where the analyte is added for recovery determination at a level corresponding to the LCL, recovery values < 100% may be calculated using single level calibration at the LCL. Calibration by interpolation between two levels is acceptable where the response factors, derived from replicate determinations at each level, indicate acceptable linearity of response. The higher response factor should not be more than 120% of the lower response factor (110% in cases where the MRL is approached or exceeded). Where three or more levels are utilised, an appropriate calibration function may be calculated and used between the lowest and highest calibrated levels. The calibration curve (which may or may not appear to be linear) should not be forced through the origin. The fit of the calibration function should be plotted and inspected visually, avoiding reliance on correlation coefficients, to ensure that the fit is satisfactory in the region relevant to the residues detected. If individual points deviate by more than + 20 ( 10% in cases where the MRL is approached or exceeded) from the calibration curve in the relevant region, a more satisfactory calibration function should be used or the determinations repeated. Extracts containing high-level residues may be diluted to bring them within the calibrated range but, where calibration solutions must be matrixmatched (see below), the concentration of matrix extract may have to be adjusted accordingly. Batch sizes for determination should be adjusted so that detector response to bracketing calibration standards does not drift by > 20% (or > 30% at < 2 x LCL, if the LCL is close to the LOD). In cases where the MRL is approached or exceeded, these maximum drift values should be 10 and 15%, respectively. If the drift exceeds these values the determinations should be repeated, except where the extracts clearly do not contain the analyte(s) - LCL and the LCL response remains measurable throughout the batch. As indicated in the paragraph dealing with the assignment of the LCL, the limits for acceptability of calibration may be disregarded for special projects, 20
Quality control for pesticide residues analysis such as large-scale screening where the accuracy of individual results is relatively unimportant. 1.7.3
Matrix effects and matrix-matched calibration
Chemicals (usually of natural origin) present in samples can influence the measurement analyte of concentrations without being detectable as interference. The magnitude of the influence can range from major to trivial but the effects are notoriously variable in occurrence and intensity. Some techniques are particularly prone to them and others are inherently less likely to be affected. Headspace partitions are frequently influenced by the nature of the sample matrix, because of increased (rarely decreased) analyte affinity for the liquid/solid phase and, of course, this is not a function of the detection system used. In general, therefore, the matrix suppresses the measured value, compared with a calibration prepared with the reagents only. The differences in the degree of effect between different types of matrix can be enormous. The differences between samples of a single matrix type are usually less but can vary according to the lipid content, for example. Gas chromatograph injectors can provide increased (occasionally decreased) transmission of analytes in the presence of certain co-extractives. The consequential apparent enhancement of the detector response is usually ascribed to a "protective effect", inhibiting losses of the analyte that would otherwise occur during injection [16,17]. Matrix effects have been attributed variously to organic acids, polyols, etc., and it may be that the range of coextractives capable of producing an effect is partly dependent upon the instrument design, materials and operating conditions. These transient effects are usually distinct from, but presumably related in some ways to, so-called priming effects in gas chromatography. "Priming" is still frequently practiced in gas chromatography as it is often observed that analyte responses are relatively low prior to injection of some poorly cleaned-up extract. The nature of the detector itself, whether mass spectrometric or otherwise, does not usually play a significant part in these effects. The differences in the degree of enhancement effect between different types of matrix are usually relatively small but the differences between samples of a single matrix type can be almost as large as those between matrices. Atmospheric pressure ionisation (API) interfaces used in LC-MS are also prone to matrix effects but, in this case, the influence is most commonly a suppression of the detector response. This is due to co-elution of co-extractives, which compete with the analyte molecules for available charge
21
A.R.C. Hill, J.R. Startin and R.J. Fussell or, in the case of electrospray, for occupancy of droplet surfaces and therefore change the proportion of analyte molecules that generate observable ions. This kind of effect is restricted to LC-MS and LC-MS/MS. Suppression of the analyte signal is rarely total but it can frequently be sufficient to render a lowlevel residue immeasurable, or, if uncorrected, an exceedance of an MRL to appear to be a compliance. The differences in the degree of effect between different types of matrix can be large, as can the differences between samples of a single matrix type. Especially subtle problems may be associated with the use of positive-ion electrospray LC-MS. The technique is mainly associated with observation of ions generated by protonation of molecules ([M + HI+), but differently cationised molecules such as [M + NH4]+ and [M + Na] + also occur and, for some compounds, the ratio of the abundances of these different ions may be highly dependent on the Na+ concentration, solvent composition and coeluting compounds derived from the sample matrix. In our experience, aldicarb sulphoxide provides an example of a compound exhibiting such variability (Fig. 1.3), whereas the ratios exhibited by aldicarb (sulphide) and aldicarb sulphone are less prone to perturbation. Such changes can strongly affect the accuracy of measurement if this is based on a single cationised form.
TESTB02 204 (7.444) '1UU~~~~~~~~~~
2: Scan ES+ 7 36e3
224
1
229 *U%
207 : I. I . 0TESTES_ B02 849 (30.9093
. I I. I
I I
I I' ,
. . ,-
. I.
207
100-
..
2: Scan ES+ `4ln0A
- I --2: !9
224
%0 0200
. ,
..
205
210
. . .
.
215
. . . .
220
..
.
.
I
1
225
.
..
-J-4 I
-/
.I. , ...,Il
230
Fig. 1.3. Positive ion electrospray mass spectra of aldicarb sulphoxide in 50 mM ammonium acetate with (a) 20 and (b) 80% acetonitrile. Ions are 207: [M + H]+; 224: [M + NH 4 ]+; 229: [M + Na] + . A similar perturbation of the spectrum may be produced by co-elution of other materials. 22
Quality control for pesticide residues analysis In contrast with the above examples, liquid chromatography with UVabsorption detection (LC-UV) is very unlikely to suffer from "hidden" matrix effects, because neither the transmission nor the extinction coefficient of the analyte is likely to be changed. Any interference by UV absorption is directly apparent. Matrix effects can be produced in UV-fluorescence detection, by quenching effects, but may be rendered more obvious if the UV absorption is monitored simultaneously. "Matrix-matched" calibration is one way to try to minimise the quantitative errors induced by matrix effects. The detection system is calibrated with known quantities of analyte added to suitable blank extracts (or test portions for headspace analysis). This is technically very simple, and can be very effective in some cases, but it does increase the costs of calibration. Costs and practicality become important considerations if batches of analyses incorporate many different types of matrix or if suitable blank samples are not readily available. If the matrix effects are minor or predictable, it may be acceptable to calibrate without matrix-matching, or to calibrate using a single matrix to represent all matrices analysed in the batch. This approach involves some risk of erroneous calibration and, in cases where the effects are major and/or unpredictable, the best ways to eliminate matrix effects are to calibrate either by standard addition or with an isotopically labelled internal standard. 1.7.4
Effects of pesticide mixtures on calibration
Calibration in multi-residue analysis, using mixed analyte solutions should be checked at method validation for similarity of detector response to that obtained from the separate analytes. If the responses differ significantly, or in cases of doubt, residues must be quantified using individual calibration standards in matrix or, better still, by standard addition. As indicated in section 1.6.2, interference between analytes may also influence calibration. 1.7.5 Calibration for pesticides that are mixtures of isomers or other components Where a pesticide is a mixture of isomers, detector response is usually assumed to be similar, on a molar basis, for each component. However, enzyme assays, immuno-assays and other assays with a biological basis may give calibration errors if the component ratio of the standard differs significantly from that of the measured residue. An alternative detection system should be used to quantify residues. In those cases where the response of a more 23
A.R.C. Hill, J.R. Startin and R.J. Fussell conventional detector to isomers differs, 3 separate calibration standards must be used. If separate standards are not available for this purpose, an alternative detection system should be used to quantify residues. 1.7.6
Calibration using derivatives or degradation products
Where the analyte is determined as a degradation product or derivative, the calibration solutions should be prepared from a "pure" standard of that degradation product or derivative, if available. Procedural standards may be used if they are the only practical option. 1.7.7 Chromatographic data acquisition rate, noise and integration Data acquisition rates can affect the uncertainty of measurement and it is important that this parameter is set correctly. If the acquisition rate is too slow, the apex of a chromatographic peak may be missed, the peak shape may appear incorrect and the integration of either peak height or area may be inaccurate. If the acquisition rate is too fast, the signal-to-noise ratio (S/N) may be too low for good integration. Calibration responses and those from extracts are affected similarly, compounding the uncertainty. Data are effectively averaged (bunched or acquired over a time period) by the acquisition software and this may or may not be under the full control of the analyst. The acquired data may also be additionally smoothed by the software, though this is usually under the control of the analyst. S/N data produced by either the software or the analyst are therefore often quite difficult to interpret. As long as S/N is measured consistently, some minimum value can be used as a criterion for determining the acceptability of instrument performance. But the same minimum value may not be appropriate for different instruments or for different operating conditions (and therefore in different laboratories). A further complication in the determination of S/N is the nature of the noise. Relative to the chromatographic (or mass) peak width, high frequency noise is reasonably easy to deal with. Low frequency, irregular noise is more difficult, not just for the determination of S/N but also for integration. "Chemical noise" (i.e., interference) is particularly problematic especially where additional clean-up or improved separation strategies are not 3 For example, the differing electron-capture efficiency of HCH isomers in ECD, or the differing
proton affinity of abamectin isomers in electrospray ionisation.
24
Quality control for pesticide residues analysis practicable. An experienced analyst may be able to provide a good estimate but there is no foolproof way of generating "correct" results under these circumstances. Chromatograms must be examined by the analyst and the baseline fitting checked and adjusted, as required. Where interfering or tailing peaks are present, a consistent approach must be adopted for the positioning of the baseline. Peak height or peak area data may be used, whichever yields the more accurate and repeatable results. Calibration of mixed isomer (or similar) standards may utilise summed peak areas, summed peak heights, or measurement of a single component, whichever is the more accurate. If none of these is sufficiently accurate, and particularly if detector response to the components differs on a molar basis, a more satisfactory detection system must be used.
1.8 ANALYTICAL METHODS AND ANALYTICAL PERFORMANCE 1.8.1
Acceptability of analytical methods
A widely accepted criterion for the acceptability of performance of an analytical method is that it should be capable of providing average recovery within the range 70-110%, for all compounds sought by the method and at appropriate concentrations. For problematic analytes, this may represent an unachievable ideal. Where the method does not permit this degree of trueness, the potentially poor accuracy of results must be considered before taking enforcement action. We define recovery as the proportion of analyte remaining at the point of the final determination, following its addition to a test portion of a blank sample immediately prior to extraction. The proportion is usually expressed as a percentage. Average recovery obtained by an analyst provides a measure of the internal bias in results but does not measure that bias against the "true" value. This can only be approached with inter-laboratory studies. Some techniques, such as solid-phase micro-extraction (SPME), certain headspace analyses, or flow-injection analysis (FIA), are incapable of producing a value for recovery, because the determination of "recovery" is the same process as calibration. This does not necessarily mean that recovery is truly 100% but, generally, this does not matter because any difference is automatically compensated for by the calibration process. Similarly, where an internal standard is added at the start of analysis, it is not normally necessary to measure recovery routinely. In this last case, absolute recovery can be 25
A.R.C. Hill, J.R. Startin and R.J. Fussell measured for both the analyte and the internal standard, using external standardisation. Where recovery cannot or is not intended to be determined, the acceptability of the method may be determined on the basis of calibration uncertainty but it is important to recognise that, in addition to precision, accuracy may become an issue at very low concentrations (section 1.7.2). For the determination of fat-soluble pesticides in products where the residues are expressed on a fat basis, the method used to determine the dry weight or fat content must be consistent, otherwise it may contribute significantly to the overall uncertainty of results. 1.8.2 Recovery for determination of acceptability of performance Ideally, recovery of the analytes determined would be measured with each batch of analyses. If this is disproportionately costly, the minimum acceptable frequency of recovery determination may be as given in Table 1.1. In addition, where a residue definition includes several components, of which one can be considered an adequate "marker" of residues of the pesticide, the AQC for screening analysis may be restricted to the marker compound. As an alternative to the above scheme for recovery determination, and especially where samples are analysed primarily to determine whether or not they contain residues at or about some limit (e.g., LCL or MRL), the recovery and calibration can be combined as a qualitative determination. In this case, the recovery is determined routinely by spiking a blank test portion at the level of the appropriate limit and this analysis is also used for calibration purposes. Residues in samples are then scored as being above or below the limit on the basis of the relative responses to the analyte. The percentage recovery is irrelevant, the only essential being that the analyte is measurably detected in the recovery determination. This simple, low-cost, qualitative approach is of particular utility where the majority of samples can be expected to contain no significant residues. The qualitative assessment is effectively "corrected for recovery" but it is difficult to provide sound information on the overall uncertainty of the determinations. This alternative approach can be refined, to make it acceptably quantitative for those pesticides detected in samples, by external calibration of these pesticides in the recovery determination. Depending upon the residue levels found in samples, it might be possible to use a single-point calibration, corresponding to the level of the recovery. If this approach is used routinely, 26
Quality control for pesticide residues analysis the recovery of all pesticides found in samples may be determined retrospectively and thus the uncertainty of results estimated. In cases where truly blank material is not available (e.g., where inorganic bromide is to be determined at low levels) or where the only available blank material contains an interfering compound at an acceptably low level, the spiking level for recovery should be -5 x the level present in the blank material. The analyte (or apparent analyte) concentration in such a blank matrix should be determined from multiple test portions. The concentration should be determined in this way each time a new blank material is to be used. As far as practicable, the recovery of all components defined by the MRL should be determined routinely. Where a residue is determined as a common moiety, routine recovery may be determined by addition of the component that either normally predominates in residues or is likely to provide the lowest recovery. Hitherto, limits have been used to define an acceptable recovery performance, such as "within the range 60-140%" or by the use of control charts and limits of ±2 RSD. Useful though such limits appear to be, the practice has a strong tendency to produce optimistic estimates of the uncertainty of measurement. The reason for this is that, following an unacceptable result and assuming acceptable average values, there is a high probability that if the recovery is repeated once (possibly more times) an acceptable recovery will be achieved and "statistical control" apparently regained. Of course, if the unacceptable recovery is due to equipment failure or other rectified mistake, the determination should be repeated. If not, the population of recovery data is artificially truncated and the analyst is deluded into thinking that the uncertainty of analysis is better than in reality. The analyst should report the recovery data whether they appear to be "good" or "bad". If the uncertainty of recovery indicates that the resultant data are unfit for purpose, a more satisfactory method should be developed or adopted. 1.8.3
Proficiency testing and analysis of reference materials
Determination of average recovery provides a partial indication of bias but it is incomplete and could, in principle, be misleading. In the continuing absence of readily available certified reference materials for most pesticide/product combinations, the laboratory should participate in all available relevant proficiency tests. Although proficiency test data may not represent an ideal way for assessing bias or accuracy, because the basis of the assigned true values may be questionable, they do provide a practical approach.
27
A.R.C. Hill, J.R. Startin and R.J. Fussell Where the result achieved in a test is questionable or unacceptable, the problem(s) should be investigated and, particularly for unacceptable performance, rectified before proceeding with further determinations of the analytes involved. Having said this, it should also be noted that a minority of questionable or unacceptable results might not be due to bias or mistakes, because they might arise as a consequence of statistical chance. The probability of this occurring depends on the uncertainty within the laboratory and this is another reason why control limits on recovery should be avoided. Nevertheless, every effort should be made to identify analytical mistakes before concluding that an adverse result is a consequence of statistical chance, because the probability of this occurring should be low. In-house reference materials may also be analysed regularly to help provide evidence of analytical performance. Where practicable, exchange of such materials between laboratories provides an additional independent check of accuracy. 1.9 1.9.1
CONFIRMATION OF RESULTS Principles
Confirmation of results has two aspects: confirmation of identity and quantity. The former is achieved by producing evidence from various techniques, etc., that supports the identification. The latter is achieved through analysis of additional test portions, to minimise the effects of sub-sampling error. Ad-ditior al confirmation of certain kinds of results would be a waste of time and money, so it is important to define those that are sufficiently important to require confirmation. "Negative" results (i.e., no residue is found or the concentration is below the reporting limit) can be considered confirmed if the recovery and LCL measurement for the batch are acceptable. The two conditions may be met by a single determination (see section 1.8.2) if recovery is at the LCL. In the case of a method that cannot or does not determine recovery, it is sufficient to be able to detect or measure the LCL. In special cases where the accuracy of individual results is unimportant, it is nonetheless important to provide information on the effective uncertainty of results below the LCL. It is impossible to confirm that a sample does not contain a residue but it is sufficient to be able to show that the residue does not exceed the LCL. In the absence of interference, all detection systems used for residues analysis are capable of demonstrating an absence of measurable residues but the criteria 28
Quality control for pesticide residues analysis outlined at the beginning of this paragraph provide the evidence that the results are not false negatives. "Positive" results may require additional confirmation but the requirements should be decided on a case-by-case basis. Generally, the more important the result is, or could be, or the greater the doubt about the result, the greater is the need for confirmation. Results which follow a wellestablished pattern of residues for a pesticide/product combination, or which are clearly of no consequence, may require little or no additional confirmation. Results which exceed an MRL or other action limit (including the detectable presence of a pesticide deemed unacceptable), or which are unusual by virtue of the identity, high quantity or high frequency of the residues found, should be further confirmed. These general rules should not be followed dogmatically-costs and requirements should be balanced-but reported results that are later proven incorrect can have costly consequences. The European Commission has developed a system of "points" for assessment of the extent of confirmation of residues of veterinary medicines in animal products, based on the relative specificity of the mass spectrometric techniques used [18]. This approach is now attracting the interest of pesticide residues analysts, as a means for providing general guidelines for deciding when confirmation is sufficient. However, no such system should be applied dogmatically, as exceptions to the points "rules" will inevitably occur, so assessments must continue to be made critically and not blindly. If the assessment remains doubtful and the result may have important consequences, further confirmation should be sought. 1.9.2
Confirmation by MS
MS, particularly when coupled with GC or LC separation (GC-MS, LC-MS) is the most useful and powerful technique for confirmation of residues. Differences in interface, ion source and analyser design can lead to significant or subtle differences in the relative abundances of ions produced, so reference spectra for the analyte should be generated using the instruments and techniques employed for analysis of the samples. To avoid distortion of ion ratios, the quantity of analyte must not overload the ion source and, depending upon the instrument and data capture system, it may be necessary to avoid generating data from very narrow chromatographic peaks. Reconstructed ion chromatograms (RICs) for diagnostic ions should show peaks of similar retention time, peak shape and response ratio to those obtained from a calibration standard analysed in the same batch. Bearing in mind the constraints outlined in section 1.7.7, ideally, the RIC peak should be 29
A.R.C. Hill, J.R. Startin and R.J. Fussell based on a minimum of seven data points and S/N at the apex should exceed 3:1. Where RICs of ions unrelated to the analyte show peaks of similar retention time and shape to those in RICs from the analyte, or where RICs of unrelated ions are not available (e.g., with selected ion monitoring, SIM), additional confirmation may be required. Where an RIC shows evidence of significant chromatographic interference, it must not be relied upon to quantify or identify residues. For data acquired from scanning, careful subtraction of background spectra is required to ensure that the resultant spectrum of the chromatographic peak is representative. Where ions unrelated to the analyte in a peakaveraged "full-scan" spectrum (i.e., from m/z 50 to 50 mass units greater than the "molecular ion") do not exceed a quarter of base peak intensity in EI spectra, or one-tenth for all other ionisation methods, the spectrum may be accepted as sufficient evidence of identity. Where unrelated ions exceed these limits, and they derive from chromatographically overlapping species, additional evidence should be sought. With EI, the absence of unrelated ions can be used to support identification if the analyte spectrum is very simple. Intensity ratios for principal ions should be within 70-130% of those obtained from the standard. Where an ion-chromatogram shows significant chromatographic interference, it should not be used to determine an intensity ratio. The most abundant ion that shows no evidence of chromatographic interference, and the best signal-to-noise ratio, should normally be used for quantification. EI, performed with acquisition of spectra, or tandem MS (MS/MS) may provide sufficient evidence of identity and quantity in many cases. Singlestage mass spectra produced by other processes (e.g., CI, API) can be too simple for confirmation of identity and further supporting evidence may be required. If the isotope ratio of the ion(s), or the chromatographic profile of isomers of the analyte, is highly characteristic it may provide sufficient evidence. Otherwise, the evidence may be sought using: (i) a different chromatographic separation system; (ii) a different ionisation technique; (iii) MS/MS; (iv) medium/high resolution MS; or (v) altering fragmentation by changing the "cone voltage" in LC-MS. The ions selected for medium/high resolution MS or MS/MS should be characteristic of the analyte, not common to many organic compounds. Where the increased sensitivity obtained by scanning a limited mass range or by SIM is essential, the minimum requirement is for data from two ions of m/z > 200; or three ions of m/z > 100. Intensity ratios obtained from the more characteristic isotopic ions may be of particular 30
Quality control for pesticide residues analysis utility. Additional supporting evidence should be provided where these requirements cannot be met or where doubt remains. Figure 1.4 shows an example of a supposed detection of dieldrin in salmon at 0.02 mg/kg on a fat basis, derived from a single quadrupole instrument operated in EI and SIM mode. Dieldrin produces a wealth of ions, all of low abundance in EI, and even the most abundant ions provided poor S/N in this case, in which the MS is capable only of unit mass resolution. Confirmation using either negative ion chemical ionisation (NICI) or EI with magnetic sector MS at high resolution provided much clearer evidence of both identity and quantity. 1.9.3
Confirmation by an independent laboratory
Where practicable, confirmation of results in an independent laboratory provides strong supporting evidence of quantity. If different determination techniques are used, the evidence will also support identification. 1.10 REPORTING OF RESULTS 1.10.1
Expression of results
Results should normally be expressed as defined by the MRL, with the concentration in mg/kg. Residues below the LCL should be reported as <(LCL) mg/kg, except in special cases of large-scale screening where individual results are not required to be quantitative. 1.10.2
Calculation of results
In general, pesticide residues data should not be adjusted for recovery. Where confirmed data are derived from a single test portion, the reported result should be that derived from the detection technique considered to be the most accurate. Where results for a single test portion are obtained by two or more equally accurate techniques, the average value may be reported. Where two or more test portions have been analysed, the average of the most accurate results obtained from each portion should be reported. Where good comminution and/or mixing of samples is undertaken, the RSD of results between test portions should not exceed 30% for residues significantly above the limit of detection. Close to the limit, the variation may be higher and additional caution is required in deciding whether or not a limit has been 31
A.R.C. Hill, J.R. Startin and R.J. Fussell A 1
O
F: T
-f
1
7
.3
4
m/z 263 7050
-
~'5C FeT
:
3 !5
7
m/z 277 _j:c _3C
70
-
KLDK_'l_
0
-
~50FT 1
:
1 7
34
D
o 7
0
e0
1
7
5
Fig. 1.4. Confirmation of a dieldrin residue in salmon fat at 0.02 mg/kg. (A) Data for the three most abundant ions produced in EI, using a single quadrupole instrument; (B) data for five high mass ions obtained by NICI, using a single quadrupole instrument; (C) data for five high mass ions produced in EL, using a magnetic sector instrument operated at high resolution. The low-resolution E data (A) do not provide an acceptable degree of confirmation of either identity or quantity, whereas the data from the two other techniques are satisfactory in both respects.
32
Quality control for pesticide residues analysis B 500' 400' 300'
m/z 382
12.07
200' 100' . .I
0
_I
I
..
.I...
Abundance 5 00o
400
m/z 380
3001
12.07
A
2001 100
i~~~~~~~1
no, Abundance 500 400
m/z 378
300 200
12.06
100 A'
I
VI
I
I
I
I
I
I
I
..
I
...
II
Abundance 12.06 500 400
m/z 346
300 200 100 0 o
I .. .. I
. .I
. I
..
I
.. I I
I ....
I II .
..
Abundance 500
400 300
m/z 344
12.06
200
.. .I I - I . . .. . . 1
100
'
4a->
0 Time--> 11.00
11.40
11.80
12.20
12.60
Fig. 1.4 (continued).
33
A.R.C. Hill, J.R. Startin and R.J. Fussell C 18.39
100.
m/z 381.8649
0
& I
- . I I. 1 I
I
II...
-
1
100-
.
I
.,
I
.... .
I
... .... I
.... I
....
,I I
.39
m/z 379.8679
%
0
.. I
I
I
I
... .
.... i.
....
.. ..
100 -
I.. . .
I
.. .. .....I ... .. . ..
I . ...
18.40
m/z %
0
377.8707
.!.. . . . . . . .
.. .. .. . .. .. .
18.39 100 %
0
m/z 346.8958
----------------------
100
------
-- -
~
~~~~~-----
-----
:--
18.40
m/z 344.8988
A ............................................................ .................................................................. i.Tim,16.00 17.00 18.00 19.00 20.00 21.00 22.00
Fig. 1.4 (continued).
34
o
Quality control for pesticide residues analysis exceeded. Alternatively, the limits for repeatability, or reproducibility, given in the European Commission's Directive 93/94/EC (Annex VI to Directive 91/414/EEC), may be applied. These values do not incorporate sub-sampling errors that can contribute significantly to the uncertainty of results, especially in the case of dithiocarbamates or fumigant determination. The ranges of variation stipulated by Fajgelj and Ambrus [19] are more realistic, because they do incorporate sub-sampling errors. For a pesticide having a residue definition that includes two or more analytes determined separately, adoption of a single reporting limit might be problematic. Three options are illustrated in Table 1.2, although there is only one option if no analyte is detected. Options (i) and (ii) are scientifically correct, whereas option (iii) is not strictly correct. The choice of option should be made according to the purpose of the results. Where the "pure" standard contains two or more components producing similar molar responses but which differ in concentration, for example chlorfenvinphos isomers, results may be calculated on the basis of the component producing the largest response. If this approach is adopted, the lack of a characteristic component profile supporting the identification of residues at or about the reporting limit may require the use of a more rigorous confirmation technique. TABLE 1.2 Reporting results for multi-component residues, using endosulfan as an example Results (mg/kg)a
Options for reporting endosulfan (mg/kg)
Alpha-endosulfan < 0.05 Beta-endosulfan <0.05 Endosulfan sulfate < 0.1 Alpha-endosulfan 0.05 Beta-endosulfan 0.05 Endosulfan sulfate < 0.1 Alpha-endosulfan 0.05 Beta-endosulfan < 0.05 Endosulfan sulfate <0.1 Alpha-endosulfan < 0.05 Beta-endosulfan < 0.05 Endosulfan sulfate 0.1
< 0.2
<0.2 -0.1 but <0.2 0.1 <0.2 -0.05 but <0.2 0.05 <0.2 - 0.1 but < 0.2 0.1
aLCLs assumed to correspond to 0.05 mg/kg for alpha- and beta-endosulfan and 0.1 mg/kg for endosulfan sulphate.
35
A.R.C. Hill, J.R. Startin and R.J. Fussell 1.10.3
Rounding of data
Results <0.1 mg/kg should be rounded to one significant figure; results 2 0.1 and <10 mg/kg should be rounded to two significant figures; results - 10 mg/kg may be rounded to three significant figures or to a whole number. Reporting limits should be rounded to one significant figure at < 10 mg/kg and two significant figures at 10 mg/kg. These recommendations do not necessarily reflect the uncertainty associated with the data. Additional significant figures may be recorded for the purposes of statistical analysis. 1.10.4
Quantifying the uncertainty of measurement
Measurement uncertainty is a useful indicator of the quantitative confidence the analyst may have in results, especially when they are to be compared with legal limits. Uncertainty estimates should take into consideration all sources of error, including sub-sampling and bias. Uncertainty data should be applied cautiously, to avoid creating a false sense of certainty about the true value. In principle, it might appear that uncertainty is a simple function of the analytical method and that, as suggested by King [20], it is possible to transfer uncertainty estimates between laboratories. This is one of the underlying assumptions of validation by collaborative study and, for certain types of analysis, it may be reasonable. However, in the sphere of pesticide residues analysis, and especially in multiresidue analysis, this should be done very cautiously. The constituent parts of the method (the reagents, the equipment, the detail of procedures) and the analysts using it can be expected to change with time. Within the sum total of a method, the equipment, reagents and the analyst(s) performing the method, changes can be expected to be frequent, with the consequence that the method is subject to evolution over time and upon transfer to another laboratory. Estimates of typical uncertainty are based, either in part or on the whole, on previous data and may not reflect the uncertainty associated with analysis of a current sample. Typical uncertainty may be estimated using an ISO [21], EURACHEM [22], or other approach. The data used may be derived from inhouse validation and AQC data; the analysis of reference materials and analysis of replicate test portions, etc. Reproducibility RSD (or repeatability RSD if reproducibility data are not available) may be used as the basis, but the contribution of additional uncertainty sources (e.g., laboratory sample heterogeneity, extraction efficiency, bias in standard concentrations) should be included where possible, otherwise the uncertainty is likely to be underestimated. Where the proficiency test performance indicates that the assigned 36
Quality control for pesticide residues analysis true value lies outside the apparent uncertainty for the result obtained, typical uncertainty data must also be reviewed. Uncertainty data relate primarily to the analyte and matrix used to generate them but, if extreme representatives are chosen, the estimates may be extrapolated to other analytes and matrices. Uncertainty as a proportion is invariably greater at low levels, especially as the limit of detection is approached. Assessment of whether or not a sample contains a violative residue is generally only a problem in cases where the level is relatively close to the MRL. The decision should take account of the uncertainty estimate. That is, if the MRL is within the uncertainty of the result, it may be inappropriate to conclude that the MRL is or is not exceeded. Given that the calculated uncertainty primarily reflects random errors of analysis and not mistakes, the possibility of residue loss or cross-contamination having occurred before, during or after sampling must also be considered. The use of reporting limits based on the LCL implies that, for practical purposes, there is no uncertainty associated with results reported as < LCL. Acknowledgements We thank F. Smith of the Central Science Laboratory for providing Figs. 1.1, 1.2 and 1.4. REFERENCES 1 2 3 4 5 6 7 8 9 10
W.J. Youden and E.H. Steiner, Statistical Manual of the AOAC. AOAC International, Arlington, USA, 1975. Codex Alimentarius Commission, Codex Alimentarius, Pesticide Residues in Food, Methods of Analysis and Sampling, 2nd ed., vol. 2A. Joint FAO/WHO Food Standards Programme, FAO, Rome, 2000, Part 1. M. Sargent, Anal. Proc., 32 (1995) 71-76. R.J. Wells, Accred. Qual. Assur., 3 (1998) 189-193. A.R.C. Hill and S.L. Reynolds, Analyst, 124 (1999) 953-958. A. Fajgelj and A. Ambrus (Eds.), Principles and Practicesof Method Validation. Royal Society of Chemistry, Cambridge, UK, 2000. A.R.C. Hill, Quality Control Procedures for Pesticide Residues Analysis: Guidelines for Residues Monitoring in the European Union, Document No. SANCO/3103/2000, 2nd ed., European Commission, Brussels, 1999/2000. A.R.C. Hill, Food Addit. Contamin., 17 (2000) 539-546. A.R.C. Hill and S.L. Reynolds, Food Addit. Contamin., 19 (2002) 733-747. Codex Alimentarius Commission, Codex Alimentarius, Pesticide Residues in Food, Section 2, Classification of Foods and Feeds, vol. 2. Joint FAO/WHO Food Standards Programme, FAO, Rome, 1993. 37
A.R.C. Hill, J.R. Startin and R.J. Fussell 11
12
13 14 15 16 17
18 19 20 21 22
38
A.R.C. Hill, C.A. Harris and A.G. Warburton, Effects of sample processing on pesticide residues in fruit and vegetables. In: A. Fajgelj and A. Ambrus (Eds.), Principles and Practices of Method Validation. Royal Society of Chemistry, Cambridge, UK, 2000, pp. 41-48. M. El-Bidaoui, O.P. Jarju, B. Maestroni, Y. Phakaeiw and A. Ambrus, Testing the effect of sample processing and storage on the stability of residues. In: A. Fajgelj and A. Ambrus (Eds.), Principles and Practices of Method Validation, Royal Society of Chemistry, Cambridge, UK, 2000, pp. 75-88. R.J. Fussell, K. Jackson Addie, S.L. Reynolds and M.F. Wilson, J. Agric. Food Chem., 50 (2002) 441-448. S.F. Howard and G. Yip, J. Assoc. Offic. Anal. Chem., 54 (1971) 1371-1372. S.J. Pattinson and J.P.G. Wilkins, Analyst, 114 (1989) 429-434. F.J. Schenck and S.J. Lehotay, J. Chromatogr.A, 868 (2000) 51-61. M. Anastassiades and S.J. Lehotay, P004. In: A. Di Muccio, T. Generali and A. Medulli (Eds.), Abstracts of the Fourth European Pesticide Residues Workshop, Istituto Superiore di SanitA, Rome, Italy, 2002. European Commission, Commission Decision 2002 /657/EC, Offic. J. Eur. Communities, L221 (2002) 8-36. A. Fajgelj and A. Ambrus (Eds.), Principles and Practices of Method Validation, 237. Royal Society of Chemistry, Cambridge, UK, 2000, Table 1, Annexe 4. B. King, Fresenius J. Anal. Chem., 371 (2001) 714-720. Anon, Guide to the Expression of Uncertainty in Measurement. ISO, Geneva, Switzerland, 1995, ISBN 92-67-10188-9. Anon., EURACHEM/CITAC guide, quantifying uncertainty in analytical measurement, 2nd ed., http://www.vtt.fi/ket/eurachem/quam2000-pl.pdf
Chapter 2
European Union legislation on pesticide residues* Luis Martin Plaza
2.1
INTRODUCTION AND SCOPE
The aim of this chapter is to provide background information and clarification about the EU legislation on pesticide residues in food and feed. The reasons for the lack of simplicity in this area can be found in the different elements involved. On the one hand, there is community and national legislation regarding active substances and on the use of products containing them. On the other hand, there are international norms and legislation on pesticide residues from third countries, as well as from different organisations such as the OECD/OCDE, FAO and WHO. These juridical regulations and norms represent, in most cases, different standards, and hence may have implications for trade and the health and environment of consumers. Despite recent food pesticide crises, such as those involving dioxin, and the finding of nitrofen (an active substance forbidden in the 1980s on account of its carcinogenic nature) on cereals and meat, or the events surrounding BSE, we are at one of the safest moments in the history of food in Europe. Food safety and a clean environment are two of the main concerns in our lives. Pesticide residues might be found through the whole food chain for two main reasons (leaving aside, of course, other sources such as accidents or fraud). First, residues of pesticides may arise in food products within the "good agricultural practice" (GAP)-which is the legal current agriculture practice in which the minimum possible amount of a pesticide is used to achieve effective control of the pest. Second, they might be due to environmental contamination owing to former agricultural practices and other sources of pollution.
* The opinions in this chapter are those of the author and not of the Health and Consumer Direction General. Comprehensive Analytical Chemistry XLIII Ferndndez-Alba (Ed.) © 2005 Elsevier B.V. All rights reserved
39
L.M. Plaza In order to protect their populations and facilitate trade, the administrations of the various countries control, monitor, and enforce the "good" use of plant protection products (PPPs). To this end, one of the systems in place is the setting of maximum residue levels (MRLs), in which every pesticide/commodity combination has assigned to it a certain legal value, expressed in mg of the pesticide residue found per kg of the commodity. Not all commodities, but the most representative in international trade are represented in this legal exercise. At the same time, not all substances have an MRL already set in the system because, among other considerations: (i) this is a time-consuming activity that requires sufficient resources to do it, (ii) the system is being fed continually with new active substances that are normally replacing the less safe ones, and (iii) of the complexity of the molecules. There are various systems or schemes for setting MRLs. Each of them has its own rules offunctioning, but for all of them, prior to having an MRL, it is strictly necessary to evaluate the active substance for the safety of consumers, the environment, and those who handle the materials. Then, the active substances can be marketed and used on the crops, and legal residues can be expected. The EU, USA, Canada, Australia, and Japan have their own national systems, which are mainly accepted by other countries exporting to them; but at the international level, the most accepted one is the Codex Alimentarius system. Another point to be taken into consideration is the analysis of the pesticide residue. Today, various methods of analysis for these substances exist. Standardisation of the methods of sampling and analysing is required as a basic principle of good laboratory practice. Several proficiency tests among the laboratories are usually run both by administrative and private bodies, in order to not only know the level of expertise of the laboratories for certain analytical methods in different pesticide/matrix combinations, but also for accreditation purposes. Last, but not least, the EU administration has in place a network to detect, inform and act rapidly in the case of any unacceptable risk for the consumers. This is the Rapid Alert System for Food and Feed (RASFF). 2.2
OVERVIEW ON EU PESTICIDES RESIDUES LEGISLATION
The Health and Consumer Protection General Direction (DG SANCO), is in charge of managing the main four Council Directives on the scope of PPP residues. Three of them apply to some fruits and vegetables, cereals, and products of plant origin (CD 76/895/EEC, CD 86/363/EEC and CD 90/642/EEC, respectively) and one to the products of animal origin (CD 86/362/EEC). 40
European Union legislation on pesticide residues During the writing of this chapter, the European Commission presented a Regulation proposal on pesticides residues to the Council and Parliament to consolidate, simplify and amend the above four main directives but also to introduce new ideas on setting and controlling the levels of pesticide residues. Council Directive 91/414/EEC, concerning the placing of PPPs on the market, is independent of the other four directives but of course is related to it, as the residues expected in crops are the consequence of legal use. From the moment an active substance is included in the positive list of substances (annex I to CD 91/414) that can be marketed within the EU, MRLs need to be set for the uses for which the substance has been granted. For other uses, not yet foreseen in annex I, if there are no other data at the time the MRL is going to be set, then the limit of quantification is proposed temporarily until the data are available and a new higher MRL may be set. Many different substances can be considered under the pesticide or PPP definition; others fall under other categories of substances, such as biocides, additives, and veterinary and human medicines. Some of them are borderline cases, but it is beyond the scope of this chapter to go into details of each of these groups. The current definition of pesticides as it appears in Council Directive 91/414 is: "active substances and preparations containing one or more active substances, put up in the form in which they are supplied to the user, intended to: (i) protect plants or plant products against all harmful organisms or prevent the action of such organisms; (ii) influence the life processes of plants, other than as a nutrient (e.g., growth regulators); (iii) preserve plant products; (iv) destroy undesired plants; (v) destroy parts of plants, check or prevent undesired growth of plants". In the EU, the active substances were classified into two categories: existing and new active substances, depending on whether the substance was present in the EU market or not in 1991, the year in which Directive 91/414 was adopted. Both kinds of substances are still under a continuing evaluation process. The European Commission put in place a program of work for evaluating all these substances. The existing ones were classified into four lists, depending on their use and concern at the time when the Directive 91/414 came into force. The new ones usually cause less concern; they are substances that are environmentally friendlier and less toxic for both the consumer and the person who applies them. It is foreseen that this evaluation program will be finalised in 2008 and then the re-evaluation will start again. In general, all the substances should be re-evaluated every 10 years, although this may be done at any time if there is any special concern.
41
L.M. Plaza For the sake of good understanding, it is also necessary to define the meaning of residues for those fungicides, insecticides, herbicides, and other substances included in the above explanation. Therefore, we shall use the definition "one or more substances present in or on plants or products of plant origin, edible animal products, or elsewhere in the environment and resulting from the use of a plant protection product, including their metabolites and products resulting from their degradation or reaction". The Community also has in place a regime permitting the setting, on a scientific basis, of EU MRLs. It is based on a shared responsibility between the Community and the Member States. The legal bases for this regime are Council Directives 76/895/EEC, 86/362/EEC, 86/363/EEC and 90/642/EEC. For those pesticide/commodity combinations where no Community MRL yet exists, the situation is not harmonised and the Member States may set MRLs at a national level. The work has been under constant review since November 1976, when Council Directive 76/895/EEC fixed MRLs for 43 active substances in selected fruits and vegetables. These first MRLs set were based on the best available data at that time. Today, with newer information and higher standards, those older MRLs are gradually being reviewed and, where appropriate, being replaced with newer MRLs. Up to now, since 1976 around 24,000 Community MRLs have been set for various commodities, and more are in the pipeline for 192 pesticide active substances. Another milestone in the legislation was the Directive 97/41/EC. From 1997, MRLs could be set not only for raw commodities as before but also for processed products and composite foodstuffs, applying for each substance specific, transfer or processing factors on the MRL fixed for the raw products. Processing factors for individual substances are normally agreed during the evaluation of a 91/414/EEC dossier, and are used in the consumer intake assessments performed to check the acceptability of an MRL. At the same time, this Directive allows the Commission to set MRLs (previously, it was just Council competence) and, last but not least, established a "conciliation procedure" to solve trade intra-community problems when non-harmonised national MRLs exist. The legislation on pesticide residues in food includes provisions not only for the setting of MRLs, but also for sampling as well as for monitoring, control and reporting. Residues in baby food are covered by a separate legislation and using a different approach (Commission Directive 96/5/EC on processed cereal-based foods and baby foods for infants and young children and its amendments).
42
European Union legislation on pesticide residues 2.3
MEANING OF AN MRL
The big challenge, and the main reasons for fixing Community MRLs for all pesticide/commodity combinations at the community level (harmonised MRLs), are to have the same minimum level of protection for the consumer populations (including infants) at the same time, to help in achieving a free single market without trade problems caused by different rules. The MRL is normally set for the product moving in trade, and not for the product as consumed by the consumer. Nowadays, there are many confusions and misunderstandings about the meaning of a pesticide MRL. The term MRL has been used for decades in other fields of public health, as in the veterinary sector. Consumers may have the feeling that breaching or exceeding a pesticide MRL means both an illegal infringement and automatically a health concern. The last is not strictly true for pesticide residues. Other considerations should be evoked first to reach that conclusion, but this issue will be discussed later when we consider risk assessment. A simple definition of MRL could be: "the maximum amount of a pesticide residue that could be found or might be expected in a food commodity, when a particular legal GAP has been followed for the crop". It is expressed in mg of residue per kg of food commodity. Although in this definition nothing is said explicitly about health, it is well known among the scientific and administrative communities that pesticide MRLs are not maximum toxicological levels but should be toxicologically acceptable for consumption purposes. So, when MRLs are set, care is taken to ensure that the maximum levels do not give rise to toxicological concerns. Nonetheless, they are based on GAPs data: the application rate of the active substance per hectare, the number of applications, method of application, and interval in days after the application and before the harvest (see Table 2.1). In cases of residues relating to persistent active substances in the environment that arise from former agricultural practice, but which are no longer registered PPPs, the maximum levels are internationally referred to as extraneous maximum residue limits (EMRLs), but in the EU they are also called MRLs. They are, of course, not set on the basis of any GAP but are established on the basis of monitoring data as, e.g., for DDT (Table 2.2). The LOQ values (0.05 mg/kg) represent the level of quantification. The MRLs proposed are at the LOQ. The monitoring data since 1997 show that the levels found in kidney fat are becoming lower and lower, as DDT is a forbidden substance and is no longer used as a plant protection product.
43
L.M. Plaza
de
Cd Cd
2E2 4
2
.
•cCCC
cC
itZ. P,C
Cd
t
-1uP4 Cd
m Cdl
0
Cd
9 a 0
"
C4 )
C)C d ., C) C ,-
go
:2
C)
0--w I
w¢ b
.Y
I
Mt
d Z .U b C)Cci
Id
Cd C
El c -.
2 042
G:,C¢
a
-24
P.
-5-C
w5d
(C) 0.
1
Cdid ,Cd4
0aCd
i
CL~
1,
d4
'
w
r
a I
72
2
[o bE
o
~, ,4P, Z
. I
-2 PCC
.2 E
a, u4
;E sC tQ
8 t
.
-. .i
Cd d
.2 ·I
-2aur
p
0
' Cd S
2
44
Cd9
p,
E
0
C)
t
C C) C
C->
0
c4
C
0
CC)a
0) 21
O i -g O C ~ooO ~. o oO a ~®o Cd4
a m
o
d
C-
C-C-~
C
C)
European Union legislation on pesticide residues 0 a) CD
0o
.2
bk Cas
.4 3
n a
.. aa
bfl
-0
.9
o
0
Ca .0
Ca
C
Ca C
m
a, a
a aO cO
nz
a
a
-ca Ca
w
iO
t0
0 Ca
I
m a) Ca m 0
0r
C
Ca
Ca
D
PQ
0)
m -
C
m CD Q)
a ,-C a P. D -
CD I
CC·CC
·
CC CXC
C C
CC~la
0CC~~
P
"Ca Ca
d
CZaz Ca
0 CdU
O&C,S"P
-
0
Ca
C w ID 0. tm GO
0 Ca
m
w t: 0
C
'i 0 Ca
Q 0
O
ICj m
O
0
Cd
a) CdAoC
.Ca
C Ca Q
Ca
-d Cd m
0
45
L.M. Plaza 0
CO ^
c
o ooo
,6 bl 000
0
in o
L o
-I 0
o
o
o
0
o'
kxo
0
0H -
C< CO -q
Lo
Lo
10
C
i
0 El
oto
L
xfc
00
O,
d
C
0:
666666 b1
10
a 0
LO000000
t
o s;
Xa
z
10 t
10
t
1
t C.) a 0.
0 4,
0
E4
ra 0 Ci
"E< U B;
o S o o dU
V000001 4
uS 4:
C
4m1 m
r:+9 >;P
a
;>
9a
cq
PW
0 0
46
- 0 m CO t 0 00 Cq O o 0 o O Cl c C s-- crl ..
European Union legislation on pesticide residues 2.4
SETTING OF EU MRLs
The goal is to have as many MRLs harmonised as possible, at the community level, in the shortest period of time. Harmonised means that the same grade of protection exists among the Member States to avoid intra-community trade problems. This task is difficult to achieve, because there are many possible pesticide/commodities combinations, with a current list of about 180 crops and up to 1000 pesticides in or out of use: then, up to 180,000 MRLs are possible for raw commodities (including animal feed), not to mention all possible MRLs in processed food. Also, first the substance has to have been evaluated under CD 91/414, and consequently to be in annex I to that Directive. The pace of evaluation is not as fast as was first thought when CD 91/414 came into force. At the current speed of evaluation, a decision on the inclusion in annex I of the last substance could be in 2008. The European Commission through Comitology (the way in which the Commission takes decisions in some fields with the vote of the Member States) reached a provisional figure (4 years from the time of first setting the MRL in a specific use) for each commodity/substance combination with which the Member States are pleased. The European Commission and all Member States have to respect the harmonised MRLs although there are always possibilities to change that provisional MRL-if, e.g., a new GAP is required. In the event of non-harmonised MRLs, it is up to the Member States to set them at the national level. This represents a source of trade conflict. To prevent this situation, the conciliation procedure (CD 97/41/EEC) should be invoked. Within a short period of time the exporter and the importer Member States have to exchange information on their MRLs (GAPs, intake assessment, respectively), and agree on an MRL to solve the problem. Another point of difficulty arises when under the World Trade Organisation (WTO) rules, the EU has to respect Codex MRLs; so, at the time of setting MRLs, Codex MRLs have to be taken into account. This means that if the Community does not agree with the Codex MRL, it can set a different one at EU level, and must provide the reasons for not approving it, and notify its partners in the WTO. Hence, it is strictly necessary to follow a program of work for setting and gradually harmonising all the above MRLs. The mechanisms and methods used to fix MRLs are fairly well standardised at the community and global levels, and well-developed data requirements exist at both levels. Member States can authorise the use
47
L.M. Plaza > m U
5 ao
m I
..
a CJg d)
5<5
's'at C~~ 8C
It
U 9 0
I Z 0 U)
trO U)B U)A
Cd 41 w w
-.
P3
d
O
+
U
m U)r o
U U U
U
Y~
I Ug U.-U),
Om
U UNU a oHcNN
CU
U r*U ?N) U:~
U)U)U
U
U *1a
0
U
g
mr C.
SHt
U)
U)t
NCo
NUU
C
M "I,
U)
,-
C o5 C:
~~~a
·
H
U,
U)d 4
B~~UU
a,
oo
aa
o' -
IL
d
o
. 7s S S
"
EE S U)
8
d
d) d.w o H--, MU
Cm cq
W4 m4
I SE~
48
I
b
Oo o
UN
ZZ P-O o1
-0
,b dUU5
UN
U)
QU
dig
.S;o
U U
P, U) U-
H P4
U
0
N
European Union legislation on pesticide residues 0
d
I
0
.D
06 O
(D
¢
a *o
~t ~~~~~ O Co c
g
c
H:
~O
C!
,.0
o
sO
,q "
6i
C°
0
P:
o
C)
eV
=
0 v
o0 ;zzV66CD <, ,
9
@ go0oeN O
O V
0~,
q5
Pr
P: Moa;=
a
a
Q
0
Ca
'6
m
C) a
D
0
.
..
E kf ~~~~~~~$Ca ,,: m
,_ m"
I
6
.0 P
6
Ca
0
6; 0
.0 0, 0 0
I
0 m 0
0
,U
6
0 l- P0 ° o
0 0
0
.6 0
S
0 0 .0
'0 0o
0
.6
g5 E ,I 0
~00
a
.0 0, Ca )
0
E Ca
*0
0-~i-
0
S
65
D|
g
0
0
-6
0-6 P.
9.
49
L.M. Plaza of a pesticide when enough information has been provided to assess the substance, and it is safe for the applicant, environment and consumer. Then, a product containing this substance can be marketed and, under certain conditions, used on crops. Various authorised uses may give rise to various levels of residues, not only in different Member States but also in different areas of Member States. These differences among Member States are justified on the basis that: the pests do not have the same virulence, there are differences in the climatic conditions, the varieties of plants, indoor or outdoor use, the time of harvesting, etc. All these conditions of use define the GAP. The objective of the GAP is to keep the pest under control with the minimum amount of active substance. So, an MRL is set as high as necessary on the basis of application as provided for authorisation, and as low as possible for reasons of preventive health care, and never under any circumstances higher than can be justified on toxicological grounds. To reach that objective, supervised trials medium residue (STMR) values are used with different GAPs. These values of the supervised residue trials of different GAPs are compared. The GAP giving rise to the highest levels, "the critical GAP", is used to set the MRL, as all other authorised uses should be covered by it (see Table 2.3). A prerequisite for the establishment of MRLs is the existence of an acceptable daily intake (ADI) and, where necessary, an acute reference dose (ARfD) acceptable to the Community for that substance. With these two concepts, the evaluation of the consumer risk can be carried out for chronic and short-term use. The ADI of a chemical is "the estimate of the amount of a substance in food or drinking-water, expressed on a body-weight basis, which can be ingested daily over a lifetime without appreciable health risk to the consumer on the basis of all the known facts at the time of the evaluation. It is expressed in milligrams of the chemical per kilogram of body weight" (WHO, 1997). In the short-term case, the ARfD of a chemical is "an estimate of the amount of a substance in food and/or drinking water, normally expressed on a body-weight basis, that can be ingested in a period of 24 hours or less without appreciable health risk to the consumer on the basis of all known facts at the time of the evaluation" (WHO, 1997). The evaluator of the intake must first make a few assumptions such as: whether it is used just by groups of consumers or the whole population, which percentile of the groups chosen are covered, which approach is going to be used-probabilistic or deterministic-and the diets available. Currently, the deterministic approach is the most used among the European Union Member States. In the deterministic approach, the consumption data tables more frequently employed are FAO/WHO diets 50
European Union legislation on pesticide residues (guidelines for predicting dietary intake of pesticide residues, prepared by the Global Environment Monitoring System-Food Contamination Monitoring and Assessment Program (GEMS/Food) in collaboration with the Codex Committee on Pesticide residues; WHO, 1997), based on adults of 60 kg bodyweight (bw). However, studies are also made on the intake assessment in the worst-case scenario, with the UK model based on toddlers of 14.5 kg and/or the German model based on 4-6-year-old girls of 13.5 kg (Tables 2.4-2.5). First of all (Step 1), the theoretical maximum daily intake (TMDI) should be calculated. It is well established that the TMDI is a gross overestimate of the real exposure. Since the TMDI is calculated using the hypothesis that each commodity contains residues at the highest possible concentration, the probability of one individual being exposed to the TMDI becomes more remote as the number of commodities included in the calculation increases. The sum of all the intakes of the pesticides through the diet is compared with the ADI or ARfD. The result of this exercise is expressed as the percentage of the ADI or ARfD. If the calculation shows that the TMDI figures do not exceed the ADI figure, then it may be assumed, without any further action being needed, that there is no risk to consumers. In the example shown in Tables 2.6 and 2.7, the intakes (TMDI) following FAO/WHO consumption data for adults and following the German data for 4-6-year-old girls are 3 and 5%, respectively, in relation to the ADI of 0.25 mg/kg bw/day for a particular substance. This means that the garlic and onions MRLs of 15 mg/kg for the pesticide X are acceptable. In some cases, however, the TMDI figure is higher than the ADI. It cannot, and should not, be assumed from this that the maximum limit necessarily poses a risk to consumers. Assessment of the estimated maximum daily intake (EMDI) (Step 2) is used to reach a final conclusion about the acceptability of the given limit value. This step gives a more realistic prediction of pesticide residue intake by taking account of: (i) the residues in the edible parts, i.e., the non-edible parts are dispensed with; and (ii) the change/drop in residue levels from the processing of a foodstuff up until its use for culinary purposes. The EMDI (see example in Table 2.8) gives a more realistic estimate of pesticide residue intake via food, but nevertheless overestimates it because the following assumptions still apply: (i) all harvested products have been treated with pesticide; and (ii) all harvested products contain residues at the maximum level. If the EMDI still exceeds the ADI, the best possible assessment of estimated daily intake (EDI) (Step 3) is provided for; usually it is called the NEDI (Step 3), from the National Estimated maximum Daily Intake. This step is intended to take particular account of: (i) actual residue levels in foodstuffs 51
L.M. Plaza TABLE 2.4 German model: average consumption of foodstuffs (g/day) for a 4-6-year-old girl
1. (i)
(ii)
(iii)
(iv)
Foodstuff
Rawa
Processedb
Total
Foodstuffs of vegetable origin Fruit (edible) and shell-fruit (nuts) Citrus fruits and juices Grapefruit (including hybrids) Lemons Limes Mandarins (including clementines and other hybrids) Oranges Citrus juices Citrus fruits without peel and citrus juices Shell-fruit (nuts) Almonds Brazil nuts-see other Cashews Chestnuts Coconuts Hazel nuts Pecan nuts see other Macadamia nuts Pine nuts Pistachios Walnuts Other Pomaceous fruit Apples Pears Other Stone fruit Apricots Cherries Peaches (including nectarines and similar hybrids) Plums Other
105.8 72.0 18.0 2.1 0.3
386.2 89.3 26.5
492.0 161.3 44.5 2.1 3.1 0.2 12.9
10.3
2.8 0.2 2.6
1.4 3.9
5.4 15.5
6.8 19.4 40.1
1.4 0.2
3.8 1.3
5.2 1.5
0.1
0.1 0.2 0.1
0.2 0.2 0.2
8.7 3.0 2.0 2.6
0.1 0.1 0.7 1.2 35.6 31.5 3.9 0.2 10.7 1.5 3.0 5.3
0.2 0.2 1.0 1.7 48.6 42.0 6.4 0.2 19.4 4.5 5.0 7.9
1.0 0.1
0.7 0.2
1.7 0.3
0.1
0.1 0.1 0.3 0.5 13.0 10.5 2.5
continued
52
European Union legislation on pesticide residues TABLE 2.4 (Continuation) Foodstuff
Rawa
Processedb
-^-----^------
(v)
(vi)
2. (i)
Berries and smaill fruit' (a) Table grapes Wine grapes (b) Strawberries (c) Brushwood berries Blackberries Loganberries Raspberries (d) Other small fruit and berries Bilberries Cranberries Blackcurrants Redcurrants/white currants Gooseberries Other (e) Wild fruit Other fruit Avocados Bananas Dates Figs Kiwi fruit see other Kumquats Lychees Mangos Olives Passion fruit-see other Pineapples Pomegranates-see other Other Banana without peel Dried fruit Vegetables Root and tuber vegetables Beetroot Carrots Celeriac
Total ---
9.0 6.1
8.6 2.6
17.6 8.7
1.6 0.4 0.1
3.2 0.4 0.1
4.8 0.8 0.2
0.3 0.8
0.3 2.4
0.6 3.2
0.1
0.2 0.1 1.3 0.5
0.1 0.1
0.3 0.1 1.5 0.8 0.1 0.4 0.1 24.8 0.1 21.7 0.2 0.2 0.2
0.2
0.2 0.2
0.4
0.5
0.9
0.7
0.4
0.6 33.0 3.3
0.6 75.4 11.1 0.4 6.0 1.8
1.1 19.5 1.2 108.4 14.4 0.4 8.6 1.8
0.2 0.3 0.1 0.1 0.1 21.3 0.1 19.5 0.2 0.1 0.1
0.3 3.5 2.2
0.2
2.6
continued
53
L.M. Plaza TABLE 2.4 (Continuation) Foodstuff
(ii)
(iii)
(iv)
Horseradish Jerusalem artichoke-see other Parsnips Parsley root Radish Salsify Sweet potatoes Swedes Turnips Other Bulb vegetables Garlic Onions Shallots/spring onions Fruiting vegetables (a) Solanacea Tomatoes Peppers Aubergines (b) Cucurbits with edible peel Cucumbers/gherkins of all types Courgettes (c) Cucurbits with inedible peel Melons Pumpkins Water melons (d) Sweet corn Brassica vegetables (a) Flowering brassica Broccoli Cauliflower (b) Head brassica Brussels sprouts Head cabbage (c) Leafy brassica Chinese cabbage Kale (d) Kohlrabi
Rawa 0.1
Processedb 0.1
0.2
0.3 0.2
0.3 0.2 0.6 0.9 0.1 0.4 0.6 0.3 10.9 0.4 8.3 2.2 32.0 17.8 15.1 2.0 0.7 11.9 11.5 0.4 1.5 0.5 0.8 0.2 0.8 25.2 7.5 1.0 6.5 13.2 3.0 10.2 3.5 1.1 2.4 1.0
0.6
2.9 0.2 2.5 0.2 18.9 9.5 8.5 1.0 8.7 8.6 0.1 0.7 0.5
0.9 0.1 0.4 0.6 0.3 8.0 0.2 5.8 2.0 13.1 8.3 6.6 1.0 0.7 3.2 2.9 0.3 0.8 0.8
0.2 2.5
1.6 1.6 0.4 0.4 0.5
Total
0.8 22.7 7.5 1.0 6.5 11.6 3.0 8.6 3.1 0.7 2.4 0.5
continued
54
European Union legislation on pesticide residues TABLE 2.4 (Continuation) Foodstuff (v)
(vi)
(vii)
(viii)
3.
Leaf vegetables and fresh herbs (a) Lettuce, etc. Cress Lamb's lettuce Lettuce Endives (broad-leaved) Other (b) Spinach and similar Beet leaves (chard) Spinach Other (c) Water cress (d) Chicory (e) Herbs Chervil Chives Parsley Celeriac leaves Other Leguminous vegetables (fresh) Beans Peas Stem vegetables Asparagus Cardoons Celery Fennel Artichokes Leek Rhubarb Fungi (a) Cultivated mushrooms (b) Wild mushrooms Dried vegetables Pulses Beans Lentils Peas
R.awa 4.8 4.4
Processedb 3.6 0.2 0.2
2.5 0.2 2.1 0.2
8.4 4.6 0.2 0.8 1.5 0.5 1.6 2.5 0.2 2.1 0.2
0.1 0.8 0.2 0.2 0.1 0.1 0.2 7.8 3.8 4.0 6.2 2.5 0.2 0.2 0.1 0.2 2.5 0.5 2.8 2.6 0.2 0.1 1.5 0.7 0.3 0.5
0.3 1.0 0.2 0.2 0.2 0.1 0.3 7.9 3.8 4.1 6.5 2.5 0.2 0.4 0.2 0.2 2.5 0.5 3.0 2.8 0.2 0.1 1.5 0.7 0.3 0.5
0.8 1.5 0.5 1.6
0.2 0.2
0.1 0.1 0.1 0.1 0.3
0.2 0.1
0.2 0.2
Total
continued
55
L.M. Plaza TABLE 2.4 (Continuation)
Foodstuff
Rawa
Processed b
Total _ __-
4.
5.
6.
Oil seed
Colza seed Linseed Peanuts Poppy seeds Rape seed Sesame seeds-see other Sunflower seeds Soya beans Cotton seed Mustard seed-see other Other Potatoes Early and ware potatoes Dried potatoes Potatoes without peel Cereals Maize Other cereals Other cereals = cereal preparations (a) Rice (husked, polished) Other grain mill products (b) Bran (c) White and wholemeal flour (flourmill products) (d) Pasta (flour content)
0.3
0.1
0.2
0.2
11.0
-
-
11.3
1.7
1.7
0.1 1.2 0.3
0.1 1.2 0.3
1.7
1.7
1.7 0.2
0.2
1.7
1.7
1.8
2.4
2.6
71.1 71.0
71.1 71.0
0.1 63.9 2.0 105.8
0.1 63.9 108.0 2.0 106.0
4.3
4.3
1.0
1.0
91.7
0.2 91.7
8.8
8.8
0.2
"Raw = not prepared or processed in any way. bProcessed, e.g., washed, peeled, boiled, fried/roasted, prepared as conserves. CExcluding wild fruit in the case of strawberries, brushwood berries, and other small fruit and berries.
introduced into the market; (ii) the proportion of a product actually treated with a pesticide; (iii) the decrease in residues during the storage, processing and culinary preparation of a foodstuff; (iv) the proportion of a foodstuff produced within the country, and the proportion imported (example in Table 2.8). If, at the end, the percentages are higher than the doses of reference, the critical GAP is not safe for the consumer, and then the MRL should be reduced using the next less critical GAP. In individual cases (i.e., existing active
56
European Union legislation on pesticide residues TABLE 2.5 WHO European diet: a few examples of commodity intakes
Fruit
Vegetables
Pulses Oilseeds
Potatoes Cereals
Animals
Commodity
WHO adult (kg/person/day)
Fruits other than grapes, apples, banana, oranges Oranges Apple Grape Banana Vegetables other than onions and tomatoes Onions Tomato Pulses Soya oil Palm oil Sunflower oil Rape oil Potatoes-total All potatoes Wheat Barley Oats Maize (cornmeal) Rice Rye Milk-cows (assumes milk solids 12.7%) Milk solids Butter Cream Meat fat Egg Poultry meat Beef Sheep Pork Meat other than beef, sheep, pork, and poultry Offal Fish and seafood
0.096 0.049 0.055 0.021 0.017 0.171 0.02 0.075 0.008 0.012 0.008 0.008 0.004 0.206 0.278 0.003 0.005 0.017 0.013 0.025 0.606 0.077 0.016 0.01 0.042 0.037 0.039 0.062 0.015 0.094 0.005 0.013 0.06
57
L.M. Plaza TABLE 2.6 Example of intake (TMDI) through diet using FAO/WHO (1998) consumption data for an adult of 60 kg bw Crop
Consumption (g/day)
Residues (mg/kg)
Intake (mg/60 kg bw)
Intake (% of ADI)
Garlic Onions dry Onions and shallots green Total
3.0 26.8 1.0
15 15 15
0.0450 0.4020 0.0150 0.4620
0.30 2.68 0.10 3.08
substances), and also if the critical application conditions provided for the authorisation cease to apply, then the MRL may be set on the basis of the next-most-critical conditions, or even at the limit of quantification in the absence of data. In a minority of cases, where residues might be expected but where there are no authorised uses, e.g., from persistent substances such as DDT or lindane, MRLs can be set using monitoring data that are reviewed regularly. The European Commission in the proposal for Regulation to the Council and Parliament already considers and opens up the possibility of setting MRLs based on monitoring data for substances still in use for minor commodities, e.g., spices, and where residues arise as adventitious contamination, e.g., honey. Since risk is a combination of hazard and exposure, and owing to differences in national eating habits, an acceptable consumer assessment in one country may not be acceptable in another. In the Community, diets in all Member States are examined, and intakes by adults, children and toddlers are assessed. The intake of a substance from all dietary sources is
TABLE 2.7 Example of intake (TMDI) through the diet using German consumption data (1991) for a 4-6-year-old girl of 13.5 kg bw Crop
Consumption (g/day)
Residues (mg/kg)
Intake (mg/60 kg bw)
Intake (% of ADI)
Garlic Onions dry Onions and spring onion Total
0.4 8.3 2.2
15 15 15
0.0060 0.1245 0.0330 0.1635
0.18 3.69 0.98 4.90
58
European Union legislation on pesticide residues
I
C) c Z) I)
3
iisi
CC00
C
:t >
cccc O
O
o
O
CD
00 00
0
O
0
6ooo
66oo
bD
M CCi C OO0
O
,)C
Cq cC
CC,
)
Ccc C-i .0C
cc r od~omor-
0
DOO
M
Cqc
'-mcc to-
oC
cc
C
x x
Cx
C1 C
Co C- O
cIn
I
CC
O
O
b-0 C1
CD
M CI
Cc
c) C)!
C O
CC C)
C
-"C
O
1, 0
oooooc6c6 OC O
cl
-C)
CS~~~ 00 O
C.
cJ
C C'
I cH 0C O
gO
Oc C
o
C
°
M C:
O CCM Cc,-c 0
°oC°c
-I O
O
.1
°OC
Oc6 OO
DtOC
to M
M
L
In
Oo
_
:O
C
M
XDX
CC Cll~ C CC= O
OCO
C CC
C
g~
O
*0
0
0
ab
co
ei
o
I'd
q
O o
CD
C) C IcC
C
csC~CI CO
0
Co
oC
0
c)
C)
0
C)c
CC C
G
C:
Ci C~o C 10
O~
0R COCLD
C~ L-
-nC
m~n
~
10
0 C
e IQ cCC
10
Z4
nl~
C) 0
O
C4
cD0
C
6
a
6
C CO
COM
cO
i0
C
C
C)
:4 CC C
cc'6
CC
6 Ca~6*-, a~~~ CC] _ ccG
o C)
v~m~ C)
CC o
Y
-
cc
C-
r
C)ICAt
-
m io
c
,0,C
C)
9
t C
N
m~~~m c
CO
Ycc
C~ri
d ~~ m ~ ~ CC)Ci C)~~C)C)
0
cc
;S> C)
cc
X
m~
m
~
Ec
C)
CC)
C)
a
~~~
·-
C-C
C C)
C)
CC Ctc .
.
C
C
.
59
L.M. Plaza CC C)
-4h Ilt5.:C)
C)
-
o
-
-
-
-
-
-
-
-
-
-
-
-
-
~-
-
-
-
-
-
-
-
;4 ei IV, Co
o
tC
b
O
i C
amb
C)
E
C
C
CX
01 X
C) , C) C -
tCC C
C)
X
X
m
m
C-
~ C)
C
c
or
o 0
co
c)
H
o
CC o CC~ H
C
C
C
C
'd
C)~
C)
C
C)
C)
CC
C
uU)C o
CC
66
I I
C
I
xn
IC) I C) C- C CDCC C o r o o O oX cxi x" X B X O OO -
b
C .2 C
8
~o
t-m CIomvoc
C) CC
x -
O
CC ) C C CC C) C C-, C) CC CD C) CC C C
O
O
x
.
IIC
x t--
x
I
C) X
A
t-
m ci
cco
O CO
n
COb
CC
x
mm
Cc)
C)
cn C) O CC C))C C)
t Co ICCC-1~Ci
C
CC c C
R
Co - 0C
C
C) o
C)
C -O)
-4 t
m Ci
. 10
C)C
100 CC
S
I
"8 C
II II C) C)
~o
COD
c
C) C C)
C
C)
CC
mD C)
C) C
C
O
C)
O
o UD 10 C)CCCCC O
C
O
C) C
C
I
:
OO
c
z~..cCC )UH~: OO
CD
10 O
I OO
C c
Z cd
c~
CC
m
CC -11
C
iM H -~
0H CC 11 D C v C-
Ii
6o
-i
6ol
11 -
c
oq,H
6oi
C0o
ca
-i C-~Nm ,C r C Coc C) C
~CC -
c e
o
l1
O
O O
CC C C I) CC
O
H w
O CC
C
CDCi
-CCCo
C)C)C)C)
C)
oCCcC C) C 0
CCO)
C-CCCC) H io
C-
cq CC-H ti
cC CC O CC m Clb
C9
CC
C
C
CC~ C
C
C
I~~~~~~-
i; C
o
u-) 0 C)
C
C
U)
CC
CC -C ¢
C
e<
C FH
60
2;m
,, h
r o;;;
m
O~ av~~a
mtm
fj
o
f u
S
European Union legislation on pesticide residues
b sb ar
Mm co ID CO
0 0~ o
0o o
0
U)
6=
O
"kC w
U7 1 0 0
T 0
a)
11 0
U
.2
x xx n OsP x'U o C
x
CU 0
CU
o 0
L-
0
0
0
0
0
GO co 0 0 CO 0 0 CD
c
0 L- L- CU1 co CO CO H 0 co O O H 0 0O -I C0 o ItiD 0
0 'U
rrir a~ Lo
to
C
~~c a,~-
:
,a
-
U)CC~
fr,(
.&1
to o666 C0
c0
6z
H
=
o
o
Cr < 4U
C)
o
~2U)
a)
21,OU) CU 0
C
C-
bh U) A
z, aa
D3UD
C 0OCUC
cCU
CU
:
i > c CU
CZ
'*v.
-
' CZ d
2.2
00
Lr~
U oH
(D H c H CU s
i oC 0C z on at113 o CU cs
a t
U c c CU
m
"
U )AH
$o
-
"Ue 2e 3~~ 0, m
A
Cds CU
3oYU)r~
U)~U
B~rn
C~
s
hib
U)CUU·~a
CU:
C Z
tU
U) U)
6~' 6
.P
~~'B
%%~~~~~~~~, .h l
~ '3 ~~C Io
d
w
.a .t~ ; 8
mo
:
' 2 g~ o
5
II )U -° agg W A Ha L
u 2 U
·
2
~ sCU
U)
44
-i
.UC
4)
C
,3'
U) C -
Q.,.
U)
c CU
b U) .0
U)
A'U) C
~
02
U
~
U ) Q U U'UU0U ' 3 1 J S ' 3 'U WC H Q 2 E r X C c C C E-uZ
C
EU)0 0 C .
C
C "C
C C .'
'
-~aQ - Uo
61
L.M. Plaza examined every time an MRL is set for a substance on a crop. Acceptable methodologies are not yet available for systematically looking at aggregate exposure (from other sources, such as the home and workplace) or cumulative exposure (intake from all dietary sources of similarly active substances).
2.5
IMPORT TOLERANCES, EXTRAPOLATION AND LEVEL OF DETERMINATION
The three concepts discussed above are also involved in the process of setting MRLs. In many cases there are no enough data to allow an MRL to be set. This could be when: (i) the crop is not harvested in Europe but it is treated with an active substance used in the EU (usually this is the case for tropical fruit, such as mango or coconuts); (ii) the crop is treated outside Europe with an active substance which is not used yet (e.g., trifloxystrobin on citrus) or is no longer used; is unauthorised because of lack of data (e.g., parathion-methyl); for environmental reasons (e.g., aldicarb) or for applicator-exposure reasons (e.g., lindane); or (iii) because the crop has a critical GAP which is higher outside the EU than in the EU. In the event of one of the three latter situations, an import tolerance can be asked to be set by the Community. The GAP and the tox data-packet should be provided. An import tolerance cannot be granted for banned substances (Dir 79/117/CEE) or substances that have been evaluated and withdrawn because of consumer concern. When there are crops that are similar, such as yams and potatoes, or peaches and nectarines, and the GAPs are comparable-all of them pre- or post-harvest-then, just with a few residue trials and using the data of the similar crop, the MRLs can be set. This is especially interesting for developing countries as they may save resources to achieve the same end. The level of determination (LOD) is the lower level of analytical determination. The analytical determination must be defined (as it depends on the matrix, the substance and the method and equipment) and agreed for all the parties involved, legislators, laboratories, and enforcement and monitoring authorities. The EU approach is to set MRLs at the LOD for non-authorised uses. In all those cases where the complete dossier for evaluating an active substance under Directive 91/414 is not available for the EC, the MRLs for those substances that will be withdrawn from the market in July 2003 will be fixed at the LOD. Fixing the MRL at the LOD 62
European Union legislation on pesticide residues does not mean that the substance will be banned and, on the contrary, not all the banned substances have an MRL set at the LOD.
2.6
MONITORING, REPORTING AND CONTROL
Since 1996, the Commission has made annual recommendations concerning Co-ordinated Community Monitoring Programs for pesticide residues in food. These annual Community Programs complement the national monitoring programs of the Member States. The report of the results of the 1996 program was published late in 1998 and the report for 2001 was posted on the Sanco Internet site in April 2003. The objectives of the programs are to: (i) better estimate the actual exposure of consumers to pesticide residues in food, and (ii) ensure compliance with residues legislation. To fulfil the first objective, the Commission has recently made a call for tender for studying the situation over the first 5 years. The second objective is implicitly to guard against illegal use of pesticides. Trying to cover the biggest number of pesticides and crop combinations, and taking into account the fact that the resources have to be maximised, the system is based on a 3-year rolling program in which for each year a list of pesticides chosen by certain criteria is monitored in a certain number of different commodities. Three of the MRL directives oblige Member States to monitor and report the results of the monitoring. The two relating to cereals and products of plant origin also require the Commission to compile and report on the results. The directive on animal-origin MRLs is weak in this respect, and most Member States report that such monitoring is done in the context of Directive 96/23/EC on "measures to monitor certain substances and residues thereof in live animals and animal products". Besides coordinated Community monitoring program, each Member State develops its own national monitoring program. Following the new sampling Directive 2002/63 (which repealed Directive 79/700/EEC), samples for the national and the EU coordinated programs were taken at different points such as retailers, wholesalers, markets, points of entry, and processing industries. National sampling plans exist in most countries, taking into consideration, e.g., consumption data, production figures, import/export relationships, and risks. For the coordinated program, samples are based on the number of inhabitants.
63
L.M. Plaza In the Community monitoring programs, more than 40,000 samples are analysed annually by the Member States. In the 2001 report, up to 60% of samples contain no residues and MRLs are exceeded in 3-4% of cases. The levels found to date do not present a health risk to consumers. There is a bad public perception of exceedences of MRLs. One easy solution to that problem could be to set higher levels and have fewer exceedences. This solution, of course, is irresponsible. So it is worth having a certain level of MRL violations in the results, owing to tightness in the GAPs, and moving to more target samples rather than random samples, than having a system with not many exceedences but which is less reliable. The top 10 list of pesticides usually found during recent years in the national monitoring plans are: the Maneb group, Chlormequat, Imazalil, Thiabendazole, Iprodione; the Benomyl group, Chlorpyriphos, Procymidone, bromide and ortho-phenylphenol. Extracted from the Explanatory Summary of 2001 monitoring report http://europa.eu.int/comm/food/fs/inspections/fnaoi/reports/annualeu/ indexen.html: "Overall, some 46,000 samples were analysed for, on average, 145 different pesticides. About 93% of the samples analysed were fresh (including frozen) fruit, vegetables and cereals, about 7% were processed products." "In 37% of the fruit, vegetable and cereal samples and processed products, residues of pesticides at or below the MRL (national or EC-MRL) were detected. In 3.6% of all samples, residues above the MRL (national or ECMRL) were found. 60% of the samples contained no pesticide residues. When only fresh products are considered the percentage of MRL exceedences increases to 3.9% instead of 3.6% and the percentage of samples without residues is 59%." "In 2001, the percentage of samples containing multiple residues has significantly increased compared to the 4 previous years. Only the 1996 data showed higher levels, but the 1996 data should be treated with caution, since only 11 countries delivered data." "Like in previous years, mainly fungicides were found on fruit and vegetables whereas, on cereals, the pesticides found were mainly insecticides. The 10 most frequently found pesticides found in 2001 were almost identical with those found in 2000 and the majority corresponded also to those found during 1996-1999." "It appears from the results that the commodities analysed in 2001 were all commodities on which plant protection products are frequently applied, which is in line with the findings of the year 1996 on the same commodities. In 47% of 64
European Union legislation on pesticide residues
the samples, residues of one of the 35 pesticides were found below or at the MRL (national or EC-MRL) and in 2.2% of the samples MRLs (national or EC-MRLs) were exceeded. Only 51% of the samples contained no detectable residues." "The most important pesticide-commodity combinations where detectable residues have been found at or below the MRL and above the MRL were maneb group/lettuce, maneb group/table grapes, iprodione/lettuce and benomyl group/strawberries. With regard to MRL exceedences, the most important pesticide-commodity combinations were maneb group/lettuce and benomyl group/strawberries."
Sum of fruit vegetables and cereals 7^
''n
_
IU
64
E 1996
60 - _
4 '""'07
o[ 1998 [ 1999 * 2000 [ 2001
E
, 50
a
_
E
W 40 - _ U)
30 - _ a) E 20 - _ a)
10 - _ 0 -
3 3.4 3.3 4.3 4.5 3.9
_
I
Samples with no detectable residues
2.7
Samples with residues below or at MRL (national or EC-MRL)
Samples with residues above MRL (national or EC-MRL)
RAPID ALERT SYSTEM IN FOOD AND FEED
The RASFF is based on a network involving Member States (in this case, Member States means all states that belong to the European Union and also those states that fall in the scope of the EEA Agreement, which at the moment are Norway, Liechtenstein and Iceland), the Commission and the European Food Safety Authority (EFSA). The idea is to have a rapid way of transmitting information and action to be taken among the members of
65
L.M. Plaza the system, on direct or indirect risk to human health from food or feed. Its principal objective is to prevent the placement on, or the recall from, the community market of foodstuffs that pose a serious risk to the health of the consumer. The legal basis of RASFF dates from 1984, Council Decision 84/133/EEC, as a general short-term surveillance and alarm system, but after a certain number of amendments and CD 92/59/EEC "on general product safety", where the scope was limited to food and industrial products but not feed, the current legal base was established: Regulation (EC) No. 178/2002 of 28 January 2002 "laying down the general principles and requirements of food law, establishing the European Food Safety Authority and laying down procedures in matters of food safety", chapter IV. The Regulation introduces, or refines, among other things in relation with the RASFF: new procedures, implementing measures, the feed sector and food imports from outside EU and the role of EFSA. There are two levels of information in the RASFF: alert (see Table 2.9) and information. Alert notification must fulfil the following conditions: (i) food on the market, (ii) more than one Member State involved, (iii) immediate action is required. Information notification: (i) immediate action not required, (ii) provide useful information. So, depending on the risk assessment and on the state of the food/feed (on the market, at a EU border post, or already consumed), the Member States trigger one of the two kinds of notification. The RASFF team in SANCO receives it and evaluates it and communicates the result of the assessment and the measures already taken by the Member States (withdraw or recall the product from the market or stop the consignment at the border). To assess the risk, it has to take into account the fact that risk is a function of the hazard (toxicity) and exposure. The ADI and/or ARfD express the toxicity. In the exposure, some factors such as the highest portion consumed or the variability factor (see Tables 2.10 and 2.11) in having residues in the composite sample, etc., must also be taken into account. So, in many cases, MRLs' violations from food that is a very minor part in the diet (tea, spices, some herbs, etc.) are not triggering any alert; in others, when the substance is very toxic, just a small amount could represent a big risk for the consumer. Focusing on pesticides alerts, where MRLs are exceeded it is up to the Member States to withdraw the product from the market or take other measures, based on their own risk assessment. They should notify the results immediately to the Commission that will make the risk management and transmit the outcome of the evaluation (alert, information, or even no notification) using the Rapid Alert System.
66
European Union legislation on pesticide residues TABLE 2.9 Example of an Alert communication: RAPID ALERT SYSTEM FOR FOODSTUFFS SYSTEME D'ALERTE RAPIDE DENREES ALIMENTAIRES SCHNELLWARNSYSTEM FUR LEBENSMITTEL DIRECTIVE 92/59/EEC-DIRECTIVE 92/59/CEERICHTLINIE 92/59/EWG NOTIFICATION GENERAL INFORMATION-INFORMATIONS GENERALESALLGEMEINE INFORMATIONEN: 01: NOTIFYING COUNTRY: PAYS DE NOTIFICATION: XXXXXXXXX MELDENDES LAND: 02: DATE OF NOTIFICATION: DATE DE NOTIFICATION: 8-03-2003 TAG DER MELDUNG: PRODUCT-PRODUIT-PRODUKT: 03: CATEGORY OF PRODUCTS: CATtGORIE DE PRODUITS: PRODUKTKATEGORIE: 04*: PRODUCT NAME/TRADE NAME: NOM DE PRODUIT/DENOMINATION COMMERCIALE: PRODUKTBEZEICHNUNG/ (VERKEHRBEZEICHNUNG): 05a*: IDENTIFICATION OF THE LOT: IDENTIFICATION DU LOT: LOSKENNZEICHNUNG: 05b: PUBLIC HEALTH CERTIFICATE: CERTIFICAT DE SALUBRITE: GENUSSTAUGLICHKEITSBESCHEINIGUNG:
Fruit and vegetables
Grapes
continued
67
L.M. Plaza 06*:
07*:
MINIMUM DURABILITY DATE OR BEST BEFORE DATE: LA DATE DE DURABILITI MINIMALE OU LA DATE LIMITE DE CONSOMMATION: MINDESTHALTBARKEITSDATUM ODER VERBRAUCHSDATUM: DESCRIPTION OF THE PRODUCT: DESCRIPTION DU PRODUIT: PRODUKTBESCHREIBUNG:
ORIGIN-ORIGINE-HERKUNFT: 08*: NAME OF THE MANUFACTURER: NOM DU FABRICANT: NAME DES HERSTELLERS/ ABPACKERS: 09*: VETERINARY APPROVAL NUMBER: NUMERO D'AGREMENT VtTERINAIRE: VETERINARKONTROLLNUMMER: 10: PERSON TO CONTACT: PERSONNE A CONTACTER: ANSPRECHPARTNER BEIM HERSTELLER: 11*: COMPLETE ADDRESS: ADDRESSE COMPLETE: VOLLSTANDIGE ANSCHRIFT: 12*: COUNTRY OF ORIGIN: PAYS D'ORIGINE: HERKUNFTSLAND: 13*: IMPORTER OR RETAILER: IMPORTATEUR OU DISTRIBUTEUR: IMPORTEUR ODER HANDLER: 14a*: DISTRIBUTION TO MEMBER STATES: DISTRIBUTION DANS LES ETATS MEMBRES:
White seedless grapes
xxxxXXXXX xxxx
xxxxxx
XXXXXxxx
continued
68
European Union legislation on pesticide residues
14b:
VERTEILUNG IN DEN MITGLIEDSTAATEN: EXPORTED TO THIRD COUNTRIES: EXPORTATION AUX PAYS TIERS: AUSFUHR ZU DRITTLANDERN:
DANGER-DANGER-GEFAHR: 15': NATURE OF DANGER: NATURE DU DANGER: ART DER GEFAHRDUNG: 16a*: RESULTS OF THE TESTS: RESULTATS DES ANALYSES: ERGEBNISSE DER UNTERSUCHUNGEN: 16b*: SAMPLING DATE: DATE DE L'ECHANTILLONNAGE: DATUM DER STICHPROBEN ENTNAHME: 16c*: PLACE OF THE TESTS: PLACE DES ANALYSES: ORT DER UNTERSUCHUNGEN: 17*: METHODS OF ANALYSIS USED: MITHODE D'ANALYSE UTILISEE: UNTERSUCHUNGSMETHODE: 18*: PERSONS AFFECTED: VICTIMES: BETROFFENE PERSONEN/ GESCHADIGTE: 19: TYPE OF THE ILLNESS: TYPE DE MALADIE: ART DER ERKRANKUNG:
Methomyl > NESTI
Methomyl 0.42 mg/kg 8-5-200x
xxxxxxx
GC-MS
MEASURES ADOPTED-MESURES PRISES-MABNAHMEN: 20*: VOLUNTARY MEASURES: MESURES VOLONTAIRES: FREIWILLIGE MAJ3NAHMEN DES INVERKEHRBRINGERS: 21*: COMPULSORY MEASURES: MESURES IMPOSEES: continued 69
L.M. Plaza
22*:
23:
24:
25:
AMTLICHE MABNAHMEN: JUSTIFICATION: JUSTIFICATION: BEGRUNDUNG/RECHTSGRUNDLAGE:
Methomyl: EU/MRL 0.05 for table grapes
SCOPE: NATIONAL OR REGIONAL PORTEE: GELTUNGSBEREICH: DATE OF ENTRY INTO FORCE: DATE D'ENTREE EN VIGUEUR: DATUM DES INKRAFTTRETENS: DURATION: DUREE: GELTUNGSDAUER:
OTHER INFORMATION-AUTRES INFORMATIONS-SONSTIGE INFORMATIONEN: 26*: MINISTRY: Health MINISTERE: ZUSTANDIGES MINISTERIUM: 27*: PERSON TO CONTACT: xxxxxxx PERSONNE A CONTACTER: ANSPRECHPARTNER: 28: OTHER INFORMATION: AUTRES INFORMATIONS: SONSTIGE INFORMATIONEN: 29*: CONFIDENTIAL: CONFIDENTIELLES: VERTRAULICH: 30*: IF YES, WHY: SI OUI, POURQUOI: WENN JA, BEGRiNDUNG: In this case, using the values from WHO and JMPR of the unit weight for table grapes of 0.5 kg and large portion of 0.485 kg, variability factor of 5, and the value of the ARfD of 0.02 mg/kg bw in a worst case scenario
70
European Union legislation on pesticide residues of a child of 15 kg, the outcome (according to the formulae from the WHO) from the Commission was that methomyl exceeded the ARfD by 381%; so it was an alert. TABLE 2.10 DEFAULT VARIABILITY FACTORS IF NO VARIABILITY FACTOR IS AVAILABLE FROM TRIALS DATA (SOURCE: JMPR; PSD) Commodity Variability Commodity Variability Commodity Variability factor factor factor Citrus fruit Grapefruit Lemon Mandarins and other soft citrus Oranges Limesa
7 7 7
7 7
Pome fruit Apple Pear
7 7
Quince
7
Stone fruit Apricot Peach Plum
7 7 7
Nectarine
7
Berries Table grape (bunches)
5
Guava Kiwi fruit Pawpaw/ papaya Pineapplea
Root and tuber vegetables Beetroot Carrot Celeriaca Jerusalem artichoke Potato Parsnip Swedea Sweet potato Turnipa Yama
7 7 7 7
Cucurbits Cucumber Courgette/ zucchini Melonsa
7 7 5
Watermelon Marrow
5 5
7 7 7 7
Pumpkina
5
Brassica Broccoli
7
7 7 7
Cauliflower Cabbage Chinese cabbage Kohlrabi
7 7 7
Bulb and stem vegetables Onions
7
Fennel bulb
7
Lettuce and leaf vegetables Lettucea Spinach
Chicory/ witloof
5 5 7 7
5 1
7
continued
71
L.M. Plaza TABLE 2.10 (Continuation) DEFAULT VARIABILITY FACTORS IF NO VARIABILITY FACTOR IS AVAILABLE FROM TRIALS DATA (SOURCE: JMPR; PSD) Commodity Variability Commodity Variability Commodity Variability factor factor factor
Miscellaneous fruit Avocado Banana Fig Mango
7
7 7
Fruiting vegetables Tomato Pepper, sweet
7 7
Pepper, chilli Auberginea
7 7
7 7
Stem vegetables Asparagus Celery Globe artichoke Leek Rhubarb
1 7 7 7 7
aA single portion of these commodities usually consists of less than one unit. TABLE 2.11 Pesticides for which no ARfD is necessary according to JMPR evaluations: Acibenzolar-S-methyl Amitrole (aminotriazole) Azimsulfuron Azoxystrobin Bitertanol Chlorpyrifos-methyl Cinidon ethyl
Clethodim Cyhalofop-butyl 2,4-D DDT Diflubenzuron Diphenylamine Diquat (dibromide) Ethofumesate
72
Ethoxyquin Fenhexamid Ferric phosphate Florasulam Flupyrsulfuron methyl Fluroxypyr Glyphosate (including trimesium, also known as sulfosate) Imazalil (also known as enilconazole) Iprovalicarb Isoproturon Kresoxim-methyl Metalaxyl-M Methoprene Metsulfuron 2-Phenylphenol (including sodium salt; also known as sodium 2-phenylphenate)
Piperonyl butoxide Prohexadione calcium Propargite Propyzamide Prosulfuron Pyridate Pyriproxyfen
Quintozene Spinosad Spiroxamine Sulfosulfuron Thiabendazole Thifensulfuron (also known as thiameturon) Thiophanate-methyl Triasulfuron
European Union legislation on pesticide residues 2.8
ACTIVITIES IN INTERNATIONAL FORA
There are some other organisations dealing with pesticide residues such as: the OECD, the World Trade Organisation, the Codex Alimentarius, the ACPLom6 countries. 2.8.1
The OECD
The "Pesticides Working Group" provides for the exchange of views and information on pesticides: some of the issues covered here are: work-sharing, to prevent duplicating work at the OECD level and having the same level of protection; the importance of zoning, etc. 2.8.2
The World Trade Organisation
Under the agreement on Sanitary and PhytoSanitary (SPS agreement) the EU has the obligation to notify in advance all decisions taken about MRLs; then 60 days are allowed from the date of the notification for comments from the other partners at the WTO level. At the same time, the EU may have comments on other legislation from other countries in the WTO. In the event that any comments arrive, the Community has the obligation to take them into account at the time of approving the legislation concerned. 2.8.3
The Codex Alimentarius commission
The work of the Codex Alimentarius Commission, and more particularly of the Codex Committee on Pesticide Residues (CCPR), deals with consumer safety. The CCPR base their decisions on the WHO-FAO Joint Meeting on Pesticides Residues (JMPR). The CCPR has the role of risk manager and the JMPR of risk assessor. The JMPR sets ADIs and fixes ARfDs for active substances, and the CCPR sets the Codex MRLs. Sometimes their values differ from the values obtained at EU level. When this happens, a trade problem may arise at the international level; then the EU may be taken to a trade court at the WTO level to defend its position with a scientific basis. 2.8.4
The ACP-EC Partnership Agreement
The ACP-EC Partnership Agreement signed in Cotonou on 23 June 2000 states that the Community notifies ACP countries of technical measures taken in the area of pesticides when they are likely to affect the interests of one or more ACP States. 73
L.M. Plaza Many decisions of exclusion from the annex I listing have had some negative impacts (MRLs at LOD) in developing countries where the use of generic substances is a common practice, as they are cheaper than substances under patent. There is also a negative impact at EU level, where there may be problems of importing to the Community some tropical fruit and vegetables. In anticipation of these impacts, the Commission has established two development programs. The first of these is aimed at promoting Integrated Crop Management in these countries, lessening their dependence on pesticides use, and reducing where possible the residue levels found in their commodities. The second, the "Pesticides Initiative", is aimed at promoting better coordination and information gathering in the ACP area with a view to providing, in good time, the data necessary for the Commission to set MRLs for tropical fruit and vegetables. 2.9
FUTURE TRENDS
Globalisation is making the world smaller. The main countries (and the international organizations as Codex and the OECD/OCDE) are much closer than before in evaluating and setting pesticide MRLs. The aim is to smooth the rules among them and avoid trade problems by trying to achieve the same standards, or as many equivalents as possible. The following solutions are in progress: ·
·
The sharing of information among administrations is growing, as in the OECD/OCDE. Why should one waste resources in evaluating substances if another organisation or country has already evaluated them? Data protection and patents seem to be problems, as it is very expensive to generate the different dossiers for the same substance. Increasing the resources of the JMPR could also improve the situation, as the evaluation of the Codex is very slow.
At the EU level, the Commission proposal on pesticide legislation is under current discussion at the European Parliament and Council. The result of this discussion at this stage is difficult to foresee. Many details need to be reflected on, such as the default value of 0.01 mg/kg as the level of determination; the role of the EFSA, the inclusion of cumulative and aggregate pesticides in the setting of MRLs, the use of monitoring data in certain cases instead of GAPs, the choice of which commodities should be in the list, etc. Whatever the state of this Regulation proposal at the end of the Council and Parliamentary discussion, it is clear that it reflects a public wish for quick harmonisation and simplification of the current situation.
74
Chapter3
Sample handling and clean-up procedures I Stewart L. Reynolds
3.1
INTRODUCTION AND SCOPE
This chapter does not include sampling techniques, which are described by Codex [1] and have been adopted by the European Commission [2], but begins at the point when the sample is received at the laboratory-with "the laboratory sample". The following text focuses predominantly on conventional solvent extraction techniques used in multi-residue methods (MRMs) for the extraction of pesticides, from "non-fatty" foods such as fresh fruits, vegetables and grains. Accelerated solvent extraction (ASE), microwave extraction, supercritical fluid extraction (SFE) and solid-phase extraction (SPE) techniques are covered in chapter 4. Similarly, gel permeation chromatography (GPC) is the only clean-up technique discussed in this chapter, and other clean-up techniques, such as adsorption and immuno-affinity, are described in more detail in chapter 4. 3.2
3.2.1
LABORATORY SAMPLE PREPARATION
Portion of the laboratory sample to be analysed
Before any pesticide residue analysis is undertaken by a laboratory, it is important to know for what purpose the resultant data will be used. In general, screening for pesticide residues in food is requested for two main reasons: 1. To monitor good agricultural practice (GAP), by checking for compliance with international (Codex) standards accepted by the World Trade Organisation (WTO), or national legislation, i.e., ensuring that maximum residue limits (MRLs) are not exceeded, and/or that non-approved pesticides have not been used. Comprehensive Analytical Chemistry XLIII Fernindez-Alba (Ed.) (C 2005 Elsevier B.V. All rights reserved
75
S.L. Reynolds 2. To provide residue data which can be used to estimate consumer dietary intakes and to subsequently perform risk assessments. The portions of the commodities to which the MRLs apply are clearly stipulated by Codex and have been adopted into many countries' national legislation. Food commodities are classified into groups, e.g., root and tuber vegetables (carrots, potatoes, radishes, etc.) or stone fruits (apricots, cherries, peaches, etc.). Codex also provides information on how to prepare the laboratory sample for analysis (i.e., to prepare the analytical sample) once it has arrived at the laboratory, e.g., by removal of soil, decomposed leaves, stalks, stems or stones. If residue analysis is to be undertaken for dietary intake purposes then the portion of the commodity that is to form the analytical sample might be quite different from that required for MRL compliance purposes. A typical example is banana. Bananas are classified by Codex as "assorted fruits with inedible peel". For MRL compliance purposes, only the crowns and stalks that have usually already been removed from bananas prepared for retail sale would be removed before analysis. However, if consumer exposure is to be assessed, then the bananas would be peeled and the skins discarded, to reflect normal consumer practice. Alternatively, the bananas could be processed and analysed as a whole (including the skins, but not the crown and stalks), and if available, an appropriate "processing factor" could subsequently be applied to the residue data so generated. In addition to home processing, e.g., washing, peeling, cooking, etc., many raw agricultural commodities are subjected to commercial food manufacturing processing before consumption. The estimation of consumer exposure, and hence consumer risk, requires information on residue levels in edible portions of raw agricultural materials and processed (home and/or commercial) commodities. FAO [3] states that the processing factor should be explicitly defined in terms of residue levels, as follows: Processing factor = the residue level (mg/kg) in the processed product/the residue level (mg/kg) in the raw agricultural commodity. The following processing methods have been identified as being widely used by industry and/or consumers: * drying (e.g., dates, figs, grapes) · canning (e.g., fruits, vegetables) · juicing (e.g., citrus fruits, tomatoes) * milling (e.g., wheat grain into flour and bran) * baking of bread · brewing (e.g., hops), wine production (e.g., grapes) 76
Sample handling and clean-up procedures I · · ·
oil extraction and refining (e.g., olives, rapeseed) refining (e.g., sugar beet) cooking, including boiling (e.g., potatoes, rice, vegetables), frying (e.g., potatoes, meat) and microwave cooking
Studies undertaken to produce processing factors must mirror home processing and/or commercial processes as closely as possible. These processing factors can then be applied to the result obtained from analysis of the raw commodity. 3.3 3.3.1
LABORATORY SAMPLE PROCESSING Homogeneity
The analytical sample is likely to consist of a number of individual units and could weigh more than 2 kg. As the whole of the analytical sample will not be analysed, it is important to homogenise the individual units so that representative sub-samples (test portions) may be withdrawn. Sub-sampling error due to heterogeneity of residue distribution in the analytical sample can contribute greatly to the uncertainty surrounding the subsequent residue data that are generated from test portions. Such errors will be increased as the size of the test portion taken is reduced. As most modern methods tend to utilise relatively small test portions (2-10 g) in order to save time, materials and money, the preparation of essentially homogeneous analytical samples is paramount. A recent study undertaken by Young et al. [4] involved the comminution of apples, cabbage and green beans containing field-incurred residues of p,p'-methoxychlor. A 40 qt vertical cutter mixer (as stipulated in the US FDA Pesticide Analytical Manual [51) was used to comminute large samples comprised of many individual fruits or vegetables. Test portions of 100, 50 and 25 g taken from the resultant homogenates of all three crops produced equivalent results. However, statistically significant differences were obtained for green beans with test portions of 2 g and cabbages with test portions of 10 g. It should be noted that Young et al. [4] used a lengthy comminution time (5 min) and that they chose to use a particularly stable pesticide, p,p'-methoxychlor. Comminution may be achieved by using an appropriate cutting/grinding device, and there are many suitable bowl choppers, mills and food processors available. Most are electrically powered but some may require manual intervention to ensure good mixing of the comminuted material. The degree of homogeneity obtained will not only be dependent on the type of equipment
77
S.L. Reynolds used, but also on a number of other key factors. For example, if a bowl chopper is used: 1. Length of comminution. In general, the longer the equipment is running the greater the disintegration of the sample, although after a certain time hardly any further disintegration will occur. It is important to consider analyte stability (see section 3.3.2). As the chopping/cutting process continues, more heat is generated within the sample. Some cutting devices are fitted with cooling jackets that can be used to prevent undue heating of the sample during comminution. 2. Design of the cutting device. This is particularly important and there are a number of design features that are necessary for rapid comminution. The motor must have the necessary power to match the capacity of the bowl. The blades should be broad and inclined and arranged at different levels. A mixing baffle will ensure a more thorough intermixing of the sample. 3. Sharpness of the cutting blades. With any cutting/chopping device, after much use the blades will become blunted and less effective. 4. Nature of the sample. Some materials are relatively fragile and disintegrate easily and rapidly to a small particle size, whilst others are much harder, fibrous or have tough skins, and will take much longer to achieve the same particle size. Materials that combine both soft and fibrous/tough/hard components are usually the most difficult to homogenise. 5. Correct amount of sample. When using any cutting/chopping device it is important not to over- or under-fill the equipment bowl with sample. Too little sample may result in poor comminution, whilst too much sample will not only produce a similar effect, but could also result in spillage from the equipment. 3.3.2
Analyte stability
The potential for loss of certain pesticides during sample processing has been known for many years. Losses of dithiocarbamate fungicide residues during sample processing of fresh plant material was first recognised in 1971 when Howard and Yip [61 demonstrated that significant losses ofmaneb occurred after addition to freshly chopped kale. Hill et al. [7] concurred that this instability problem could be overcome by adopting one of the following strategies: 1. Take the whole laboratory sample and analyse as a single determination without disintegration. However, this is seldom practical as more than a single determination is often required, and large volume glassware and large quantities of reagents would be needed.
78
Sample handling and clean-up procedures I 2. Cut segments (causing the minimum possible disruption to the sample tissue). With this approach the sub-sampling error will be high, and it is prudent to analyse a number of replicate segments, and calculate the mean result. 3. Use cryogenic processing to slow down degradation of the dithiocarbamate residue (see below). Comminution processes are normally performed at room temperatures (20-25°C), but it is important to realise that enzymes and other labile chemicals will be released from the disrupted tissues, which may react with pesticide residues if present in the sample. As simple chemical reaction rates decrease as the temperature decreases, one way of eliminating, or at least reducing, pesticide losses is to freeze the sample prior to processing and then comminute it in the presence of "dry ice" (solid carbon dioxide). Such processing is often referred to as "cryogenic milling" and comminution can take place at more than 40°C below room temperature. Fussell et al. [8] demonstrated that losses of bitertanol (95%), heptenophos (50%), isofenphos (40%), and tolylfluanid (48%), which occurred at room temperature, could be eliminated by using cryogenic milling. Also losses of dichlofluanid (54%) and etridiazole (40%) at room temperature were reduced to 10 and 14%, respectively, at - 20°C. Following cryogenic milling, it is advisable to store the samples in open bags in the freezer for at least 24 h to allow the dry ice to completely evaporate before weighing out test portions for analysis. 3.4
3.4.1
EXTRACTION OF PESTICIDE RESIDUES WITH ORGANIC SOLVENTS Extraction techniques
In most classical methods for the determination of pesticide residues, the pesticides are extracted from samples using a single organic solvent, a mixture of organic solvents, or an aqueous/organic solvent mixture. The choice of extraction equipment may be influenced by the degree of comminution that has taken place during sample processing (see section 3.3). Extremely finely comminuted analytical samples may only need to be shaken/agitated with the extraction solvent using a shaker or vortex mixer to achieve maximum extraction efficiency. If possible, the extraction efficiency should be checked using test samples containing known concentrations of incurred residues. Analytical samples that have received little or no laboratory processing will require further comminution and this can be achieved as part of 79
S.L. Reynolds the extraction procedure. Bottom and top-driven macerators/blenders (e.g., Ultra Turrax or equivalent) and sonicators may be used to comminute the sample and mix it with the extraction solvent simultaneously. Thus the analyte should be in equilibrium with the solvent and any insoluble sample matrix. 3.4.2
Properties of organic solvents
There are a number of solvents and solvent mixtures that can be chosen to extract pesticide residues from foods. Choice, in terms of extraction efficiency, will be largely dependent on the polarity of analytes that are to be targeted. Non-polar pesticides, e.g., the persistent organochlorines, can be readily extracted using non-polar, hydrocarbon solvents, whereas the more polar pesticides, e.g., many of the organophosphorus compounds, will require a low to medium polarity solvent for efficient extraction. The polarity of a solvent is broadly reflected in its dielectric constant, and values for the solvents most commonly used for the extraction of pesticides from food and environmental samples are given in Table 3.1. If sample extracts must be concentrated before clean up or determination, then the boiling point of the solvent is also important, and these values are also presented in Table 3.1. However, it is worth remembering that many food samples, particularly fruits and vegetables, contain a high proportion of
TABLE 3.1 Important physical properties of solvents [9] Solvent
Dielectric constant (20°C)
Acetone Acetonitrile Cyclohexane
20.7a 37.5
Dichloromethane Ethyl acetate Hexane Methanol Pentane Petroleum ether Water
2.0 9.1 6.0a 1.9 32.6a 1.8 78.5 --
"25C.
80
Boiling point (°C)
Vapour pressure (kPa at 25°C)
56 82 81 40
30.8 11.8 13.0 58.2
77 69
12.6 20.2
65 36 30-60
16.9 68.3
100
Sample handling and clean-up procedures I water. If a water-miscible extraction solvent, such as acetone, is used then the resultant sample extract will also contain a high percentage of water that may need to be removed prior to concentration. 3.4.3
Basic safety with solvent usage
The two most important considerations with regard to the use of organic solvents within the laboratory are flammability and toxicity. With the exception of dichloromethane and water, all the solvents listed in Table 3.1 are readily flammable at room temperature. It is therefore essential that adequate ventilation is provided in the extraction areas of laboratories, and that all electrical equipment utilised therein are "spark-proof'. Indeed all extraction and evaporation procedures should be performed in fume cupboards. As well as the potential dangers posed by flammable solvents, worker inhalation exposure must also be considered. Table 3.2 gives the flash points and exposure limits for some of the solvents commonly utilised in the laboratory. 3.4.4
Solvents used as extractants in multi-residue methods
Many MRMs have been developed over the past four decades that allow the simultaneous analysis of a wide range of pesticide residues in a variety of different foods commodities. Without MRMs, residue analysts would be faced with using literally hundreds of specific, single residue methods (SRMs),
TABLE 3.2 Flammability and toxicity data for solvents [10] Solvent
Flash point (°C)
Long-term exposure limit (8-h period) (ppm)
Long-term exposure limit (8-h period) (mg/m3 )
Acetone Acetonitrile Cyclohexane Dichloromethane Ethyl acetate Hexane Methanol Pentane Petroleum ether
- 20 6 - 20 -4 - 22 11 -40 - 22 to - 40
500 40 100 100 200 20 200 -
1210 68 350 350 72 266
81
S.L. Reynolds even though many of them would be very similar, if not the same. However, as it is impossible to create a single set of optimum conditions for extraction and clean up of all possible pesticide/commodity combinations, MRMs must have limitations. With all MRMs, and in particular multi-class MRMs, recoveries approaching 100% for all pesticides from all possible food matrices is unlikely. Numerous different organic solvents, and mixtures of organic solvents, have been used to extract a wide range of pesticides with different physicochemical properties from foods. The use of three solvents (acetone, ethyl acetate (EtAc) and acetonitrile (MeCN)) has predominated in MRMs and all three continue to appear even in the most recent publications. It seems that residue analysts still cannot decide which of these solvents is the most suitable for multi-residue extraction. Acetone, MeCN and EtAc have all been, and still are being, used in hundreds of analytical laboratories around the world to extract both non-polar and polar pesticides from a wide diversity of raw agricultural materials and processed food products. Acetone and MeCN are both fully miscible with water, whereas EtAc exhibits only low miscibility with water. MeCN and acetone extracts from fresh fruits and vegetables will contain water coincidentally extracted from the sample. Schenck and Lehotay [11] noted that it is usually necessary to remove this water before any gas chromatographic determinations can be made, and this can be achieved by partitioning the pesticides from the aqueous/organic extract into a water-immiscible solvent. 3.5
3.5.1
HISTORICAL DEVELOPMENT OF MULTI-RESIDUE METHODS BASED ON THE USE OF ACETONITRILE, ACETONE AND ETHYL ACETATE AS EXTRACTION SOLVENTS Acetonitrile
MeCN has the disadvantage of being both more expensive and more toxic than acetone and EtAc. However, it also has advantages over acetone and EtAc, not least is that, because of its higher polarity, much less lipophilic material, such as oils and chlorophyll and to a lesser extent waxes, are co-extracted with the pesticides. One of the first MRMs to be published was by Mills et al. [12] in 1963. They used MeCN to extract organochlorine pesticides (OCs) from a range of different fruits and vegetables. These relatively non-polar compounds were then partitioned into petroleum ether (PE) after the addition of a large 82
Sample handling and clean-up procedures I volume of water and a small volume of saturated salt (NaCl) solution. The PE solution was then applied to a Florisil column as a clean-up step, prior to determination using a gas chromatograph (GC) fitted with an electroncapture detector. By the late 1960s the widespread use of organophosphorus (OP) insecticides in agriculture meant there was a requirement for a number of these predominantly more polar compounds to be included in MRM analytical suites. The partition using non-polar PE was unsuitable for polar compounds and meant that recoveries of many OPs through this step would be poor. Alternative methods were developed which retained the use of MeCN as the extraction solvent, but involved alternative partitioning solvents and/or cleanup techniques. In 1971, Storherr et al. [13] used the higher polarity dichloromethane (DCM) to replace the non-polar PE and acid-treated charcoal to replace the Florisil. This method was used to determine a wide range of OPs in samples of fruits and vegetables. By the early 1990s MeCN was being used to extract more than 100 target analytes from many different classes of pesticide, e.g., organochlorines, organophosphorus compounds, carbamates, etc. In 1991, Ton Joe and Cusick [14] used MeCN to recover 143 pesticides from 13 different crop samples. They extracted 50 g of chopped sample with 100 ml of MeCN, and added solid NaCl to the filtered extracts to effect a separation into two phases. An aliquot of the upper MeCN phase was dried and concentrated before using splitless injection into a gas chromatograph fitted with a mass spectrometer, without any clean up. In the same year, Lee et al. [15] used the identical MeCN extraction procedure, but added an SPE (C-18) cartridge clean-up step so that the extracts were compatible with selective GC detectors (FPD and ECD) and HPLC. In 1995, Fillion et al. [16] also adopted the same extraction procedure, but used charcoal-Celite to effect a clean up for 199 pesticides with GC-MS and HPLC-fluorescence detection. In 1999, Cook et al. [17] again used the same basic extraction procedure with a variety of SPE clean-up steps to analyse 89 pesticides in a wide range of fruits and vegetables. 3.5.2
Acetone
Acetone is the least toxic, least expensive, and most volatile of the three solvents. In 1971, Becker [18] utilised acetone as a solvent to extract OC and OP pesticides from samples of plant-based materials. However, his method per se is unlikely to be used in laboratories in the 21st century as it involves
83
S.L. Reynolds the use of benzene in the clean-up step. Luke et al. [19], 4 years later, developed an MRM that included not only OCs and OPs, but also various organonitrogen (ON) compounds. Samples of fruits and vegetables (100 g) were extracted with 200 ml of acetone and the extracts then partitioned into a PE/DCM mixed solvent followed by a Florisil clean up similar to that used by Mills et al. [12]. In 1981, Luke et al. [20] improved this procedure by eliminating the Florisil clean-up step and adding petroleum ether following the initial concentration to remove traces of DCM. About the same time, Specht and Tillkes [21] published their MRM for 90 pesticides in samples of both vegetable and animal origins. Like Luke, they also utilised DCM to partition the pesticides from the aqueous acetone extracts, but added a GPC clean-up step. The method was adopted in Germany by the DFG into their Manual of Pesticide Residue Analysis. The status and popularity of this manual amongst residue analysts meant that the Specht and Tillkes DFG method (S-19) became widely used during the 1980s and early 1990s, not only in German laboratories, but also in many other laboratories throughout Europe. By the early 1990s, analysts were coming under increasing pressure to eliminate usage of chlorinated solvents, such as DCM. In 1994, Koinecke et al. [22] investigated the use of several less toxic solvents as possible replacements for DCM. They concluded that cyclohexane, light petroleum (also known as petroleum ether) and tertiary butyl methyl ether all gave acceptable recoveries for a wide range of pesticides, including those of high water solubility, by partitioning from acetone extracts of plant materials. A year later, Specht et al. [23] published a paper that updated the DFG method S-19 by replacing the DCM with EtAc/cyclohexane (1:1) in the liquid-liquid partition. This publication also simplified the S-19 method by combining the acetone extraction step with the liquid-liquid partition step. An added bonus was that EtAc/cyclohexane was used as the GPC elution solvent, so no solvent exchange was required for the sample extracts prior to clean up. In more recent years, SPE has been used by Casanova [24] and Nordmeyer and Their [25] to replace the DCM partition from aqueous/acetone extracts. Because the word "extraction" appears in SPE, it is easy to assume that this is simply an alternative to solvent extraction, whereas in most methods SPE acts as a clean up, not an initial extraction, step. Adou et al. [26] used acetone in combination with other solvents to apply a newer technology, pressurised liquid extraction (PLE), also known commercially as ASE, to automate the extraction procedure and reduce analysts' exposure to solvents. SPE and PLE are covered in detail in the following chapter of this book.
84
Sample handling and clean-up procedures I 3.5.3
Ethyl Acetate
Whilst MeCN and acetone are water miscible, EtAc exhibits only very low water miscibility. The main advantage of EtAc is that the small amounts of water present in sample extracts can be easily removed by the addition of anhydrous salts, intended for removing water, without the need to perform any additional liquid-liquid partitioning. In comparison with the other two solvents EtAc is less polar, and with oily samples, such as avocados and animal products, more non-polar lipophilic materials may be present in the extracts. One of the first MRMs to be based on an EtAc extraction was published by Watts et al. [27] in 1969. They obtained good recoveries of 60 OPs from apple, carrot and kale crops extracted with EtAc. The extracts were cleaned-up on a charcoal column and the pesticides determined using GC-NPD. For the next decade there appeared to be a paucity of published methods that employed EtAc, and it was not until the late 1980s that EtAc gained in popularity as the primary extraction solvent. Roos et al. [28] used EtAc as an extractant for OC and OP residues from a wide range of foods, including fruits and vegetables. They used a GPC column to clean up the sample extracts and reported recoveries of OCs from cereal grains when using EtAc that were virtually the same as those obtained using acetone. In 1994, Ferndndez-Alba et al. [29] used EtAc to extract residues of OCs and pyrethroids from fruits and vegetables. They used silica gel cartridges to effect clean up of the extracts and employed GC-ECD and GC-MS to determine the pesticides. In the same year, Holstege et al. [30] used EtAc modified with 5% ethanol (EtOH) to extract OCs, OPs and carbamates from a range of both plant and animal-based commodities. They reported that the EtOH was required to produce quantitative recoveries of the most polar OPs (acephate, methamidophos and monocrotophos) from water, without affecting the recoveries of the most non-polar OP pesticide, chlorpyrifos. In 1999, Obana et al. [31] also used EtAc to extract pesticides, of mixed classes, from fruits and vegetables. They used a high-speed homogeniser to comminute the samples thoroughly, and substituted sodium sulphate with a high water capacity absorbing industrial polymer. One gram of this polymer of acrylic acid (Aquapearl A3, Mitsubishi Chemical Industry Ltd, Tokyo, Japan) was reported to absorb 200 ml of water and was extremely cheap (around US $10/kg). Recoveries of 107 pesticides (from many different classes) from a wide variety of fruits and vegetables were > 70% and were generally in good agreement with recoveries using acetonitrile as the extraction solvent (with the exception of methamidophos, and possibly methidathion), as shown in Table 3.3.
85
S.L. Reynolds TABLE 3.3 Comparison of EtAc/polymer and MeCN in residue analysis [31] Sample
Pesticide
Lettuce Orange Pineapple Grapefruit Cucumber an
EtAc/polymer
Acetonitrile
Average result (mg/kg)a
RSD (%)
Average result (mg/kg)
RSD (%)
Permethrin Chlorpyrifos Triadimefon
0.06 0.12 0.50
2.7 15.1 5.2
0.07 0.13 0.51
10.7 1.8 6.5
Triadimefon
0.17
7.8
0.17
3.6
Methidathion Methamidophos
0.09 0.44
5.6 2.8
0.12 0.63
19.1 2.7
5.
3.6 3.6.1
FACTORS AFFECTING EXTRACTION EFFICIENCY pH
The pH of the extraction solvent can be extremely important for a number of pesticides, as it may not only affect their dissociation and salvation, but in some cases, also their stability. The pH of homogenates of fruits and vegetables varies widely, ranging from <2-7. Many pesticides, particularly esters, some of which are commonly found as residues in fruits and vegetables, are pH sensitive and will rapidly hydrolyse at extremes of pH. For example, captan and captafol are readily hydrolysed to tetrahydrophthalimide, folpet to phthalimide, and dicofol to 4,4'-dichlorobenzophenone (Fig. 3.1). As none of these hydrolysis products are included in the definition of the MRLs, for compliance monitoring and enforcement it is important that the parent compounds are determined intact and not degraded at any stage of the analysis. Table 3.4 gives some examples of pesticides that are pH sensitive. Degradation of these pesticides may occur at any stage of an analytical method, during sample processing, extraction, clean up and the subsequent instrumental measurement. Gilvydis and Walters [33] reported 70-90% losses of the phthalimide fungicides captan, captafol and folpet following spiking onto fresh cauliflower and blending as part of an inter-laboratory study. They presumed that the fungicides were decomposed by reaction with fresh cauliflower constituents, as these losses were overcome when the same sample was spiked after freezing. Martinez et al. [34] experienced losses of 86
Sample handling and clean-up procedures I
-SCCI
-H
---
3
Folpet
Phthalimide
-SCCo 3
--.-
Captan
0 N -H
•o~~ Tetrahydrophthalimide
OH
Dicofol Dicofol 0 ---i-,--
Cl
C
Cl
4,4-Dichlorobenzophenone Fig. 3.1. Examples of chemical structures of parent fungicides and their breakdown products.
the same three fungicides due to hydrolysis in predominantly aqueous samples. Instead of lowering the pH, they successfully overcame this problem by adding a surfactant Triton X-114. The presence of the surfactant stabilised the fungicides and prevented hydrolysis. Colina et al. [35] reported losses of a number of pesticides, including captan and folpet, when stored on (SPE) Cls8 cartridges, even when stored at low temperatures. They concluded that the losses were not due to hydrolysis (as no water was present), or volatility (both have relatively low vapour pressures). Although they could not identify the mechanism(s) of loss, they found that all the pesticides
87
S.L. Reynolds TABLE 3.4 Examples of pesticides that are prone to hydrolytic breakdown [32] Pesticide
Hydrolyse at low pH
Hydrolyse at high pH
Bupirimate Captafol Captan Chlorothalonil Dichlofluanid Dicofol Folpet Tolylfluanid Triazophos"
Yes Yes No No No No No No Yes
No Yes Yes Yes Yes Yes Yes Yes Yes
"Many organophosphorus insecticides are susceptible to hydrolysis and triazophos is just one example. they tested were stable for up to 30 days when stored at - 18"C as dried organic extracts. Some pesticides are weakly basic, and a few, such as dichlorophen, are weakly acidic. Hence the pH of the sample can have a marked effect on the extraction efficiency. For example, carbendazim (pKa= 4.2), imazalil (pKa = 6.5) and thiabendazole (pKa = 4.7) are all weak bases. As such they become protonated in aqueous solution at low pH and their salts, in particular, have low solubility in non-polar and low polarity organic solvents. For fresh fruits and vegetables differences in variety and maturity will often affect the pH, immature fruit containing more naturally occurring acids. Variation in pH between samples of the same commodity may be due to differences in variety and maturity. By raising the pH to about 7.5, the free bases of carbendazim, imazalil and thiabendazole are greatly favoured and, being much more soluble in water-immiscible solvent than their salts, they are more readily extracted from an aqueous phase under these conditions. This means that, with the exception of bupirimate, all the other pesticides listed in Table 3.4 may undergo some degree of hydrolytic breakdown. Another way to reduce the solubility of polar pesticides in aqueous solution is by "salting out". This involves the addition of high concentrations of various salts (as solids or in solution), e.g., NaCl, Na 2SO 4, MgSO 4, etc., to compete with the pesticide molecules for solvation by water, effectively decreasing their affinity for the aqueous phase and encouraging their migration into a waterimmiscible solvent. 88
Sample handling and clean-up procedures I 3.6.2
The use of salts for drying solvent extracts
Removal of trace amounts of water from sample extracts in EtAc may also be very important prior to clean up and/or gas chromatographic analysis. Anhydrous salts used as desiccants remove water from organic solvents by forming hydrates. Of the three salts discussed in the above section on liquidliquid partitioning, only Na 2SO 4 and MgSO 4 are effective as they both form hydrates. MgSO 4 forms a monohydrate and heptahydrate, whilst Na 2SO 4 forms a heptahydrate and decahydrate. Therefore on a molar basis, Na 2SO 4 should be a slightly more effective dehydrating agent. 3.6.3
The use of salts in liquid-liquid partition
Liquid-liquid partitioning is utilised to transfer pesticides from a predominantly aqueous sample extract, e.g., fresh fruit and vegetable samples, that have been extracted with a water-miscible solvent (such as acetone or MeCN), into a non-water-miscible solvent. This not only facilitates easier concentration of the extract, but may also serve as a clean-up step by removing potentially interfering co-extractives. The liquid-liquid partition of the more polar compounds, from aqueous extracts can be improved by the addition of water-soluble salts such as NaCl, Na 2SO 4, MgSO 4, etc. As more "salt" dissolves in the aqueous phase, more of the pesticide is partitioned into the non-aqueous phase. However, the addition of salt may reduce the effectiveness of the clean up as more polar co-extractives will also become more likely to partition into the non-aqueous phase. The solubility of NaCi is almost unaffected by temperature, whereas the solubility of MgSO 4 and Na 2SO 4 increases markedly with temperature (Table 3.5). In effect, at higher temperatures and hence higher concentrations, the competition for solvation by water is increased and other solutes, including pesticides, will have a lower apparent affinity for water as a consequence and be "driven" either out of solution or into an immiscible solvent if it is in contact with the water. Of course the concentration of salt in TABLE 3.5 Water solubility of salts commonly used in MRMs at different temperatures [361 Salt
0°C
10°C
20°C
25°C
30°C
40°C
50°C
MgSO 4 NaCl Na 2SO 4
18.2 26.3 4.3
21.7 26.3 -
25.1 26.4 16.1
26.3 26.5 21.9
28.2 26.5 26.5
30.9 26.7 32.4
33.4 26.8 31.6
89
S.L. Reynolds TABLE 3.6 Comparison of different salts for the removal of water from MeCN extracts [37] Salt
Amount of salt added
Water in MeCN before addition of salt (%)
Water in MeCN after addition of salt (%)
NaCl Na 2SO 4 MgSO 4
2 g/27 ml 0.8 g/4 ml 0.8 g/4 ml
27 6 6
6.0 3.9 0.6
the water phase not only affects the recovery of pesticides but also the partition of other compounds co-extracted from the sample into the organic phase. This may influence the degree of clean up that is subsequently needed before the instrumental determination of the pesticide(s) is performed. Lehotay et al. [371 undertook an experiment to compare the effectiveness of NaCl, Na 2SO 4 and MgSO 4 on the removal of water from MeCN extracts (egg extracts). Table 3.6 presents the results obtained from NMR measurements of the water content in MeCN following the use of salts to dry a water/MeCN mixture. They concluded that MgSO 4 was more effective for removal of water from MeCN extracts than Na 2 SO 4 (90% was removed using MgSO 4, compared with 35% using Na 2SO 4). However, the exothermic hydration of MgSO 4, compared with the endothermic hydration of Na 2SO 4, may have had a considerable affect on the outcome of the experiment, as there was no temperature control and the authors did not state that the mixtures were allowed to equilibrate to room temperature. It is also important to bear in mind that these inorganic salts can be the source of organic contaminants, such as phthalates, which may carry through the clean up and may interfere with the final determination. These can be removed by heating the salt in a muffle furnace at 500-600°C for several hours. 3.7 3.7.1
CLEAN-UP TECHNIQUES Gel permeation chromatography
GPC, sometimes more aptly referred to as size exclusion chromatography (SEC), is ideally suited as a clean-up technique in MRMs, because it predominantly separates pesticides from co-extractives based on their relative molecular sizes. A wide range of gels is available but the technique has been applied mostly to pesticides soluble in organic solvents and so separation is generally performed using columns of divinylbenzene-linked polystyrene gels. 90
Sample handling and clean-up procedures I As many pesticides have molecular weights that are low in comparison with common co-extractives such as chlorophyll, carotenoids, waxes, triglycerides, etc., separation can be readily achieved. Generally a column containing a gel (e.g., Bio beads SX-3) that has already been pre-swollen with eluent is prepared. The sample extract is applied to the top, which is then eluted with a suitable solvent. In passing down the column, the higher molecular weight species penetrate the pores of the gel less effectively than the smaller molecules and therefore travel more rapidly through the column. Thus, a separation based largely on physical size is achieved. The early fractions of eluate containing high molecular weight co-extractives are discarded, whilst later eluting fractions containing the target pesticides are collected. Usually the last eluting fraction containing very small molecules is also discarded. Unlike adsorption-based clean-up procedures, differences in polarity between pesticides have little or no effect on their retention times on the GPC column, so a wide range of analytes can be collected in one fraction, without the need to adjust the polarity of the column eluent. In practice, there is usually some evidence of other interactions-e.g., aromatic ring stacking-but the effects do not completely negate the size separation. Another advantage of GPC is that the gel is eluted clean and requires no reactivation, etc., so that large numbers of extracts can be cleaned-up in series without loss of efficiency. It is also easy to automate by connecting a pump and auto-injector to the column and adding a fraction collector (see Fig. 3.2). The idea of using GPC, or gel filtration chromatography, as it was often referred to in the past, came from its use in the field of protein chemistry. The first material to be used in an attempt to separate pesticides from co-extracted
Fig. 3.2. A typical automated GPC/HPGPC clean-up system.
91
S.L. Reynolds materials, such as chlorophylls and carotenoids, was a modified dextran, Sephadex LH-20. In the late 1960s, Ruzicka et al. [38] attempted to separate a number of OP pesticides from cabbage co-extractives using Sephadex LH-20 columns pre-swollen with acetone and ethanol. However, they found that the elution profiles of the pesticides and co-extractives were too similar and ineffective in cleaning-up cabbage extracts. By 1973, Masud et al. [39] had demonstrated that both Sephadex LH-20 and Bio beads SX-8 (a cross-linked polystyrene gel) could be used to clean-up extracts of rice containing OP pesticides. About the same time, Tindle and Stalling [40] evaluated a number of different gels (Sephadex-LH-20, Bio Beads S-X2, SX-4 and S-X8) for separating OCs from fish oil. They concluded that S-X2 gave the most satisfactory separation with 95% recoveries of the OCs and < 0.5% of the total lipid material appearing in the pesticide fraction. The Bio Beads SX-2 column was soon automated by Stalling et al. [41] and later adopted into the DFG Manual of Pesticide Residue Analysis [42]. By 1980, Specht and Tillkes [21] had developed an MRM for 90 pesticides (both water- and fat-soluble) in which they utilised a column of Bio Beads SX-3 (25 mm i.d. x 30 cm length) with a 1:1 EtAc:cyclohexane elution solvent. Bio Beads SX-3 has since become the gel of choice for nearly all MRMs that involve a GPC clean-up step. In the 1990s, a number of papers were published that utilised SX-3 columns. Andersson and P6lsheden [43] used an SX-3 column with a mobile phase of 1:1 cyclohexane:DCM as a clean-up technique to compare the extraction efficiencies of acetone and EtAc. Anastassiades and Scherbam [44] also used an SX-3 column with a mobile phase of 1:1 EtAc:cyclohexane to clean up citrus fruit sample extracts for multi-residue analysis. More recently, Nordmeyer and Thier [25] used a similar SX-3 column and elution solvent for the multi-residue determination of pesticides in apples, carrots, spinach and red cabbage. In 1997, Gelsomino et al. [45] used the same column packing and elution solvent to clean up sample extracts of carrot, melon and tomato before determining 77 different pesticides. They used a 10 mm i.d. x 30 cm column containing only 8.5 g SX-3 gel, so that solvent consumption was reduced to 30-40 ml per sample extract. Sannino et al. [46] used the same column and mobile phase to clean up samples of fruit juices and canned peas prior to the simultaneous determination of 19 fungicide residues by GC-MS. Van Rhijn and Tuinstra [47] had already demonstrated that a 2 mm i.d. x 600 mm column containing Bio Beads SX-3 gave comparable results for the clean up of extracts of peppers and animal fats for the determination of organochlorine compounds. They were able to use a mobile phase flow rate of only 40 l/min so that total solvent consumption had been reduced to around 3 ml per sample extract. The Dutch Inspectorate for Health 92
Sample handling and clean-up procedures I Protection has since adopted this procedure in their manual of analytical methods [48]. Clean-up procedures based on GPC with SX-3 continue to be popular in many laboratories, and many of the newer pesticides are amenable to this technique. In 2001, Christensen and Granby [49] published a paper in which they described the use of an SX-3 column to clean up sample extracts of wheat, apple and grapes prior to the gas chromatographic determination of the new class of strobilurin fungicides (azoxystrobin, kresoxim-methyl and trifloxystrobin). 3.7.2
High-performance gel permeation chromatography
By the late 1980s, rigid polystyrene/divinylbenzene copolymers became available. These phases are packed into preparative sized HPLC columns and potentially offer the advantage over conventional 25 mm i.d. x 40 cm long SX-3 resin columns of increased speed of separation and a reduction in solvent consumption. However, this advantage may be offset by a reduction in the loading capacity of the column. Like GPC, high-performance gel permeation chromatography (HPGPC) is also easily automated. In 1996 and 1997, Rimmer et al. [50,51] used HPGPC to clean up vegetation samples prior to the determination of range of pesticide residues, including the phenoxyacid herbicides. The HPGPC column was a 300 x 19 mm Waters Envirogel column fitted with a guard column (150 x 19 mm) of the same gel. In 2001, MagtovskA et al. [52] used a longer but smaller internal diameter column (600 x 7.5 mm) from Polymer Laboratories containing PL gel to clean up extracts of wheat samples prior to the determination of organophosphorus pesticide residues using flash gas chromatography and NPD detection (Table 3.7). Clean up of plant samples by GPC using Bio Beads SX-3 is discussed in more detail in a review article, published in 1996, by Tekel and Hatrik [55]. 3.8
COMPARISONS OF MRMs FOR EXTRACTION EFFICIENCY
Although many laboratories have undertaken in-house comparison studies involving MRMs based on EtAc, acetone and/or MeCN, there appear to be few published papers on this topic. In 1991, Andersson and Plsheden [43] published the results of a study that compared the relative extraction efficiencies of acetone and EtAc, used with two MRMs, on fresh fruit and vegetable samples (20 different commodities). Results from both MRMs for laboratory spiked samples, and also a few samples containing "agriculturally incurred" residues, were compared. The higher mean relative recoveries for 93
S.L. Reynolds TABLE 3.7 Multi-residue methods involving the use of gel permeation chromatography (Bio Beads SX-3 or Envirosep SX3 styrene-divinylbenzene copolymer) as a column clean-up step Bed Column eluent dimensions length (cm) x i.d. (mm)
Elution Volume Reference/year rate of eluent (ml/min) per sample (ml)a
32 x 25
Cyclohexane/EtAc (1:1)
5
165
32 x 25
Cyclohexane/EtAc (1:1)
5
165
32 x 25
Cyclohexane/EtAc (1:1)
5
165
32 x 25
Cyclohexane/EtAc (1:1)
5
165
30 x 10
Cyclohexane/EtAc (1:1)
1
31
30 x 10 45 x 10 60 x 2
Cyclohexane/EtAc (1:1) Cyclohexane/EtAc (1:1) Cyclohexane/EtAc (1:1)
1 1 0.04
25 30 2.5
60 x 20
Cyclohexane/EtAc (1:1)
4.5
280
30 x 20 18 x 25 27 x 20 15 x 10
Cyclohexane/acetone (7:3) EtAc/hexane (3:7) EtAc:toluene (3:1) Cyclohexane:DCM (1:1)
5 5 4.5 2
150 210 245 20
Specht and Tillkes, 1980 [21] DFG Manual, Vol. 1, 1987 [4 21b Koinecke et al., 1994 [2 2]b Anastassiades and Scherbaum, 1997 [44 ]b Gelsomino et al., 1997 [45] Sannino et al., 1999 [46] Roos et al., 1987 [28] Van Rhijn and Tuinstra, 1991 [47] Nordmeyer and Their, 1999 [25] Obana et al., 1999 [31] Holstege et al., 1994 [30] Johnson et al., 1976 [53] Hong et al., 1993 [54]
"This is the total volume of column eluent required for each sample (including the first "discard" volume and the second "collect" volume), but not including any "wash" volumes between samples. bFor these references, exactly the same GPC conditions as those described by Specht and Tillkes [21] were adopted. the polar pesticides acephate (131%), methamidophos (184%) and omethoate (147%) using EtAc are consistent with some losses occurring during the partitioning step from aqueous acetone into dichloromethane. However, the lower mean relative recovery of tecnazene (75%) when using EtAc was almost certainly not due to it being relatively non-polar, as was reported, but its loss by volatilisation during the evaporation of the solvent. Because of the higher boiling point of EtAc (77C) compared with dichloromethane (40°C), there is a much greater risk of losing tecnazene (vapour pressure 240 mPa at 15°C) [32] during solvent evaporation. The authors concluded that although the EtAc 94
Sample handling and clean-up procedures I method had the advantage of being more rapid and using less solvent, many of the GC chromatograms of the sample extracts showed the presence of more potentially interfering co-extractives. Overall they concluded that, in general, both methods gave acceptable recoveries and equivalent results for the 76 pesticides tested.
3.8.1 Inter-comparison study of the relative extraction efficiencies of acetone and EtAc During the period 1996-2000, an inter-laboratory study was conducted under the auspices of the European Commission [56] to investigate the relative extraction efficiencies of acetone and EtAc for a range of pesticides with differing physico-chemical properties in fruits, vegetables and a cereal grain. Acetone and EtAc were chosen, as these were considered to be the most common extraction solvents in MRMs used by laboratories within Europe. Indeed the procedures chosen were based on three methods adopted by CEN (Comit6 Europ6en de Normalisation, the European Committee for the Standardisation of Methods) in a standard for the determination of pesticide residues in non-fatty foods [57]. The use of MeCN in MRMs has been more popular in laboratories based in the USA and Canada. The study was unusual in that it used test materials containing agriculturally incurred residues, specially prepared from field-treated crops, in addition to test materials that were laboratory spiked (as is the more common practice for ascertaining analyte recovery). From 14 different European countries, 25 expert laboratories participated in the inter-comparison study. 3.8.1.1 Preparationof the test materials Six crops were chosen as representatives of: a tree fruit (apple), a root vegetable (carrot), a leafy green vegetable (spinach), a soft fruit (strawberry), a salad crop (tomato), and a cereal grain (wheat). These crops were grown and commercially treated with pesticides specifically for the purpose of harvesting the crops and preparing test materials containing incurred residues that would be used in the study. From different classes of pesticides, 27 compounds with differing physico-chemical properties were chosen for the field treatments. The pesticides were also chosen because they are amenable to multiresidue gas chromatographic analysis, so that participants in the intercomparison study would only have to perform a single determination procedure. After treatment of the crops in the field, and on reaching maturity, they were harvested. Immediately following harvest, each crop was "blastfrozen", and cryogenically milled in the presence of dry ice to prepare a bulk 95
S.L. Reynolds composite of test material. Portions containing 100 g of the bulk sample were placed in polythene jars and stored, without their lids, overnight in a freezer at - 20°C to allow the dry ice to evaporate. The jars were then sealed prior to circulation to participants for analysis. In addition to the treated crops, similar untreated crops were grown, harvested, blast frozen and milled to prepare "blank" test materials that could be used for recovery experiments and also for the preparation of matrixmatched calibration standard solutions. 3.8.1.2 Homogeneity and stability testing of the test materials and spiking solutions One of the most important factors in the success of any inter-comparison study is to ensure that participants receive representative and stable test/spiking solutions and test materials. This is usually achieved by checking the homogeneity and stability of the test materials. For all four phases of the study, homogeneity and stability studies were undertaken on the test and spiking solutions and the test materials containing incurred residues. The intra-bottle (within bottle) and inter-bottle (between bottle) homogeneity of each pesticide in the test/spiking solutions and the test materials containing incurred residues were measured and compared to check for any statistically significant differences using the F-test [58]. For the majority of the pesticides, the inter-bottle mean square values did not differ significantly from zero when compared with the intra-bottle mean square values, demonstrating that these compounds were homogeneously distributed in the solutions/materials. In the test materials, four pesticides failed the F-test. Although it was not possible to prove statistically that these compounds were homogenously distributed, the residue data generated by participants in Phases III and IV of the study produced mean %CVs of between 10-16%, which indicated that any small amount of heterogeneity was acceptable. A stability study was also undertaken during the period allowed for the participants to undertake the analyses involved in each phase of the study (12-15 weeks). Bottles of test/spiking solutions and test materials were stored under a variety of conditions (ranging from - 18C in the dark to + 60°C with constant UV light) selected randomly and analysed at 2-week intervals for 20 weeks. Although under the most extreme conditions, a few of the pesticides showed some degradation, under the storage and transport conditions adopted for the study, no evidence of degradation of any of the pesticides was detected. 96
Sample handling and clean-up procedures I For both the homogeneity and stability studies tetraphenylethylene was added to the test material extracts to act as an internal standard and minimise GC injection errors. 3.8.1.3 Analytical protocols Analytical protocols were written and supplied to every participant for each of the four phases of the inter-comparison study. For all phases, participants could opt to use any appropriate gas chromatographic system and choose the operating conditions from those prescribed in the CEN standard. In Phases II-IV, they were also free to choose whether or not to use a clean-up procedure from those prescribed in the standard. However, the exact extraction conditions for each of the three methods tested were carefully specified in the protocol to ensure that all participants used the same extraction procedure and that the ratio of test material to extraction solvent was constant. Participants were also instructed to blend the wheat test material for 2 min with an aliquot of water and allow the resultant mixture to stand for 20 min prior to extraction with the organic solvent. This was to overcome any possible problems of residues "binding" to the cereal matrix and is discussed in section 3.9. 3.8.1.4 Phase I[59] Participating laboratories were provided with certified reference standards of the 27 selected pesticides from which they were to prepare their calibration solutions in solvent. The aim of Phase I was to check the ability of participants to measure these pesticides in solution using gas chromatography. Vials of six test solutions containing combinations of the pesticides at known concentrations were sent to each participant for analysis within a 6-week period. Duplicate analyses were performed on two separate occasions, at least 24 h apart. All 25 participants returned results, and with one or two exceptions (some participants encountered problems with fenoxycarb, metalaxyl, omethoate and thiabendazole), all produced results close to the expected values with acceptable precision (repeatability < 10%, reproducibility < 15%). 3.8.1.5 Phase 11 [60] In this phase participants were sent samples of six blank test materials (prepared from the untreated crops) and six spiking solutions containing combinations of the 27 pesticides chosen for the study. The participants were also supplied with analytical protocols that specifically defined the extraction conditions to be used for each of the three methods to be studied, as defined by 97
S.L. Reynolds the CEN Standard [54]. The methods were coded as presented in the CEN Standard: Method P: Acetone extraction, followed by liquid-liquid partition into DCM. Method P1: Acetone extraction and simultaneous partition into cyclohexane/EtAc. Method R: EtAc extraction. Initially, when the study was designed, only two methods were planned to be studied, but just prior to the commencement of Phase II, it was decided to include a third method, Method P1. Method P1 has the advantage of replacing DCM with a less toxic, and more environmentally friendly, solvent mix of cyclohexane/EtAc. Participants were free to select the clean-up procedure that would be the most appropriate to the GC detector they would use for measurement of the pesticides, as stipulated in the CEN Standard [54]. Each participant was requested to analyse the test materials in duplicate, on two separate occasions at least 24 h apart. Calibration standards were prepared in blank test material extracts (matrix matched) to avoid any possible chromatographic enhancement or suppression effects that could affect analyte quantification. Twenty-four participating laboratories returned results. There were 15 sets of results for Method P, 10 for Method P1 and 10 for Method R, although not every set of results was complete. The results for phase II are summarised in Table 3.8. Regression analysis [61] performed on the mean results for all compounds demonstrated that there was no significant difference in overall accuracy (P = 0.05) between the three methods. This was confirmed using a paired t-test [61]. The pooled variance data for all the pesticides demonstrated that Method P1 was significantly more precise than Methods P and R, and that the precision of Method R was slightly better than Method P. Low and variable recoveries for omethoate (the most polar pesticide included in this study) using Methods P and P1 were almost certainly caused by losses during the liquid-liquid extraction step. Low and extremely variable recoveries for thiabendazole, using Methods P and P1, may also have occurred during partitioning. In this case, the aqueous acetone apple extract, which was likely to be fairly acidic, would compete for the solvation of the sodium salt of thiabendazole, even in the presence of added sodium chloride. Adjustment of the pH to neutral with sodium hydrogen carbonate would almost certainly have improved the partition into DCM and/or cyclohexane/ethyl acetate. Additionally thiabendazole is notoriously difficult 98
Sample handling and clean-up procedures I
P, v
5
Pi
c
It N
ClCq
M
M
C
o
o
cc
oc
oo
cM
c
CO
cx
N
0
Cq
01
c
0
Go M M
c
cs c
Cl
-
c
M
cc
0M
o0
co
0C
M N
1 iN
0
M M
N
=
cD
to
co
t i
cc
-
C
00
0)
10
cs
cc 10
1Lo
o
0
0N
0
'-4 tl
o
0 0
X4
0
c
0 N
CO 00
cQr C
0 o
t CO0o
Lc
0)
0-
0 cg o
X 0)
I-
t
0
0
N
C
N
0
C 0
ccc
0o )
t-
10 0
a C
0
-
0
00
0)
0
C0
0X
-4
-
C
X C
X
'-I
.0 0
m s0
· ~~
v
~ ~'~~~
0)
O Cl
~~~~N~
~
o~~o ~~ O' 09 N
o
~~ ~~ Oc 10
m ND N
cl
X
C
HCS cc
c
C0
N
I'
o
0
g; C) -t
c
u
V
E
4-°,
C B
99
S.L. Reynolds O
10
CD
c
101
P-,
s
(0
-
c
r
(0
m
Q
0 10
Lo
c
l
CO
o c-
0
N
occ
P,
x co
x ooo
>
o
00
P;
o
tc
CN
C
t x
oox 0
o Q0
CD M MC
cc o - D c~ bccc
C
N
c
0
m o CM
o~oO~-
oO c
ccN(
c
oc
-c3
-
0
0 5H
00
cr
o
coo xesm
rlH
O
O
zs
~~O OD
H
o
H
H 0
C
l cqcOc(OfabooXoob 0 0 N Nq c 1.0
cq1.0oc
-i
CD o
0 0 s
Q
1: o0
k
s
'&
v
9 OU
4.) P
P~
0
g~~~~~~~ .0
In
a
vo
>q
CQ a% X
0 0 0
d
0 0
A-
1
100
3
90
,
F9UE
0
,2 oU F
10 CX0
0
co
E-
~
N~~Clcc~~Cl~~Cl0
(0N (50t-NccccccccN
Q P4 1o
9w
0 0j 0
io 0
q
Sample handling and clean-up procedures I to transmit through GC columns. There is a tendency for the peak to tail badly, and thus cause problems with accurate quantification, on the nonpolar or low polarity stationary phase capillary columns that are normally used in MRMs. 3.8.1.6 Phases III and IV [62,63 Phases III and IV involved not only the analysis of laboratory spiked test materials (as for Phase II), but also the analysis of the prepared test materials containing incurred residues. The analytical protocol as used in Phase II was retained and three of the six test materials were analysed in each of the two phases. The three analysed in Phase III were strawberry, spinach and carrot, and the three in Phase IV were tomato, apple and wheat. Results from the analysis of the spiked test materials are presented in Table 3.9 and results for the test materials containing incurred residues are presented in Table 3.10. The results obtained for the spiked test materials (Table 3.5) were remarkably consistent with those obtained in Phase II (Table 3.4). There was some improvement in the precision of the results obtained for bupirimate in strawberry using Method R and in tomato using Method P. As in Phase II, there was no overall significant difference (P = 0.05) in accuracy between the three methods [61]. As for the laboratory spiked test materials, and using the same statistical test [611, there was no overall significant (P = 0.05) difference in accuracy between the three methods. Similarly, as in Phase II Method P1 was significantly (P = 0.05) more precise than Methods P and R [61]. 3.8.1.7 Conclusions from the inter-comparisonstudy The inter-comparison study was focused on the extraction procedures and it would have been impractical to impose a common approach to the clean up and determination steps. Within the limitations of the study (27 pesticides and six test materials), and with the exception of omethoate, the results from Phases III and IV clearly indicate that the performances of the three extraction procedures are virtually equivalent. Omethoate was the most polar compound included in the study, and the lower recoveries obtained using Methods P and P1 were undoubtedly due to losses occurring during the partition from aqueous acetone into DCM or EtAc/cyclohexane. Thus the losses probably did not occur during extraction, but during clean up. Because of the generally good agreement in results from participants using the same extraction procedures, but different clean-up techniques and
101
S.L. Reynolds 9
C0o CQl
0
CC
I
0
0 '-4i
C C) P-4
m
-4-
10 1 Ct c- cN
i
0
-1
,4
10 CC 1o Cl 1--I 10 1 -4 CC m ,-.4 . '- Cl
0
m
00 1
Cl I--
Cl
-10 Cl Cl
t -
or
IC1
m Cl
L C o --I. -I
q
-
C
C9 .. 1
N 0 - 4 -
4
0 C N 1 H Cl -..
N C0 4
O> fDU 10 10
~
Cl
1 0 -4
LI H
co
I'
0
GO N DObeQ0
> m
CC H4
CC LO H. CC CCD t
CC0 9rc 0
t
H
Cxl
00
o
LI
0
0)
cs CC Cl
4 N
Om
'C C)
CD10
P4P
Ct)
00
XCC
m0
C
VD 10
CC
0
)
CC
-4
-4MM~~O~O CC CC Cl t4 XC 10 N: oo C 0) m CCOM m H
N 0O N
1
N
'
C
CC X0
c
N
Pl P,
-
CC CC CC Cl
C) 0)
m a)P
'CC
10
N
10
C
C
-
0)0)0)010
N
N
N
0)
Cl
I
ON
z
U1
l
4
00
N
N
N
m
1
0--
C
Cl Cl
Cl
)
)
CC CC 00
X N
Nw
00 N
C cC
o
Ci
C
cl
CC C
10
r0D N
I Cl
uCi).
0
Cl) rHooH CC5
c
4 4-4
C)
CE
C)oHC
Q
O O OO
CQ
HO O
O x
O
aC) O
d
CC a)
Cl)
a: S'
Am:
S"8 > o
~~ua~~
~~~
v
O
)0O
z v n S o oC o·) -E o CC
.CC-'Cm
r
c) 4-
CZ 41'n
C)
Cl)
4-
CC
C CC
.C
O C
E~~~~~~~4
CC mCC , C)
102
Cl
C~~~~~~C O,
'I C)
00
C F-i
CC
C 41 C
41 Cl
C-CC
Sample handling and clean-up procedures I Q0
Cl
w
H H cs cs
H
H
Lol
0 o t.
0o
c0 L
co
M
oN CC_
co
M
0
0
0 CC
Cx t
t- x
-0 X0 H
~
~04C
I> moo
CC
C OXC
C0 M CD r C1 CQ U
cx
00 Cq
0
o CS ID 0
~
0> 0 IQ CC r 00l Cl 00
M 0 O
c
rM x
0
q CCOCC
C-
V
00Ux
Q
O 0 C
o:
CC
o t-
M C Lo-
Lo s-
Ot
ICCl
C
C
rl~rl
~~~~~4 0 ~
C
0
0 0 -~-
'C; C C 0
C o~ o-
C CC CC o
~
l~Jh CC CC Cl CC O
CC CC
I-
C
CC - CC CC
1~1~PCZU C IC C Cl O> 0
V Cl IC Cl0
Oa Cs O4
N
C
C
4~~~~~~~~~~~~~-
Cl
P6,4
103
S.L. Reynolds TABLE 3.10 Summary of results from phases III and IV of the inter-comparison study for test materials containing incurred residues [62,63] Test material
Strawberry
Spinach
Carrot
Tomato
Apple
Wheat
Pesticide
Bupirimate Chlorpyrifos Dichlofluanid Iprodione Bifenthrin Dimethoate Metalaxyl Omethoate Permethrin Chlorfenvinphos Cypermethrin Dimethoate Metalaxyl Omethoate Triazophos Bupirimate Chlorothalonil Alpha endosulfan Beta endosulfan Endosulfan sulfate Tetradifon Tolylfluanid Bromopropylate Captan Fenoxycarb Phosalone Thiabendazole Chlorpyrifos-methyl Deltamethrin Lindane Permethrin Pirimiphos-methyl
Number of results
Mean result (mg/kg)
P
P1
R
P
P1
R
P
P1
R
6 7 7 7 7 6 5 7 7 6 3 2 3 0 6 7 6 7 8 8 7 6 7 8 6 8 8 8 8 8 8 8
9 9 8 9 8 9 7 8 9 9 7 4 7 1 9 9 8 7 8 8 8 8 8 8 9 8 9 8 8 8 8 8
11 13 11 13 12 13 9 11 12 14 4 2 7 2 14 13 12 13 13 13 10 12 12 12 12 13 14 13 13 13 12 13
0.56 0.21 0.11 1.32 0.72 0.26 0.03 0.14 3.67 1.59 0.03 0.04 0.03 0.83 0.15 0.27 0.08 0.15 0.18 0.02 0.08 2.14 1.77 0.33 3.21 3.16 3.21 0.75 0.20 0.73 3.63
0.53 0.21 0.11 1.53 0.77 0.27 0.03 0.09 4.25 1.69 0.02 0.02 0.03 0.01 0.84 0.16 0.36 0.11 0.15 0.18 0.01 0.08 2.31 1.73 0.33 3.35 2.64 3.26 0.84 0.22 0.85 3.80
0.52 0.17 0.10 1.29 0.69 0.25 0.03 0.19 4.00 1.36 0.02 0.02 0.03 0.03 0.63 0.15 0.36 0.09 0.17 0.20 0.01 0.09 2.42 1.75 0.32 3.28 3.69 2.84 0.78 0.19 0.78 3.23
18 16 40 9 13 16 15 46 32 13 85 52 4 17 15 40 43 20 16 28 14 14 13 16 12 56 17 22 30 17 18
23 10 8 12 9 12 10 42 19 14 60 26 10 12 14 33 11 30 27 21 26 12 11 10 10 58 12 17 21 14 12
20 14 9 17 13 18 27 35 14 19 26 2 17 74 23 15 26 35 21 19 32 24 14 26 13 11 50 27 22 31 19 29
CV (%)
Figures in bold type are outside criteria considered to be acceptable (Mean CV < 30%).
104
Sample handling and clean-up procedures I gas chromatographic equipment, any quantitative differences in the residue data were more likely to have been influenced by the individual practical skills and experience of each analyst, rather than any of the three extraction solvents.
3.9
OVERALL CONCLUSIONS
It seems likely that acetone, EtAc and MeCN will continue, at least for the foreseeable future, to be used to extract a wide range of chemical classes of pesticides from fruits and vegetables. SFE using liquid carbon dioxide has not proven to be an ideal replacement for organic solvents for fruits and vegetables, because of their high moisture content. The problems are twofold. First, so much desiccant is required that test portion sizes are very small and therefore the amount of pesticide available for determination will also be small. Secondly, with small test portions the sub-sampling error can be very large. Organic solvents will continue to be utilised but perhaps in conjunction with newer extraction technologies, such as pressurized liquid extraction and microwave extraction. Each of the three solvents has certain advantages/disadvantages when used as the primary extractant in MRMs, as have already been discussed in some detail in this chapter. The choice for the pesticide residue analyst remains difficult, and opinions as to which is the "best" extraction solvent for fruits and vegetables will remain divided. The best solvent is likely to continue to be determined much on the basis of the analyst's preference and particular requirements, rather than on results that can be used to differentiate their effectiveness. Also, with the exception of a few pesticide/commodities that are in the margins of being amenable to accurate quantification using multi-residue analysis, there is little difference between the solvents. If polar pesticides such as acephate, methamidophos, monocrotophos and omethoate must be determined then MRMs based on an EtAc extraction are most likely to yield higher recoveries, as there will be no necessity for a liquid-liquid partition step. Acceptable recoveries (> 70%) of these polar pesticides can be achieved by partition from acetone/water and MeCN/water into a non-water-miscible solvent, provided the predominantly aqueous phase is first saturated with a suitable salt such as sodium or magnesium sulphate. It must be stressed that the inter-comparison [56] and inter-laboratory [43] studies described in this chapter were only aimed at assessing "relative" extraction efficiencies. Determination of "absolute" extraction efficiency involves the application of radiolabelled pesticides, which is very costly 105
S.L. Reynolds and can be challenging. Previous studies [64,65] have demonstrated that pesticides can "bind" to food commodities of both plant and animal origins. Such residues may be either "bound" or "unextractable". Skidmore et al. [64] describes bound residues in the simplest terms as those residues that cannot be dissociated from the sample matrix by exhaustive extraction or digestion without changing their chemical nature. Matthews [65] reported that 28% of the applied dose of [1 4C] chlorpyrifos-methyl was unextractable from cereal grains using methanol after a prolonged storage period. However, when a 1:1 methanol/water mixture was used to extract the grains 86% of the radioactivity was released. It was postulated that the residue was retained due to physical entrapment, perhaps in the fibrous layers of the wheat grain and that the water/methanol mixture changed the matrix sufficiently to allow release. As a precaution against the possibility of this type of binding, participants in the inter-comparison study [56] were instructed to soak the wheat test material in water prior to the organic solvent extraction step (see section 3.8.1.3). GPC and HPGPC as clean-up techniques have the appeal of offering a broad range of applicability, both in terms of pesticides and commodities, and will continue to be used as a clean-up step in MRMs. However, the performance of alternative clean-up technologies and detection systems improves year by year. As automated, on-line clean up of sample extracts becomes more reliable and robust, and instruments such as triple-sector quadrupole mass spectrometers appear in more laboratories, so the use of a GPC/HPGPC clean-up step may decline. Nonetheless, there remains plenty of scope for further miniaturisation of this technology. REFERENCES 1 2
3 4 5
106
Codex Alimentarius, Pesticide Residues in Food. Methods of Analysis and Sampling, 2nd ed., Vol. 2A, 2000, Part 1. Official Journal of the European Communities, Commission Directive 2002/63/ EC, Community methods of sampling for the official control of pesticide residues in and on products of plant and animal origin and repealing Directive 79/700/ EEC, 11 July 2002. FAO manual on the submission and evaluation of pesticide residue data for the evaluation of maximum residue levels in food and feed, Food and Agriculture Organisation of the United Nations, Rome, 1997. S.J.V. Young, C.H. Parfitt Jr., R.F. Newell and T.D. Spittler, Homogeneity of fruits and vegetables comminuted in a vertical cutter mixer, J. Assoc. Off. Anal. Chem. Int., 79(4) (1996) 976-980. PesticideAnalytical Manual, 3rd ed., Vol. 1, US Food and Drug Administration. Rockville, MD, 1994.
Sample handling and clean-up procedures I 6 7
8
9 10 11
12 13
14
15
16
17
18
19
20
21
S.F. Howard and G. Yip, Stability of metallic bisdithiocarbamates in chopped kale, J. Assoc. Off Anal. Chem., 54 (1971) 1371-1372. A.R.C. Hill, C.A. Harris and A.G. Warburton, Effects of sample processing on pesticide residues in fruit and vegetables. In: A. Fajgeli and A. Ambrus (Eds.), Special Publication No. 256, ISBN 0-85404-783-2, RSC, 2000. R.J. Fussell, A.K. Jackson, S.L. Reynolds and M.F. Wilson, Assessment of the stability of pesticides during cryogenic sample processing. 1. Apples, J. Agric. Food Chem., 50 (2002) 441-448. T.J. Bruno and P.D.N. Svoronos, CRC Handbook of Basic Tables for Chemical Analysis. CRC Press, Boca Raton, FL, 1989, p. 89. EH40/2002 Occupational Exposure Limits, HSE Books, Sudbury, Suffolk, UK, ISBN 0-7176-2083-2, 2002. F.J. Schenck and S.J. Lehotay, Does further clean-up reduce the matrix enhancement effect in gas chromatographic analysis of pesticide residues in food?, J. Chromatogr. A, 868 (2000) 51-61. P.A. Mills, J.H. Onley and R.A. Gaither, Rapid method for chlorinated pesticide residues in nonfatty foods, J. Assoc. Off. Anal. Chem., 46(2) (1963) 186-191. R.W. Storherr, P. Ott and R.R. Watts, A general method for organophosphorus pesticide residues in nonfatty foods, J. Assoc. Off. Anal. Chem., 54(3) (1971) 513-516. W.L. Ton Joe and W.G. Cusick, Multiresidue screening for fresh fruits and vegetables with gas chromatography/mass spectrometric detection, J. Assoc. Off. Anal. Chem., 74(3) (1991) 554-565. S.M. Lee, M.L. Papathakis, H.-M.C. Feng, G.F. Hunter and J.E. Carr, Multipesticide residue method for fruits and vegetables: California Department of Food and Agriculture, Fresenius J. Anal. Chem., 339 (1991) 376-383. J. Fillion, R. Hindle, M. Lacroix and J. Selwyn, Multiresidue determination of pesticides in fruit and vegetables by gas chromatography-mass-selective detection and liquid chromatography with fluorescence detection, J. AOAC Int., 78(5) (1995) 1252-1266. J. Cook, M.P. Beckett, B. Reliford, W. Hammock and M. Engel, Multiresidue analysis of pesticides in fresh fruits and vegetables using procedures developed by the Florida Department of Agriculture and Consumer Services, J. AOAC Int., 82(6) (1999) 1419-1435. V.G. Becker, Gaschromatographische simultanbestimmung von chlorierten Kohlenwasserstoffen und phosphorsaureestern in pflanzlichem material, Dtsch. Lebensm. Rundsch., 4 (1971) 125-126. M.A. Luke, J.E. Froberg and H.T. Masumoto, Extraction and cleanup of organochlorine, organophosphate, organonitrogen, and hydrocarbon pesticides in produce for determination by gas-liquid chromatography, J. Assoc. Off. Anal. Chem., 58(5) (1975) 1020-1026. M.A. Luke, J.E. Froberg, G.M. Doose and H.T. Masumoto, Improved multiresidue gas chromatographic determination of organophosphorus, organonitrogen and organohalogen pesticides in produce, using flame photometric and electrolytic conductivity detectors, J. Assoc. Off. Anal. Chem., 64(5) (1981) 1187-1195. W. Specht and M. Tillkes, Gaschromatographische bestimmung von ruickstinden an pflanzenbehandlungsmitten nach clean-up fiber gel-chromatogrphie und
107
S.L. Reynolds
22
minikieselgel-saulen-chromatogrphie, 3. Mitt.: methode zur aufarbeitung von lebensmitteln und futtermitteln pflanzlicher und tierischer herkunft fir die multirtickstandsbestimmung lipoid- und wasser 1oslicher pflanzenbehandlungsmittel, FreseniusZ. Anal. Chem., 301 (1980) 300-307. A. Koinecke, R. Kreuzig, M. Bahadir, J. Siebers and H.G. Nolting, Investigations on the substitution of dichloromethane in pesticide residue analysis of plant
materials, Fresenius J. Anal. Chem., 349 (1994) 301-305. 23
24
25
26
27
28
29
30
31
32 33
34
108
W. Specht, S. Pelz and W. Gilsbach, Gas-chromatographic determination of pesticide residues after clean-up by gel-permeation chromatography and minisilica gel-column chromatography, FreseniusJ. Anal. Chem., 353 (1995) 183-190. J.A. Casanova, Use of solid-phase extraction disks for analysis of moderately polar and nonpolar pesticide in high-moisture foods, J. AOAC Int., 79(4) (1996) 936-940. K. Nordmeyer and H.-P. Their, Solid-phase extraction for replacing dichloromethane partitioning in pesticide multiresidue analysis, Z. Lebensm. Unters Forsch. A, 208 (1999) 259-263. K. Adou, W.R. Bontoyan and P.J. Sweeney, Multiresidue method for the analysis of pesticide residues in fruits and vegetables by accelerated solvent extraction and capillary gas chromatography, J. Agric. Food. Chem., 49(9) (2001) 4153-4160. R.R. Watts, R.W. Storherr, J.R. Pardue and T. Osgood, Charcoal column cleanup method for many organophosphorus pesticide residues in crop extracts, J. Assoc. Off Anal. Chem., 52(3) (1969) 522-526. A.H. Roos, A.J. Van Munsteren, F.M. Nab and L.G.M.Th. Tuinstra, Universal extraction/clean-up procedure for screening of pesticides by extraction with ethyl acetate and size-exclusion chromatography, Anal. Chim. Acta, 196 (1987) 95-102. A.R. Fernandez-Alba, A. Valverde, A. Agiiera and M. Contreras, Gas chromatographic determination of organochlorine and pyrethroid pesticides of horticultural concern, J. Chromatogr. A, 686 (1994) 263-274. D.M. Holstege, D.L. Scharberg, E.R. Tor, L.C. Hart and F.D. Galey, A rapid multiresidue screen for organophosphorus, organochlorine and N-methyl carbamate insecticides in plant and animal tissues, J. AOAC Int., 77(5) (1994) 1263-1274. H. Obana, K. Akutsu, M. Okihashi, S. Kakimoto and S. Hori, Multiresidue analysis of pesticides in vegetables and fruits using high capacity absorbent polymer for water, Analyst, 124 (1999) 1159-1165. C.D.S. Tomlin (Ed.), The Pesticide Manual, 12th ed., British Crop Protection Council, Surrey, UK, 2000. D.M. Gilvydis and S.M. Walters, Gas chromatographic determination of captan, folpet and captafol residues in tomatoes, cucumbers and apples using wide-bore capillary column: interlaboratory study, J. Assoc. Off. Anal. Chem., 74 (1991) 830-835. R. Carabias Martinez, E. Rodriguez Gonzalo, Ma.G. Gracia Jim6nez, C. Gracia Pinto, J.L. Prez Pav6n and J. Hernandez Mendez, Determination of the fungicides folpet, captan, and captafol by cloud-point preconcentration and high-performance liquid chromatography with electrochemical detection, J. Chromatogr.A, 754 (1996) 85-96.
Sample handling and clean-up procedures I 35
36 37
38
39 40
41
42 43
44
45
46
47
48
49 50
C. De la Colina, F. Sdnchez-Rasero, G. Dios, E. Romero and A. Pefia, Effect of storage on the recovery of different types of pesticides using a solid-phase extraction method, Analyst, 122 (1997) 7-11. David R. Lide (editor-in-chief) (Ed.), CRC Handbook of Chemistry and Physics, 83rd ed., CRC Press, Boca Raton, FL, 2002-2003. S.J. Lehotay, A.R. Lightfield, J.A. Harman-Fetcho and D.J. Donoghue, Analysis of eggs by direct sample introduction/gas chromatography/tandem mass spectrometry, J. Agric. Food. Chem., 49 (2001) 4589-4596. J.H. Ruzicka, J. Thomson, B.B. Wheals and N.F. Wood, The application of gel chromatography to the separation of pesticides. Part I. Organophosphorus pesticides, J. Chromatogr., 34 (1968) 14-20. Z. Masud, V. Batora and Kovai6ova, Gel filtration clean-up multi-residues of organophosphorus pesticides in rice, Pest. Sci., 4 (1973) 131-136. R.C. Tindle and D.L. Stalling, Apparatus for automated gel permeation cleanup for pesticide residue analysis, applications to fish lipids, Anal. Chem., 44(11) (1972) 1768-1773. D.L. Stalling, R.C. Tindle and J.L. Johnson, Cleanup of pesticide and polychlorinated biphenyl residues in fish extracts by gel permeation chromatography, Anal. Chem., 55(1) (1972) 32-38. Deutsche Forschungsgemeinschaft, Manual of Pesticide Residue Analysis, Cleanup Method 4, Vol. 1. VCH, Weinheim, 1987, pp. 65-69. A. Andersson and H. Palsheden, Comparison of the efficiency of different GLC multi-residue methods on crops containing pesticide residues, FrenseniusJ. Anal. Chem., 339 (1991) 365-367. M. Anastassiades and E. Scherbam, Multimethode zur bestimmung von pflanzenschutz- und oberflichenbehandlungsmittel-ruckstanden in zitrusfruchten mittels GC-MSD, Dtsch. Lebensm. Rundsch., 93(10) (1997) 316-327. A. Gelsomino, B. Petrovicova, S. Tiburtini, E. Magnani and M. Felici, Multiresidue analysis of pesticides in fruits and vegetables by gel permeation chromatography with electron-capture and mass spectrometric detection, J. Chromatogr. A, 782 (1997) 105-122. A. Sannino, M. Bandini and L. Bolzoni, Multiresidue determination of 19 fungicides in processed fruits and vegetables by capillary gas chromatography after gel permeation chromatography, J. Assoc. Off. Anal. Chem. Int., 82(5) (1999) 1229-1238. J.A. Van Rhijn and L.G.M.Th. Tuinstra, Miniaturisation of size-exclusion chromatography as a powerful clean-up tool in residue analysis, J. Chromatogr., 552 (1991) 517-526. Analytical methods for pesticide residues in foodstuffs. In: P. van Zoonen (Ed.), General Inspectoratefor Health Protection, 6th ed., Rijksinstituut voor Volksgezondheid en Milieu (RIVM), Bilthoven, The Netherlands, 1996. H.B. Christensen and K. Granby, Method validation for strobilurin fungicides in cereals and fruit, FAC, 18(10) (2001) 866-874. D.A. Rimmer, P.D. Johnson and R.H. Brown, Determination of phenoxy acid herbicides in vegetation, utilising high-resolution gel permeation chromatographic clean-up and methylation with trimethylsilyldiazomethane
109
S.L. Reynolds
51
52
53
54
55
56
57
58 59
60
61 62
63
110
prior to gas chromatographic analysis with mass-selective detection, J. Chromatogr.A, 755 (1996) 245-250. P.D. Johnson, D.A. Rimmer and R.H. Brown, Adaption and application of a multiresidue method for the determination of a range of pesticides, including phenoxy acid herbicides in vegetation, based on high-resolution gel permeation chromatographic clean-up and gas chromatographic analysis with mass-selective detection, J. Chromatogr. A, 765 (1997) 3-11. K. Mastovskd, J. Hajglovd, M. Godula, J. KiivAnkovd and V. Kocourek, Fast temperature programming in routine analysis of multiple pesticide residues in food matrices, J. Chromatogr.A, 907 (2001) 235-245. L.D. Johnson, R.H. Waltz, J.P. Ussary and F.E. Kaiser, Automated gel permeation chromatographic cleanup of animal and plant extracts for pesticide residue determination, J. Assoc. Off. Anal. Chem., 59(1) (1976) 174-187. J. Hong, Y. Eo, J. Rhee and T. Kim, Simultaneous analysis of 25 pesticides in crops using gas chromatography and their identification by gas chromatography-mass spectrometry, J. Chromatogr., 639 (1993) 261-271. J. Tekel and S. Hatrik, Pesticide residue analyses of plant material by chromatographic methods: clean-up procedures and selective detectors, J. Chromatogr.A, 754 (1996) 397-410. S.L. Reynolds, R.J. Fussell and M. Caldow, An inter-laboratory study of two CEN multi-residue methods for use in the enforcement of maximum residue levels for pesticides in fruit, vegetables and grain within the European Union, Pest. Sci., 50 (1997) 164-166. CEN Non-fatty Foods-Multi-residue Methods for the Gas Chromatographic Determinationof Pesticide Residues-Part2: Methods for Extraction and Cleanup, EN 12393-2, European Committee for Standardisation, Brussels, 1998. J.C. Miller and J.N. Miller, Statistics for Analytical Chemistry, ISBN 0-13030990-7, 3rd ed., Ellis Horwood Ltd., Chichester, West Sussex, UK, 1993, p. 84. S.L. Reynolds, R.J. Fussell, M. Caldow, R. James, S. Nawaz, C. Ebden, D. Pedlington, T. Stijve and H. Diserens, IntercomparisonStudy of Two Multiresidue Methods for the Enforcement of EU MRLs for Pesticides in Fruit, Vegetables and Grain, Phase I Intercomparison Study of Pesticide Solutions, Report EUR 17870 EN, European Commission, Brussels, 1997. S.L. Reynolds, R.J. Fussell, M. Caldow, R. James, S. Nawaz, C. Ebden, D. Pedlington, T. Stijve and H. Diserens, IntercomparisonStudy of Two Multiresidue Methods for the Enforcement of EU MRLs for Pesticides in Fruit, Vegetables and Grain, Phase II IntercomparisonStudy of Spiked Test Materials, Report EUR 18639 EN, European Commission, Brussels, 1998. W.J. Youden and E.H. Steiner, Statistics Manual of the AOAC. Association of Analytical Chemists, Arlington, 1975. S.L. Reynolds, R.J. Fussell, M. Caldow, R. James, S. Nawaz, C. Ebden, S. Lovell and H. Diserens, Intercomparison Study of Two Multi-residue Methods for the Enforcement of EUMRLs for Pesticides in Fruit, Vegetables and Grain, Phase III IntercomparisonStudy of Test Materials Containing Incurred Residues, Report EUR 19306 EN, European Commission, Brussels, 2000. S.L. Reynolds, R.J. Fussell, M. Caldow, R. James, S. Nawaz, C. Edben, S. Lovell and H. Diserens, Intercomparison Study of Two Multi-residue Methods for the
Sample handling and clean-up procedures I
64
65
Enforcement of EU MRLs for Pesticides in Fruit, Vegetables and Grain, Phase IV Intercomparison Study of Test Materials ContainingIncurred Residues, Report EUR 19443, European Commission, Brussels, 2001. M.W. Skidmore, G.D. Paulson, H.A. Kuiper, B. Ohlin and S. Reynolds, Bound xenobiotic residues in food commodities of plant and animal origin, Pure Appl. Chem., 70(7) (1998) 1423-1447. W.A. Matthews, An investigation of the non-solvent extractable residues of [14 C] chlorpyrifos-methyl in stored wheat, Pest. Sci., 31 (1991) 141-149.
111
Chapter4
Sample handling and clean-up procedures II new developments Michelangelo Anastassiades and Ellen Scherbaum
4.1
INTRODUCTION
Pesticide residue analysis plays an indispensable role in estimating the exposure of humans and the environment to pesticides in controlling the compliance of farmers to good agricultural practice rules, in facilitating regulatory decisions and trading and in strengthening the consumers' trust towards food safety. In official government programmes and the private sector alike, residue control is gaining importance and there is a growing pressure on laboratories to improve cost-effectiveness and analytical performance and to decrease sample turnaround times. To address these needs, instrument manufacturers and residue analysts around the world are continuously developing and implementing new analytical techniques and approaches with the aim of simplifying and speeding-up procedures, improving quality and the scope of analysis and reducing chemical consumption and manual labour. In pesticide residue analysis, analyte concentrations are generally too low and samples too complex to be analysed without preliminary sample preparation. Because measurements are typically made at low levels, background interference is a problem to be addressed. The main goal of sample preparation is therefore to provide a sample fraction, which is enriched in all analytes of interest and as free as possible from interfering matrix components that will certainly be present in the extract. Any analyte losses occurring here cannot be compensated for in the subsequent measurement steps. Thus, sample preparation is a crucial part of the whole analytical process. Sample preparation begins with sample processing and ends with the generation of the final extract used for instrumental analysis. In the extraction step, analyte traces are released from the sample material Comprehensive Analytical Chemistry XLIII Fernindez-Alba (Ed.) ( 2005 Elsevier B.V. All rights reserved
113
M. Anastassiades and E. Scherbaum and transferred into the extraction medium. This is followed by the removal of potentially interfering co-extractives (i.e., purification or clean-up). Traditional methodologies include numerous manual sample-handling steps such as filtration, volume transfers, column chromatography, evaporations and reconstitutions. This not only adds to the overall complexity of these methods but also makes them time-consuming and prone to systematic and random errors. For a long time, sample handling has therefore been considered as the bottleneck in residue analysis. Compared with the field of instrumental determinative analysis (injection, chromatographic separation, detection and data analysis), where technological advances in hardware, software and computing have resulted in sophisticated and highly automated analytical instruments, developments in sample preparation were significantly slower. There are a variety of reasons for this, including: (a) in many research facilities sample preparation has traditionally been considered as a low-tech process of little academic interest and thus little activity was devoted to how procedures could be improved, (b) major instrument manufacturers did not invest in the development of automated sample preparation techniques, leaving this niche to smaller companies, and (c) the complexity of natural matrices, which in many cases discourages more fundamental research. Since the early 1990s, however, as a result of rapidly rising labour costs, the growing demand for residue controls and the call for a reduction of chemical waste, this trend has begun to change and sample preparation turned from being an "unpleasant necessity" into an interesting and challenging research task and a focus for improving overall laboratory efficiency. Since then, numerous sample-preparation approaches focusing on simplification, automation and miniaturisation, as well as on coupling with chromatographic analysis, have been introduced. It is important to note that, in numerous cases, simplifying sample preparation has only been made feasible by the enormous advancements in the field of determinative analysis, including injection technology, chromatographic separation and, most importantly, sensitive and selective detection. Sample preparation includes a vast number of more or less time-and labour-intensive sample manipulation and liquid handling tasks such as homogenisation, weighing, pipetting, dilution, agitation, filtration, centrifugation, drying and evaporation. There have been important developments in each of these fields in recent years. Nevertheless, this chapter will mainly focus on techniques dealing with extraction, purification and enrichment of analytes, which are the most critical steps in analysis. The distinction between extraction and clean-up depends on the point of view and there is 114
Sample handling and clean-up procedures II-new developments often disagreement as to how these terms should be used. In this chapter, there will be no strict division between extraction and clean-up techniques, as in several other publications, because in most cases these analytical steps cannot be strictly distinguished as they both deal with a more or less selective separation or isolation of the target analytes from matrix components. Various novel approaches and techniques will be presented, describing the theory behind each technique, discussing some critical aspects in method development and compiling some representative applications. Each section concludes with a critical discussion on the possibilities and limitations of the various techniques with emphasis on multi-residue method (MRM) applicability. As regards the sample types, the focus is on the analysis of fruit and vegetable samples while high fat content matrices (e.g., of animal origin) and environmental substrates (e.g., water, soil, air) are only covered marginally. Headspace analysis and derivatisation reactions are not covered. At the end of this chapter, there is a discussion about the difficulties encountered and the considerations that have to be made in the process of implementing a new sample preparation technique in a laboratory. 4.2
SAMPLE PROCESSING AND HOMOGENISATION
Pesticide residues in biological samples are usually unequally distributed, not only from unit to unit but also within single units. Thus, before the analytical portion is taken (sub-sampling), intensive cutting, chopping, shredding and blending or grinding is necessary to reduce particle sizes and ensure a statistically well-mixed homogenate that can be used for checking the compliance of the entire laboratory sample with maximum residue limits (MRLs). A thorough comminution reduces the variability of results within replicate test portions and improves the accessibility and extractability of residues. In terms of produce, commodities with soft flesh and relatively hard peel, such as grapes and tomatoes, are especially problematic and require special attention during comminution to sufficiently reduce the size of the peel pieces, which may contain large amounts of non-systemic pesticides. In the past, little attention has been paid by the analytical community to the improvement of sample-processing procedures. This was surely related to the fact that traditional multi-residue methodologies [1-4] were employing relatively large sub-sample sizes (50-100 g) and were thus less prone to subsampling variations. To improve the extractability of residues, most of these "macro-MRMs" involved an additional comminution with special blending devices (e.g., Ultra-Turrax) during the initial extraction step to further break up the sample particles. Since the mid-1990s, with the introduction of novel 115
M. Anastassiades and E. Scherbaum
extraction techniques such as supercritical fluid extraction (SFE) and pressurized liquid extraction (PLE) that typically employ small sample sizes and with the emerging trend to miniaturise analytical procedures in general, the homogeneity aspect has become increasingly important. Many studies have been conducted since then to describe the influence of sample processing on the degree of homogeneity and to estimate the uncertainty as a function of the analytical portion size [5-9]. In general, the smaller the analytical portion, the larger the derived uncertainty. Today, the degree of standardisation as regards sample processing is still low compared with other steps in pesticide residue analysis and the procedures followed in the various laboratories vary significantly in terms of the equipment that is employed and the sample temperature during comminution. The comminution offrozen fruits and vegetables in the presence of dry ice (cryogenic processing), which usually results in a free-flowing powdery material, is nowadays generally accepted as the most effective, yet feasible, sample-processing procedure for pesticide residue analysis. Cryogenic processing leads to a significantly better degree of homogeneity, thus measurably improving the accuracy in replicate sample analysis [8-12]. Allmendinger et al. [9] have investigated the variability of sub-sampling of cryogenically-processed apples, grapes and tomatoes, when 2, 5, 10 or 20 g sub-samples are used for analysis. With the exception of the 2-g grape sub-samples, all other combinations gave acceptable variations (RSD in % at n = 5) ranging between 4.7 and 11.1%, which is, considering the variation resulting from the residual sample preparation and analysis steps, a highly acceptable value. Ambrus et al. [11] have further demonstrated that the homogeneity of the samples is substantially improved when an aliquot of the initially blended samples is further blended after adding some water to it, a procedure previously proposed by Kadenczki et al. [13]. Cryogenic processing not only enhances homogeneity but also pesticide stability. Recently, Fussell et al. [14] and El-Bidaoui et al. [15] have shown that processing at ambient temperatures can lead to considerable losses of susceptible pesticides and thus to substantially biased (underestimated) results. For some extremely labile pesticides, such as most dithiocarbamates, degradation during cryogenic processing is unavoidable so determination is performed by measuring the degradation products. The deceleration of chemical reactions when maintaining low temperatures during processing also decreases the decomposition of sample components and can reduce the number of potentially interfering compounds in the extracts. This is often observed in the case of onion samples where, compared with traditional comminution, liquid nitrogen treatment followed by grinding significantly 116
Sample handling and clean-up procedures II-new developments reduces the amount of interfering sulphur-containing compounds in the extracts (note: such compounds are for the most part glycosidically bound to sugar molecules and enzymatically released as soon as the onion cells are broken). On the other hand, the reduced particle size resulting from the more thorough comminution at frozen conditions also leads to a more exhaustive extraction of sample components. This has been shown in the case of grapes where, in traditional processing, the seeds remain mostly intact while, in cryogenic processing, they are crushed to expose their content (e.g., oils and phenolic compounds) to the extraction solvent. In a broader sense, sample preparation also entails all the sampling steps performed outside the laboratory and many agree that the variabilities derived from this process often affect the analytical result more than any other part of the analytical procedure. The way sampling should be performed when controlling the MRL conformity ofcommodities is prescribed in several national and international guidelines that define the minimum number of units and sample amounts required. These sampling procedures have been developed with practicability in mind and do not necessarily ensure that the sample taken fully represents the whole lot. In recent years, there has been a growing interest in studying the unit-to-unit variability within composite samples [16,17], mainly in relation to the need to establish suitable models for the assessment of acute risks from pesticide intake through food consumption. 4.3
RECENT ADVANCEMENTS IN TRADITIONAL MRMs
Extractions with organic solvents followed by liquid-liquid partitioning (LLP) steps for clean-up purposes have been the standard techniques in residue analysis for a very long time and are still commonly used today. The main advantages over many newer approaches include the fact that they are based on familiar and established principles, that no expensive or complicated instruments are needed and that organic solvents of high purity are easily available, although at a relatively high price. Today, the most commonly used MRMs for the analysis of pesticides in fruits and vegetables involve initial extraction with acetone [3,4,18,19], acetonitrile [1,2,20-23], or ethyl acetate [24-29] followed by LLP, during which the analytes of interest are transferred into the organic layer, leaving unwanted hydrophilic co-extractives as well as some highly polar pesticides in the aqueous phase. When employing ethyl acetate, which is quite hydrophobic, the formation of a separate organic layer occurs readily. In the case of acetonitrile and acetone, however, which are highly water-miscible, phase separation requires the addition of non-polar organic solvents and/or salts. The types and amounts of the solvents and salts 117
M. Anastassiades and E. Scherbaum employed decisively influence the partitioning of compounds and consequently the selectivity of the methods. Following LLP, further purification usually involves one or more clean-up steps such as size-exclusion chromatography (SEC) or adsorption chromatography using normal-or reversed-phase sorbents. Traditional sample-preparation approaches are often very laborious and troublesome and have thus often been considered as the bottleneck steps in pesticide residue analysis. Some of their most typical practical disadvantages are: (a) the need to perform numerous labour-intensive and error-prone samplehandling steps (blending, evaporations, drying, phase separations, etc.), (b) the use of large volumes of toxic and inflammable solvents (high purchase costs, waste-disposal problems), (c) the extensive use of glassware items (and the associated dishwashing requirements and breakage losses) and (d) the need for laboratory fume hood and extensive storage and bench space. Furthermore, the analyte range covered by most traditional procedures is not broad enough to encompass important analytes. If at all, most laboratories cover such analytes using equally troublesome single-residue methods (SRMs) or moiety-specific (single-class) methods, the latter targeting multiple residues of chemicallyrelated pesticides. A great extent of the inefficiency and complexity of typical MRMs is related to their "macro" design, which leads to many of the aforementioned unnecessary drawbacks. Figure 4.1 summarizes some of the key factors contributing to the overall inefficiency of classical MRMs. MRMs have always been subject to numerous modifications aimed at improving analytical performance and clean-up efficiency, simplifying sample handling, achieving better amenability to automation and reducing solvent Main drawbacks
Consequences
/-----U,,
large san "macro-a
Too often labo
"'<7yn
I.--a 1 tat
Fig. 4.1. Main drawbacks in classical MRMs. 118
End result
Sample handling and clean-up procedures II-new developments consumption and manual work. A brief overview of some of the most important recent developments and trends in "traditional" sample preparation is presented in the following. An important trend in the mid-1990s was the replacement of toxicologically and environmentally critical solvents such as dichloromethane, which has been widely used in methodologies employing acetone for initial extraction [18,19,30]. As regards the clean-up of extracts, an important innovation was the introduction of SEC, also known as gel-permeation chromatography (GPC). The technique was introduced in the late 1960s and widely employed in pesticide MRMs since the 1980s, is performed in a fully automated fashion and offers the possibility to separate relatively large amounts of high molecular mass components, such as lipids and pigments from the pesticide fraction. The removal of these low-volatility matrix components may not result in a reduction of semi-volatiles interfering in GC analysis, but it helps to reduce matrix build-up in GC injection systems that largely contributes to the progressive deterioration of instrument performance. GPC was therefore very quickly incorporated into many MRMs as a simple alternative to traditional adsorption chromatography clean-up approaches (see section 4.9.1.5). Negative aspects associated with the use of SEC are the adsorptive losses of certain basic pesticides [19,31], the insufficient separation of the triglyceride fraction from high molecular pesticides (e.g., pyrethroids) and the extensive solvent consumption of ca. 300 ml per sample, when using the traditional macro-column formats (25 mm I.D.). Nevertheless, many analysts currently employ miniaturized, so-called high-performance SEC minicolumns (HPmSEC) that use more pressure-resistant PS-DVB co-polymers packed into HPLC-like columns of typically < 10 mm I.D. The solvent consumption is thus reduced to less than 40 ml per sample [32-34]. An important contribution to the simplification of pesticide multi-residue analysis was the introduction of solid-phase extraction (SPE) (see section 4.9.1), a technique that has very quickly replaced traditional extraction with solvents in the residue analysis of water samples. In the field of food analysis, however, the adoption of SPE has initially been very slow (with the exception of the classical NP-adsorption chromatography). In recent years, however, there is an increasing trend of using SPE either to remove matrix components from extracts (see Table 4.11) or for the enrichment of pesticides from diluted extracts (see Table 4.10). The latter approach of pesticide enrichment has also been performed in a similar manner using other sorption techniques such as solid phase micro-extraction (SPME) (see section 4.10.1) and stir bar sorptive extraction (SBSE) (see section 4.10.2). 119
M. Anastassiades and E. Scherbaum Another approach that has been introduced to simplify and potentially automate the time-consuming LLP in traditional methods is the dispersion of crude sample extracts over the large surface area of macroporous adsorbents filled in columns. Partitioning is then performed by simply eluting the column with suitable solvents (see section 4.4). Over the years, there have been many attempts to introduce miniaturized and simplified methodologies for pesticide multi-residue analysis [29,35-39]. However, despite the obvious benefits in terms of convenience and the savings in time and materials, such methods were only very slowly accepted as a replacement for the traditional macro approaches. One reason was surely the erroneus perception that a miniaturised method can, if anything, only serve for screening purposes. A decisive contribution towards simplifying, streamlining and potentially miniaturising traditional methodologies was the introduction of the so-called "on-line" principle, in which the extraction and a single partitioning step are performed within one (sealable) vessel ("one-pot" procedure) that can be put in a centrifuge to assist phase separation [38]. The troublesome separation of the initial extracts (traditionally mainly achieved by filtration) and their transfer to separate vessels for partitioning is thus avoided. After partitioning, instead of accurately and completely separating the organic from the aqueous layer, as is done in most traditional approaches, an aliquot of the organic layer is taken for further analysis. This simple principle not only drastically simplifies the procedures but also facilitates their miniaturisation [37] and automation [40]. Some MRMs based on this principle are shown in Table 4.1. The dramatic developments that have recently been made in the field of gas and liquid chromatographic analysis in terms of sensitivity, specificity and the introduction of large volumes have also opened new horizons in simplifying sample preparation by reducing the need for excessive purification and concentration steps. Few innovations have had such a profound impact on multi-residue pesticide analysis as the recent developments in HPLC/MS instrumentation that dramatically expanded the range of pesticides that can be analysed within multi-residue approaches. As a result, various authors have introduced MRMs specially designed for LC/MS combination. Jansson et al. [26], Taylor et al. [25] and Mol et al. [41] have employed ethyl acetate/Na 2 SO 4 for extraction, avoiding any additional clean-up. Klein and Alder [42] employed methanol for the initial extraction, followed by a partitioning with dichloromethane on a disposable Chem-Elut column. In some cases, the use of LC/MS-MS has even resulted in methods where sample preparation is drastically reduced or totally omitted. Hogenboom et al. [43] have developed 120
Sample handling and clean-up procedures II-new developments C-d
0
C M a
0
pZM C:
¢
C.
C)
<
a
PJ4
02
, X C s g4
W
4)
P.~~ r
o
.r 01 .a
cab
~~
z
:E i
U
a)
v
0
n
.
P, 7'.
a a)
20
Pa _1C
COQ .,
P
c
0
C;C0 0Q
,s4
¥
0 CJ
AC
g: 0
dA) C
x
a) C J
0
- PqaZ
I
CO c.) ;4 C
C
w ,
CZ
~d b. 0
4:1
a)
0
0
c
i
a)
.i
.i
Q
U -
?. i
.)
a) a)c) O - 0
0
C) 0 -z
a)
a)
.h
.h
Q
0
'a
-
,. z
0
..
0
.dz
:,
0
z
a)'
U
Or0
o -
O O 1 _
0 0
O
rc cl o
0 o
o
m
tC aCaO
I
Cd
CO
bC
co
'I
4
a)
a)
0 U
X_° X _Q
a)
.0)
E0C oC qv
7
C )
a) O
C
a
--
CZ
-
-
M
.U
o
a LC -i
M C)
o0.
OZM
9 M -a) 0 C-
~
~
bn CO
9 x
CD
m bh am ,h
> X *
a) P)
iO
Oa)
bC 0 0
d
C
CQ
1
~C
to
a)
.0 0 a)
P
C-)
a)
a
O 0 -
I
a
U 0
I
M
~ ~bn ~ ~ ~
W)
o
bC
g 1-
0:1M
M
, a.
P
bU LO ,;i
on O
Ld
0
M
;I
bC 0 0
cf
-. 0
L_
a >
-0
X1
') 0
z
0
Z
a)
¢
a)
~
o
ID
,a
a O
CI
0
SS
a)0 a
¢d a
C
CO CO rH Cq
-
r4
4Ca 2P.
0
o
0a)
W--
a)
0*-
.~4
,$~
a)
o
..0
.
C'
0
W
.a
a
aC) W scC)C -s - -n
-
a)
a)
(D
00
a
0
fC
f
M
4
Z M
M
~ 024bpnUS
f'.C
4
4z)
0
.x
>
P
Pa)
.4
>
>
02
C)
C)
M.
0
C) Ca)
FI
i E 119 lg
F CD
O 0
C CO
;5_
CO
SE
p
~CO
F CO
P" m
CO
1
121
M. Anastassiades and E. Scherbaum a method where 2-g vegetables are extracted with 3 ml acetonitrile, filtered, diluted with ammonium formiate buffer and injected (900 bl) onto an HPLC/ MS-MS system, achieving detection limits of 0.5-2 Ag/kg. Ingelse et al. [44] have directly analysed polar organophosphorous pesticides (that could not be satisfactorily recovered by traditional procedures using SPE) in water samples by directly injecting 1 ml into a HPLC/APCI-MS system equipped with a C18 polar end-capped column). Hyotylainen et al. [45] have presented a method where wine samples were directly injected onto an HPLC coupled on-line to a GC using a specially designed interface. The capabilities offered by large volume injection (LVI) in GC were exploited by Forcada et al. [46]. They developed a method where pesticides were extracted from 10 ml water into 1 ml of MTBE after addition of NaCl. 50 .ldof the extract were injected into the GC using a PTV. The whole procedure was performed in an automated fashion using a contemporary sample-preparation and auto-sampling station. A recently published MRM that takes advantage of the enhanced possibilities offered by modern analytical instrumentation is the QuEChERS method (see Fig. 4.2), which was designed to deliver extracts that are directly applicable to both GC and HPLC analysis. During the development of this method, great emphasis was put on streamlining the procedure wherever "QuEChERS" -eth: i(0 miL OTEE-tub (ph-adjust t if
Weigh 10 g1of samtiple
ShIke vigorously0I m7
Optional: adjust the pH of the extract, add analyte protectants [53]
Fig. 4.2. Flow-chart QuEChERS-method [521. 122
Sample handling and clean-up procedures II-new developments possible by simplifying or omitting impractical, laborious and time-consuming steps. The method involves initial extraction with acetonitrile, LLP after addition of a mixture of MgSO 4 and NaCl, which removes a significant amount of polar matrix components, followed by a simple clean-up step in which the extract is mixed with bulk SPE sorbent ("dispersive SPE") [52]. The advantages of this method include: (a) rapidity (sample preparation of eight previously homogenised samples in ca. 30 min), (b) simplicity, (c) reliability and robustness (few, simple steps), (d) low costs, (e) low solvent consumption (only 10 ml acetonitrile), (f) practically no glassware needs, (g) amenability of acetonitrile extracts to GC and LC applications alike and (h) coverage of a very broad pesticide spectrum (including basic, acidic and very polar pesticides). Excellent recoveries and low variations have been achieved in intralaboratory validation experiments [20]. The most important simplifications introduced in this method are shown in Table 4.2. These developments show that, despite the introduction of novel and highly sophisticated extraction and TABLE 4.2 Simple alternatives to troublesome analytical steps in conventional MRMs [201 Time and material consuming, complicated or error-prone steps in traditional methods
How to simplify?
Sample processing/homogenisation Blending (e.g., with Ultra-Turrax) during initial extraction Filtration of matrix bulk Use of multiple glassware for extraction, filtration and partitioning
No way around this Shaking
Multiple partitioning steps Separation/transfer of entire extract Evaporation/reconstitution in smaller volumes
Classical SPE with columns and manifold
Centrifugation Extraction, partitioning and centrifugation are performed in a single vessel Single partitioning (on-line approach) Take aliquots (use ISTD if volume is not known) Use smaller amount of organic solvent to obtain a more concentrated end-extract (check extractability of incurred residues). Employ large volume injection and use sensitive and selective instruments for measurement Dispersive SPE
123
M. Anastassiades and E. Scherbaum clean-up approaches, LLP will most likely remain a viable approach in the foreseeable future. 4.4
4.4.1
EXTRACTION AND PARTITIONING ASSISTED BY SOLID SUPPORT MATERIALS Introduction
In view of certain drawbacks associated with traditional LLP procedures employing separatory funnels, alternative procedures have been developed that make use of adsorbents to disperse samples (or sample extracts) in order to facilitate extraction and/or LLP. These procedures may involve macroporous normal-phase adsorbents such as diatomaceous earth (see sections 4.4.2 and 4.4.3) or reversed-phase silicas (see section 4.4.4). In a typical procedure, samples are mixed with sorbents (e.g., using pestle and mortar) to form a flowing powder that is filled onto columns to be eluted with appropriate solvents. Alternatively, liquid samples or extracts may be poured directly into columns already filled with the sorbent. These procedures have several practical advantages including: (a) the mechanical grinding with the irregularly shaped sorbent particles destroys tissue structures and cells, releasing enclosed residues, (b) the dispersion of the samples over a large surface area facilitates analyte accessibility and partitioning, the sample gets loosened-up and is more easily penetrated by solvents without the need of applying too much pressure for elution, (c) water and solid matrix particles are physically retained and (d) depending on the sample-sorbent combination, the sorbent may also act as a retentive, thus providing an additional selectivity potential. Compared with traditional extraction and partitioning approaches, the use of dispersing materials helps to avoid repetitive partitioning steps, troublesome separation of layers and potential formation of emulsions and filtrations. The approach is thus much more straightforward and more amenable to automation (see PLE and SFE in sections 4.5 and 4.7). 4.4.2 Dispersion of samples on macroporous normal-phase adsorbents In the extraction of biological samples, the use of solid support materials, such as diatomaceous earth, Celite, Florisil, silica gel and sea sand, has a long tradition. Having a surface which is highly wettable by water, such normal-phase adsorbents can disperse the sample water as a thin film over 124
Sample handling and clean-up procedures II-new developments a very large area, thus facilitating extraction and partitioning. This is usually performed by simply filling the sample sorbent mixtures into columns and eluting with organic solvents. Drying salts, such as NaSO 4 , have also frequently been used in combination with the above-mentioned sorbents to control sample moisture better. In some cases, various additional adsorbents such as alumina have been employed on-line in series to remove interferences from the eluted extracts. The concept of distributing aqueous samples over a large surface followed by LLP was already introduced for drug analysis in the mid 1970s. In pesticide residue analysis, the approach has traditionally been mainly employed for samples of animal origin such as milk and milk products, fish, meat, fat, etc. More recently, however, several applications for fruit and vegetable samples have been presented as well. Table 4.3 compiles some applications with emphasis on samples of plant origin. Owing to its simplicity, the approach has often been performed in automated or semi-automated fashion, as in the case of Soxhlet extractions and more recently in SFE and PLE, where Hydromatrix, cellulose and, more recently, synthetic polyacrylbased polymers are also employed. 4.4.3
Dispersion of extracts on support materials
The above-mentioned macroporous normal-phase support materials have not only been employed for the direct dispersion of pre-homogenised samples, but also for the dispersion of sample crude extracts previously generated using traditional methodologies. While the initial extraction is still performed as in traditional MRMs, the troublesome and time-consuming clean-up by LLP in separatory funnels is avoided. Typically, the crude sample extracts are filtered and an aliquot is poured into a column already containing the macroporous support material. In most applications, the organic solvent used for the initial extraction is fully or partially evaporated before or after the dispersion of the extracts onto the sorbent. In the latter case, the solvent is purged by passing a nitrogen stream through the column, thus leaving the support material covered by a thin aqueous layer (in the case offruit and vegetable extracts) or a thin film of fatty material (in the case of lipid extracts). In a process that essentially resembles both LLP and chromatography, the columns are then eluted with a relatively large amount of solvent that is preferably nonmiscible with the dispersed aqueous or fatty layer. This results in a more or less selective partitioning of the analytes into the eluting solvent. The required adsorbent columns can be manually prepared in the laboratory, but several manufacturers also offer ready-to-use disposable cartridges filled with macroporous adsorbents that can be used for this purpose. 125
M. Anastassiades and E. Scherbaum M f~a
M a
C
a
g
~j
Ca
ca '
CCa a Mm
C
a)
w
r
Q)1~ m)JOm.
5t Id
a
a~~a M
r.
c
I
I
8 Ic
Ca
00
r: 0
0P
0
0
0
V
PI 0
-I
u0
V
a
C
Ca 0
*Ca
u w
U)
0
Z
4) Ca
0
0d
0u
C
&
03 Ca
0
.C o,5
M Ca
0
o
rp d
c
0 O Ca 0
bD U
40Ca
0 '.
0,
P
z
6 Z C
ri
a
bb, Un
o
Z
0 6a
¢V
42
.2 0
Ca
0 0
wo ra 0
.
0
0
Ca
Ca
O
0
c
c0 -~4
Lo
l C'l
0
12
M't 0 C 1 0
c
Ca
0
4)
'a
cc
PCa
cc~i
0
1
0 Ca M Ca Ca
Cai
00
.2
2 0 cc
;1 Ca C t a CO~
4)0
0 u
bn
1-4 0c
cc
4)0(
0
bn
'jm,
MO Cac
(MD~
4
-C o
CaC
ho Ca
Ca 4)0 M 0
r4
M Pa U) a k Ca
CM
4)0
m Ca '0 ,c)
0 Ca
0
M M
Ca
M
M aH
4)
'Ca 0 .. 0·
a u
4)1)
Clq M
Q) Ca
0
Ca
b.C
Ca
.C ,-IM . C a)0a
a2
.,Ca-
r
,
+
ccm
a
a
t Ca Ca 49
:)
'0Ca
Ca a) C M CZ
Z
M
0 M0
0
(0
(
0. Ca
0...
.
C)
126
> 4
Pa i3~~a 4C
>I
4 Pao a)
C
I)
m
0.
4n .
~a
-
o
0. Ca m>-t
Sample handling and clean-up procedures II-new developments Table 4.4 shows some applications where this approach has been used to analyse pesticide residues. Podhorniak et al. [62] have dispersed crude fruit and vegetable extracts (acetone:water 2:1) on Hydromatrix filled in a column. Elution was performed with dichloromethane while Hydromatrix was re-used after a thorough cleaning procedure. However, total solvent consumption was even higher than in the Luke procedure [4] that was intended to be simplified. Di Muccio et al. [63] also used acetone:water 2:1 extracts and dispersed them on disposable Extrelut-20 columns. Before eluting with dichloromethane, acetone was partly removed. By purposely leaving excessive adsorbent (not wetted) at the bottom of the column, additional clean-up was achieved (the rest of the adsorbent was deactivated by the sample water, thus merely serving as a support in the partition process). Iijima et al. [64] dispersed crude acetone extracts of tomato on Chem-Elut diatomaceous earth adsorbent after previously removing most acetone by evaporation. The 49 selected analytes from various classes were sequentially eluted with hexane and ethylacetate followed by silica gel clean-up. Most analytes gave high recoveries except for the very polar acephate and some degradation-prone analytes. Low recoveries were also reported for highly non-polar pesticides such as pyrethroids, which obviously started precipitating following the evaporation of acetone and thus becoming inaccessible to the water-immiscible solvents employed for elution. In preliminary experiments without a real matrix, the authors observed a correlation between the log P,ow values of 171 pesticides and their elution behaviour during the partitioning on Chem-Elut and the silica clean-up. In a recent publication, Klein and Alder [421 describe a very fast and effective method that uses this partitioning concept for the isolation of pesticides amenable to HPLC analysis. Fruit and vegetables samples are initially extracted with methanol (water:methanol 1:2) and an aliquot of this extract is dispersed on Chem-Elut cartridges after the addition of salt. Elution/ partitioning is performed with dichloromethane. After solvent exchange to water/methanol, the extracts are directly analysed by HPLC -MS/MS without any additional clean-up. A different type of water-adsorbing material based on polyacryl was used by Obana et al. [65] who poured ethyl acetate extracts of fruits and vegetables into a column containing the polymeric adsorbent as well as carbon for clean-up. In an earlier study [66], the water absorbent was directly added to the sample before extracting with ethyl acetate. Argauer et al. [67] have analysed carbamate insecticides in meat by distributing an aliquot of the concentrated acetonitrile extracts on Hydromatrix that was filled in a SFE thimble to be re-extracted by supercritical CO 2 . This resulted in an additional discrimination of matrix co-extractives.
127
M. Anastassiades and E. Scherbaum
6
Ci
C-) C
CE C
.0
a *O e
mfa
o
aC)
bD
o
t
·%
4)
z
.E 0
C)
r.
SCC CD CID
o cC
C.0 CD
r~
.
C
0w
wC1
)
C'C
a
Cd 00 ),
.
Q
;,oS
C)O4
t
E Qa
0 0
0 "0
X
s
C)
C
WXm
o
C a ;B cC C. 0 4 ) c r
B 0m a
x
i
C, 0 C
o
nw
b
,CS
C)
C..
o-
U z = 0C-
MdX
o
0
C)
Cl
t
'C CQ
C) CC
C-C C.C
.
0
aa
>
)
CC-
C
C)
E-
aS
C)
c C)
~ M
~d
Q
o
o m o
128
Sample handling and clean-up procedures II-new developments 4.4.4
Matrix solid phase dispersion (MSPD)
Instead of mixing the samples with normal-phase dispersing materials, as described above, MSPD uses reversed phase silica-based sorbents. The concept was introduced in the late 1980s by Barker et al. to simplify sample preparation of high fat content matrices. Meanwhile, however, it is also widely employed for the analysis of pesticides in fruits and vegetables. MSPD applications involve direct blending of a small sample amount (e.g., 0.5 g) with bulk RP-silica-based sorbent to form a semi-dry, free-flowing powder that can be filled into columns to be eluted with small solvent volumes (Fig. 4.3). Grinding is usually performed with a pestle in a mortar, while syringe barrels are often employed as reservoirs, using the syringe plunger for placing a frit on the top of the bed, for compressing the sample and for applying positive pressure during elution if necessary. Some authors recommend washing and pre-conditioning of the RP-sorbent prior to blending with the sample to remove potential interferences and improve wettability and thus facilitate interactions with the matrix [71]. During grinding, the sorbent acts as an abrasive to destroy tissue structures, thus improving the accessibility and extractability of enclosed residues. This is very important in the case of animal tissue samples that often contain proteins, lipids and sturdy conjunctive
Sorbent
\11L 'T :~f
Homogenized sample Column
Fig. 4.3. MSPD procedure schematically.
129
M. Anastassiades and E. Scherbaum tissue. When samples of high fat content are processed, the lipid material is dispersed as a thin film over the lipophilic RP-surface, which not only facilitates the extraction process but also instigates an additional clean-up effect. In a way, MSPD is a form of chromatography, the general principles of which apply. However, the whole process is very difficult to predict because the sample forms part of the chromatographic system and because analytes are dispersed throughout the sorbent-matrix mixture rather than being concentrated at the top of the bed as in typical chromatographic procedures. The efficiencies of extraction and clean-up, which are performed simultaneously, greatly depend on the dynamic interactions between the dispersed matrix, the sorbent, the elution solvent and the analytes. Additional adsorptive materials are often placed on-line in series to the MSPD column to remove further coextractives. To facilitate this process, dual-compartment cartridges have recently been introduced, where the adsorbent/matrix-mixture can be filled in the upper compartment while the lower compartment, which is separated by a frit, can contain a clean-up sorbent or drying salt of choice. To further optimise clean-up, several authors have performed fractional elutions, initially using extraction conditions that retain the analytes and remove interfering matrix components (inverse extraction) followed by conditions where the analytes are eluted. In many cases, direct analysis of the collected extracts, without any additional clean-up, was reported. MSPD is a very straightforward and simple extraction technique that requires neither sophisticated and expensive apparatus nor extensive amounts of materials and solvents and is thus more economical and faster than many traditional approaches. The elution procedure is potentially amenable to automated sequential processing using robotics and on-line hyphenation with chromatographic determination. However, care should be taken to avoid degradation of analytes while samples are awaiting analysis. The very small sample size (0.1-2 g) employed in MSPD can be an advantage if limited sample is available but, in most cases, it is a decisive disadvantage because of the difficulties associated with achieving the degree of homogenisation that is necessary to ensure that such a small sub-sample is representative of the initial laboratory sample. In general, the sample size in MSPD is limited by the cost of the sorbent and the fact that large bed sizes may cause high backpressures and plugging. It is thus generally not recommended to use sorbents with particle sizes smaller than 40 gm [71]. A practical difficulty of MSPD is the quantitative transfer of the sample into the reservoir, which requires rinsing mortar and pestle with the elution solvent.
130
Sample handling and clean-up procedures II-new developments Applications: A vast number of analytical methods involving MSPD have been published to date. Initially, the primary interest has been in the analysis of drugs and their metabolites as well as toxic pollutants in animal tissues, but the number of pesticide residue applications is meanwhile rapidly increasing. Several reviews summarise the use of MSPD in the analysis of animal tissue samples [72,731 and food in general [741. Valuable information about how to develop MSPD methodologies is presented in Ref. [71]. As shown in Table 4.5, most MSPD applications for fruit and vegetables employ Cs or C1 s sorbents at a sample/adsorbent ratio of 1:1. Several methods involve post-elution clean-up with normal-phase adsorbents, while fractional elution (e.g. washing step prior to the elution of analytes) has only been occasionally used. Automated elution has been accomplished by Kristenson et al. [801, who miniaturized the approach using a very small aliquot of the sample/adsorbent mixture equivalent to only a 25-mg sample. The aliquot was filled into a stainless steel vessel that was connected to an automated pumping system for pre-washing and analyte elution into a micro-vial for GC/MS analysis. 4.5 4.5.1
PRESSURIZED LIQUID EXTRACTION (PLE) Introduction
PLE is an automated extraction technique that uses heat to take advantage of the faster analyte kinetics at elevated temperatures, thus achieving fast extractions with relatively small amounts of solvents. In order to keep the solvent in a liquid state and enable safe instrument operation, pressure is applied on the extraction cell using a pump. The approach was developed in the mid-1990s following the introduction of automated SFE at a period of growing interest for extraction techniques that reduce solvent consumption and manual work. Depending on the author or instrument manufacturer, the technique has been also referred to as pressurized fluid extraction (PFE), pressurized solvent extraction (PSE), enhanced solvent extraction (ESE) and accelerated solvent extraction (ASE), the latter being the registered name of the most prominent PLE instrument manufacturer. A typical ASE instrument set-up is shown in Fig. 4.4. 4.5.2
Analytical procedure and critical parameters
In a typical procedure, the sample is packed into a special pressurisable vessel, which is placed in a carousel to be sequentially extracted without 131
M. Anastassiades and E. Scherbaum o 0
a
.
a
a
rn
rflR
a
00 I
C-) w
04
00~4
0U
o
-
0 C 00
I 0C
0 0 pq
0
uI
c
g .0 u
a
m a
a
: O
a
a
a
aa
Oa
a a
a
a
z
ao
z
0
a a
0a P3
0~-I
SO~
60
06
a 9
a
0 0
00
C9
ai a
x
z -a
a
a
0
0,
0
z
-4
a
a·
a
-
z
aC.) a d
a
a)
o
o
r
aD
0
o
ba
bn
b
a > ..
aC
Q) 0
w 10
° LO
U
z
UZ
c
c
+)
r.a a 0
U= 0M LO 0 Q
o0i u 0
to co
0
C~U6
a
U i"
a +3 a
a
,l 0
0
o
0
cq
Z¢
cs
aa
bu
bO
bD
bn 0
0
bt 0
Lo
a
0
a
Io .2
~4a
a 'a
a
a,
w
+30
o
U a
bn
0 10
6 *cI;
a
' ¢-
CO
o< a ,-: 0 a Q
O a2 co
U
U 0
e=
>0 x
°
0 )
0
co
t0 00
0 0
LO10
M
'a a)
iD
co
n
c
N
O
bk
M
ao M w -amaam
a, o
132
a
C
CZ$
~~ et
r
t
O
bn~~~b C-a >
aa
4C.
oa
0
M
g
aa
+3
oaU '".
0
Ew
ro
*^ X
a
0
a
aG a
00
a, C0 U
o 0
bD
-zO G?
a-
d
C-
F
-
C-
-
5L-
mA a)
Sample handling and clean-up procedures II-new developments
Fig. 4.4. PLE instrumentation, scheme [84]. operator attendance. In many cases, it is necessary to mix the samples with supporting materials such as sand, diatomaceous earth and Hydromatrix to control moisture or to avoid agglomeration of the sample and ensure unhindered and uniform solvent flow. The development of PLE methods is usually quite straightforward and simple because of the few experimental parameters involved and because the principle of extraction is more or less familiar to analysts having previous experience with traditional extraction/ partitioning procedures and column chromatography. Besides the sample type, the most important parameters to be considered in PLE applications are: (1) solvent type, (2) temperature, (3) extraction time, (4) number of extraction cycles, and (5) pressure. Solvent type: The selectivity and efficiency of PLE extractions is primarily controlled by the choice of solvent. In general, solvents typically used in conventional extraction approaches also work well in PLE. It should always be considered, however, that the properties of solvents, including polarity and miscibility, can alter considerably at different temperatures. Unfortunately, the physicochemical properties of many common solvents are not yet known well enough at the elevated temperatures (and pressures) employed in PLE. Temperature: Increased temperature enhances the solubility of analytes, promotes their diffusion within the matrix and accelerates their desorption kinetics by weakening various inter-molecular forces between them and active sites on the matrix surface, such as hydrogen bonds and dipole-dipole
133
M. Anastassiades and E. Scherbaum
attractions. The viscosity and the surface tension of the solvents are reduced, thereby increasing their ability to "wet" and penetrate the matrix and solubilise the target analytes. In general, raising the temperature reduces selectivity by increasing the extraction of matrix components and thus cleanup is often necessary prior to chromatographic analysis. Because of elevated temperatures, possible thermal degradation of susceptible analytes should always be explored in PLE applications. Increasing losses of dichlofluanid, captan and folpet, which are known to be sensitive to hydrolysis, have been observed during extraction from various samples as extraction temperatures were increased from 80 to 140°C [85]. Okihashi et al. [86] investigated the behaviour of N-methyl-carbamates during PLE at 100°C without noticing any degradation. It should be noted that drying salts such as Na 2SO 4 and MgSO 4, which have been successfully employed in many SFE applications, lose much of their ability to entrap molecular water as temperature increases. In addition, hydrated MgSO 4 tends to melt at elevated temperatures and is thus not recommendable. Good water-binding capacities at elevated temperatures have lately been observed for polyacryl-based drying polymers. Cycles: Extraction is performed in static cycles (typically 5 min). The dynamic extraction in PLE is quite negligible compared to SFE (see section 4.7), merely comprising a simple flushing out of the solvent into the collection vessel using additional solvent (between two extractions) or an inert gas (after the last static extraction). Several static cycles have been proven to be useful in the case of very high analyte concentrations or when matrices are difficult to penetrate. When low-temperature extractions are necessary to avoid degradation of analytes, extraction kinetics are less favourable, so multiple static cycles may be necessary to obtain higher recoveries. Pressure: In PLE applications, the pressures applied are typically far higher than needed to maintain the solvents (which are usually heated at temperatures exceeding their atmospheric boiling points) in the liquid state. Changing the pressure will normally have very little impact on analyte recovery; however, high pressure is claimed to force the solvents into areas that would be inaccessible under normal elution conditions such as small pores sealed by air bubbles. Sample type: As described in section 4.4, wet samples have to be mixed with adsorbents to control water and distribute the matrix over a large surface so that analyte transfer to the extraction solvent is facilitated. In the case of very non-polar analytes, a thick water film may act as a barrier that prevents non-polar solvents from reaching the analytes. In this case, the use of polar
134
Sample handling and clean-up procedures II--new developments co-solvents can assist the extraction and this may result in a higher content of water and polar co-extractives in the final extracts. Extractions at higher temperatures will also facilitate the analyte transfer to the extraction solvent, but this raises the potential for analyte degradation. Low-humidity samples can be applied to PLE as such, but particle size should be small enough to allow fast extraction. The use of support materials such as sand, cellulose or glass balls can help to prevent clogging and facilitate elution if necessary. In many cases, the addition of water to the samples will enhance the recoveries of analytes (especially the most polar ones), since water weakens polar analytematrix interactions such as hydrogen bonds. Following the extraction of fruits and vegetables, all authors report the presence of water in the collection vial. This water was either removed by adding a drying salt directly to the collection vial [85] or, more inconveniently, by LLP after addition of non-polar solvents and salting out. 4.5.3
Published applications
Remarkably soon after its introduction, PLE has become established in environmental laboratories. Helpful in improving the acceptance of PLE was the fact that the approach is comparable with traditional solvent-based procedures and that instrument manufacturers have actively pursued the establishment of an official EPA method for various contaminants and residues in soil. In many environmental laboratories, the introduction of PLE resulted in a drastic reduction of extraction times for soil and solid waste samples from hours (Soxhlet) to minutes [87-91]. The adoption of PLE in routine pesticide residue analysis of food was not that fast. Some PLE applications for pesticide residues in food are listed in Table 4.6. In Italy and Germany, ASE procedures have already gained official status for the analysis of fruit and vegetables and plant material with low water content [92,93]. In a recent application, Korta et al. [94] have employed PLE to extract six acaricides from honey that was previously dispersed on diatomaceous earth using a mixture of hexane:propanol for extraction. In general, PLE can achieve high recoveries for most pesticides in food matrices; however, extraction selectivity tends to be lower compared with traditional extraction methods and much lower compared with SFE. In most applications, instrument conditions during extraction vary between 60 and 120°C and 80 and 150 bar and typical extraction times range between 10 and 20 min. Total sample processing is, however, longer due to the need for mixing the sample with adsorbent, filling the vessel, post-extraction water removal, clean-up and evaporation prior to chromatographic analysis. 135
M. Anastassiades and E. Scherbaum
~I
0
a
O °a '
;
~
°
N
I~~~~~~~~~~~
i4N
1
¢
o~ .0g x
~
CtH
g
t
aj
U S Htnm
a)
aa
$ o
8
-U
rt
' *?
t 0 !:, t
)N
aa° o
44
a
't.a
Q
rl
0
i
4
-,
cqa
El
1(
--
=
a .1
a)
b
oU).AN
~ r~o"
a))
a'U 'a'
~ co
8
a
> 04*44'
0
o
~
.
)°
o
oo
N
'a
'a
4
C
zis a)
'a: .
r~
~U).-
a
*c
x
F 'j
w
'a4
"0
0
CC
2.c
.4 a
0'U)U) a
~~00
0lz Uea
ci)
'
4U)U)
'aU)
9-. $4'a~~~~·
-X
a) r 9 un 0 C)
~~~~1 ~ca
'a
co
'
40.
C9 C)
B~
4444
~
00
e,,
P~ k-C
W
Uz 0
136
A
a.
m
01
I
a'a -q
0
WA ~
~~ ~~s
r~~~~~~~~~.a 4
04'
I 0r 4CMw '
'a'
4~4'
w
4,4
I
.
wU
iei
~=
Sample handling and clean-up procedures II-new developments
:
t"
C)
~)Z
Z
O
c
c°
W
a, 4C
CC
CC
.12O
C
o~~tCCQ
8a
9
4
C)z
E
CCC
;a
-c
>
C
o
'~
o
.. mj,,
u ·)
O gtt
a,
~
c a
-a t 1 C.)
o
C
C
a
cccCC
a
s:
1"'x
g r;Z
4
cC)z C o <2s
Z
..
C± -C
:
a
z°
C.e
5C)8 o
o"~~~~~~C
C)
-
~~~~~~~CiC ~:~; ~~ zC) g u1 .Esg
· ·-
CCe
C)
C)C
O~ kt: +CCCC t =
C)$O
.2
CC7)C)>
CCC
C
~
Csia aQ~ii t 2
54~
e
t~~~~ CC~~~~~~~PC) CC 54
CCC
-
mS~~~~~~~~~~~~-
CCC
~
C CCC)
~
cc
·
B
g5 mm
-
~a
r~
.- 40C
rC~
CCC)C
-F
a-~~~~~~~~~~i
~I
C).0~ cc~
D
acc ccc
com
aS
S-
a 2 a
137
M. Anastassiades and E. Scherbaum 4.5.4
Discussion and future perspectives
Despite its numerous practical advantages and its wide acceptance in environmental analysis, PLE technology has not yet managed to gain an important place in laboratories analysing pesticide residues in food. The development of PLE methods is generally simpler than in SFE and, with proper analyst experience, PLE generally performs as well as traditional extraction methods, requiring less time and solvent. However, there are various factors that have impeded this technique from being established, including the high initial investment costs of more than C60,000, the relatively low research activity and the general reservations of analysts towards using elevated temperatures in multi-residue analysis due to the potential loss of analytes and selectivity, which implicates the need for more thorough cleanup procedures to reduce matrix interferences. However, with recent and future developments in final determinative analysis instruments in terms of detection selectivity and sensitivity, the importance of selectivity will surely decline further, opening new prospects for the advancement of exhaustive extraction techniques such as PLE. Practical problems associated with automated PLE include the degradation of analytes during storage of samples in the carousel and troublesome instrument operation due to frequent plugging of the frits or tubes and other technical malfunctions. 4.6
HOT WATER EXTRACTION
The enhanced cleaning and dissolving properties of water at elevated temperatures have been widely used for many centuries in households, craftsmanship and industry. As a vapour steam, water has also frequently been employed for the distillation of essential oils from plants in perfumery and pharmacy. In pesticide residue analysis, however, the use of heated water as an extraction agent was for a long time limited to steam distillations of certain compound classes. The use of hot pressurized liquid water for the extraction of organic residues began in the mid-1990s, at a time of growing interest in environmentally-friendly and economical extraction methods. The initiation point was a 1994 publication by Hawthorne et al., who reported the extraction of non-polar organic pollutants from environmental solids, with sub-and supercritical water [102]. Water is a highly attractive extraction solvent because it is readily available, very cheap and does not pose any safety or environmental concerns. Water may be a very poor solvent for non-polar organic compounds due to its high polarity and dielectric constant at ambient conditions, but its properties 138
Sample handling and clean-up procedures II-new developments gradually change as temperature increases due to the increased molecular motion and the disruption of the hydrogen-bond network. This is reflected by the decrease of the dielectric constant from ca. 80 at ambient temperature to 50 at 150°C, ca. 30 at 250°C and between 5 and 15 at supercritical conditions [103]. In other words, increasing the temperature makes the water resemble common organic solvents such as acetonitrile, acetone and ethyl acetate, with dielectric constants at ambient conditions of 37, 21 and 6, respectively. Besides the favourable changes in the polarity of water, other factors favouring extractions at elevated temperatures are the reduced surface tension and viscosity and the improved analyte kinetics and diffusivity. In the supercritical region, water characteristics change dramatically and supercritical water is reported to be highly corrosive in the presence of oxygen and is thus a difficult medium with which to work. Most analysts thus perform extractions at subcritical conditions, which refer to all temperatures below 374°C and pressures below 221 bar. Besides "hot-water extraction", there are several other terms that have been used for this extraction approach, such as "PLE using water", "subcritical water extraction" (SWE), "pressurized hot-water extraction" and finally "superheated water extraction". These terms are used to denote the region between 100°C and the critical point [89,104]. In general, hot-water extraction can be considered a PLE-type procedure, with sample preparation and sample sizes often being similar to those used in PLE applications. The moderate pressures (e.g., 15 bar at 2000 C) that have to be applied to maintain the water in liquid state can easily be achieved with typical PLE instruments or other means. Hot-water extractions can be performed in static or dynamic mode. In any case, the dilution of the extracts in water and the co-extraction of polar matrix components are often detrimental to subsequent chromatographic analysis. Water is difficult to evaporate and employing LLP with organic solvents to isolate the analytes from the aqueous extract would eliminate the advantage of performing the extraction automatically and of not using hazardous organic solvents. Various strategies have thus been developed to selectively enrich the analytes using minimal amounts of organic solvents: (a) aqueous extracts are passed through SPE sorbents to trap the analytes [105], (b) aqueous extracts are subjected to SPME or SBSE, thus allowing GC analysis without the use of any solvent [106-108], (c) a sorbent is placed in situ to trap the analytes as the extract is being cooled down, and (d) a non-polar organic solvent is added to the extraction cell, which is miscible with water under hot conditions but separates from it during cooling, thus concentrating the lipophilic analytes. Most applications of subcritical water deal with environmental samples such as soils and sediments. Three excellent reviews with a focus on
139
M. Anastassiades and E. Scherbaum environmental analysis have recently been published by Smith [104], Ramos et al. [88] and Luque de Castro [109]. Only a few publications, however, cover the analysis of pesticides from food. Wennrich et al. [106] employed subcritical water modified with 10% acetone to extract organochlorine pesticides in strawberries, which were mixed with cellulose prior to extraction. SPME and SBSE were employed to isolate the analytes from the cooled aqueous extract. The method was later extended to other fruits and vegetables [1081. Pawlowski and Poole [110] extracted thiabendazole and carbendazim from different foods at 75°C, 50 bar and employed LLP with ethyl acetate for clean-up. Extracting under acidic conditions increased the recoveries by enhancing the water solubility of these basic and thus ionisable compounds. Curren and King [107] employed ethanol-modified subcritical water (100°C, 50 bar) to extract atrazine from beef kidney previously dispersed on Amberlite XAD resin and Hydromatrix. A Carbowax-divinylbenzene SPME fibre was used to sample the aqueous extracts. In an environmental application, Lou et al. [111] have extracted chlorinated acidic herbicides from freshly spiked and aged soil samples employing hot water at 100°C. The extractions were performed in a sealed vial in which a SAX diskwas placed in order to trap the anionic pesticides while the solution was cooled. The trapped herbicides were then derivatized using a sililating agent and further analysed by GC/MS and GC/ECD. Unlike in SFE, pressure has little effect on the extraction properties in hotwater extractions and thus optimising extraction conditions is relatively simple, merely requiring the adjustment of temperature. Compared with the supercritical CO 2 used in SFE, water exhibits a wider potential polarity range and the possibility to adjust the polarity by means of temperature programming gives the opportunity for selective or fractionated extractions. Furthermore, water permits control of pH and ionic strength, which can be very useful in the partitioning of polar or pH-dependent compounds after extraction. The problem with water, however, is that it is not amenable to GC analysis. Nevertheless, the amenability with HPLC, CE and immunochemical assays opens new routes for a wide range of potential automated applications. In addition to the above-mentioned drawbacks arising from the dilution of the extracts with water and the co-extraction of polar interferences, other disadvantages of hot-water extractions include the potential re-deposition of non-polar analytes back onto the matrix or the surfaces of the connection tubing during the cooling process and the potential degradation of analytes at the high temperatures required for the extraction of the most non-polar analytes. These disadvantages limit the suitability of hot-water extraction for multi-residue applications.
140
Sample handling and clean-up procedures II-new developments 4.7 4.7.1
SUPERCRITICAL FLUID EXTRACTION (SFE) Introduction
SFE offers an alternative to traditional extraction approaches by employing supercritical fluids (SFs) as extraction solvents. Among all substances for which the critical conditions can be attained under reasonable conditions, carbon dioxide (CO 2) has been shown to be the most attractive because it can be readily transferred to the supercritical state (31°C, 73 bar), it is non-toxic, quite inert, inflammable, easy to evaporate at ambient conditions and available in good purity grade for a reasonable price. Supercritical CO 2 (SCCO 2) has been used for many decades in a number of industrial extraction processes. The first laboratory-scale extractors were developed in the mid1970s but the real breakthrough of SFE began in the mid-1990s as the growing need for reducing the consumption of conventional solvents animated scientists into pursuing SF extractions. Compared with many other extraction approaches, SFE is easier to automate, requires less hazardous solvents, produces little waste and reduces glassware and laboratory space required for sample preparation. Being a gas at ambient conditions, CO 2 can be easily removed by evaporation into the atmosphere after the extraction process, leaving analytes trapped on the surface of an adsorbent or dissolved in a small volume of an organic solvent. This simplifies the whole sample preparation. To become supercritical, a substance must be heated and pressurized above certain critical temperature and pressure values. The physical properties of SFs are intermediate between those of liquids and gases. While their densities lie close to those of liquids, providing them with a high solvating power, their viscosities are close to those of gases, which gives them favourable hydrodynamic properties and allows a good diffusivity of analytes within them. Moreover, the small surface tension of SFs provides them with a good "wettability" and allows them to penetrate porous solids and packed beds much better than liquid solvents. This distinctive combination of properties ensures a high mass transfer and fast extraction kinetics and makes SFs particularly suited as extraction solvents. 4.7.2
Instrumentation and analytical procedure
SFE instruments briefly consist of a fluid delivery system, a pressureresistant extraction vessel, a restrictor that controls the solvent flow and a collection device, which can be either a solid or a liquid. A typical instrument set-up for SFE is schematically shown in Fig. 4.5.
141
M. Anastassiades and E. Scherbaum
Fig. 4.5. Schematic presentation of a typical instrumentation for SFE with solid-phase trapping system.
SFE methodologies typically include the following steps: (a) sample processing, (b) sample preparation, (c) vessel packing, (d) extraction, and (e) trapping followed by elution or evaporation, depending on the trapping device. Sample processing is a critical aspect in SFE applications because of the small size (typically 5-10 ml) of typical extraction vessels that limits the amount of sample that can be extracted. Thus, good comminution is always required to obtain homogeneous and representative sub-samples. Furthermore, reducing the size of sample particles benefits extraction and mass transfer by shortening the pathways of analyte diffusion from the interior to the surface. However, when particles become too fine, care must be taken to avoid high back pressures due to clogging of the extraction cell or the restrictor. Sample preparation depends on the consistency of the samples. Dry samples can be filled into extraction vessels directly or, if necessary, after mixing with appropriate inert materials to ensure proper flow (see section 4.7.3 on the effect of water addition on recoveries). Samples with high water content, however, require more care to prevent water from being carried out of the vessel where it may cause clogging or restrictor damage (due to icing) or affect the trapping process. Several approaches to remove or control water have been described in the literature such as lyophilisation (freeze drying) [112], use of chemical desiccants (like MgSO 4 and Na 2SO 4) [113-117] and 142
Sample handling and clean-up procedures II-new developments addition of highly porous adsorbents, which can help to disperse the sample material (e.g., Hydromatrix (Varian), diatomite, Celite) 118-121]. Lyophilisation may allow a larger amount of sample to be extracted since no addition of drying agents is needed, but it is time-consuming and increases the possibility of losing volatile analytes. Furthermore, a certain amount of water is often needed to breakup analyte-matrix interactions. For performance reasons, most applications for samples with high water content focus on the use of water-adsorbing materials, sometimes in combination with chemical desiccants. The extraction process starts with pumping CO 2 into the extraction cell, with the restrictor being closed, until the selected temperature and pressure conditions are reached. In most applications, a static extraction is performed for a certain interval (e.g., 1-5 min) to allow the solvent to penetrate and soak the matrix. Afterwards, the restrictor is opened, allowing the fluid to flow through and dynamically extract the sample. As the extract-loaded CO 2 fluid exits the restrictor, it suddenly expands and loses its solvating power, which forces the extracts to precipitate on the trappingdevice, while the CO 2 gas is allowed to escape. Because fluid expansion is always connected to a severe temperature drop, the restrictors are typically heated during the extraction to prevent CO 2 from converting into ice and clogging the system. The subsequent trap, however, is usually cooled to better retain the analytes and minimise losses. In the case of solid traps, the analytes have to be eluted with an appropriate solvent into a collection vial. This is followed by a clean-up step to condition the sorbent for the next extraction. In the case of liquid traps, an evaporation step may be necessary to reach the desired extract concentration. Most commercial instruments are equipped with a carousel that enables sequential automated extractions, but there are also some instruments that have the capability of performing several extractions simultaneously. 4.7.3
Critical analytical parameters
SFE is clearly a powerful technique with unique properties but method development is often not straightforward due to the vast number of parameters involved in each analytical sub-step, including: (a) sample processing (as discussed above), (b) sample preparation (water control, salting out), (c) extraction (adjustment of temperature, pressure, time, flow rate and modifier addition), and (d) trapping (choice of collection medium, size, temperature, elution conditions). In the following, some of the parameters affecting SFE performance will be discussed. For additional information on method development, see also Refs. [122-125].
143
M. Anastassiades and E. Scherbaum Water control and salting out: As mentioned above, SFE of highly aqueous samples requires the control of water, which is mostly accomplished by mixing the samples with highly porous adsorbents. By distributing the sample water over a large surface area, the analytes become more accessible to the fluid (see also section 4.4). Several potential dispersing and drying agents for SFE applications have been investigated by various authors with Hydromatrix, a pelletized modified diatomaceous earth material, probably being the most extensively studied agent in pesticide applications. The extraction of dry samples (e.g., cereals) with pure SC-CO 2 usually results in low recoveries for pesticides with polar moieties. The recoveries of such compounds usually increase dramatically when water is added to the samples since water, being a highly polar and protic solvent, is able to disrupt these pesticide-matrix interactions and effectively mask the active matrix sites. Moreover, the small percentage of water being dissolved in SC-CO 2 improves its ability to accept polar molecules. In other words, water in the sample acts as a modifier for SC-CO 2 and helps to speed up extractions of polar pesticides [126,127]. In the presence of excess water, however, the partitioning of very polar pesticides becomes more difficult, requiring longer extraction times and greater fluid volumes. The recoveries of polar pesticides increase by addition of soluble salts (salting out process) or drying salts that incorporate water in their crystal structure (water ofhydration). The latter are often combined with Hydromatrix to provide a sample consistency that is easier to handle. Salting out, however, often worsens the extractability of non-polar pesticides by forcing them to precipitate and accumulate preferably on lipophilic surfaces such as waxes and oil droplets. These lipophilic particles tend to adhere to the porous and irregular surface of the sorbents used to mix the samples, where they are likely to get covered by a thin aqueous layer, which prevents the fluid (which is non-miscible with water) from reaching the analytes, thus leading to lower recoveries. For the most non-polar compounds, such as pyrethroids and benzoylureas, this effect can also be observed when no salts are added. Several authors have reported recovery improvements of such compounds when mixing samples with a greater amount of water-adsorbing material [12,127,128]. Addition of modifiers: The non-polar properties of SC-CO 2 limit its applicability for the extraction of polar analytes. To enhance the extractability of such compounds, it is sometimes necessary to add a co-solvent or modifier to the fluid. Several low-molecular-weight substances including alcohols (mostly methanol) and acetone have been employed for this purpose. CO 2 modified with polar co-solvents provides better partitioning conditions for polar analytes than pure CO 2. When dry samples are used, modifiers may help to swell the matrix and facilitate the extraction process. Most appropriate are 144
Sample handling and clean-up procedures II-new developments modifiers that are able to interrupt hydrogen-bond interactions between analytes and adsorptive sites of the matrix, thus decreasing retention [129]. The addition of modifiers to the fluid can be performed either in situ (i.e. directly to the sample or to the extraction vessel), usually followed by a static extraction step, or dynamically by mixing them to the pressurized CO 2 prior to entering the extraction vessel (see Fig. 4.5). When working with solid traps, care should be taken to avoid trap overflow and analyte breakthrough. Heating the traps at temperatures where the modifier can be evaporated is common but this may result in losses of volatile or degradation-prone analytes. Another negative aspect associated with the use of modifiers is the decrease in extraction selectivity, and thus the need for more thorough clean-up prior to further analysis [130,131]. In general, solvent modifiers are not recommended unless CO 2 alone cannot perform the extraction. Some manufacturers offer instruments that are designed to perform extractions at any SC-CO 2 to co-solvent ratio, thus filling the gap between SFE and ASE. Such instruments offer the analyst a great degree of versatility. However, the additional parameters that have to be considered further complicate method development. Water is surely the most important modifier and the primary choice in method development since it effectively eliminates analyte-matrix interactions and does not reach the trap in large quantities due to its very low solubility in SC-CO 2 (0.3% [132]). Being virtually non-miscible with SC-CO 2, water is added directly to the sample. Acids and bases can also be employed to bring acidic and basic analytes in a non-ionic state and facilitate partitioning to the non-polar fluid [12,133]. Acetic acid as a static modifier has been used in the extraction of organotin pesticides to mask active sites on the surface of the matrix and the tubing of the SFE instrument that tend to interact with the central tin atom [12]. Temperature,pressure; density: An important advantage of SFE is the fact that the density and thus the solvent strength of the fluid can be altered through control of temperature and pressure, thus allowing the user to adjust the selectivity of the extraction process to some degree. In traditional methodologies, this aspect is not managed that easily with individual liquid solvents. In general, the higher the density and thus the dielectric constant of the SF, the more effectively the fluid can shield electrostatic interactions between molecules and the better its solvating properties will become. However, the solvent strength of SC-CO 2 is comparably weak and, even at very high pressures, the dielectric constant does not exceed 1.8, which is comparable to ambient hexane. This explains the low recoveries often achieved for highly polar pesticides.
145
M. Anastassiades and E. Scherbaum At elevated temperatures, the molecules possess a higher kinetic energy (mobility), which helps them to overcome inter-molecular interactions. Diffusion and desorption processes thus become easier and extraction times are shortened. However, higher temperatures require higher pressures in order to avoid a density drop and thus a decrease in dissolving power. In theory, the maximum temperature that can be employed for extraction depends on the maximum pressures that can be achieved by a specific instrument. In reality, however, the extraction temperature is limited by the thermal instability of analytes and the loss of selectivity as the temperature increases. When raising the pressure at constant temperature, the density and thus the solvation power of the SF will increase. At the same time, however, the SF becomes more viscous, which has a negative effect on diffusivity and extraction kinetics. There are consequently several competing factors that have to be balanced to achieve optimal extraction conditions. Conditions near the critical point should be avoided because in this region the properties of the SC fluid change markedly with temperature and pressure and are not easy to control. Trapping conditions: Analyte collection is a critical step because the gas flow generated by the expanding CO 2 can blow analytes out of the system. In a solid-phase collection, the decompressed CO 2 stream passes a device filled with a bed of cooled sorbent material (e.g., C s, 8 silica, Florisil) or an inert material (e.g., stainless steel beads). After complete extraction, the analytes are eluted from the solid-phase trap with a suitable solvent. For the elution of analytes from the trap, it is generally recommended to employ watermiscible solvents such as acetonitrile and acetone in order to elute the small amounts of co-extracted water that reach the trap and to avoid the formation of separate layers. Solvent collection is most commonly achieved by keeping the restrictor outlet immersed in a vessel containing a small volume of an organic solvent, such as methanol, hexane or acetone. While the extracts are dissolved in the solvent, the CO 2 gas is left to escape to the atmosphere. The extraction flow should be such to ensure that analytes are given enough time to dissolve and diffuse into the solvent and to avoid evaporation losses. Solvent type, solvent volume, solvent temperature, CO 2 flow-rate, restrictor temperature and pressurisation of the collection vessel are important parameters to consider. Compared with solid trapping, liquid trapping generally requires higher amounts of solvents, which sometimes have to be replenished periodically during extraction. Liquid trapping was one of the first collection techniques used in SFE, but it has since lost favour with pesticide residue analysts. 146
Sample handling and clean-up procedures II-new developments An alternative trapping approach that has been used to enhance the selectivity of extraction is the so-called "in-situ" (also referred to as "in-line" or "build-in") trapping, where a bed of adsorbent material is placed on the outlet site of the extraction cell. In a typical procedure, a normal-phase adsorbent (e.g., alumina, Florisil) is used to retain triglycerides and other more polar matrix components, allowing non-polar analytes, such as pyrethroids and organochlorine pesticides to pass through [134-139]. An alternative approach involves initial trapping of polar analytes allowing the non-polar matrix components to pass through, followed by a subsequent extraction to recover the analytes of interest [140,141]. Time: In general, extending extraction time is a good approach to improve recoveries and often helps to enhance the ruggedness of the method. However, when determining the extraction time, several factors have to be considered and balanced, including: (a) greater fluid consumption, (b) reduced sample throughput, (c) increased solvent evaporation in the liquid trap, (d) enhanced losses of analytes due to breakthrough and evaporation in the trap, (e) increased amount of water condensation in the trap, (f) reduced selectivity, and (g) enclosure of analytes by matrix components in the trap. The extraction efficiency of analytes is sometimes limited by slow mass transfer processes such as slow diffusion from the core of the matrix particles to their surface or slow transfer of non-polar analytes through the stagnant water layer to reach the fluid. In these cases, it is advantageous to employ longer static extraction times. In most applications concerning pesticide analysis, a combination of static and dynamic extraction is used. Lipid content: The extraction of pesticides from samples of high fat content such as animal products is not straightforward because target analytes (especially the highly non-polar ones) may be encapsulated within the lipid matter. Sample-preparation design should thus ensure the physical accessibility of these residues. In practice, this often means that the fats in the sample should be completely extracted and subsequently separated from the target analytes using various approaches. An alternative option is the dispersion of the fat material over a large surface, which makes the residues more accessible, thus reducing the need for total fat extraction. Most authors employ NP adsorbents within the extraction vessel or at the trap to retain the fat. Fortifying fatty samples in the laboratory often does not result in a residue distribution as in real samples, where the analytes are incorporated into the lipid matter, and complicates method development. It is thus highly preferential to optimise extraction yields on samples containing incurred residues.
147
M. Anastassiades and E. Scherbaum 4.7.4
Applications
There are numerous publications describing the use of SFE in pesticide residue analysis. Early applications in the late 1980s and the beginning of the 1990s mainly focused on the extraction of relatively non-polar compounds from environmental samples with low water content like soil, sludge and solid waste. Extraction of pesticides from fruit and vegetables initially seemed to be more difficult due to the relatively non-polar nature of SC-CO 2 and the high water content of the samples. The introduction of water-adsorbing materials to prepare the samples for SFE considerably facilitated sample preparation for such sample types, paving the way for a vast number of new applications and publications, which are summarized in several reviews [122,142-145]. Some examples of different approaches employing SFE for the extraction of pesticide residues from various food matrices are listed in Table 4.7. Lehotay and Eller [119] have studied the extractability of the 46 pesticides that were included in the USDA pesticide data programme in 1996. In recovery studies, most pesticides were shown to be very well extractable except for methamidophos, omethoate and captan. In a further series of experiments, Lehotay et al. [120] have investigated how sample preparation for SFE (including chopping, freezing, controlling moisture and storage time) affects recoveries of 40 pesticides (representing various classes including OCs, OPs, carbamates and triazines). Degradation of certain analytes in the extraction vessels was observed during storage at room temperature. Stefani et al. [116] investigated the extraction of pesticides of a wide polarity range fortified on apple matrix using Celite and anhydrous Na 2SO 4 to control moisture. Very high recoveries were achieved for 92 pesticides. It should be noted, however, that the fortifications were performed by adding 1 ml standard mixture in cyclohexane to 2 g of sample, which may have influenced the recoveries. Tena et al. [146] investigated the extraction of ten organophosphorous pesticides from oranges that were previously mixed with various types of adsorbents, including anhydrous Na 2SO 4, Extrelut and diatomaceous earth. Increasing the sample to diatomaceous-earth material ratio from 1:3 to 1:1 increased the recovery of several pesticides but the highly polar methamidophos could not be recovered at all. The addition of Florisil improved the cleanness of the extracts but reduced the recoveries of certain pesticides. The addition of methanol as modifier did not improve recoveries and negatively affected the reproducibility. Yoshii et al. [147] have extracted ten chloracetanilide pesticides from fruits and vegetables employing Arasorb S-310 and Celite, a polyacryl-based water-absorbing polymer, to immobilise water (sample/polymer/Celite 5:1:1). The extracts were trapped on a solid trap 148
Sample handling and clean-up procedures II-new developments In
o
w CD Lo
Lo 10
rt
Lo 1 -4~~~~~~~~~~~~~~~~
-4
Ci)
~.~ mo ",. O C
m
.s o ~-4 ~a~ a
o,
CD~
100~J~*l~IB Cq)
42 r:j (D 0
0
rj"
m .
N
B O o 0
a
CZ
0
Q
m
o
XX
v
i
3
-
C
a
a
°4 01
0_ -O
O 10
eQ
VO
U?
inc
))
g4
ia2
ID 33y ° E
o
t
.0
a
C9g
0 0O
C) a
a) 0 0
Lo
C) 0M
0
.0
a)
I
tt
a) -C)
W
.
C) C +I a 0 C) M C) U.
C)
17-
nC; bD
Ua
0
Ez
n
C)
0 C
C-)
Ci)
-C)
° ~~~~
lC) 0
C)
t
d° ~~~~~
z 0 )
Cl
C)
a ,acS~~
¢ co
cd
4
C)
0
a)
U) Q
C
.-
Cd
Q o C)
,
~C Ci)~s 0~ ~,
0
Ic
.-
'i
Cl) a)
-o
5 , XU) ~
VO C
k 'fZ M
9
Ca
Z 5<
0
41
L1-
(d C) 4 Oc ' IU) 0 C: 0 I'm
0N V
a) C P.
O
0
,-R
O
aO4
0o
rxh
0
0
r,~ Wm Z . Cl)
Ci))P,0
C)bi%0 "C LP'O -
C) Fo 0
0
149
M. Anastassiades and E. Scherbaum n
o C) C) a)
ca C) .)
P,
C
O
P4
CCCC
o
;0
A
a.,
4.ci
C) C)
P~
~ 0a8o ar
.
·, ~
C.)
SC) P, C) CC rl
C
a
u0 d C)
I
M ,l
.-
o:n
co 9.0
eO Mo
m
c
oLZ CC cs CCF C
-z
C
o
CO CC.)
O
C
oo
oor
0 r? 0 Q
.-
Co
0
C.
4
.-
*-
cC .BA
X
0 4 IdC
0
gi c
C- d-g
C
(d
0
P
bh
C P. d C) *ae o::
0 MW °j;
O
4
m
;.
t~
x 1C
C% '
.)
9
.-
Z
) bb
C
0
U2
0 0
C) (D
w
UJ)
-el a)
'O4 m
150
,C
.g 'ed
C>~
i
,
g
Q) CZ : O.1 ;: .
o
P
0 'r
Sample handling and clean-up procedures II-new developments
CCq r
r
D
cg
co
Q0~~~~~
Fa
· a
,c,3
8
z BS X
Lo
um
"v
b9 o VaE rr 'i
=a,
6, <
s
OLP
a
UX
O .,C) Z
¢] O,,.) C Z V Lo
cc
I
nh ·
u *
V
Q =
a
-tc~X
V O
*n
cO
o c M
M
N U dd ' iOr
!;d
,r h
0~~~~~~~~~~~~~~ · ,c
0 S9 4O
xU na)u9
O-.
L
g
a)
U;
eq 4:~
c~qx:~o,.C
<
C 10 cq
4
p ,,: 5
M S,
8
Z
D a'
Cqm Ci3 S
Ooo Cq lt:l D5'(0
g
|
$:
~~ d
+
W
P
a
U P3;
A
Czd
(d z ~
<
4
o
,P ;
to Um a a,
~"c:
· 3
p.
3
o C~I k
t
O
c~~~~~~~~~~~~~~~~~~~~~j CF:~~~ M > 04
(
4
O
0
151
M. Anastassiades and E. Scherbaum consisting of SAX and PSA sorbents achieving good clean-up when eluting with acetone/hexane (9:1). Kaihara et al. [160] have extracted 27 pesticides belonging to various classes from various fruit and vegetable samples using the same methodology; however, trapping was performed on an Extrelut/C18 mixture. The eluted extracts were then transferred into an SPE column filled with Florisil and PSA and eluted with solvent mixtures of increasing polarity into several fractions that were analysed separately. Low recoveries were noticed for thiabendazole, imazalil and clofentezine. Nemoto et al. [147] studied the extraction of 88 pesticides from various classes from fortified Celite as model matrix. They observed a dramatic increase of the recoveries of polar pesticides when water was added to the sample before extraction. However, the recoveries of the most polar pesticides, methamidophos, acephate and propamocarb, remained low. Various polar and non-polar solvents were tested as modifiers with methanol being the most promising, giving the highest recoveries for acephate and methamidophos. With increasing extraction time, however, recoveries of several pesticides decreased, probably due to a saturation of the trap with the modifier and a decrease in the trapping efficiency. Valverde-Garcia et al. [113] have achieved very high recoveries for methamidophos by mixing the samples with MgSO 4. Recoveries improved further when 200 l of methanol was added to the extraction vessels. However, using this method the results of various pesticides of low polarity were lower than with a traditional solvent-based MRM. In a separate study, Valverde-Garcia et al. [114] have shown that, using the above-mentioned method, imidacloprid (a neonicotinoid insecticide) was very badly extractable. A number of investigations to assess the possibilities of SFE as a routine method for the extraction of pesticide residues from fruit and vegetables have been performed by Anastassiades et al. [12,128,133]. A basic method and various possible modifications to enhance the extractability of "difficult" pesticides, such as basic fungicides, acidic herbicides and organotin pesticides, have been presented. The basic method, in which comminuted samples are mixed with Hydromatrix, gave very high recoveries and low variations for 81 GC-MS-amenable pesticides as well as more than 60 LC-MS-amenable compounds, including 22 N-methyl-carbamate insecticides [12], eight benzoylphenyl-urea insecticides [128], 23 phenylurea herbicides [12] and six sulfonylurea herbicides [12]. Very polar compounds, having log K/ lower than 0.5 (omethoate, methamidophos, acephate, oxamyl and aldicarb sulfone), were found to give low recoveries. Recoveries increased gradually when various salts were added such as NaC1, MgSO 4 and Na 2SO 4. Furthermore, various experiments were performed to find the optimal pH conditions for 152
Sample handling and clean-up procedures II-new developments a multi-residue approach, where basic fungicides are still extracted and decomposition of compounds that are base-sensitive (e.g., folpet, tolylfluanid) and acid-sensitive (e.g., carbosulfan, benfuracarb, dioxacarb) is minimized. pH values between 4 and 5 were found to be a viable compromise. pH adjustments were also performed to enhance the extractability of basic (e.g., imazalil, thiabendazole, carbendazim) and acidic compounds (e.g., phenoxyalcanoic acids) [12,1331. Difficulties in terms of adsorption on various sites of the instrument and carry-over have been observed for thiabendazole and organotin compounds. In both cases, cleaning up the tubing with water in an ultrasonic bath to remove precipitated impurities containing active sites and the use of stainless steel balls instead of Cs material for trapping helped to reduce these negative effects. In the case of organotin compounds, the addition of acetic acid as a modifier to the extraction vessel helped to improve recoveries significantly by reducing the retention of the compounds in the vessel, tubing and trap [121. Several promising approaches to extraction of pesticides from cereals have been reported (see Table 4.7). Ohlin and co-workers [149] have developed an MRM for pesticides in dry and dried foodstuff using both solvent-based methods and SFE (EU project SMT4). The results of a proficiency test indicate the comparability of the SFE procedure (without clean-up) to a solventextraction method involving GPC clean-up for wheat samples. Yoshii et al. [150] have extracted 71 pesticides from ground cereals without any prior addition of water. Following a first clean-up effect on the Extrelut/C18 trap, the extracts were subjected to column chromatography on Florisil, resulting in two fractions that were separately analysed. Recoveries for most compounds exceeded 70%. Low recoveries were reported for fenthion and DDVP while propamocarb could not be recovered at all. There is considerable controversy about the effectiveness of modifiers in improving the extraction of pesticides from foods. Numerous examples exist in the literature for environmental samples, particularly soils and sediments, where it is apparent that modified SC-CO 2 (usually with methanol) is superior to unmodified SC-CO 2. In food analysis, many analysts claim little benefit from using static modifiers [151,152]. A positive effect of methanol as a dynamic modifier has been reported by Khan [153] for the extraction of pesticides from spiked wheat. Howard et al. [1211 used SC-CO2 modified with 2% methanol to extract methomyl, methiocarb and eptam from apple matrix while using Celite to immobilise water. Skopec et al. [154] extracted organophosphates from rice using 5% v/v methanol-CO2, while Nerin et al. found that a combination of methanol as static and acetone as dynamic modifier can be used for the extraction of pesticides from strawberries [155]. Nevertheless, most 153
M. Anastassiades and E. Scherbaum SFE-based MRMs for fruit and vegetable samples work best with intrinsic moisture as a "natural" modifier. 4.7.5
Discussion and future perspectives
Many applications demonstrate that SFE methods are clearly faster, less expensive and more environmentally-friendly than traditional solvent-based approaches. The advantages of automated SFE include: elimination or reduction of traditionally troublesome manual steps (such as extraction, partitioning, solvent evaporation and reconstitution with solvent), a high degree of selectivity (less need for clean-up), reduced organic solvent usage and thus less waste disposal and personnel exposure problems, and reduced space and glassware requirements. A crucial advantage of SFE over liquidbased methods is that the extraction solvent becomes a gas after extraction, leaving the analytes conveniently concentrated in the collecting medium that can, in the case of solid trapping, also be used as a clean-up device. However, despite these impressive abilities and advantages and the remarkable boom in research activities in the 1990s, SFE has not managed to become widely established in the field of pesticide residue analysis and the adoption of SFE methods as official approval has been rather slow [158,162]. There are many reasons contributing to this, some of which are similar to those discussed in the PLE section including: (a) high capital investment costs for automated commercial instruments, (b) questionable reliability of instruments and lack of interest on behalf of manufacturing companies to improve the technology, (c) reluctance on behalf of analysts when it comes to adopting an extraction technique that uses an extraneous solvent and performs extraction in a closed system ("black-box" effect), (d) need for a high degree of sample homogenisation and careful sub-sampling to ensure that the very small sample sizes employed are sufficiently representative, (e) inability of the SC-CO 2 to cover a broad enough pesticide range using a single sample-preparation method due to its very lipophilic nature as well as difficulties to overcome analyte-matrix interactions, (f) the need to frequently exchange gas cylinders (cooling gas), (g) complicated method development due to the great number of parameters that have to be optimised, and (h) the potential degradative loss of analytes while samples are awaiting extraction in the carousel (when performing sequential operation). In the past, SFE has been frequently advertised as an exceptionally fast technique and SC-CO 2 as being a solvent that provides an immense extraction power and at the same time an extraordinary degree of selectivity. However, experience has shown that SC-CO 2 does not confer any "super"-enhanced 154
Sample handling and clean-up procedures II-new developments properties as the name would suggest and that it behaves similarly to conventional non-polar solvents in terms of partitioning behaviour, miscibility and salvation power. In terms of speed, sequentially processed SF extractions of pesticide residues in produce take even longer than some "old-fashioned" manual extractions employing conventional organic solvents [52], and this without usually achieving a more efficient extraction. The merit of performing automated unattended extractions is further reduced by the risk of analyte loss during the waiting time associated with sequential extraction. The high degree of selectivity claimed for SFE is related to the non-polar nature of the SC-CO 2. It should be kept in mind, however, that this selectivity always implies limitations regarding the analytical scope (range of analytes covered). An advantage of SFE is that increasing the dynamic extraction time and thus the volume of the extraction fluid does not result in additional extract dilution, and solvent waste. Critical reviews on SFE have been published by Smith [163] and Luque de Castro et al. [164]. SFE has been demonstrated to be very effective in many applications for the extraction of a variety of residues from various matrices. However, the technique has not yet fully matured and there is a lot of room for further improvements. The future of SFE thus mainly depends on whether the instrument manufacturers are willing to invest in new developments that would provide higher sample throughput and a better robustness and reliability. This includes the ability to extract multiple samples in parallel, a higher flexibility in vessel sizes, improved restrictor design and better trapping devices with possibilities for automated clean-up. It remains to be seen if the technique can overcome its drawbacks and become more widely accepted. 4.8 4.8.1
OTHER ENERGY-ASSISTED EXTRACTION TECHNIQUES Microwave-assisted extraction (MAE/FMAE)
4.8.1.1 Introduction Microwave-assisted extraction (MAE) uses heat that is generated by microwave energy to accelerate extractions. Heating with microwaves is directly applied to the sample molecules and is generally more efficient than conventional convection-based heating that has to be transferred from the vessel to the solution. The interest in employing MAE for extraction in residue analysis started in the mid-1990s with the growing demand for heat-assisted extraction approaches, which are faster and require less solvents than traditional methodologies. The technique has meanwhile become relatively mature and is mostly employed in environmental analysis [165,166], with 155
M. Anastassiades and E. Scherbaum some methods having already obtained official status. Only a few applications deal with the extraction of pesticides from plant material. 4.8.1.2 Theoretical background Microwave radiation causes molecular motion by ionic conduction and rotation of dipoles. The heating effect is attributed to the friction generated by ion flow and the thermal energy released when molecules previously aligned by microwaves return to the normal randomly disordered state. When 2450 MHz, the frequency of most commercial extractors, is applied, this process happens at almost 5 billion times per second. The ability of a solvent molecule to absorb microwave energy and pass it on in the form of heat to other molecules is roughly proportional to its dielectric constant (a measure of its polarizability in an electric field) and the dielectric loss (describing the efficiency of converting microwave energy into heat). Polar solvents (high dielectric constant) such as water and methanol have the ability to strongly absorb microwave energy and dissipate it into heat, whereas non-polar solvents such as hexane do not respond to microwaves and do not heat up. The MAE approach simply involves placing the sample with the extraction solvent in a specialised container consisting of a microwavetransparent material (e.g., quartz or fluoro-polymers) and heating with microwaves of the preset power for the required time. When preparing the extraction, various strategies can be followed: (a) the sample is immersed in a solvent that strongly absorbs microwave energy, (b) the sample is immersed in a mixture of solvents with both absorbing and non-absorbing properties, (c) samples with microwave-absorbing properties are mixed with a microwavetransparent solvent (such as hexane), and (d) the sample is immersed in a microwave-transparent solvent and a microwave-absorbing stirring bar is added [167]. More detailed information about the theory behind MAE can be found in Ref. [168]. 4.8.1.3 Instrumentation Early instruments were mainly laboratory-built systems based on domestic ovens. Modern commercial MAE units are equipped with temperature and pressure feedback devices that allow the control of the extraction process. MAE extractions are performed in both closed and open systems with nonfocused or focused microwave energy. In focused systems, the microwave radiation is directly applied to the sample, which results in a much stronger electrical field than in the former case, in which microwave radiation is less efficiently dispersed in the extraction chamber, which can contain a large number of usually rotating extraction cells. 156
Sample handling and clean-up procedures II-new developments When using closed vessels, the solvent can be heated well above its normal boiling point, thus achieving a drastic enhancement in efficiency and speed of extraction. Closed-vessel MAE systems, which are mainly used when microwave-absorbing solvents are added, require the use of appropriate equipment and pressurisable extraction vessels to increase operational safety. Usually, these instruments allow the simultaneous processing of a number of extraction cells, which are placed on a 360 ° oscillating turntable. Some commercial instruments provide the facility of stirring the samples with magnetic bars to agitate the sample and ensure a uniform distribution of the temperature throughout the extraction mixture. Since these stir bars also absorb microwave energy, they are used to heat the samples, which is especially interesting when extractions are performed in the absence of any polar solvent. Most closed-type commercial instruments have been designed to perform chemical digestions and only a few of them to perform extractions with organic solvents, as required in pesticide multi-residue analysis. In open systems, extractions are performed under atmospheric pressure conditions and vapour losses are prevented by the presence of a reflux system on top of the extraction vessel. Open MAE systems mostly consist of a single vessel, which is irradiated using focused microwave irradiation, and they are thus traditionally referred to as focused MAE systems. These systems can usually extract larger samples and offer higher operational safety than closed systems by avoiding overpressure. Several articles describe in detail the most commonly used commercial extractors [169-171]. Recently, extractors have been developed that allow a dynamic MAE in closed pressurised systems. This PLE-like approach opens the possibility for further automation and potential hyphenation with subsequent analytical steps, e.g., clean-up devices [172,173]. Since MAE is often more exhaustive than selective, several clean-up techniques have been employed to purify the extracts, including GPC [174], SPME [175-177], SPE [178] and LLE [179]. 4.8.1.4 Parametersinfluencing the extraction process The fundamentals of pressurised MAE have been described in various publications [169,180,181]. Compared with SFE, MAE is much easier to optimise, with the main parameters that need to be considered being solvent composition and volume, temperature, extraction time and sample composition [165,167,182]. Solvent: A correct choice of solvent is fundamental for obtaining an optimal extraction process. Consideration should be given to the microwave-absorbing properties of the solvent, its polarity, potential matrix interactions and compatibility with subsequent analytical steps (evaporation, clean-up,
157
M. Anastassiades and E. Scherbaum chromatography). Preferably, the solvent should have a high selectivity towards the analytes of interest, excluding unwanted matrix components. Typical microwave-absorbing solvents employed are water, methanol and dichloromethane, while mixtures with microwave-transparent solvents such as hexane and acetone (1:1) [183] or ethyl acetate and cyclohexane (1:1) [184] have also been used. For samples with high water content, e.g., plant tissues, efficient extractions have been performed using pure, microwave-transparent solvents. This is reported to be particularly useful for thermolabile compounds to prevent their degradation. The solvent volume must be sufficient to ensure that the entire sample is immersed, especially when having a matrix that will swell during the extraction process. Temperature, time and microwave power: Similar to PLE, elevated temperatures generated by microwaves will improve extraction efficiencies by facilitating analyte desorption from active sites, speeding up diffusion and mass transfer processes and enhancing the solvent capacity to solubilise analytes. Additionally, at higher temperatures, surface tension and solvent viscosity decrease, allowing a better wetting and penetration of the matrix. However, increasing the temperature mostly leads to a greater amount of undesired matrix components in the extract and requires more thorough clean-up procedures. In applications dealing with thermolabile compounds (such as many pesticides), degradation can be a problem at higher temperatures; thus, choosing extraction temperature requires a compromise between extraction yields and selectivity. Extraction times in MAE are usually very short and sometimes 10 min is sufficient. With thermolabile compounds, longer extractions may increase degradation. The temperatures achieved by open MAE systems are limited by the boiling point of the solvent used. Matrix characteristics: Since water is a strongly microwave-absorbing solvent, water content in the sample is of major importance. Microwave energy should, therefore, be always adjusted according to the water content in the sample because the water content will dictate the energy needed to bring the non-absorbing solvent to the required temperature. A matrix dependency in the extraction efficiency has been reported by Pylypiw et al. [179] when several pesticides were extracted from crops (lettuces and tomatoes). When samples contain high water contents, extractions of non-polar analytes using non-polar solvents is hindered due to the limited accessibility. 4.8.1.5 Applications MAE has frequently been applied in the environmental analysis sector for the extraction of contaminants in soils and sediments. However, only a few applications deal with the extraction of pesticide residues from biological 158
Sample handling and clean-up procedures II-new developments samples and food in particular. Vetter et al. [184,1851 have extracted organochlorine pesticides from fatty tissues, including seal blubber and fish, using a cyclohexane:ethylacetate (1:1) mixture followed by GPC clean-up after water removal by Na 2SO 4. Stout et al. [186] extracted imidazolinone herbicides and their metabolites from plant tissue using MAE and water as a solvent. Determination was performed with an LC-MS/MS system. Bouaid et al. [187] extracted various pesticides from orange peel using hexane/acetone (1:1), achieving recoveries of 93-101%. Diagne et al. [188] extracted fenitrothion from beans with hexane/acetone (1:1) using a household microwave oven. After silica gel clean-up, determination was performed using HPLC/DAD or GC-ECD. Recoveries were comparable with Soxhlet extraction. Pylypiw et al. [179] analysed seven field-incurred pesticides from several matrices using MAE. The microwave settings required to achieve sufficient recoveries were shown to be dependent on both crop matrix and pesticide and a 10-min extraction at 100°C was chosen as a compromise between minimising degradation of chlorothalonil and still achieving good recoveries for all investigated pesticides. In an alternative procedure, Chee et al. [189] employed a closed-vessel MAE with acetone to extract C1 8 SPE disks previously used to pre-concentrate a number of pesticides from water samples.
4.8.1.6 Discussion and future perspectives The major benefits of MAE versus traditional methods are the reduced solvent consumption and the increased sample throughput. The technique is easy to use and the instrument purchase costs are lower compared with other modern techniques like SFE and PLE. MAE instruments can conduct batch extractions to further increase sample throughput, which is an advantage over other automated extractors that perform sequential extractions. However, although careful method development may result in some extraction selectivity, there is often a need for clean-up steps after extraction. The "onepot" approach furthermore requires the separation of the bulk matrix by manual means such as filtering and centrifuging. The need to wait for the extraction solutions to cool down after extraction in the closed-vessel approach also extends the total analysis time (newer instruments contain a cooling system but still over 15 min is required). In open systems, there is a potential for losses of thermolabile and volatile analytes. These disadvantages have hindered the widespread use of this approach for pesticide residue analysis in food.
159
M. Anastassiades and E. Scherbaum
4.8.2
Sonication-assisted extraction
Sonication-assisted extraction, also known as ultrasonic extraction (USE), makes use of ultrasonic energy (ultrasonic waves) to support extraction processes. The technique has been employed for this purpose for more than 40 years. By definition, ultrasonic waves are acoustic waves above the audible frequency range, covering the spectrum from approximately 20 kHz to over 1 GHz. They are intrinsically different from electromagnetic waves since they can only travel through matter (solids, liquids or gases) but not through a vacuum. Like any other sound waves, ultrasounds are mechanical vibrations, which involve alternating expansion and compression cycles. During the expansion cycles, ultrasounds applied on liquids induce the formation of very small bubbles or cavities, which initially grow and subsequently collapse by implosion. This process in known as cavitation. The collapse of the vapour bubbles is associated with the release of energy in the form of high temperatures and pressures. These energy forms may be rapidly dissipated within the solution but are still advantageous for the extraction process by locally increasing the solubility and diffusivity of analytes and by favouring matrix penetration. Close to solid surfaces, cavities tend to collapse asymmetrically, forming high-speed liquid jets, which can have a great mechanical impact on the solid surfaces [1901. Ultrasound is thus of great help in the pre-treatment of solid samples, such as clays and soils, which are known to tightly retain certain analytes. When dealing with plant samples, sonication can help the extraction process by facilitating the swelling of the material, by destroying and releasing the contents of oil glands, by dilating cell-wall pores and even by causing cell-wall rupture. A negative aspect associated with sonication is that the high temperatures generated can locally lead to chemical reactions (sonolysis) and the formation of free radicals, which may react with the analytes, causing losses. The main advantage of this technique is that it uses simple and cheap equipment. There are two common devices for ultrasonic application: baths and probe units. Baths are more widely used but have the disadvantage of a non-uniform distribution of energy, which leads to a limited experimental repeatability. Probes, which are immersed into the sample, have the advantage of efficiently focusing their energy on a localized zone [190]. By creating localised turbulences in the solution, sonication is a type of agitation procedure that increases mass transfer. Although sometimes even more effective compared with conventional shaking, USEs are often performed repetitively, which increases solvent consumption and makes 160
Sample handling and clean-up procedures II-new developments the procedures time-consuming. After the extraction step, phase separation and clean-up of the extracts are often performed. So far, most applications for pesticide residue analysis deal with environmental samples [191-193]. Bushway et al. [194] have employed sonication with methanol to release residues of benzimidazoles from various fruit samples. Therdteppitak et al. [195] employed microwaves to enhance the extraction of 16 organochlorine pesticides from fish using a mixture of hexane and acetone 9:1 for extraction. Navarro et al. [47] employed ultrasonic energy to assist the extraction of 17 fungicides from wine using acetone/dichloromethane (1:1) as solvent and Schenk et al. [196] used it to assist the extraction of organophosphorous pesticides from milk previously mixed with a mixture of acetone/acetonitrile/methanol. The ability of ultrasound to improve desorption processes has also been frequently used in combination with other analytical techniques such as LLP on the surface of macroporous adsorbents (see section 4.4) [60,68], SPME (and see section 4.10.1) [197,198]. 4.9
ADSORPTIVE EXTRACTION TECHNIQUES
Adsorptive extraction techniques rely on the partitioning of analytes between a liquid or gaseous phase and the surface of an adsorbent. Adsorbents are usually porous materials with a very large surface area (up to 1200 m 2/g) that contain active groups with which the analytes interact. Analyte adsorption (trapping) on the surface is usually followed by the reverse process, i.e., their desorption (release, elution) using a small amount of an appropriate solvent. By choosing the sorbent and the conditions of trapping and elution, the analyst can influence the recovery and selectivity of the process. As an alternative to liquid desorption, thermal desorption under an inert gas stream is also possible; however, this approach is more common for sampling devices that involve partitioning onto thermally stable liquid-like phases such as polydimethylsiloxanes (PDMS) (see section 4.10). Thermal desorption of analytes from adsorbents is problematic because most adsorbents are either thermally unstable or undergo interactions with analytes that are too strong to allow thermal desorption without analyte degradation being induced. There are several types of adsorbents exhibiting different surface chemistries and thermal stability: (a) inorganic carbon-based adsorbents: e.g., activated carbons and (graphitised) carbon blacks, which have a very high affinity towards organic (especially aromatic) compounds and can be heated up to 450°C without alteration; (b) metal oxide-based adsorbents: e.g., silica and alumina, which have a strong affinity towards polar compounds and are thermally stable up to 400-600°C; (c) modified (bonded) silicas: these are
161
M. Anastassiades and E. Scherbaum coated with organic moieties containing various lipophilic or hydrophilic functionalities and are thermally unstable, thus mostly used with liquid desorption; (d) polymeric adsorbents: these are based on various types of synthetic or natural (co-)polymers such as the intermediately polar polyamides (e.g., PA-6) or the polystyrene-divinylbenzenes (PS-DVB), which are highly lipophilic if unmodified (e.g., XAD-Amberlites, PRPS, Chromosorb). With few exceptions (e.g., Tenax), polymeric adsorbents are thermally highly unstable and thus not suitable for thermal desorption since artefacts and monomer units (e.g., benzene, styrene) are released upon heating. A new generation of polymers based on polyacryl is especially designed to contain the various functionalities in such a sterical arrangement to selectively adsorb certain analytes (see section 4.9.2 on immunoaffinity sorbents and section 4.9.3 on MIPs). Since the beginnings of pesticide residue analysis, adsorptive partitioning techniques have frequently been employed in sample preparation, serving various analytical purposes such as the selective removal of interfering matrix components (clean-up), the enrichment of analytes, the exchange of solvents (from aqueous to organic) and the storage and transport of sample extracts. 4.9.1
Solid phase extraction (SPE)
4.9.1.1 Introduction SPE is a physical extraction process involving a solid adsorbent and a liquid phase. The technique, which has sometimes also been referred to as "liquidsolid-extraction" [199] or "sorbent-extraction", is frequently used to selectively extract, concentrate and purify analytes from liquid samples. SPE adsorbents are most commonly packed into columns or cartridges. Thus, SPE usually constitutes a chromatographic procedure that starts with percolating a liquid sample or sample extract through the adsorbent bed in the column, followed by a complete or fractional elution (displacement) of the various components using relatively small solvent volumes. There is a wide choice of SPE adsorbents with various types of active sites on their surface with which analytes and matrix components can, more or less strongly or selectively, interact. Any selectivity or separation accomplished is based on the different affinities that analytes and matrix components exhibit towards both the mobile and stationary phases. To achieve the desired separation effect, the analyst should know how to control these interactions properly. Historically, SPE has evolved directly from traditional adsorption chromatography, which was first introduced as far back as the beginning of the 20th century by the father of chromatography, M. Tswett. Adsorption 162
Sample handling and clean-up procedures II-new developments chromatography, also known as "column chromatography" or "liquid-solid chromatography", has traditionally employed polar mineral adsorbents such as silica, Florisil' and alumina and has been among the most frequently employed clean-up approaches in pesticide residue analysis for many decades, with numerous official methods still using this technique today. Modern SPE still makes use of these traditional "normal-phase" sorbents but also employs a vast number of additional sorbents with different surface chemistries. The advent and growth of HPLC in the late 1960s and 1970s led to the development of new phases and thus to more choices and better production standards of stationary phases. A milestone in this respect was the development in the late 1960s of silica-based reversed-phase (RP) materials consisting of organic moieties covalently bound on silica particles. By the early 1970s, these new stationary phases started to be employed for the trace enrichment of various organic compounds from aqueous samples as an effective alternative to liquid-liquid extractions, replacing other RP adsorbents used at this time such as Amberlites (polymeric resins) and granular active carbon, the latter being employed for this purpose since the 1950s [200]. This early form of RP-SPE, then described as "RP-column extraction" or "RPadsorption chromatography", usually employed columns that were manually packed in the laboratories. The commercial introduction of pre-packed disposable cartridges took place in the late 1970s, but the term SPE was only introduced in 1982. Today, SPE continues to be strongly related to HPLC and most sorbents developed for HPLC are sooner or later offered for SPE applications as well. Differences pertain in the size and shape of the particles, which are spherical with a diameter of 3-5 Aim in HPLC and mostly irregularly shaped in SPE with a diameter of ca. 50 gm to allow rapid flow and prevent clogging. Driven by the analysts' needs for simpler and more economical sample-preparation approaches, which can be easily automated for high-throughput analysis, SPE has undergone a steady growth in its popularity and scope of use with improvements and diversifications in formats, sorbent types and automated apparatuses. In general, SPE has been much more readily adopted by analysts working in the biomedical and pharmaceutical fields, where analytes are predominantly polar in nature and thus more often analysed by HPLC, the principles of which are closely related to SPE. Analysts of pesticide residues in food have traditionally predominantly employed GC technology and were thus much more reluctant to incorporate SPE into their methodologies. Bad experiences with variable sorbent activities, purity problems and a concern that the large amount of matrix components in food extracts would overload the adsorbents and displace analytes has surely contributed to this reluctance of pesticide 163
M. Anastassiades and E. Scherbaum analysts to use SPE. In contrast to food analysis, SPE has been readily adopted in water analysis due to its numerous and evident advantages over the traditional solvent-based approaches, including the considerable solvent savings, automation and the on-site sampling possibilities. 4.9.1.2 Method development Defining an efficient strategy for the development of an SPE method is not always easy. Method development is thus often described as a largely empirical, labour-intensive and time-consuming trial-and-error process [201]. The complexity of the topic, with numerous possible interaction mechanisms to be considered and countless new products entering the market each year, contributes to this situation. Method development primarily involves the determination of the most appropriate retention mechanism (i.e., sorbent selection), taking into account the chemical structure of the analytes and the composition of the sample. Both the surface chemistry of the sorbent and the composition of the surrounding liquid phase are equally important. It should be kept in mind that slight differences in the constitution of the matrix, mobile phase or adsorbent can have a great impact on the chromatographic behaviour of the analytes of interest, additionally complicating the subject. Fortunately, due to strict quality control during adsorbent production, batch-to-batch sorbent variability is not as critical as in the past. In general, experience with SPE and a basic understanding of the properties of the stationary phases and the physicochemical interactions that may be formed with the analytes and the matrix components will help to realize the potential of SPE to a fuller extent and save much effort in method development. Nevertheless, often too little attention is given to the chemistry involved in the interaction processes and, despite its associated difficulties, SPE method development is in practice often delegated to less trained personnel [201]. Valuable information for selecting the analytical parameters for SPE can be found in various review articles [200-206], books [207-209] and the numerous guidelines released by SPE manufacturers. The analogy of SPE with HPLC can be helpful in method development, since HPLC retention data can provide valuable information for the selection of sorbents and mobile phases. A linear relation between the retention factors (log Kw) of analytes on C18 phases and the octanol-water partition coefficients (log Ko/w) was observed by various authors [201]. Computer programs, based on more general models for retention prediction in HPLC, have been developed meanwhile, allowing the calculation ofthe composition and volumes of solvents that may be used in each step of SPE methods. However, the acceptance of these tools will depend on their user-friendliness and reliability and remains to be seen.
164
Sample handling and clean-up procedures II-new developments 4.9.1.3 Interaction mechanisms In SPE, as in any chromatographic technique, retention and separation largely depend on the nature of the stationary and liquid phases and the analytes involved. Sorbent-analyte interactions fall into three main categories: hydrophobic (non-polar), polar and ionic. In principle, all these interaction types involve some kind of electrostatic forces and cannot always be clearly distinguished. The so-called hydrophobic or dispersive interactions are based on electrostatic forces between molecules with temporarily induced dipoles (Van der Waals forces). However, these interactions are too weak to explain the relatively strong retention of hydrophobic molecules on non-polar sorbent sites (e.g., C18 chains). Rather, it is the strong interactions between water molecules of the sample that are responsible for the hydrophobic retention by forcing the non-polar analytes to separate from the aqueous phase and align themselves with the non-polar sites of the sorbent, thus avoiding the formation of energetically unfavourable cavities in the water. Of course, the energetic relations change as the percentage of organic co-solvents in the mobile phase increases. It should be also kept in mind that the building up of a large amount of matrix components onto the surface of a RP sorbent can dramatically alter its capacity and retentive properties. The so-called r- 7r interactions are of intermediate strength and are counted among the hydrophobic interactions by some authors and the polar ones by others. They are based on dispersive forces between electron clouds of molecules containing a degree of unsaturation (double-bonds, aromatic rings). Polar interactions are formed between molecules that contain strong permanent dipoles. When proton donor and proton acceptor molecules (e.g., alcohols, amines) are involved, these interactions are mostly formed via H-bridges (hydrogen bonds). Such interactions are also of intermediate strength and ca. 10-fold less strong than ionic interactions,which are set up between molecules of opposite charge. Often, the pH of the liquid phase has to be adjusted to ensure the ionic character of the analytes or the sorbent. Depending on the mechanism of interaction involved, one can distinguish between various separation principles, i.e., normal phase, reversed phase and ion exchange. In general, normal phase SPE is the process where the adsorbent is more polar (hydrophilic) than the mobile phase and primarily involves polar interactions between analyte and sorbent. In reversed-phase SPE, the adsorbent is more lipophilic than the mobile phase and analytes are primarily retained by hydrophobic mechanisms. The term ion exchange SPE is used when analytes are retained by ionic interactions.
165
M. Anastassiades and E. Scherbaum The analyst can choose from a variety of sorbents that can undergo the above-mentioned interaction mechanisms. However, one has always to keep in mind that most SPE adsorbents have the potential to undergo interactions of more than one type, which provides them with a broader analytical scope (e.g., carbon and mixed-mode sorbents). In the case of immunosorbents (ISs) (see section 4.9.2) and MIPs (see section 4.9.3), however, the diverse functional groups of the sorbents are sterically arranged in such a way to provide them with a very high selectivity for compounds with similar moieties. Even common RP silicas always exhibit mixed-mode properties to a certain extent due to the presence of residual silanol groups. These active groups can undergo ionic and hydrogen-bond interactions with analytes depending on the composition and pH of the mobile phase. The acidity of silanol groups can vary considerably depending on their location relative to other silanol groups and the influence of trace metals embedded in the silica structure. It is usually assumed that pH below 3.5 would fully protonate the silanol sites, but a fraction of highly acidic silanol groups is reported to start dissociating even at pH values below 2. Once deprotonized, silanol groups strongly contribute to the strong retention and elution difficulties of basic analytes due to the formation of ionic interactions. 4.9.1.4 SPE procedure SPE adsorbents can be used either in the batch mode (also known as dispersive SPE [52]) or in the column mode. In the batch mode, the adsorbent is simply poured into the liquid phase containing the analytes, shaken and subsequently removed by centrifugation or filtration. The distribution equilibria are thus simpler and easier to predict than in the column operation, which in fact constitutes a complex chromatographic process where parameters such as flow rate and breakthrough volume have to be considered. Nevertheless, the column mode is far more frequently employed due to its higher versatility for performing various elution strategies and its better amenability to automation. The objective of the analyst is to isolate the analytes of interest from a complex sample in a concentrated state and there are two basic approaches for achieving that goal, depending on the elution order of analytes and matrix components. In the first case, the sorbent has a high affinity for the sample contaminants so that the analytes can be eluted and isolated first while, in the second case, the adsorbent retains the target compounds while the impurities can be washed out before eluting the analytes. The selection of one of these two modes will depend on the molecular structure of the analytes, the nature of the sample and the subsequent determinative step. The first approach has often been used in multi-residue
166
Sample handling and clean-up procedures II-new developments analysis offood to remove unwanted matrix components from extracts (e.g., in traditional column chromatography where normal phase sorbents are used to separate polar compounds in a fractional elution scheme). In the simplest form of this approach, the sorbent merely acts as a chemical filter that retains impurities. In this case, batch mode SPE is a good alternative to column SPE with many advantages, including simplicity and cost savings [52]. The second approach is often used for the enrichment of pesticides from aqueous samples (water, juices) on RP stationary phases as a straightforward and economical alternative to traditional LLE (see Table 4.9). The same approach has also been successfully employed to enrich pesticides from raw MRM extracts of fruit and vegetable samples after diluting them with water, thus obviating troublesome steps such as LLP and evaporation (see Table 4.10). The enrichment of target analytes using the batch mode SPE approach is not popular due to the difficulty of quantitatively recovering the sorbent particles that tend to adhere to surfaces. A typical procedure involving SPE columns entails the following steps: (1) Conditioning and equilibration: This step usually entails eluting the cartridge with an appropriate solvent of intermediate polarity such as methanol, followed by a liquid similar in nature to the sample to equilibrate the sorbent (e.g., water in the case of aqueous samples or a non-polar solvent in NP-mode applications). Conditioning is a critical step to ensure proper sample-sorbent contact and slight variations of the procedure are reported to have a dramatic influence on the retention behaviour. A typical error source leading to poor recoveries and low reproducibility in SPE applications is the de-conditioning of the adsorbent if the device is left under vacuum and goes dry. A new generation of polymeric adsorbents containing embedded polar groups allow water to effectively wet their surface and are claimed not to require this activation step. (2) Sample loading (retention): The sample (previously treated to be amenable to the SPE application) is applied on the head of the conditioned cartridge and the analytes, together with some matrix components, are retained. The sample volume to be applied will depend on general analytical requirements (e.g., limits of determination) but it will be also limited by the capacity of the sorbent and its ability to retain the analytes (to avoid breakthrough losses). (3) Interference elution: Appropriate solvents are passed through the cartridge to rinse away interfering compounds while leaving the analytes undisturbed. Care should be taken to avoid analyte losses.
167
M. Anastassiades and E. Scherbaum (4) Drying: In RP applications, drying may be necessary if the final eluent is immiscible in water to ensure a better access of the analytes. (5) Analyte elution: The solvents chosen should disrupt analyte-sorbent interactions to selectively elute the analytes and should further be amenable to the subsequent sample preparation or measurement steps. Fractional elution is also possible and has been frequently employed in normal-phase applications. Finding the right conditions is sometimes difficult and involves the judicious choice of the sorbent and the proper adjustment of the mobile phase conditions during loading, rinsing and elution steps. The analyst should always be aware of pesticide losses that might result due to irreversible adsorptions, deactivation of the sorbent and the presence of competing coextractives. Flow through the cartridges is achieved by pressure differentials that are easily achievable with laboratory vacuum systems (vacuum manifolds). Using a syringe attached to the SPE cartridge, it is possible to accelerate elution by applying positive pressure (pressing). Nearly all modern systems for automated SPE use the positive pressure approach because it allows the flow to be kept constant and independent of the density of the cartridge packing. Centrifugation is an alternative approach that allows parallel processing of a great number of cartridges, which are placed in appropriate collection tubes. 4.9.1.5 Sorbents The key element to any SPE procedure is the sorbent. The physicochemical properties of the sorbent determine the interactions and the extraction efficiency. Over the years, a multitude of sorbent materials with a wide range of surface chemistries, pore sizes (typically 60-300 A), particle sizes (typically 10-100/ m), surface areas (typically 100-1200 m2/g) and base supports (silica, alumina, polymers) have been developed and commercialized. In general, two main trends can be observed in terms of SPE sorbent applicability, one focusing on "universality" (to allow broad spectrum multi-residue analysis) and the other on selectivity and specificity. Table 4.8 summarizes some properties of the most common SPE sorbents. In the following, some recent developments on SPE stationary phases will be discussed. Reversed-phase silicas: For many years, n-alkyl-silicas have been the universal SPE sorbents, with octadecylsilan (C18) modified silicas being the most popular because of their greater capacity. Over the years, there have been many developments in this field, mainly initiated by the need to address problems experienced in HPLC. A major trend has been to minimize 168
Sample handling and clean-up procedures II-new developments 0.
k
rj)~
0 0Z 0
. >
0~
0(.~ r0
oo 0
o~
a~.
~°
'
:
0~
Sa
0,
a
a E
4~a
U
B
cdw0
oo ~o~
'A o 4: P)
0o
rj
00
0 o
0
~o~
V0
0oL N
m -2 0
~cI 0 .0 0~~~.
I
I
x I~
0
a) -
rl~
m
~~~~~~~~C ~ ~~ 0
169
M. Anastassiades and E. Scherbaum
(D~~~~b0
.g
M
m a '~
0
Ch
.°2
4
1
E
X L!m r° i d
I-C~
t;y*g; a
B3~U
k
4
o P . ,.
E5
a) o
.4 o
.C)
C
-
p
Sa~~~~~~
tI4 0 O i x Q °l .
v 0)
Go
;L
Pm
a,
I tr3
I
A
o
9
-41 =1 ct
o 9
,g
,
H
u
r. 0
·a °
d.~
-9
,~a
bM 0
W Ao
r
o·
o
.
o Ac
0~
a I
0, N
z I
Z
[~
-I
k)
I .I_
;i
I
-7-
-7O-
f1
r~) ll d
.D o V
170
a, 7
I? · :E
a~C,
ChNW
f
.~o
dt.
~
o
Sample handling and clean-up procedures II-new developments
..Q
.'
a
0~~~~~~
co
00 I
11 I
oI
LHC
V
UWW
¢o,
I
a
o
MV
~~~~~~~~~II
~ oo
r
o
o,
ci6 0 P.
0 ' I1 .! h, t
DJ~M--
o aM o oa.a p
0Cd),. +F
o _E
I C)
oO
z
0
o
z I
-r-I
_r
I
N
'Uz
I
- C) I
I -)
Icn
~.~.~ ~. .Ea.o '"" · h : '~
¢
z
°
¢-
Cbf
.
~ '~
~
°
$
I
°.
l
"
~.
C
§ 171
M. Anastassiades and E. Scherbaum a
-a
a Ca
.r~~~
ba
~
a -a,
o~ b ho
O·
>
b
0 7~ M
ro 0
Co
C)11-;
0.M F
d
O
Fl 4
~0
bn DOa
~O~~O I-
0
0
C)
U t
a
Po aa aID
O-W
0 o a
a
0
zrQ
"
c$D W a4
z
0 .U
o 0
P.
5O 0 ©-
o-O
.EE I 0 0
4-,
-C g., a
I O IIN
I 0 I
O
I
z
O-0O I o I
-co
.)
I
-- j)1
V~B
O
M
0
Cd o
a
0
0C
x
>
~u
U
u"
o
V2
P..
00 51 P 0
a
pq
172
a
C
0
u
Lt
;
>,
Pd
Q -r.
,
C
~~a
M Cl
m~ 0
~-
.C
01
'P. P-
Sample handling and clean-up procedures II-new developments
=
a *t m
QQ
i;
zflu
Cd LOO
O
r(
0
a
~c~In~
~
I
+
0~
..
~oa~
z
*I
0
*
~-
~4
Q"
173
M. Anastassiades and E. Scherbaum the activity of residual silanol groups on the surface of RP-modified silicas. In early days, silicas were generated from natural diatomaceous earth material employing special sol-gel processes and were generally characterised by a high content of metal impurities (e.g., Fe and Al). After discovering that the presence of these metals within the silica structure enhances the acidity (and thus activity) of neighbouring silanol groups, great efforts have been undertaken to improve the silica purity. The problem has finally been successfully addressed by introducing the so-called "ultra-pure" silicas, which are synthesized from tetraethoxysilan (TEOS), allowing the production RP sorbents with silanol groups of low acidity. A reduction of the number of residual silanols has been also accomplished by performing the alkyl bonding with tri-functional silanes (e.g., trichloroctadecylsilane), which can simultaneously react with neighbouring silanol groups. Residual silanol groups are then covered by endcapping with small-sized silanes (e.g., trimethylchlorosilane). This trend of producing end-capped sorbents for HPLC was initially also followed in SPE; however, this began to change after the usefulness of such silanol groups in providing additional (secondary) interactions and in enhancing the sorbent wettability, which is very important in the extraction of aqueous samples, was understood. So, various manufacturers began to reintroduce C18 sorbents with unmodified silanol groups, prepared either by modifying silicas with monofunctional alkylsilanes or by keeping the alkyl chain saturation low (low carbon loading). Some common descriptions for such sorbents are C18/OH, C18-light or polar-C18. Nevertheless, despite the great efforts to improve the retention of polar analytes (pKo < 1.5) from aqueous samples, alkyl-silicas are still not strong enough and are thus rarely used for multi-residue analysis of water samples when polar pesticides are to be included. Another limitation of bonded silicas is the narrow pH stability range. Below pH 2, the silyl bond can be hydrolysed while above pH 8 the silica base is liable to dissolution. This, however, is rarely a problem in practice as elution times in SPE tend to be short and the columns are intended for single use. Polymeric sorbents: The use of polymeric resins in SPE-like procedures is not new. Highly hydrophobic resins based on polystyrene divinyl benzene (PS-DVB) such as the Amberlite XADs have been used since the late 1960s for the extraction of contaminants from water samples. The retentive power of these polymeric adsorbents is based on a combination of hydrophobic mechanisms and l-II interactions. The retention capabilities of these early PS-DVB resins towards polar analytes were typically higher than of C18 silicas, but still not strong enough to meet the needs of multi-residue analysis, mainly because of the relatively low surface areas of these sorbents (e.g., 300 m 2/g for XAD-2 and 500 m2 /g for PLRP-S). The inadequacy of alkyl-silicas 174
Sample handling and clean-up procedures II-new developments and early polymeric sorbents to address the problem of sufficient retention of polar pesticides from large-volume water samples has led to the development of highly cross-linked PS-DVB resins that are characterised by a higher porosity and a larger specific surface area (700-1200 m2 /g). This translates into higher sorptive capacities and significantly larger breakthrough volumes, as confirmed in numerous studies [210,211]. A study has shown that highly cross-linked polymers can even retain ionic analytes from water samples due to the strong interactions with the lipophilic part of the molecules [2101. Many authors have reported that, compared with alkyl-silicas, polymeric resins give extracts containing more polar matrix interferences. These can be discriminated by adding a small amount of an organic solvent directly to the sample before extraction and/or subsequent to the washing solution. This is, of course, only feasible when the targeted analytes are better retained (less polar) than the interferences. This approach is also beneficial in the analysis of highly nonpolar pesticides such as pyrethroids and organochlorine compounds, which are notorious for their tendency to precipitate on containers and tubes [2121. A parallel development to the highly cross-linked PS-DVB is that of the functionalized polymeric resins. Several types of functional groups have been introduced to various types of PS-DVB polymers, including acetyl, hydroxymethyl, sulfonyl, o-carboxybenzoyl, and benzoyl [205,213,214]. This functionalization improves the contact with aqueous samples and several studies have shown that the recoveries of polar analytes are higher than those obtained by the unmodified analogues. An alternative type of such polymers is generated by co-polymerization with polar monomers such as N-vinylpyrrolidone (NVP). Such a co-polymer is the patented Oasis HLB (Waters) that is claimed to possess an excellent water wettability and not to require pre-conditioning with bipolar solvents as is the case with reversed-phase silica and unmodified PS-DVB sorbents. It is also reported that drying out of the sorbent during the extraction procedure does not diminish its ability to retain analytes. Owing to its relatively large surface area of 800 m2 /g and the hydrogen acceptor properties of the pyrrolidone group, the sorbent furthermore has an excellent retentive power. Other sorbent producers, meanwhile, also offer polymeric sorbents combining high specific area with polar groups such as Abselut NEXUS (Varian), Strata X (Phenomenex), H 2 0-philic DVB (JT Baker) and ENV + (IST). Most of these are of undisclosed chemical structure. A recent development, mainly driven by the needs of drug analysis, are the so-called mixed-mode polymeric sorbents that contain lipophilic (e.g., C8, C18) and ionic (e.g., sulfonic acid, carboxylic acid, diethylenetriamine) groups attached to the same polymeric PS-DVB backbone. Mixed-mode sorbents can 175
M. Anastassiades and E. Scherbaum retain compounds by both reversed-phase and ionic mechanisms and can thus be used to efficiently separate them from both non-ionic and permanently ionic interferences. This is achieved by properly adjusting the composition (pH, polarity) of the mobile phase during the extraction, washing and elution steps. Of great importance is the possibility to eliminate inorganic ions, which often pose a competition problem in ion-exchange chromatography and suppress signals in LC/MS applications. This principle was used by Young et al. [215,216] to isolate thiabendazole and carbendazim from juices, using a mixed-mode polymeric sorbent (Oasis MCX). Prior to the introduction of mixed-mode sorbents, such separation strategies were performed using two types of sorbents, which were either contained in one cartridge (as disks or sorbent beds) or in two separate cartridges sequentially arranged (tandem cartridges) [2171. The recent advances in the field of polymeric adsorbents have opened new horizons for pesticide residue analysis. Due to the higher capacity and retention power (especially for polar analytes), smaller bed volumes can be employed to achieve the same retention capabilities, which translates in higher flow rates and reduced clogging problems. Highly cross-linked PS-DVB sorbents have meanwhile replaced silica-based sorbents for the extraction of water samples and it can be expected that many more polymeric sorbents with various functionalities will be introduced in the future. In on-line SPE-HPLC applications, due to the fact that polymeric sorbents are more retentive than the silica-based ones that are contained in the analytical columns, special elution strategies are required to avoid band broadening, such as columnswitching, backflush elution and eluent dilution prior to entering the analytical column. The low-pressure resistance of polymeric sorbents should also be considered. Carbon-basedadsorbents:These sorbents occupy a special place because of their unique retention properties. In the past, carbon was notorious among chemists due to the great number of charcoal types and the irreproducibility of applications. The situation dramatically improved with the introduction of graphitised carbon blacks (GCBs), which are obtained from heating carbon blacks at 2700-3000°C in an inert atmosphere. These are essentially nonporous sorbents with a surface area of about 100 m 2/g, consisting of hyphenated hexagonal rings in graphitic layers held together by dispersive forces. Analytes are retained via 7r-ir- and hydrophobic interaction mechanisms, strongly depending on their structure and less on the presence of functional groups. Strong retention is usually obtained for planar molecules containing delocalized electronic bonds and hydrocarbons with potential for multiple surface contact points. Positively-charged chemical heterogeneities on their surface give carbon-based sorbents an additional anion-exchange 176
Sample handling and clean-up procedures II-new developments character, which has even been used to fractionate acidic pesticides from neutral and basic ones [218]. Carbons can be run in reversed-phase or normalphase applications. RP mechanisms contribute to, but do not rule, the retention. The complex interaction mechanisms, however, make it difficult to predict the retention. Newer Carbograph sorbents have surfaces greater than 200 m2 /g and have been reported to provide better recoveries of certain polar pesticides than highly cross-linked polymers. A drawback of carbon-based sorbents in general is their excessive, even irreversible, retention of certain analytes that complicates elution [77]. Typical SPE elution solvents such as acetonitrile and methanol are sometimes too weak, so methylene chloride, toluene or tetrahydrofuran has to be used to disrupt the interactions. Another drawback is the poor mechanical stability (pressure resistance) of carbon sorbents, which makes them inappropriate as HPLC column materials. Inorganic normal-phase adsorbents: Normal-phase (NP) adsorbents have been widely used for several decades for the clean-up of extracts in a procedure described as column extraction or liquid-solid partitioning (LSP). Normalphase sorbents show their highest retentive power in aprotic media of low dielectric constant such as non-polar organic solvents and are thus mainly used for the clean-up of sample extracts dissolved in such solvents (e.g., hexane, isooctane). Elution sequences with solvent mixtures of increasing polarity allow a separation into fractions on the basis of polarity. In traditional MRMs, NP chromatographic clean-up was performed using alumina, Florisil or silica columns (sometimes mixed with charcoal) prepared by the analyst in the laboratory. The poor batch-to-batch reproducibility of the sorbents, their need for deactivation and partial reactivation and the troublesome manual column preparation led to rather time-consuming procedures and variable results. Nowadays, NP-sorbents can be purchased in disposable cartridges. NP materials are usually intended for single use, since many of the polar co-extractants bind firmly to their surface and are difficult to remove. The fractional elution typically starts with a highly non-polar solvent, which elutes very non-polar pesticides (e.g., organochlorine compounds), thus separating them from the more polar triglyceride fraction. The elution continues with solvent mixtures of increasing polarity, allowing the displacement of more and more polar pesticides [3]. In a multi-residue approach, however, the plethora of pesticides of interest usually cover a very broad polarity range, making a clear separation between individual groups impossible. In principle, this procedure merely splits pesticides and matrix components into different fractions based on their polarity. NP clean-up is thus often performed following GPC clean-up that complementarily removes fat and pigments on the basis of the molecular-size-exclusion principle. 177
M. Anastassiades and E. Scherbaum Probably the most decisive drawback of fractional clean-up is that the different fractions have to be handled and injected separately, which translates into more manual and administrative work. Losses for certain polar pesticides have been reported by various authors. There are numerous applications where NP adsorbents were applied in pesticide residue analysis. While, in the early days, large amounts of sorbents were used, e.g., 20-40 g, newer applications employ miniaturized self-made or commercial columns filled with, for example, 0.5-2 g sorbents [3,219]. Restricted access materials (RAMs): RAMs are dual-coated, silica-based
sorbents with controlled small pores. The sorbent surface within the pores (inner surface) is modified with groups that allow retention of analytes through hydrophobic, ionic or affinity interactions while the external surface is modified with hydrophilic moieties that are non-retentive when aqueous samples are injected. Such sorbents are also described as internal surface reversed-phase (ISRP) materials. Owing to the small pore diameter, only analytes with a low molecular weight have access to the retentive sites while macromolecular matrix components remain in the void volume and can be directly flushed into the waste. Thus, in principle, RAM combines size exclusion of high-molecular-mass matrix components with the simultaneous adsorptive enrichment of low-molecular-mass analytes. RAMs are almost exclusively used as pre-columns in on-line SPE-LC systems with column switching arrangements that allow direct injection of biological and environmental samples containing macromolecular components such as proteins and humic acids. A typical procedure starts with trace enrichment of the analytes on the RAM pre-column and at the same time the separation of macromolecular compounds followed by the elution of the analytes into the analytical column and the regeneration of the RAM column in the backflush mode. One of the main advantages of using RAMs on-line to HPLC is the protection of the analytical column from being contaminated by large bio-polymers. Such compounds tend to precipitate on the column surface and block the access of analytes to the adsorptive sites. Furthermore, they may modify the retention properties of the sorbent and increase the backpressure during elution. The most popular RAMs are the alkyl diol silicas (ADS), which are diol-modified silicas with an internal pore surface modified with lipophilic alkyl groups (C 18, C8, C4). 4.9.1.6 Formats
The developments in SPE technology not only concern the available sorbents but also the different formats designed to provide better handling, 178
Sample handling and clean-up procedures II-new developments performance and automation possibilities. While in the 1960s adsorption chromatography was merely performed in laboratory-filled glass columns, today there is a great variety of pre-packed, disposable SPE formats to choose from, ranging from simple packed syringes and cartridges to disks, 96-well plates and SPE pipette tips. The traditional cartridge (previously syringe barrel) is still the most popular SPE format. Disposable SPE cartridges, as we know them today, were introduced in the late 1970s and usually consist of polypropylene or glass. The sorbent bed is contained between two frits usually made from polyethylene or PTFE. Analysts can choose between a great variety of cartridge sizes and shapes, some of which have been specially designed to meet the requirements of automated SPE. The cartridge design has certain disadvantages, including the occurrence of channelling that negatively affects repeatability, and the small cross-sectional area. The latter limits the tolerance to blockage by suspended particles, thus leading to longer extraction times and low sampleprocessing rates, especially when dealing with large sample volumes, as in the case of water analysis. The SPE disk format was introduced in the early 1990s as an alternative to particle-filled cartridges. SPE disks (also called SPE membranes) do not contain the sorbent particles loosely packed, as in traditional SPE columns, but incorporated onto a support membrane that consist of porous PTFE or glass fibres, the latter being more rigid. The SPE particles used in PTFE membrane disks are smaller than those used in traditional SPE columns (e.g., 8 m versus typically 50 Am) and make ca. 90% of the total weight of the membranes. A great variety of sorbent types have already been embedded on SPE disk membranes, including various types of silicas (e.g., C18, C8, SCX, SAX), PS-DVB, modified PS-DVB (e.g., with cation and anion exchange functionalities) and ISs. The millimetre-thin disks are commercialized in three main sizes (2.8, 4 and 9 mm) and are placed in special holders to perform SPE by letting the liquid samples flow through. Green et al. presented an alternative arrangement for achieving trace enrichment of analytes from water samples by directly submerging C18 disks into the water, which was stirred with a stir bar [220]. SPE disk membranes are characterized by a uniform packing density and a large diameter compared with the thickness, which allows high and steady flow rates and faster throughput of large volume samples. Compared with loose-particle filled cartridges, the occurrence of channelling that causes breakthrough losses is significantly reduced and more reproducible results are reportedly achieved. Disks are also prone to clogging caused by particles in the sample, which is effectively prevented when a prefilter is used. One of the drawbacks of using disks instead of cartridges is their
179
M. Anastassiades and E. Scherbaum limited capacity and the smaller breakthrough volumes. When dealing with analytes that show weak retention, the use of two or more disks in the same device helps to increase retention. On the other hand, the lower retention power of disks compared with packed beds results in very small preconditioning and elution volumes and thus less need for post-elution concentration steps. SPE disks have lately also been incorporated into various other formats such as cartridges (cartridge disks), 96-well plates and pipette tips (see below). The call for lower solvent consumption and the higher sensitivity of analytical instruments have initiated a trend for miniaturization of SPE applications, both in the packed column and the disk format. The use of lower solvent volumes furthermore reduces the amount of solvent that has to be evaporated, thus speeding up the whole procedure. A survey by Majors [221] has shown a trend away from 500 mg and towards 100 mg packed beds in SPE cartridges. Fritz et al. [222] have employed 0.7 mm disks impregnated with polymeric sorbent particles that they incorporated into a syringe needle, achieving comparable results to conventional 4 mm disks. Recently, Saito and Jinno [223] have introduced a novel miniaturised adsorptive extraction device for dynamic extractions. Numerous thin fibres consisting of"Zylon", heterocyclic polymers packed into PEEK tubes, are used as adsorbents. The extraction takes place by passing the sample through the device [223]. With miniaturisation, automation and high-throughput sampling in mind, 96-well plates were introduced in the mid-1990s. The 128 x 86 mm-sized plates have been designed to fit on automated plate-handling systems and are equipped with 96 miniaturised devices filled either with sorbent particles (10-100 mg) or with appropriately sized SPE disks. Early 96-well platehandling systems were based on manual vacuum manifolds; however, newer, fully automated systems employ positive pressure. Elutions are performed with as little as 100-200 ul solvent. Lately, even 192-and 384-well plates have become available, which allow even higher sample throughput. The well plate format has enjoyed widespread application and rapid acceptance in laboratories working in the bioanalytical and pharmaceutical fields, where it is used for rapid sample preparation, in clinical studies and combinatorial drug synthesis. So-called modular devices (e.g., Versa-Plate) allow individual equipping of the plates with different sorbents, which can be very useful in automating method development. The 96-well plate technology has been reviewed by Wells [2241. SPE has recently been commercialised in disposablepipette tips that allow convenient performance of miniaturised applications. There are numerous manufacturers and designs of SPE pipette tips. Some contain loose sorbent 180
Sample handling and clean-up procedures II-new developments particles filled between two frits inside the pipette tips. Here, the sample is drawn and mixed with the stationary phase and then dispensed again. In other designs, the sorbent particles (normal or reversed phase) are impregnated onto the interior walls of the pipette tips in order to minimise plugging. 4.9.1.7 Automation and hyphenation The amenability to automation (or semi-automation) is one of the advantages of SPE and numerous efforts have been undertaken in this direction in the last three decades. Automation has been widely applied in SPE applications dealing with extraction/pre-concentration (mostly in water analysis) but less often for clean-up purposes [225]. Today, most, if not all, laboratories that use SPE utilise some form of semi-automation but still few utilise computercontrolled robot arms (workstations) to fully automate some or all of the steps. For many years, SPE has been mainly performed using manual vacuum manifolds that allowed single and multiple sample processing employing vacuum. However, these manual vacuum stations have some disadvantages: (1) they require attention by the personnel, (2) the flow rate is difficult to adjust, which may result in poor reproducibility, (3) the sorbent may dry out after conditioning, and (4) clogging may occur when dealing with certain sample types. Almost all fully automated modern workstations apply positive pressure using high-precision pumps, which eliminates a lot of the abovementioned disadvantages. SPE automation allows unattended operation, helps reduce the amount of monotonous repetitive work done by laboratory staff, helps increase sample throughput, provides better repeatability, facilitates method development and consequently helps to better exploit the potentials of SPE. A review on automation of SPE, which also focuses on online hyphenation, has been published by Rossi et al. [226]. With early automated SPE systems, individual samples were processed in series with the next sample starting after the preceding one had been completed. Contemporary serial processing equipment is able to extract 20-50 samples per hour. As regards speed, such systems are comparable or even slower than manual systems that allow extraction in batches of, for example, 12 samples using vacuum manifolds. Nevertheless, time savings still result from the ability to operate continuously during non-working hours as well. It should be kept in mind, however, that when analyte stability is an issue, sequential processing may be a problem. Starting in the 1990s, instruments were introduced that allowed automated parallel processing of samples. Such systems can process up to many hundreds of samples per hour. The configuration of SPE makes it easy to be hyphenated on-line to other analytical techniques, resulting in fully automated systems. Being highly 181
M. Anastassiades and E. Scherbaum
compatible with liquid chromatography, SPE is most often on-line-connected to HPLC [227,228]. SPE/LC is probably the most robust on-line arrangement used in residue analysis. Following the enrichment of analytes on the SPE cartridges, the extracts are subsequently transferred to the analytical column for further separation and detection. Modern instruments employ so-called "column switching arrangements", equipped with special valve systems to regulate the flow. It is important to consider that when the SPE sorbent used is more retentive than the analytical LC-phase, this may result in peak broadening due to the fact that the strong elution solvents required to displace the analytes from the SPE column will not allow proper analyte focusing and separation on the analytical column. An elegant approach to overcome this band-broadening problem involves the elution of the SPE pre-column in the backflush mode (in the reverse direction from extraction) and the dilution of the eluate with water before it reaches the analytical column [229]. 4.9.1.8 Applications Of all sample preparation techniques described here, SPE is the most widely used in pesticide residue analysis, with countless extraction and clean-up applications being published each year. Water analysis is probably the most prominent application field and SPE has been very well adapted to the handling of such samples. Very large volumes of filtered water can be passed through SPE cartridges/disks in a short time, providing simultaneous extraction and concentration as well as the possibility to conveniently store and transport sample extracts. For many years, multi-residue analyses of pesticides in water have been mainly performed using alkyl-silicas (mainly C18) [212,230,231] but this has changed with the introduction of polymeric high-capacity sorbents [212,231,232] and GCB [233]. The applications employing SPE are numerous and have been covered in a number of recent reviews [201-204,234]. Compared with water analysis, the use of SPE in food analysis is far less extensive. Some applications employing SPE for pesticide residue analysis in food are summarised in the following three tables: Table 4.9 focuses on the use of SPE for the enrichment of pesticides from liquid samples directly or after dilution and Table 4.10 shows some applications involving RP-SPE for the enrichment of pesticides from raw extracts of samples after dilution with water. A common problem associated with these two types of applications is that highly non-polar analytes (e.g., pyrethroids) may start to precipitate when the organic content becomes too low. On the other hand, even a small percentage of the organic solvent may drastically limit the extractability of polar analytes onto the SPE phase 235]. Similar observations have been
182
Sample handling and clean-up procedures II-new developments -C~ C) C C)
C
c
C) Q
~~0 B 'd Cu
S oO C'C ~aS-~C)~ C~~ C)C)C) 1'=E 8 ~~~! ~~U,
9
t: iS
S
,Ca PQ
:j 0
C' '
Z,
Z
3CCo )~
C) )~
I~C)
0 1
~C)
)C . C
C
1
zz0
5 O
-a o C S c' CCC) C)-.) C~a0 Ca Ca
C
j Q
C
- a) C
c>o
6
5
5
b"
S
a
Cb
Z1
a
5
C)·
C)
C
0
0
¢
z
a E
"I
C) ),
c)
_Q . Ca o C Co -Cs
b)
CO
)
C
z
W C)1
0
0
Co
bb 'C C P
, l 11 Co
o
C)
0.
o
W Al
o
C)
o
W) K
Ca
O Go
co
O .
I)
O
CA -
z
Co
G 0D
00 o
0
M X
0 iX
or
o
c
o
-5 I
0
Ets Fu
i
Co C) C)
e
ri
C>
i
r
1:11
a
a
w
w
Co
C)
rr ;a I a)
0 ,
C) C)
z
s
z
'S
y
X
-E-i ID
C) 3
C
;5 "
A--aI .
:
iNa ID
z
O
-o C)
ID a,
z 2; -
d C
CC
I C-C)
t
C' C
W I
IQ
i -g
CO co
g
.g
C)
C):~
C)i
C)
'C
C)
ia " 4D
0
ii
a
Co Co
o Co
C)
Ca t4
0
.g
m'O~~~~~~~C
;: Pq$:
Co
Co
C' z
Co "t
a
i
,i 'C'
Co 'C
183
M. Anastassiades and E. Scherbaum
4~m0·~~ S'g
o Co:
7Co
dobn '- 0 0- 0 O ^$ O Co 0.
.0 M
wh ¢:
0 Z P:
°
-
CC4 4 P; 0s
Q
R 5R °r
O~
5
w 00
Co
5
tX
.~ P
oo
o
g
2G
a
· ~d .0.0 Co a
4
Co
)~
C
o a 2
0
d 4 @aO 0.
a
0
o0
0d 0h 0r 0
0
5, Co
a) 0 0 a
0 o
0 O
P,
h
d
o
Z w
o
Z
0
Z
a
7 02
Z
N
pq u
0 hq
M
1.4
00 '0-O
E o
SZ
a,
:2lr 0d
Co .
a
a
4
Z 0
-v
0
g~: to
a
0
~
Co
a) 0
0 C)
rn
w
0 0
0O 0
0
4
-i
S
,.
Co3
180C, Uz cz00
C)
C C c N
Q
W
C
4) g
0
d~~~~~~~~~L
I
0
c
a .,
0m
ga
C) Z
,
0
02
OqGO
0 e0! U
P.
F
0
o
0w 00~
.2E o
0
0
w1 0 w
0 F4
.~o
i bk Z~. CO *o OC CC on ID S ao
on
0 0 0-
0
0 Id
Co
0 C)
La
;
o, -
Q
a
C9
.
M"
6O
M
04 :
0~rl
,B C
3m oO
0Cot
0 c -lt
0 Ca
t.
bD q
C, S
,0_O .-a
0
0 4;
.2a
rz -. .;2 -a 00
; o
Z a
0 o
-?i It
o
0~
.
0.
0.
Q Oo
4
a co o rL4i
'C )
g
'a
'
~
D
-0 C
>h
5o
a H 'o
P
Co
Ix
u h
a0 N
184
-f0
N
zi~
0
4 P0
oO
g .2
0
02
O~ w
0
a R.2
0 !N
0 Cl
Cl
Cl
Cl
Nl .t,
p.)-
Sample handling and clean-up procedures II-new developments noted in corresponding SPME and SBSE applications, as will be shown in the corresponding sections. In the case of oily matrices, analyte enrichment cannot be performed with reversed-phase sorbents; thus, NP sorbents are used to trap the analytes. For example, Barrek et al. [236] have isolated 22 pesticides from essential oils of citrus by dissolving them in pentane and pouring the extracts into a Florisil column. Various non-polar components such as terpenes were washed off with pentane, while the analytes were subsequently eluted with dichloromethan and analysed by GC/MS. Table 4.11 gives some examples of the use of SPE for the clean-up of extracts in traditional MRMs. The applications shown there employ SPE as a chemical filter to selectively remove certain co-extracted matrix components, allowing the analytes to pass through. 4.9.1.9 Discussion and perspectives Within the last 30 years, SPE, in its various forms, has steadily gained acceptance among analysts and has become one of the most frequently employed clean-up and trace-enrichment procedures in a variety of applications, including multi-residue analysis of pesticides. In water analysis, where SPE provides simultaneous extraction and concentration of multiple residues, the technique has meanwhile almost completely replaced the traditional extractions with organic solvents. Driven by the need for high-throughput analysis (mainly on behalf of the pharmaceutical industry), SPE is steadily improving in terms of sorbent chemistries, automation and applicability to high-throughput analysis. The importance of SPE is also reflected by the growing number of companies producing and marketing SPE products [221]. SPE offers many benefits, including high enrichment factors, ease of automation and hyphenation with final instrumental analysis, the ability to rapidly process multiple samples in parallel, reduced labour and also simplicity (although the devil lies in the detail). Compared with LLP procedures, SPE is characterised by an enormous reduction of solvent-related problems (purchase and disposal costs, human exposure, environmental pollution) and the elimination of emulsions. SPE further facilitates on-site sampling and eliminates the need for transporting large volumes of water back to the laboratory under refrigerated conditions. The great variety of sorbent types and retention mechanisms (RP, H-bond, Tr-Tr, ionic) give SPE a high potential for the selective enrichment of analytes and the removal of matrix interferences. This selectivity potential can be exploited to a fuller extent in single class or single analyte methods and less in multi-class-multi-residue applications, a limitation that is inherent to any 185
M. Anastassiades and E. Scherbaum
C
8~~~C)
S t0
o~~~~~ o o
z~~~~~~K~~~~ z z
o ¢
az
o
= z$ a
w CO
3
>
bt IY °
o
0
0. °
o
as
O
Co
a s t-¢g C
..q
g 4 a0¢
c
W
a
<
C)
r~
C-¢
g m C)
C)
a
2
,,
C O
C) C) ,
,,)
"rJ
C)
,o
+.
cq
-C)
CO C)
C)
..
.
C .s cs c 'o 3
0
c -i
c
5
,
3
z
-CC)
43
ID )
SC) fr O
d . NC)
Nd
Naa
ad
£
P-
s
43 9
*C) *C
S
'C
C
0
aC
Coa,
~ COZ~ nC)
C)Z
.)C
Oa m
>0.aC
C
C
a 9~~~~C
C,
·
~~~~~·
C C
0g a, r·o a
PC.
43 C) aAb ,,
EC, 5)
3
°-C) C):
$0
.5C
'0 C)C)~ C
C)
5) O 4
. 4 C)vao CSo C) CO
CCC. N~
~
'
CI CC)
C) ,C),! CCC)~
C) )C
0 CC5CC % )C
C, 'C
~o ~ C)C) C
),C
'C)O'C)
'CC a.
~
C)b C) C)C C)3 4 43CCC)
C)C
CO
'C ))C)C 43C
4 -
)C 3
C)
8) t
186
t'
~
C
O
CN
C
Sample handling and clean-up procedures II-new developments
4
S I C8
O §
C)C
~
O
o ;doU ~~~~~ C < C)B
.4 :
.cB
, Sna
¢
C)~L ,-) .
CC -pPS
C)
z~t-
So~So
C-)
C a
)
a
4-)Q
CC~~~~~~C * -~ C) z~z
_W R Mk ~wC 1.
zm m 0
<
Pqa
4
.
S
0
CCi
eC)
C)C)
.5 O8 o C
C
e
z
uz
M
M
r; M "
Ci)
O
Cz
C.)CC
O
0 C) - g~
W, a U
a a
C)
-
aI
o
C)¢^ ) v
O
-d
as
3I3
I
:t)
I
O
CCM
z
¢
)
,
4-) 4c
PC
Q
a
z e-
O
C)
e
0
O i
oC) C)
-
C) C)))))z W
.00
,
a
(D
C).. 0D
Ci
¢" t¢
¢
&
.50CC
¢ C) ¢
`4
t QO
C CC
C
CCOCC O)
C)
C0
CC3
z
s.d°
0*·3
C-
0<
0 C
S~~~~N C) C0 $ %
~
c-C CVN
C~d ca
C)CC c
od
E -~
N~~~~~~~~~R
C
C
lx~w -C C)
*C C
'e
C
187
M. Anastassiades and E. Scherbaum
~
©>t
s
>
et
5
=
<
li
co
C) o) cC
h
)
C
X
.o
Z
o
')
I
C°
o.
C
o
oSo
rom
rbz
a)
o
,tCM
¢i
¢Z
¢c
.C) O)
3 (=.
9
a)
z
xtz
B
gh
A
g ) 3
C)
rC
a
0
-Q
<2
~t.·
n
b
oe.
-
C)
bk
bi C:) CD
C=
(
._,
Z
Z
z
~Cjm 0
')
o
Co
o
o4
cD
5
C)
CO
,,s
>
c
SC>C
'
-
.-
a
CD
GO
4..
I m d
188
_
.
.
.
.
_
°
Co °
Sample handling and clean-up procedures II-new developments
-v
v
^
z
U
E7
o~~~~~~~
B
¢
¢4
F
Cq
¢
C9'
C>
aq~~~= In~~
zr
F
i
n
~~~~~~~~~X
=_o P<
t
151S
bn~P
r
U
co
t
3t0
Cq
1
co
C
n; CQa s
CS
o
m
°
o
m EO
kme
o X CQ
o >
.
;
~~~~~~~~~~t
°4E4 ° O
o
e
DG
b
g ;
b D!
z A X m O *
W
189
M. Anastassiades and E. Scherbaum partitioning technique. Multiple sorbents can increase selectivity but this results in higher cost and may cause losses of the most polar or non-polar analytes. SPE sorbents are often among the most expensive materials in sample preparation. Nevertheless, SPE devices are almost always discarded after a single use with only a few applications, comprising regeneration and reuse of sorbents. However, compared with traditional alternatives, SPE often still provides lower per-sample costs due to the increased speed, reduced solvent usage and decreased labour. The efficacy and economy of SPE is now well documented in a great number of articles and reviews. Some common concerns with SPE are related to: (a) the possible introduction of interferences, (b) factors associated with manual SPE such as reproducibility of elution and drying steps, and (c) the influence of matrix on extraction rates due to competition phenomena. Meanwhile, other drawbacks have been largely overcome, including: (a) the problem of limited breakthrough volumes for hydrophilic analytes, which has been addressed with the introduction of highly cross-linked PS-DVB-sorbents, (b) the need for tedious pre-conditioning to ensure wettability of the sorbents, which has been eliminated through the introduction of RP sorbents with polar functionalities, and (c) the lot-to-lot reproducibility of sorbents, which has been minimised through standardisation and quality control during production as well as the careful selection of materials in terms of particle and pore size, distribution, purity and silanol activity. However, significant variability may still exist between SPE sorbents of seemingly the same type from different manufacturers. The most important recent trends include miniaturisation, automation and the switch from silica-based to polymeric sorbents, especially in multiresidue analysis of water samples. It is foreseeable that many new functionalised polymeric sorbents, both for more selective and wide-scope analysis, will be introduced in the future. New miniaturised formats will enhance automation, handling and high-throughput capabilities. 4.9.2
SPE with immunosorbents ISs
4.9.2.1 Introduction ISs are solid-phase extraction materials, the surface of which has been covered with antibodies to achieve molecular recognition and thus a high degree of selectivity. These properties make them especially suitable for enrichment and clean-up purposes, mainly in single residue or moiety specific applications. Compared with classical SPE, ISs provide extracts with less co-extractives and thus less interferences in chromatography. Extraction and 190
Sample handling and clean-up procedures II-new developments concentration of analytes from complex aqueous environmental samples are possible in a single step from large volumes of sample. 4.9.2.2 Theoretical background and analyticalprocedure The antibodies used to prepare ISs are produced by animals (polyclonal) or in cell cultures (monoclonal). Since compounds of low molecular weight (< 1000) are unable to evoke an immune response, it is necessary to attach them to a larger carrier protein. This is usually accomplished by introducing an additional functional group into the selected small molecule to form a socalled hapten. A typical preparation of an IS based on polyclonal anybodies involves the following steps: a hapten is formed by introducing a functional group into the analyte; the obtained hapten is coupled to a large protein (such as bovine serum albumin); * animals (e.g., rabbits) are immunised; * serum is isolated from blood; * the G-type immunoglobulin (IgG) fraction is isolated and purified; · the polyclonal antibodies are covalently bonded to an activated silica sorbent. * ·
The support materials should be ideally rigid and porous to allow high flow rates and should provide functional groups to enable the coupling with antibodies. Diol-or aldehyde-activated silica supports meet all these requirements. In both cases, the antibody is coupled via amino groups using the Schiff-base approach. Silica-based ISs are furthermore highly pressure-resistant and thus amenable to on-line coupling with HPLC. The shapes of the antibody receptors and antigen (analyte) are complementary and may involve ionic and hydrophobic attractions as well as hydrogen bonds. The formation of the antibody-antigen complex can be affected by the chemical composition of the sample. The commercialisation of ISs requires a high batch-to-batch reproducibility that can be better guaranteed by monoclonal antibodies that consist of a single monospecific antibody population, which is produced by specially modified cell cultures. Experiments by Pichon et al. indicated a higher capacity of monoclonal antibodies compared with polyclonal ones [261] but, up to now, monoclonal antibodies are more difficult and more costly to prepare than polyclonal ones. In terms of cross-reactivity, however, monoclonal and polyclonal antibodies behave similarly. This is usually not the case when dealing with larger antigen molecules where a polyclonal mixture will most 191
M. Anastassiades and E. Scherbaum likely contain a much larger number of specific antibodies for different parts of the molecule [262]. Unlike in competitive immunoassays (ELISA), in ISs cross-reactivity of the antibodies with other structurally related analytes may be advantageous because of the possibility of trapping and isolating pesticide families (like triazines, ureas, etc.). Method development A typical SPE procedure with ISs entails the following steps: (a) conditioning of the IS with water, (b) loading the sample on the immunosorbent (e.g., water or sample extract diluted with buffer solution), (c) desorption of the analytes (e.g., with methanol-water), and (d) reconditioning of the ISs. The conditions for the desorption of analytes from the IS should preferably be measured employing the compound that has been used to provoke the immunoreaction because this antigen-antibody interaction is most likely to be the strongest and thus the most difficult one to breakup compared with the interactions with other members of the same compound class. In most off-line procedures, desorption is achieved with methanol, ethanol or acetonitrile, resulting in a small volume of eluent and a high enrichment factor. The binding capacity of an IS must be measured for each single analyte because it may differ significantly for different members of the same pesticide family and is best for the hapten used in the immunisation process. The capacity of an IS greatly depends on the concentration of active antibodies in the IgG fraction. The specific surface area of the support material is also important because the higher it is, the higher the number of antibodies bonded and thus the higher the extraction capacity. An incomplete recovery can be the result of a low capacity and/or a low retention. Binding curves typically show a linear range followed by a plateau, which is reached when the analytes start breaking through. The breakthrough volumes may be influenced by competition between analytes of the same group. 4.9.2.3 Published applications In contrast to the field of mycotoxins, ISs for pesticide analysis have only recently managed to move on from the laboratory stage to industrial production and commercialisation. Thus, only a limited number of applications have been published so far. A general overview of applications involving ISs in environmental and food analysis is provided by DelaunayBertoncini et al. [262,263] and Hennion and Pichon [264,265]. Pichon and co-workers successfully employed and evaluated a mixed bed of ISs to allow the multi-class analysis of phenylureas and triazines in surface water and soil samples [261,266]. The same group has later presented an automated 192
Sample handling and clean-up procedures II-new developments procedure for the analysis of these two pesticide classes in water samples, coupling immuno-SPE with LC-MS [267]. Martin-Esteban et al. employed anti-isoproturon and anti-chlortoluron ISs in a mixed bed that was on-linecoupled with LC for automated trace enrichment and analysis of phenylureas in environmental waters [2681. The same mixed immunosorbent was then also employed for the analysis of methanolic extracts of vegetables. However, unspecific interactions with matrix components occurred, making it necessary to perform a clean-up by GPC or anion exchangers prior to IS-SPE [269]. Anion exchange SPE was also performed by Lawrence et al. to clean up methanolic extracts of various fruit and vegetables prior to IS-SPE isolation of triazines and phenylurea herbicides [270,271]. Watanabe et al. developed a method for the determination of imazalil in citrus fruit using anti-imazalil monoclonal antibodies [272]. 4.9.2.4 Discussion and future perspectives The development of ISs has been driven by the need to analyse compounds at low concentration levels from samples with a high load of potentially interfering components. In water-sample extractions, trace enrichment and clean-up is achieved in one step, giving clean extracts and low detection and determination limits. The enormous selectivity may be a decisive advantage for specific applications when the analyte spectrum is limited and known; however, it also drastically limits the applicability of ISs for multi-residue analysis. Compared with the artificially-produced molecularly-imprinted polymers (MIPs), ISs are in a more advanced stage. A barrier to a broader exploitation of ISs is seen in the cumbersome and thus expensive generation of custom-made ISs for every type of analyte that may take as long as a year. Further drawbacks include the susceptibility of the ISs towards organic solvents, pH extremes and high temperatures, the possible non-selective interactions of the carrier materials (silicas) with analytes and matrix components and the irreproducibility of IS production, a problem that has been more or less overcome with the introduction of monoclonal antibodies. 4.9.3
SPE with molecular-imprinted polymers (MISPE)
4.9.3.1 Introduction MIPs are synthetic polymers that possess molecular recognition sites that are complementary to the three-dimensional shape and positioning of the functional groups of analytes. MIPs are prepared by synthesising the polymer in the presence of a template molecule (e.g., target analyte), which is subsequently removed, leaving behind highly selective, tailor-made
193
M. Anastassiades and E. Scherbaum binding sites. The imprinted polymers are capable of recognising and selectively binding analytes with equal affinity and selectivity as ISs that are based on antigen-antibody recognition. As in the case of IS, molecular imprinting can yield analyte or group-selective sorbents. MIPs can in principle be prepared against any target molecules, even for those that are too small for immunoaffinity. They can furthermore endure harsh working conditions and exposure to organic solvents, which makes them extremely attractive as class-or compound-specific adsorbents. The potential for MIPs as SPE sorbents was first reported by Sellergren in 1994 [273]. For more detailed information about MIPs see Ref. [274]. 4.9.3.2 Theoretical background Synthesis of MIPs: Molecular imprinting exploits the simple but elegant principle of using the target molecule (or a close structural analogue) to create its own recognition sites (see Fig. 4.6). To achieve this, the template analyte and a selected monomer, containing various active sites, are mixed in a solvent also containing cross-linking monomers. The functional groups of the template molecule (recognition elements) interact with complementary functional groups of the monomer to form a so-called "self-assembly complex". The selective binding of the template to the functional monomers may involve a number of different interaction mechanisms such as hydrogen bonds, ion
1 t
Eq;;;P
1 Fig. 4.6. Schematic presentation of the principle of MIP preparation using the non-covalent imprinting approach where the template can be a structural analogue of the analyte or the analyte itself.
194
Sample handling and clean-up procedures II-new developments pairing, rr-T and hydrophobic. Since the electrostatic interactions are the most characteristic, MIPs are usually produced in aprotic solvents of low dielectric constant, such as toluene, methylene chloride or acetonitrile where these interactions are the strongest (in the presence of protic and polar solvents, the complex usually destabilises). These interactions are then frozen by incorporating the whole assembly into a highly cross-linked polymeric network, which is achieved by initiating a radical polymerisation using an azo-initiator. The formed monolith is ground, sieved and exhaustively extracted (e.g., by Soxhlet) to reveal the binding sites on the polymer. Some small amount of template molecule, however, always remains behind in the polymer despite the best attempts to extract it. This may be a drawback as it leads to bleeding and false positive results in the practical use of the polymer. This can be avoided by employing a structural analogue of the analyte as template. It is essential, however, that the functional group types of the template and their 3-D orientation are very similar to those of the target analytes. Furthermore, the non-interacting remaining functional groups in the template molecule should be similar in size or greater than the corresponding groups of the analyte to avoid a possible size-exclusion effect. In pesticide residue analysis, where normally not only a single analyte is of interest, imprints should be selective towards common structures of related compounds. To produce a multi-residue amenable MIP, a mixture of template molecules may be used or imprints of different templates can be mixed afterwards. The most interesting analytes to be used for the production of MIPs are those that contain functional groups that form hydrogen bonds. A critical decision in MIP synthesis is to choose what functional groups are to be involved in recognition. For analytes with proton-acceptor (basic) sites, methacrylic acid is the most frequently employed monomer while, for acidic analytes containing proton-donor functions, 4-vinylpyridine or N,N-dimethylaminoethyl methacrylate (DMA) is used. Besides the non-covalent imprinting approach described above, MIPs have also been produced using the much less common covalent imprinting approach where the template is covalently coupled with the functional monomer during polymerisation, followed by a cleavage reaction to release the binding sites. For more details on polymer-and template-related factors in MISPE, see Refs. [275,2761. 4.9.3.3 The MISPE procedure A small amount of imprinted polymer (50-200 mg) is packed in a cartridge. The selectivity achieved in MISPE will, as in any other SPE application, 195
M. Anastassiades and E. Scherbaum depend on the careful selection of the most appropriate solvents for conditioning, sample loading, washing and elution. To avoid breakthrough of the analytes when larger volumes of sample and washing solvent are applied, conditions should be such to ensure that the analyte is strongly bound to the sorbent. The most optimal selective binding usually occurs in the solvent used during the polymerisation of the MIP. In addition to the highly selective interactions of analytes with the imprinted cavities, MISPE sorbents also undergo non-selective adsorptions with both analytes and matrix components. Non-selective interactions should be eliminated during the washing steps to enhance the selectivity of the procedure. In general, a high density of imprint cavities in the polymer is advantageous because it reduces interactions of analytes and matrix components with the non-imprinted part of the polymer. The higher the selectivity of the MISPE, the lower the requirements for the selectivity of the analytical technique used for the subsequent determinative analysis. Aqueous samples (e.g., water or biological fluids) are typically directly applied to the MIP column, which is then washed with a solvent capable of selectively disrupting the non-specific interactions of the matrix components with the polymer matrix. This is followed by the elution of the analytes using water-miscible organic solvents such as acetonitrile or methanol that may be modified with small amounts of weak acids and bases. The recognition properties and binding affinity of the MIP sorbents in different mobile phases can be investigated very well in an LC characterisation study. 4.9.3.4 Published applications Up to now, most research activities have focused on the optimisation of polymer synthesis conditions. The relatively few MISPE applications have been reviewed by various authors [277-280]. Ramstrom et al. reviewed the use of MIPs in the field of food analysis [281]. Muldoon and Stanker [282] were the first to employ MISPE for the analysis of pesticide residues in biological samples. They used a self-made atrazine-imprinted polymer to enrich atrazine from beef liver extracts in chloroform. There are several publications for MISPE of triazines from water samples. Matsui et al. have done extensive work to optimise the extraction of atrazine residues. While initially employing an atrazine-imprinted polymer [283], they later decided to use a "dummytemplate" to avoid problems with insufficient washing out of the template [284,285]. Ferrer et al. [286] used terbutylazine as the template for the production of a group-selective MIP that was used to extract six chlorotriazines from groundwater and sediment samples. Bjarnason et al. [287] developed a triazine-specific MIP sorbent using simazine as the template. 196
Sample handling and clean-up procedures II-new developments The polymer was filled in a column that was coupled on-line to a C18 SPE cartridge and HPLC. This arrangement was successfully employed to selectively enriched triazines from urine, apple extracts and humic acidcontaining water samples. Martin-Esteban et al. [288] investigated the effect of template size on the selectivity of MIPs for phenylurea herbicides and showed that fenuron-imprinted polymers are highly selective for fenuron (which is the smallest phenylurea), whereas isoproturon-imprinted polymers recognise all phenylurea herbicides. Using propazine as a template, Cacho et al. [289] synthesized a methacrylic acid based MIP for the analysis of triazines in various vegetable samples. Non-specifically bound interferences were elegantly removed using a non-imprinted MIP that was prepared in the same way as the analytical MIP although in the absence of the template. An MIP sorbent that is amenable to polar and protic solvents has been developed by Haupt et al. [290] for the isolation of 2,4-D residues from aqueous samples. Imprinting was performed in a mixture of methanol/water with the interactions involved being of ionic and hydrophobic nature. 4.9.3.5 Discussion and future perspectives Molecular imprinted polymers are very useful tools for the selective trace enrichment of organic contaminants in complex mixtures. They combine the advantages of synthetic plastics such as low cost, durability and robustness with the ability for a highly selective recognition of analytes that is comparable with that exhibited by natural receptors. MIPs are stable to pH extremes and temperatures up to 120°C, allow the fast development of rugged methods and can be regenerated and reused several times. The production of a new MIP material is straightforward and possible within weeks with the costs being low enough to allow the production of disposable MISPE cartridges even for single use. Until now, the use of MIPs in pesticide residue analysis is still scarce. Future advances are to be expected in order to widen the field of applications and a real breakthrough can be expected when MIPs become commercially available in the future. Imprinted polymers generally work better in organic rather than aqueous media, so they could function complementary to antibodies. As in the case of any affinity-based technique (e.g., immunosorbents), the high selectivity of MIPs towards specific analytes limits their applicability for multi-residue analysis due to the need to develop a custom-made MIP for any new analyte. Another disadvantage concerns the leaching of the template during use. Future challenges include the reduction of the non-specific binding of analytes and matrix components to the MIPs, the development MIPs that can selectively remove interfering matrix components from 197
M. Anastassiades and E. Scherbaum
extracts, the development of sorbents that cover a broad analyte range and are applicable to multi-residue analyses of a defined analyte spectrum and the application of MIPs in other formats such as the disk, the stir bar, etc. 4.10
MICRO-EXTRACTION TECHNIQUES INVOLVING LIQUID-LIQUID PARTITIONING
Micro-extraction techniques are characterised by the use of extraction media (extractants) of a very small volume compared with the volume of the samples. Analyte extractions are thus often not exhaustive with the extraction rates being determined by the distribution behaviour of the analytes between the two participating phases, the sample matrix and the extractant. The amount of analytes extracted eventually reaches a maximum (equilibrium). The most significant problem encountered with equilibrium-based micro-extractions is that the partitioning equilibria are very sensitive to numerous parameters, including temperature, time and sample composition. Matrix-matched calibrations, automation and on-line connection to the terminal determinative analysis are thus of paramount importance for achieving good reproducibility. Furthermore, it is important and challenging to ensure that the extractant is physically supported so that it can be entirely and easily isolated from the sample when the extraction is over. The extractants used in liquid-liquid micro-extractions are either ordinary solvents or non-porous gum-like polymeric materials such as those used to coat GC columns (e.g. PDMS). The former are usually immobilised by membranes (see section 4.10.4), while the latter are more rigid and often just coated on the surface of supporting devices such as fibre rods (SPME, see section 4.10.1), inner walls of tubes (in-tube SPME and OTT, see section 4.10.3) and stirring bars (SBSE, see section 4.10.2). Having a high thermal stability and an extremely low volatility, such gum-like extractants allow their introduction into GC injectors for the thermal desorption of analytes in entirely solvent-free procedures. Multiple extraction/thermo-desorption cycles may be performed without any major alteration of the extraction properties. Liquid desorption is also viable but less preferred since it makes use of solvents, dilutes the extracts and complicates the overall procedure. In contrast to solid sorbents, such as those employed in SPE, where the analytes are retained by interactions with binding sites located on their surface, liquid-like extractants such as PDMS are capable of incorporating (dissolving) the analytes. The first mechanism is described as adsorptionand the second as absorption. In absorptive extractions, the equilibrium requires a uniform distribution of the analytes within the liquid-extractant phase. 198
Sample handling and clean-up procedures II-new developments Since the consistency of the gum-like extractant often does not allow a fast diffusion of the analytes, this process can be rate limiting. In comparison, equilibration times in adsorptive extractions, where solid extractants are employed, are much shorter. There is the disadvantage, however, that the limited amount of binding sites on the surface of solid adsorbents can lead to competitions between analytes and matrix components and thus to non-linear calibration curves in equilibrium sampling [291]. Adsorbents are thus more suitable for dynamic, SPE column-type extractions, where the goal is to retain the analytes of interest in their entirety. This exhaustive extraction principle also remains the same in miniaturised versions, where smaller adsorbent beds correlate to smaller sample volumes. In contrast, LLP-based (e.g. absorptive) micro-extractions mostly rely on equilibrium partitioning, regardless of whether they are performed in the static or dynamic mode. Numerous micro-extraction techniques have already been introduced in recent years and many more are expected to follow in the future. Aiming at bringing these novel techniques into prominence, their developers often label them with distinctive names and acronyms, which unfortunately are sometimes more misleading than descriptive. For example, the term "solid-phase micro-extraction" conveys the impression that this technique may be a miniaturized variant of SPE. The term "sorptive extractions", which was adopted a few years ago by Sandra and Baltussen [291] to describe microextractions involving polymeric liquid phases such as PDMS, has also created some confusion since sorption has been traditionally regarded as the collective term for absorption and adsorption. 4.10.1 Solid-phase micro-extraction (SPME) 4.10.1.1 Introduction SPME, which was developed by Pawliszyn et al. in the late 1980s, is a solventfree extraction technique that employs a small amount of a polymeric extractant that is coated onto a fused-silica fibre rod. For sampling, the coated fibre is exposed to the sample for a certain amount of time so that analytes can partition into the coating. When the extraction is over, the fibre is introduced into a GC injection port where thermodesorption of the analytes takes place. Liquid desorption is also possible with special SPME-HPLC interfaces but is much less common [292]. SPME mostly employs liquid-like fibre coatings such as PDMS and is thus a typical example for a liquid-liquid micro-extraction technique. Besides the absorptive extraction phases, there are also SPME fibres available that are coated with solid adsorptive phases. Although not technically belonging to this chapter, the latter will be also mentioned here.
199
M. Anastassiades and E. Scherbaum The major advantages of SPME are the avoidance of hazardous solvents, the amenability to automation and the simplicity of use. Detailed information on the technique can be found in several books and articles by Pawliszyn et al. [293-296]. 4.10.1.2 Instrumentation and method development Figure 4.7 visualises how SPME devices are utilised. The polymer-coated fused silica fibre (1 cm x 0.5 mm) is fixed to a stainless steel plunger that is installed onto a microlitre syringe-like device. The plunger moves the fused silica fibre into and out of the hollow needle. For extraction to take place, the analyst (or autosampler) draws the fibre into the needle, passes the needle through the septum that seals the sample vial (I) and depresses the plunger, exposing the fibre to the liquid sample or to the headspace above the sample (II). The analytes are sorbed to the coating of the fibre and, after equilibrium is attained, the fibre is drawn back into the needle and the device is withdrawn from the sample vial (III). Finally, the needle is introduced into the GC injector (IV) where the analytes are thermally desorbed (V). In SPME, as in any other LLP technique, there is a multitude of parameters that can influence both the time required until the equilibrium Thermodesorption step
AdsorptionlExtraction step II I
IV
III
V
VI
H SPME-syi tinge
I
T
GC inlet
GC Column
Fig. 4.7. Schematic presentation of a typical SPME procedure. 200
Sample handling and clean-up procedures II-new developments state is reached, and the distribution of the analytes between the sample and the extracting phase at equilibrium conditions. Accurate quantitative analysis requires keeping all experimental parameters as constant as possible. Some of the most important parameters influencing SPME equilibria will be discussed in the following: Extraction mode: Using SPME devices, sampling can be performed in two main modes: (1) direct immersion SPME (DI-SPME) where the fibre is immersed into the liquid sample and (2) headspace SPME (HS-SPME) where the fibre is exposed to the headspace of the sample. Both modes have been employed for the analysis of pesticide residues from various samples. When choosing the extraction mode, the nature of both the sample matrix and the analytes should be considered. In general, DI-SPME is only employed for liquid samples and is more sensitive than HS-SPME for analytes having low vapour pressures. On the other hand, HS-SPME is amenable to all types of samples (gases, liquids and solids) and exhibits a higher selectivity by discriminating low-volatility compounds. This selectivity also results in longer fibre lifetimes compared with the DI-SPME approach. Headspace sampling deals with a three-phase system and one should consider that the analytes must first cross the liquid-gas interface before reaching the sorbent. In general, the equilibration times for non-polar and volatile analytes, which readily partition to the headspace, are shorter in headspace sampling than in direct immersion. The following discussion will mostly focus on directimmersion SPME, which has been more widely employed for pesticide residue analysis in food commodities. Type and amount offibre coating: The vast majority of applications dealing with pesticide residues in food or water employ PDMS and PA sorbents but there are several additional types of fibre coatings currently available, as shown in Table 4.12. Since the extraction media employed in SPME are of a rather lipophilic nature, they are best suitable for the extraction of non-polar analytes from aqueous samples ("like dissolves like" principle). As mentioned above, when employing SPE fibres with solid sorbents like PS-DVB, carbon or Carbowax, SPME is no longer a purely absorptive extraction. In static sampling, the application of these materials is likely to lead to irreproducible results because of competition and displacement phenomena between target analytes and matrix components for the available adsorptive sites. Such sorbents are thus not recommendable for samples of a high matrix load. When dealing with aqueous samples, the use of polar sorbents such as CW does not necessarily lead to higher recoveries of polar analytes since water interferes with the formation of H-bonds between analytes and active sites on the sorbent. In any case, analysts should always 201
M. Anastassiades and E. Scherbaum TABLE 4.12 Some commercially available SPME fibres Fibre-coating
Film thickness
Polarity
Properties
PDMS, polydimethylsiloxane
30, 100, 7 fm
Low
CAR (carboxen, activated carbon support)-PDMS copolymer DVB-CAR-PDMS copolymer
75, 85 m
Low
Up to 300°C, non-bonded Partially cross-linked
50, 30 ,tm
Medium-low
PA (polyacrylate)
85 gm
Medium
PDMS-DVB (divinylbenzene) copolymer CW (Carbowax)-DVB copolymer
65 g/m
Medium
65, 70 gzm
Medium-high
Highly cross-linked Partially cross-linked Partially cross-linked Partially cross-linked
consider that the choice of the extractant should not only meet the need of efficient extraction but also allow fast desorption because, when desorption is too slow, peak broadening and tailing may occur, which can only be eliminated if the analytes are cryogenically focused on the beginning of the analytical column. High inlet temperatures will help to evaporate the analytes more rapidly but care should be taken to minimise analyte decomposition, injector septum bleeding and the contamination of the GC column with matrix components of low volatility. The amounts of polymeric coatings on commercial SPME fibres can vary significantly, with the thickness of the coatings ranging from 7 to 100 prm. Thick fibre coatings have a large capacity and will thus extract more of a given analyte compared with a thin coating. At the same time, however, the desorption rate will be prolonged, which may result in band broadening or carry-over problems. Thinner films may have a lower capacity but they ensure faster equilibration times. Compared with other LLP techniques, SPME employs very small extractant amounts (up to 2 lA[2971). The resulting small extractant to sample ratio is often the cause of the lack of sensitivity in SPME, especially for polar analytes with small partitioning coefficients. Equilibrationand extraction time: During sampling, the concentration of the analytes in the fibres increases and eventually reaches a maximum as soon as equilibrium is attained. The development of SPME methods typically includes a study to determine the time required for each analyte to reach the equilibrium. In many cases, equilibration times may take as long as several 202
Sample handling and clean-up procedures II-new developments
hours, but for practical reasons analysts often perform the extractions over shorter times. As long as parameters such as extraction time, stirring speed and sample composition are kept constant, non-equilibrium sampling can still lead to accurate results due to the nearly linear relation between analyte concentration in the sample and the extracted amount. On-line SPME methodologies are typically optimised so that extraction and determinative analysis times are similar. Temperature: Constant temperature is essential to obtain good analytical precision in SPME applications. Temperature is surely of greater importance when dealing with HS-SPME but one should consider that also in direct immersion sampling the headspace over the liquid sample can also participate in the overall equilibrium and compete with the partitioning into the SPME extraction phase. To eliminate this source of error, headspace volume and temperature should be kept constant. Agitation: Sample agitation like stirring, vibration and sonication reduces the time until equilibrium is attained. Inconsistent agitation causes poor precision and is worse than no agitation. In the latter case, however, the equilibration time is limited by the slow diffusion rate of the analytes through the sample. Thorough stirring reduces this factor and the diffusion of the absorbed analytes within the polymer becomes time-limiting. Sample composition: Suspension of the sample with water and DI-SPME creates a three-phase (fibre/water/matrix) equilibrium where several factors (fibre type and thickness, physical and chemical properties of the pesticides) are important considerations. In the extraction of ionisable compounds (e.g., acidic herbicides), sample pH has to be properly adjusted to shift the acid-base equilibrium towards the non-ionic forms that will readily partition into the nonpolar SPME phase. For polar analytes, the addition of salts to the samples often results in higher extraction efficiencies since it forces these compounds out of the aqueous phase and promotes their partitioning into the lipophilic extractant (equilibrium shift). In principle, this effect applies to all organic analytes; however, when dealing with analytes of intermediate or low polarity, salt addition can sometimes lead to the reverse effect, i.e., lower recoveries [298]. This can be explained by the fact that, upon the addition of salts, such analytes will eventually start precipitating or adsorbing onto lipophilic surfaces of the sample. This phenomenon leads to a drastic extension of the equilibration time as well as a shift of the equilibrium. For the most non-polar compounds, this effect has also been observed even without any addition of salts with such analytes precipitating on filters, vessel walls or even the PTFEcoated stir bars used to agitate the solution [291]. The addition of 203
M. Anastassiades and E. Scherbaum
water-miscible organic modifiers such as acetone is highly recommendable in these cases, although it may decrease the recoveries of the most polar analytes. Quantitation:Different analytes have different kinetics and partitioning coefficients, so SPME does not work uniformly for all types of pesticides. The extraction efficiencies of the analytes mainly depend on their polarity, with non-polar analytes giving the highest and polar ones the lowest recoveries. Various authors have reported a close correlation between Kow with the KPDMS/w values [291,299]. In general, the larger the log Ko/1 value, the higher will be the recovery at fixed water:sorbent ratio but, in any case, recoveries are mostly far from quantitative. For quantification purposes, additional samples that have been fortified with pesticides are extracted and used for calibration. This surrogatestandard procedure obviates the need for determining the recoveries. Keeping the same analytical conditions for samples and calibration standards is essential and automation has been shown to improve precision. The use of matrix-matched calibrations is extremely important. In practice, however, blank matrices of the same type are often difficult to obtain so most analysts prefer to employ the procedure of standard additions. Unlike other methodologies, where the matrix-matched calibration merely requires the fortification of blank extracts, SPME requires the entire procedure (i.e., sampling and determinative analysis) to be performed for each calibration point. Another fact to consider is that the addition of organic solvents to the samples during standard addition will change the composition of the sample and may significantly shift the partitioning behaviour of the analytes. These factors and the need to perform a standard addition for each single analyte diminish the attractiveness of SPME for laboratories performing multiresidue analysis of samples of unknown pesticide-treatment history. Many analysts thus consider SPME as more suitable for qualitative analyses and screening purposes but even here the inefficiency in the extraction of polar compounds limits the applicability of this technique. 4.10.1.3 Published applications SPME fits perfectly for the extraction of aqueous matrices and has been quite well established in the field of water analysis, at least as far as semi-polar and non-polar pesticides are concerned [3001. Inter-laboratory studies on pesticide analysis in water by SPME showed good accuracy, repeatability and reproducibility. A determination limit below 1 pg/l can be achieved in many cases but the 0.1 g/l limit, required for drinking water analysis, is often hard to reach. Only a few publications deal with pesticide residues in food (see Table 4.13) [301] where the technique, despite the initial high expectations, has not
204
Sample handling and clean-up procedures II-new developments Co Co
2o c4) Cou
*0
uO 0)1
.~
M
E 9
7
Co·u
i-
C
o
V 0C 5
CoGo~C,~0Co .o
C)
Z! Q5 C
o
K
.4
Z
r 3
P. O %%:ia
). Co
C
Co
U
oo CdV
CZ°,i
CID
5
Co O
o
sC
Co
S
P:CoA
'
O~~C
C Q 05 )0,Coi)Co5z
3C Oo
,
: o
a GO
S PO a
(n DC
o
C
OQ CC
O
m
~o
Cor4oio
"0 o 0
Co
Co o
o - Co
cH
CoOW n
Co
.o
*o
0
,
Co"'C _
CoC)n-C U
*
Co C
o"~ O
0
'o
C)
04
Co
0 Iz
0 a
Co A K
a)
Do t;
,g
0
Co
C
co
Co
C
.·
C; S c:
e
~~ ~ ~ ~ ~
1~~~~~C Co
C
Co
Co C
~'C
CoC Co1 o
oC
w
Ko
(DI W
4 Po
0 O
M
"
m C".~
"
Co 4w
O a)
205
M. Anastassiades and E. Scherbaum
0
CC
V t-
CF
F-C)
CC CC
*C)C rll
~,
+
:5>~
~
4
C
C) Wz
~6)5
.C) CC ' a
C
B a>,
,~C
.E
a t C .$ o i) C
)~)d
~o .~7
C)
'
CC*
4 IC
0
L
II
u
C)P
0 u00 R k 9 O M o
5
C)
CC C
sgx
P-
C C4 E t U),E
. M c) CC
B
CC4
F-F-5 'F-
S -I")
P4 -V
C , C C'
c'oC bL
*
C)
£
Sg t
0
g
9
UP F0)
E
.
P,
o
C)
¼ C
"
(
.0
]-
0
0
to
V i-
M z C) 0
C)
0 C)
5C) a) C)
.0
0. F-
4C)
OS/
0
7S
C) C)
CC tH , cO
a)
B
C)
w
Fr~~~~~~.,
8
F-C '5
a) r
r
o
oC)
z0. ii co
a4 a~
c
C)
'C Cm
I
oo
.2 a'CCX8 j-,= I: C,
,qr
z
'o
C)F
mcC cqL
C)
C) o
F42 C
206
o CC
CC CC C3
CC os
C c
Sample handling and clean-up procedures II-new developments managed to become widely established. In the case of fruit juices or beverages, a direct extraction is possible and the main difficulties arise from possible interferences from the matrix. For fruit and vegetable samples, direct extraction by fibre immersion is, however, more problematic and different sampling approaches have been proposed: (1) the homogenised samples (of very high water content) are centrifuged before performing DI-SPME, (2) the sample matrix is suspended in water before DI-SPME, and (3) the samples are pre-extracted with a water-miscible organic solvent and an aliquot of the extract is diluted with water before DI-SPME is performed. Considering the need to extract lipophilic pesticides from the sample matrix, the latter approach, which has also been applied in SPE applications (see Table 4.10), appears to be the most promising for multi-residue analyses. The presence of a certain percentage of organic solvent in the diluted extract (e.g., 6-10% acetone), however, will surely affect the partitioning equilibria and thus the recoveries, as is the case in analogous SPE applications. The difference, however, is that here the organic co-solvent will partly partition into the liquid-like SPME extractant and change its properties as well. Volante et al. [302] found that suspending homogenised vegetable samples in water leads to under-estimated results for certain pesticides and is thus not suitable for multi-residue analysis. The addition of 4 ml acetone to a 10 g sample to improve the extractability for pesticides followed by a dilution with water and DI-SPME led to good results for almost all of the 100 pesticides investigated. Unsolved problems existed with very polar organophosphates, very unpolar pyrethroids and with unstable compounds like captan or dicofol. Falqui Cao et al. [303,304] combined focused MAE of the whole fruits with SPME and developed a method for the determination of pesticides in fruit and vegetables. In the case of pyrethroids, acetonitrile had to be used as co-solvent to increase their solubility and transfer into the aqueous solution. 4.10.1.4 Discussion and future perspectives Few analytical techniques have proven to be as versatile and universal as SPME. This is impressively reflected in the numerous articles that are published every year dealing with the analysis of a great number of analytes in a diverse spectrum ofmatrices, including liquids, solids and gases. In general, SPME meets its potential advantages best when dealing with vapour-phase sampling or liquid samples with a low matrix load (e.g., water samples). The general advantages of SPME include: the elimination of organic solvents; the compact, inexpensive device; and simple operation that allows repetitive, unattended sampling when autosamplers are employed. Furthermore, SPME offers a great selectivity potential, especially when performed in the headspace 207
M. Anastassiades and E. Scherbaum sampling mode. The injection into GC is straightforward and contamination of the system with non-volatiles is prevented. SPME is suitable for multi-residue pesticide analysis although the small amount of coating as well as the highly non-polar properties limit the range of analytes that can be efficiently covered. On the other hand, the limited polarity spectrum increases the selectivity, which is beneficial as long as the analyte spectrum is limited to low-polarity compounds. The main concern with SPME is its usefulness for quantitative analysis when dealing with complex samples since this requires the consideration of a multitude of factors. In routine pesticide multi-residue analysis of agricultural products, SPME is not very useful for quantitative analysis due to the need to perform a standard addition for each single analyte present in the sample. The technique further suffers from the relatively long equilibration times during the extraction procedure and thus significantly shorter extraction times are chosen in most applications. Nevertheless, the convenience and simplicity of SPME make this technique a valuable tool for screening purposes. Other concerns are that SPME fibres can be prone to memory effects and the fibre can become contaminated with non-volatile matrix components as well as with components from the ambient air. The latter concern is addressed with special sealing systems. 4.10.2 Stir bar sorptive extraction (SBSE) SBSE (trade name "Twister", Gerstel, Milheim a.d. Ruhr, Germany) employs a stir bar that is coated with a layer of PDMS (Fig. 4.8). Sampling is performed by introducing the device into an aqueous sample and stirring it for a defined time with a magnetic stirrer. The stir bar is then removed from the sample and the extracted compounds are desorbed, either by introducing the SBSE device into a special GC injector system or by immersing it in a small volume of an organic solvent and stirring. In contrast to the SPME device, the SBSE stir bar cannot be introduced in conventional GC inlets for thermal desorption. However, a commercial instrumental arrangement exists that can conveniently perform the whole procedure in a fully automated fashion. This combines thermodesorption in a special unit followed by cryo-focusing on a cooled PTV injector liner and subsequent heating and transfer of the analytes into the GC column. PDMS-Coating Magnet Glass
Fig. 4.8. Schematic presentation of an SBSE device.
208
Sample handling and clean-up procedures II-new developments Two stir bar sizes are currently available: 10 x 3.2 mm and 40 x 3.2 mm. The smaller one is recommended for sample volumes from 10 to 50 ml and the bigger one for larger volumes. The stir bars are coated with 50-300 Al of PDMS (maximum of 125 A1 for the smaller one), which is significantly more than the maximum of 2 bl [297] employed in SPME. Since the volume of extractant is an essential parameter for the extraction efficiency in static sampling, SBSE ensures higher real recoveries and thus a better overall sensitivity compared with SPME. As for any other LLP technique, the recoveries also depend on the partition coefficients of the analytes. The approximate recoveries from purely aqueous solutions can be calculated using an algorithm based on the Ko/W values. From a 10 ml water sample (using a 125 bl PDMS coating), quantitative recoveries (> 90%) can be theoretically achieved for compounds with log Ko/w values greater than 2.9 (e.g., iprodione). The theoretical recoveries of more polar pesticides are ca. 6, 1.2 and 0.2% for analytes with log K/W values of 0.7 (e.g., dimethoate), 0 (e.g., methomyl) and - 0.8 (e.g., methamidophos), respectively. Theoretical (calculated) recovery data from water for more than 400 pesticides with logK 0 /w values above 1.7 are shown in Ref. [314]. The recoveries using SBSE may appear low, but the total amount of analyte extracted and introduced into the GC system can be favourably compared with traditional multi-residue methodologies, as shown in Table 4.14. It should be considered, however, that the real SBSE recoveries will be lower due to nonequilibrium sampling and the presence of matrix components and methanol (ca. 6%) in the diluted extracts. Due to the more quantitative extraction, SBSE procedures are reported to be more robust than the SPME ones as far as differences in stirring time and temperature are concerned. Several applications for SBSE in the analysis of pesticide residues in plant material have been published. In his thesis, Baltussen [315] describes the analysis of procymidone in wine using SBSE and GC-AED. The same working group described the use of SBSE for the determination of dicarboximide fungicides in wine [316] and of pesticides in vegetables, fruit and baby food [317]. Following the extraction with methanol (ultrasonic bath), an aliquot was diluted with water 10-fold and SBSE was performed for 60 min. After thermal desorption, analytes were cryo-focused in the PTV (- 150°C) and analysed by GC-MS in full-scan or SIM mode. A similar approach for SBSE was followed by Blasco et al. [76], who analysed oranges for pesticide residues and compared the results with those obtained by a MSPD method. The authors concluded that SBSE is not suitable to determine polar pesticides such as carbendazim, imidacloprid and trichlorfon. Hyasaka et al. [318] combined compositional analysis of wine with the analysis of 24 pesticides 209
M. Anastassiades and E. Scherbaum TABLE 4.14 Comparison of SBSE with a traditional multi-residue method Conventional procedure (QuEChERS)
SBSE
Sample amount Division factor
10 g 1 (No dilution or aliquotation)
Volume of final extract End concentration of extract Assumed recovery of compounds (A) e.g., iprodione; (B) methomyl; (C) e.g., methamidophos Amount of analyte in final extract (assuming 1 mg/kg initial concentration) GC-injection volume Amount of analyte introduced in GC
10 ml 1 g/ml in acetonitrile (A) 100%; (B) 100%; (C) 90%
15 g 45 (+ 30 ml MeOH, 1 ml aliquot diluted to 10 ml) 0.125 ml 2.67 g/ml in PDMS (A) 90%; (B) 1%; (C) 0.2%
(C) 9 g
(A) 300 ng; (B) 3.3 ng; (C) 0.67 ng
3 Al(PTV) (A) 3 ng; (B) 3 ng; (C) 2.7 ng
125 ld(entire stir bar) (A) 300 ng; (B) 3.3 ng; (C) 0.67 ng
(A) 10
jig;
(B) 10 jig;
using SBSE and GC-MS. Bicchi et al. [319] presented a SBSE method for the analysis of 11 pesticides in tea infusions (Passiflora alata) and reported a significant decrease in recovery versus spiked water. After PLE with water as extracting solvent, Wennrich et al. [106] employed SBSE to enrich pesticides from strawberry extracts. The results were compared with those obtained using SPME. All in all, the principal advantages and disadvantages of SPME also apply to SBSE but the higher sorbent volume employed in SBSE improves the extraction recoveries and allows the analysis of a broader analyte spectrum. SBSE can be performed in a fully automated fashion but it is not amenable to conventional GC injection inlets and thus requires specialized and expensive thermodesorption and cryo-focussing units. 4.10.3 Other microextraction techniques 4.10.3.1 In-tube SPME and related techniques Open tubular trapping (OTT), which was introduced in the mid-1980s, uses capillaries (i.d. 0.3-0.5 mm) that are internally coated with a polymeric sorbent. The capillaries resemble GC columns but have thicker films of up to 210
Sample handling and clean-up procedures II-new developments 165 Am (typically 10-15 jim). Similarly to SPME, OTTs are useful for the trapping of analytes from both gaseous and aqueous samples. Sampling is performed by passing (pressing or sucking) the sample solution through the capillary using a microflow pump. Both liquid and thermal desorption have been described in the literature. On-line coupling to LC/MS employing liquid desorption is lately gaining importance. A recent variant of OTT is the socalled in-tube SPME (ITSPME) that was developed by Eisert and Pawliszyn [320]. The coated capillary is placed between the injection loop and the injection needle of an HPLC autosampler while sampling is performed by repeatedly aspirating and dispensing the sample through the capillary. Desorption is carried out by flushing with a volume of organic solvent, which is finally injected on-line in the HPLC system. Pawliszyn and Eisert determined several phenylurea herbicides from aqueous samples with detection limits of about 10 /xg/l water. Another similar technique introduced recently is the so-called Solid-Phase Dynamic Extraction (SPDE). Here, a syringe is employed, the interior walls of which are coated with a polymeric phase. The analytes are concentrated by repeatedly moving the plunger up and down and thus drawing and ejecting the sample. The system is also amenable to liquid and gaseous samples and extractions are performed using a fully automated autosampler. Thermal desorption is achieved by injecting the needle in a GC inlet, while the analyte transfer is assisted by gas flow through the syringe. Recently, Saito et al. [321] have introduced another modification of this technique named wire-in-tube SPME. Here, a stainless steel wire is introduced into the coated ITSPME capillary. This reduces the internal volume of the capillary (e.g., 3-fold) and the diffusion distances, which results in more effective extractions. The technique has been successfully coupled online to a micro-LC system. A common advantage of all in-tube-absorption-type extraction systems is the protection of the extractant film from damage during stirring, as commonly happens in SPME. In-tube SPME applications have been reviewed by Kataoka [322], Saito and Jinno [223] and Zambonin [292]. Gum-phase extraction (GPE) is another related technique that employs polymeric sorbents (e.g., PDMS) filled as a bed in a column, most commonly in the form of particles. The technique thus strongly resembles SPE. In principle, GPE can be used for both liquid and gaseous samples. Desorption can be performed by heating or with a liquid. For liquid samples, the packed bed must be dried between sampling and thermal desorption, which may lead to a loss of more volatile analytes. SPME or SBSE are thus more suitable for such samples.
211
M. Anastassiades and E. Scherbaum 4.10.3.2 Single-drop extraction (SDE) SDE is another micro-LLE approach that uses a single drop of a non-watermiscible solvent (e.g., hexane) for the enrichment of analytes from aqueous samples. Similar to SPME and SBSE, the extractant is injected into a GC system directly after sampling. The difference, however, is that the extractant evaporates during GC injection. Various SDE approaches have been proposed so far. Jeannot and Cantwell [323] and He and Lee [324] proposed a procedure where the solvent drop hangs from the tip of a GC syringe needle. In this approach, the syringe is immersed in the sample vial and the solvent drop is exposed to the sample. After the extraction is over, the drop is drawn back and the needle is inserted in a GC injector. This procedure can be readily automated. In principle, the distribution of the analytes between the sample and the solvent follows similar rules as for the above-mentioned static sampling techniques (SPME, etc.). The control of sample-to-solvent volume ratio, temperature, immersion time and agitation conditions is fundamental for achieving high reproducibility [325]. Equilibration times are similar to SPME but extraction times are usually kept smaller. A factor that limits the extraction time is the solvent loss due to dissolution in the water phase. Agitation and stirring may cause damage to the fragile drop. Nevertheless, if all parameters are properly controlled, the linearity and repeatabilities achieved compare well with those of SPME [326]. Buszewski et al. reported higher enrichment factors using this technique compared with SPME. Overall, SDE is a relatively cheap alternative to SPME, merely requiring a standard syringe. 4.10.4 Membrane-assisted micro-extractions A multitude of different enrichment and cleanup approaches have been developed that employ membranes. In principle, membranes are used to separate two phases, i.e., the sample (donor) phase and the acceptor phase, and at the same time provide a direct contact between them for the analytes to be transferred. In many cases, the membranes consist of hydrophobic materials that hold the organic phase via capillary forces. In the so-called microporousmembrane liquid-liquidextraction (MMLLE) technique, the membrane consists of a highly porous hydrophobic material (polypropylene) that separates an aqueous sample phase (donor phase) from the organic solvent that forms the acceptor phase. This approach is often interfaced to GC. A similar approach is the so-called polymeric membrane extraction, where a thin layer of an inert polymeric material such as silicone or polyethylene constitutes the membrane. These membranes may be mechanically more stable than microporous membranes, but the extraction is 212
Sample handling and clean-up procedures II-new developments generally slower. In the so-called supported liquid membrane extractions (SLME), the membranes also consist of hydrophobic materials that hold the organic phase; however, the arrangements are more complex, consisting of three-phase systems. Both the donor and acceptor phases are aqueous, while the analytes are extracted from the aqueous sample to the membranesupported organic layer and from there to the aqueous acceptor phase that is subsequently used for further analysis. The concept is thus similar to classic liquid-liquid extraction and back extraction. As in the traditional approach, this principle applies to analytes with variable partitioning behaviour such as acidic and basic pesticides. In practice, the pH of the donor phase is adjusted so that the analytes are uncharged in order to partition readily into the organic phase, while the pH of the acceptor phase is such as to transform the analytes into the hydrophilic ionic state. This provides a driving force for the extraction. The back-extraction step considerably increases the selectivity of the process. A number of authors have performed extractions in the dynamic mode (in a flow-system format), with the donor phase flowing through a channel and the acceptor phase being stagnant or flowing depending on the application. This approach permits an automated on-line operation. A typical device consists of two mirrored blocks with grooves. The two blocks are clamped together with the membrane between them so that the donor and acceptor channels are formed, one on each side of the membrane. Various channel designs have been reported, including linear, serpent and spiral shapes with volumes typically ranging from 10 to 1000 [L1. To enhance the analyte transfer, the membrane surface-to-channel volume ratio should be large. Low donor flow rates increase the residence time of the analytes in the donor channel and result in higher extraction efficiencies (absolute recoveries) but the overall analyte enrichment is limited by the smaller sampling volume. Higher donor flow rates allow more sample volume to be processed and lead to considerably higher enrichment factors in the same time frame. In practice, this is more favourable because it is more time-efficient and leads to larger signals that usually increase proportionally to the sampling volume. Matrixmatched calibrations are to be preferred [327]. The so-called hollow-fibre membranes employ a cylindrically shaped polymeric membrane that separates the organic acceptor phase, which is inside the membrane, from the aqueous sample. This design ensures a relatively high contact surface between the phases and allows easy withdrawal of the solvent from the acceptor phase for the injection. Various designs of extractors have been developed. In the so-called liquid phase micro-extraction (LPME) technique, the extraction is performed in an 213
M. Anastassiades and E. Scherbaum
autosampler vial containing the aqueous sample [329]. The porous polypropylene hollow fibre forms a bag that is fixed on top of the vial and submerged into the sample, which is agitated. When the equilibrium is reached, the extract located inside the bag is withdrawn for further use. A similar approach has been presented by Hauser et al. [329] under the name membrane-assisted LLE. Using this device, 15 ml of aqueous sample were extracted with 500 ful of hexane and 100 dulwere injected into a GC system (PTV). Zhao and Lee [3301 developed a semi-automated liquid micro-extraction procedure employing a hollow fibre mounted on a syringe and impregnated with solvent. The device is immersed in the aqueous sample and the extraction is processed under stirring. The technique allows the direct transfer of extracted analytes to a GC/MS system for analysis. A similar approach was presented by Norberg et al. [331] under the name extracting syringe (ESy). Conclusions and outlook: With the use of membranes, liquid-liquid extractions can be performed in a very economical way. The advantages include low solvent consumption, high enrichment factors and excellent amenability to automation. Furthermore, the three-phased supported liquid membrane approach provides a high degree of selectivity. All these approaches can be automated and connected to chromatographic systems. Several companies are currently making attempts to commercialise membraneextraction techniques. Applications: Most applications so far deal with the analysis of drugs in biological fluids. Basic drugs liquid are well amenable to the supported membrane extraction where a high selectivity is achieved. Most applications dealing with the analysis of pesticides so far concern environmental samples. A more detailed overview of the theoretical background and the applications of membrane extractions can be found in various articles [327, 332,333]. 4.11
STRATEGIES FOR THE INTRODUCTION OF NEW ANALYTICAL APPROACHES
The introduction of a new analytical approach in a laboratory is a slow, labourintensive, expensive and thus sometimes a discouraging process. Before even considering implementing a new analytical technique, the analyst must therefore carefully define his analytical goals and priorities, which may include: (a) reduction of costs, (b) reduction of manual labour, (c) reduction of analysis time and increase in sample throughput, (d) reduction of chemical
214
Sample handling and clean-up procedures II-new developments consumption, (e) expansion of the spectrum of analytes and/or sample types that can be analysed, and (f) improvement in the analytical quality (better recoveries, limits of detection and confirmation, accuracy, precision and selectivity). Due to the multitude of new analytical approaches that exist in the market, the analyst has to gather and evaluate a lot of information to facilitate decision-making. Other factors to be considered include: (a) the costs for initial investment and operation, (b) the amenability of the approach for routine applications, (c) robustness, (d) ease of operation and degree of expertise required by the users, (e) safety and environmental issues, and (f) the fact that additional laboratory space and materials are needed. It is obvious that substantial knowledge, experience and background information are required to make the right decisions and choices. Newcomers to the field are often overwhelmed and confused by the tremendous amount of analytical possibilities and usually lack the experience needed to evaluate this information and to recognise the strengths and limitations of each technique. On the other hand, more experienced analysts may have the knowledge and expertise but are usually more sceptical and reluctant in adapting modern analytical approaches. This reluctance can have a variety of reasons: often there is a lack of time, money or personal energy to go through the whole process, which also includes the instrumentation purchase formalities, the training of the upcoming users and the development and validation of new methods. Sometimes, the reluctance to change is associated with a general distrust towards the suitability and/or reliability of new technologies and the risks involved if the new approach fails to reach the goals that have been set. Therefore, many analysts often wait until the approach has been sufficiently tested and recognised by the scientific community or until the associated costs decline. On the other hand, analysts working in research are much more enthusiastic about employing the latest technological achievements because they give them the ability to perform pioneering work. The success of a novel analytical approach ultimately depends on whether users are able to explore fully the potentials of the technique and develop methods that are more efficient, selective, rapid and cost-effective than existing alternatives. In the past, the implementation of many modern analytical approaches was additionally hindered by the policy of regulatory bodies to prescribe standardised official methods, which lacked flexibility, even prescribing the instrumentation to be used for sample preparation and measurement. Fortunately, in recent years, these policies have changed and modern regulations, rather than prescribing the exact methodology, increasingly concentrate on defining the general laboratory practices necessary to ensure 215
M. Anastassiades and E. Scherbaum a high quality of results. By addressing the performance criteria for methods and laboratories, this policy encourages the implementation of new techniques. Another recent trend that offers more flexibility to analysis is the introduction of modular methods. Here, a number of alternative approaches (modules) exists for each analytical step (extraction, clean-up and instrumental analysis), thus allowing the analyst to assemble a method that best fits his purpose. Numerous descriptive, evaluative, comparative and critical publications are published every year with the aim of helping the analysts form their opinion and better assess the possibilities and limitations of each technique. The following journal articles [180,334-343] and books [344-350] cover more than one of the sample preparation techniques that have been presented in this chapter and can be consulted to obtain information from a different perspective. Table 4.15 synoptically provides a brief description of some of the techniques discussed in this chapter. 4.11.1 Interdependence of analytical steps When choosing a new analytical approach, it is always important to keep in mind that the analytical steps within a procedure are always highly interdependent. For example, the need to remove matrix components by extensive clean-up can be substantially reduced, either by putting more emphasis on the selectivity of the previously performed extraction step or by employing determinative techniques that provide a better chromatographic resolution and detection selectivity. However, both these options are subject to some practical limitations. On the one hand, enhancing the selectivity of the extraction step unavoidably limits the range of analytes that can be satisfactorily recovered. This is, for example, the case in SFE, SPME, SBSE and SPE where the extraction media employed restrict the polarity range of the procedure (with the advantage, however, of not requiring additional cleanup steps). On the other hand, even when highly selective and sophisticated instruments are used, high concentrations of matrix components in the extracts may contaminate and negatively affect the ruggedness of the chromatographic system. The analyst must, therefore, take all these factors into account in order to find the optimal solution for a specific analytical problem. Often, the implementation of a new sample preparation technique requires investment in the instrumental analysis area. Since most novel sample-preparation approaches depend greatly on the use of highly selective and specific determinative techniques, any decision for introducing a novel sample-preparation technique should always consider the potential 216
Sample handling and clean-up procedures II-new developments 0
U)6
'C
3
0:
0 0
L
4
w
0' 6;
o
C
e:
9t3 0 El
0 0
O
r
l0 C)
C
t 4 2a)
~9
Ee 2I
C
C= C)
Pq
0 NL
U)
bD
S 24299.94' ." 0
M
.9
0O O .9 4
-60
CD
So
V 0
4
0
.1
. 04.9
0
R
O
0
u CO
IB
o
I
-0 CA C)
0 A
c
I
o C cc
00 . 0
g
,O 5b
A:
34 Q0't IC, 4
0
99
~~
0" 0-
>
0,
+4 +4
0. 0
co
0 06
6
C 0 a
s
0 U)
a)
C)
-
00
9999
.9 '9 . . vo'o 9
C)
it 9
-. 4
0
400 I I
.n
0 s
U) n
_,
& 42
99i C
0
0 Uo
o
9
o
o
s U)
U
o
bO:4 C)
.
60
.r
°t
0
ME 0o
o
O
o
01 3 0
Ei
40 0n ~O to
00 '.0C
IZ
0
0
0)
o-'
0 0_Z5 . i
0 o Cd 000
I
co
I
6 9
O C) c) 0
o 6 '4-4
9.
.0 ' C
0 :r0
o
t
'
0
9
I
co
40-
I
q 0
-V a
0
C)
h 0
C-
~4 a46U)
C)
NO
.0
0
O
U)
-4. 0
4
.01 -44 C
. 0 4 2
5.02v, .
-·
o
0.~ah 0~g·d~
C)z
·
4
0
4
.
.
0
0
0
o
0
tO .0
$
0 .9
i
XD 1 I O o I
-Mr s pq 0
0114.
110
U)m 0 FO C)%
0
5
5
0; C)
0
w " 0 0. O M
0 o U)
0)
0 M
9
°
C
o 6 O
217
0
C
M. Anastassiades and E. Scherbaum
UO a
a
-a a °o.
a)oa)
®
zn t
,
a)as~~~~~~~~~~~~~~~>
0 .a
c S0
aa
na
Oc=bD °
a)
4
C
S
0
U)
Q
A
Is
r
a)
~~0C-9~
M
U) 9
P4 a) cc c9
O
Ed aa a >
II -
;5
Q~'a'o5
iYO ·
a)
a
_
a),
C
-6 -a) 5,a) 'c cc ro-CA4 -a)
w
A-
C- 0C 0'
5
,"It
. zS > a)>-~C) _ t,a >
=
A-
r>
a)
C
C) a)a) -. -Id -W, c·- C E-
a) '0
v-a)C 3M0 @
~
-5
P a
.1 a a)
w
i a)
C
w
c
5
O
xa)
a
0
0 C
- -5a
0
P4 cc U)
C > '0 'C -Cqs
g
~
o 5 a$) 5 50>
5;W
U<
rQ Co
r
a)c °a
m;
e gaO
)
, S S Cd E
C aG
~ =;
;g , L 5
O
-C-C O
g t
a)
-C5+
25't
-4 a)
O
-Q
E-
218
U.a
o
a )t
C)
$
0 0
LCa·ga
Sample handling and clean-up procedures II-new developments availability of the required instrumentation. In recent years, dramatic improvements have been accomplished in the field of determinative analysis that were extremely helpful in enabling the simplification of samplepreparation approaches. These include the higher selectivity and sensitivity of mass spectrometric (MS) detectors, the improved resolution of GC and LC columns and the significant advancements in the field of versatile GC injection and automated on-line sample preparation. Modern GC injector systems offer the possibility of automated solvent evaporation, thus allowing the introduction of large extract volumes, which eliminates the need for performing troublesome evaporation and solvent-exchange steps. Direct sample introduction (DSI) systems enable the automated exchange of injector inserts (e.g., liners, mini-vials), offering the possibility of introducing samples/extracts with a high concentration of non-volatile matrix components. Meanwhile, numerous sample-handling tasks can be performed fully automated by highly sophisticated and functional xyz robots. Such instruments have contributed to the automation of various micro-extraction techniques such as SPME or SBSE, the latter also employing specially designed thermodesorption units. At the end, the purchase costs of these high-tech instruments for the final determinative analysis is the factor with the most decisive influence on the choice of the sample preparation approach. 4.11.2 Trend for more analytical efficiency The task and desire of laboratories to increase productivity, sample throughput and cost-effectiveness have always been important driving forces for scientists and instrument manufacturers to improve existing and seek out new sample preparation approaches. Often, the most successful of these techniques are characterised by automation, miniaturisation and a greater simplicity. Trend for simplification: Residue analysis in complex biological matrices often requires sophisticated sample preparation strategies to obtain the degree of enrichment and separation required for achieving specific and accurate determination. As explained above, the more selective and sensitive the final determinative analysis, the less selective the sample preparation can be. Many of the new simple and streamlined sample preparation approaches and methodologies presented in this chapter take advantage of the tremendous developments in MS detection. Reducing the number of sample preparation steps not only translates into less labour, time and costs, but also often results in procedures that cover a broader analyte range and are less prone to errors. In some cases, sample preparation has already been simplified
219
M. Anastassiades and E. Scherbaum to a basic "dilute and shoot" approach and one can anticipate that this trend will continue in the future. Trend for miniaturization:Miniaturisation is probably the easiest way to improve the efficiency of a method. Miniaturisation is often indicated and advisable when the method consumes more resources (e.g., chemicals) than are affordable. The use of less solvent volumes also reduces the time and effort required to concentrate the extracts and is thus economically and ecologically highly desirable. Miniaturisation of procedures has often also been driven by the need for more automation in sample preparation. Many of the modern single-equilibrium and flow-through sample preparation techniques (e.g., SPE, PLE, SFE, etc.) can, in principle, be scaled up or down as required (e.g., to facilitate automation). However, not all types of procedures can be easily miniaturised. It is, for example, much easier to scale down a one-pot on-line methodology than a traditional method that involves multiple phase separations. Limiting factors to consider are the sample homogeneity and the availability of additional equipment required in the periphery (e.g., pumps, injectors, autosamplers). Trend for automation and on-line hyphenation: The development of automated sample-handling techniques has been stimulated by the growing desire to simplify procedures and reduce manual labour and intervention. In the early days of automation, equipment manufacturers endeavoured to build instruments (robots) that would exactly copy the analytical tasks as humans would do them. This often resulted in highly complicated and unreliable instruments that were not accepted by the analytical community. In today's concepts of automated sample preparation, the sample-handling steps are typically designed to be amenable to the capabilities of the existing instrumentation from the very beginning (e.g., SBSE, SPME). The advantages of introducing automation in a laboratory include: (a) increased efficiency and productivity through time savings, higher sample throughput and unattended (e.g., overnight) performance of repetitive tasks, (b) improved analytical results in terms of accuracy, precision and reproducibility, (c) increased safety through less contact of personnel with hazardous chemicals, (d) reduced manual work so personnel are freed from routine, monotonous, cumbersome and labour-intensive tasks and can devote their time and energy to other functions, (e) better process control and documentation, (f) easier method development, and (g) savings in space. However, automation by itself does not always eliminate the problems associated with manual approaches. Thus, before switching to a new approach, both the manual methods to be replaced and the automated alternatives should be critically evaluated to find out whether the resulting 220
Sample handling and clean-up procedures II-new developments benefits would justify the efforts. The advantages must be seen in the context of the associated, often high initial investment in labour, time and money. Whether automation of a procedure is worthwhile or not generally depends on the number of samples that are analysed. Furthermore, one must consider that the complexity of many automation instruments also limits their reliability. Instrument breakdown, which is often associated with additional costs and stress, is not uncommon and thus alternative action plans should exist in every laboratory. In off-line hyphenation, specific sample-handling tasks are performed by a stand alone instrument and operator intervention (in the simplest case, manual transfer of the extracts to the next apparatus) is required before the sample is ready to be processed by the next instrument. In on-line arrangements, no operator intervention is required for transferring the extracts to the next device. In many cases, automated sample preparation is directly hyphenated to the instrument performing the final chromatographic analysis (e.g., SPE/HPLC, SPME/GC, etc.); however, the hyphenation of two or more sample preparation procedures (e.g., GPC/evaporation, SFE/SPE) is also common. Off-line automation arrangements (using standalone workstations) are often preferable to on-line automation because sample preparation can be performed in parallel while on-line automation usually calls for sequential processing. Parallel processing enables a higher sample throughput and is preferable to sequential processing when analyte stability in the original samples is an issue. However, if immediate further processing of the extracts is essential, on-line sequential automation is a better choice. This is the case, for example, when employing SPME, SBSE or headspace analysis or when the stability of the analytes in the extracts is limited (e.g., following derivatisation). Often, the goal is to adjust the processing times of the two instruments working in tandem so that, as soon as the second instrument starts the processing, the first instrument is already beginning preparation of the next sample. A problem with on-line arrangements is that the entire set-up is blocked when one part of the instrument has a failure. Even if only the second instrument has a malfunction, the preceding instrument will in most cases be hindered from continuing its work. Also, the use of each component individually is not as straightforward as in off-line set-ups, requiring the undoing of the connections. Among the most useful modern devices employed in sample preparation are the so-called xyz-workstations equipped with robotic arms, tube racks, solvent reservoirs, vortex mixers, shakers, dispensers, centrifuges, etc. These instruments can automatically perform a number of sample-handling tasks 221
M. Anastassiades and E. Scherbaum such as pipetting, dilutions, mixing, derivatisation, addition of internal standard, heating, shaking, etc. Most manipulation steps (e.g., pipetting, vial transport) are performed in a linear fashion along the xyz plane. Such workstations, which can be very agile and operationally highly reliable instruments, have become increasingly popular as autosamplers for GC and LC, for the automation of chromatographic clean-up procedures such as SPE and GPC and also for the on-line hyphenation of these and other sample preparation procedures (e.g., SPME and SBSE) with LC or GC instruments. Today, automatic instruments are commonplace and have become indispensable in almost every laboratory. The dramatic developments in electronics and information technology will continue to open up new prospects and possibilities and more useful, sophisticated and reliable automated instruments will be available in the future. However, the role of humans as the last instance of decision making will surely continue to exist.
REFERENCES 1 2 3 4 5 6 7 8 9 10 11
222
S.M. Lee, M.L. Papathakis, H.-M.C. Feng, G.F. Hunter and J.E. Carr, Fresenius J. Anal. Chem., 339 (1991) 376. J. Fillion, F. Sauve and J. Selwyn, J. Assoc. Off. Anal. Chem. Int., 83 (2000) 698. W. Specht and M. Tillkes, Fresenius J. Anal. Chem., 322 (1985) 443. M.A. Luke, J.E. Froberg, G.M. Doose and H.T. Masumoto, J. Assoc. Off. Anal. Chem., 64 (1981) 1187. A.R. Hill, C.A. Harris and A.G. Warburton, In: A.R. Faigelj and C.A. Ambrus (Eds.), Principles and Practices of Method Validation. Royal Society of Chemistry, Cambridge, 2000, p. 41. A. Ambrus, In: T.R. Roberts (Ed.), Pesticide Chemistry and Bioscience, The Food-Environment Challenge. The Royal Society of Chemistry, Cambridge, 1999, p. 339. B. Maestroni, A. Ghods, M. El-Bidaoui, N. Rathor, O.P. Jarju, T. Ton and A. Ambrus, In: A. Ambrus (Ed.), Principlesof Method Validation. Royal Society of Chemistry, Cambridge, UK, 2000, p. 49. A. Di Muccio, S. Girolimetti, D. Attard Barbini, T. Generali, P. Pelosi, A. Ausili, F. Vergori, G. Merulis, A., Santilio and I. Camoni, In: A. De Kok (Ed.), European PesticideResidue Workshop, Alkmaar, 1996. H. Allmendinger, B.R. and P. Ohs, In: J.R. Finney (Ed.), 9th IUPAC Congress of Pesticide Chemistry, The Food-Environment Challenge, The Royal Society of Chemistry, London, 1998. E.A. Bunch, D.M. Altwein, L.E. Johnson, J.R. Farley and A.A. Hammersmith, J. Assoc. Off. Anal. Chem. Int., 78 (1995) 883. A. Ambrus, E.M. Solymosne and I. Kovs6s, J. Environ. Sci. Health B, 31 (1996) 443.
Sample handling and clean-up procedures II-new developments 12
13 14 15
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
34 35 36 37 38
M. Anastassiades, Entwicklung Schneller Verfahren zur Rickstandsanalytik von Pflanzenschutzmitteln in Obst und Gemise unter Einsatz der SFE-ein Beitrag zur Beseitigung analytischerDefizite, Shaker Verlag, 2001. L. Kadenczki, Z. Arpad, I. Gardi, A. Ambrus, L. Gyorfi, G. Reese and W. Ebing, J. AOAC Int., 75 (1992) 54. R.J. Fussell, K. Jackson Addie, S.L. Reynolds and M.F. Wilson, J. Agric. Food Chem., 50 (2002) 441. M. El-Bidaoui, O.P. Jarju, B. Maestroni, Y. Phakaeiw and A. Ambrus, In: A. Ambrus (Ed.), Principles of Method Validation. The Royal Society of Chemistry, Cambridge, UK, 2000, p. 75. A.R. Hill and S.L. Reynolds, Food Addit. Contam., 19 (2002) 733. A. Andersson, Food Addit. Contam., 17 (2000) 547. W. Specht, S. Pelz and W. Gilsbach, Fresenius J. Anal. Chem., 353 (1995) 183. M. Anastassiades and E. Scherbaum, Deutsche Lebensmittel-Rundschau, 93 (1997) 316. M. Anastassiades, E. Scherbaum and D., Bertsch, In: M. Montury (Ed.), MGPR 2003, Aix en Provence, 2003. J. Cook, M.P. Beckett, B. Reliford, W. Hammock and M. Engel, J. Assoc. Off. Anal. Chem. Int., 82 (1999) 1419. R.S. Sheridan and J.R. Meola, J. AOAC Int., 82 (1999) 982. L.E. Sojo, A. Brocke, J. Fillion and S.M. Price, J. Chromatogr.A, 788 (1997) 141. A. Valverde-Garcia, E. Gonzalez-Pradas, J.L. Martinez-Vidal and A. Aguera Lopez, J. Agric. Food Chem., 39 (1991) 2188. M.J. Taylor, K. Hunter, K.B. Hunter, D. Lindsay and S. Le Bouhellec, J. Chromatogr.A, 982 (2002) 225. C. Jansson, T. Pihlstrom, B.-G. Osterdahl and K.E. Markides, J. Chromatogr. A, (2004) 93. A.R. Fernandez-Alba, A. Valverde, A. Aguera and M. Contreras, J. Chromatogr. A, 686 (1994) 263. A. Andersson and H. Palsheden, Fresenius J. Anal. Chem., 339 (1991) 365. H. Steinwandter, Fresenius J. Anal. Chem., 343 (1992) 887. A. Koinecke, R. Kreuzig, M. Bahadir, J. Siebers and H.-G. Nolting, Fresenius J. Anal. Chem., 349 (1994) 301. J. Zrostlikova, J. Hajslova, T. Kovalczuk, R. Stepan and J. Poustka, J. Assoc. Off. Anal. Chem. Int., 86 (2003) 612. A. Sannino, J. AOAC Int., 81 (1998) 1048. A. Di Muccio, P. Pelosi, D. Attard Barbini, T. Generali, S. Girolimetti, P. Stefanelli, A. Leonelli, G. Amendola, L. Vergori and E. Viana Fresquet, J. Chromatogr.A, 833 (1999) 19. K. Mastovska, J. Hajslova, M. Godula, J. Krivankova and V. Kocourek, J. Chromatogr.A, 907 (2001) 235. J. Rado and S. Gorbach, FreseniusJ. Anal. Chem., 302 (1980) 15. R.J. Hemingway, N. Aharonson, P.A. Greve, T.R. Roberts and H.-P. Thier, Pure Appl. Chem., 56 (1984) 1131. H. Steinwandter, In: T. Cairns and J. Sherma (Eds.), Emerging Strategies fbr PesticideAnalysis. CRC Press, Boca Raton, FL, 1992. H. Steinwandter, Fresenius Z. Anal. Chem., 322 (1985) 752.
223
M. Anastassiades and E. Scherbaum 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
62 63 64 65 66 67
224
H.B. Wan and M.K. Wong, J. Chromatogr.A, 754 (1996) 43. A. Colume, S. Cardenas, M. Gallego and M. Valcarcel, J. Chromatogr. A, 882 (2000) 193. H.G. Mol, R.C. van Dam and O.M. Steijger, J. Chromatogr. A, 1015 (2003) 119. J. Klein and L. Alder, J. AOAC Int., 86 (2003) 1015. A.C. Hogenboom, M.P. Hofman, S.J. Kok, W.M. Niessen and U.A. Brinkman, J. Chromatogr.A, 892 (2000) 379. B.A. Ingelse, R.C. van Dam, R.J. Vreeken, H.G. Mol and O.M. Steijger, J. Chromatogr.A, 918 (2001) 67. T. Hyotylainen, K. Jauho and M.L. Riekkola, J. Chromatogr.A, 813 (1998) 113. B.M. Forcada, J. Beltran, F.J. L6pez and F. Hernandez, Chromatographia,51 (2000) 362. S. Navarro, A. Barba, G. Navarro, N. Vela and J. Oliva, J. Chromatogr. A, 882 (2000) 221. P. Cabras, A. Angioni, V.L. Garau and E.V. Minelli, J. AOAC Int., 80 (1997) 867. A. Aguera, L. Piedra, M.D. Hernando, A.R. Fernandez-Alba and M. Contreras, Analyst, 125 (2000) 1397. J.L. Martinez Vidal, F.J. Arrebola and M. Mateu-Sanchez, J. Chromatogr.A, 959 (2002) 203. M. Gamon, A. Saez, R. Pelegri, I. Peris, J.G. de la Cuadra and R. Coscolla, J. AOAC Int., 81 (1998) 1037. M. Anastassiades, S.J. Lehotay, D. Stajnbaher and F.J. Schenck, J. Assoc. Off. Anal. Chem. Int., 86 (2003) 412. M. Anastassiades, K. Mastovska and S.J. Lehotay, J. Chromatogr.A, 1015 (2003) 163. L. Dagna, G. Gasparini, M.L. Icardi and E. Sesia, Boll. Chim. Igien., 44 (1993) 383. B. Morzycka, J Plant ProtectionRes, 42 (2001) 17. E. Viana, J.C. Molto and G. Font, J. Chromatogr. A, 754 (1996) 437. Y.C. Ling and I.P. Huang, J. Chromatogr. A, 695 (1995) 75. P. Branca, Industrie Alimentari, 41 (2002) 556. D. Perret, A. Gentili, S. Marchese, M. Sergi and G. D'Asceno, J. Assoc. Off. Anal. Chem. Int., 85 (2002) 724. B. Albero, C. Sanchez-Brunete and J.L. Tadeo, J. Chromatogr.A, 1007 (2003) 137. A. Di Muccio, T. Generali, D.A. Barbini, P. Pelosi, S. Girolimetti, P. Stefanelli, G. Amendola and F. Vanni, In: A. Di Muccio (Ed.), European PesticideResidues Workshop, Rome, 2002. L.V. Podhorniak, J.F. Negron and F.D. Griffith Jr., J. AOAC Int., 84 (2001) 873. A. Di Muccio, A. Ausili, I. Camoni, R. Dommarco, M. Rizzica and F. Vergori, J. Chromatogr., 456 (1988) 149. K. Iijima, M. Saka, Y. Odanaka and 0. Matano, Nippon Noyaku Gakkaishi, 22 (1997) 17. H. Obana, K. Akutsu, M. Okihashi and S. Hori, Analyst, 126 (2001) 1529. H. Obana, K. Akutsu, M. Okihashi, S. Kakimoto and S. Hori, Analyst, 124 (1999) 1159. R.J. Argauer, K.I. Eller, M.A. Ibrahim and R.T. Brown, J. Agric. Food Chem., 43 (1995) 2774.
Sample handling and clean-up procedures II--new developments 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92
93 94 95 96 97 98 99 100
C. Sanchez-Brunete, B. Albero, E. Miguel and J.L. Tadeo, J. Assoc. Off. Anal. Chem. Int., 85 (2002) 128. A. Di Muccio, P. Pelosi, I. Camoni, D.A. Barbini, R. Dommarco, T. Generali and A. Ausili, J. Chromatogr.A, 754 (1996) 497. P. Gros, J. Tourte and S. Couturier, In: A. Di Mucchio (Ed.), EPRW 2002, ISS, Rome, 2002, 172p. S.A. Barker, J. Chromatogr. A, 885 (2000) 115. M.D. Crouch and S.A. Barker, J. Chromatogr.A, 774 (1997) 287. C.C. Walker, H.M. Lott and S.A. Barker, J. Chromatogr.A, 642 (1993) 225. S.A. Barker, J. Chromatogr. A, 880 (2000) 63. S.C. Stafford and W. Lin, J. Agric. Food Chem., 40 (1992) 1026. C. Blasco, G. Font and Y. Pico, J. Chromatogr.A, 970 (2002) 201. C.M. Torres, Y. Pico and J. Maies, J. Chromatogr. A, 778 (1997) 127. M. Fernandez, Y. Pico and J. Manes, J. Chromatogr.A, 871 (2000) 43. A.I. Valenzuela, Y. Pico and G. Font, J. Assoc. Off.Anal. Chem. Int., 84 (2001) 901. E.M. Kristenson, E.G. Haverkate, C.J. Slooten, L. Ramos, R.J. Vreuls and U.A. Brinkman, J. Chromatogr. A, 917 (2001) 277. M. Navarro, Y. Pico, R. Marin and J. Manes, J. Chromatogr. A, 968 (2002) 201. F.J. Schenck and R. Wagner, Food Addit. Contam., 12 (1995) 535. A.R. Long, M.M. Soliman and S.A. Barker, J. Assoc. Off. Anal. Chem., 74 (1991) 493. F., H6fler, Beschleunigte Losemittelextraktion (ASE), Dionex GmbH, 2000. F. Schroter, M. Anastassiades and E. Scherbaum, Chem. Labor Biotech., 50 (1999) 4. M. Okihashi, H. Obana and S. Hori, Analyst, 123 (1998) 711. B. Richter, B.A. Jones, J. Ezzell and N.L. Porter, Anal. Chem., 68 (1996) 1033. B. Richter, LC-GC Suppl., 17 (1999) S22. L. Ramos, E.M. Kristenson and U.A. Brinkman, J. Chromatogr.A, 975 (2002) 3. S. Pyle and A.B. Marcus, J. Mass Spectrom., 32 (1997) 897. J.L. Ezzell, B.E. Richter, W.D. Felix, S.R. Black and J.E. Meikle, LC-GC Int., 13 (1995) 390. ISS, Instituto Superiore di Sanita, Estrazione per Dispersione su Terra di Diatomee a Temperaturae PressioneElevate (PLE), PurificazioneMediante GPC o Mediante SPE-Silice, 2002. BGVV, L 00.00-34 E 9 Beschleunigte Losungsmittelextraktion (ASE), Amtliche Sammlung von Untersuchungsverfahrennach §35 LMBG, 1999. E. Korta, A. Bakkali, L.A. Berrueta, B. Gallo and F. Vicente, J. Food Prot., 65 (2002) 161. K. Adou, W.R. Bontoyan and P.J. Sweeney, J. Agric. Food Chem., 49 (2001) 4153. A. Franchi, L. Bensi, S. Viti and F. Abballe, Boll. Chim. Igien., 49 (1998) 119. B. Richter, F. Hofler and M. Linkerhagner, LCGC, 19 (2001) 408. H. Obana, K. Kikuchi, M. Okihashi and S. Hori, Analyst, 122 (1997) 217. T. Pihlstrom, G. Isaac, M. Waldeback, B.-G. Osterdahl and K.E. Markides, Analyst, 127 (2002) 554. J.C. Chuang, K. Harta, J.S. Changa, L.E. Bomanb, J.M.V. Emonc and A.W. Reedc, Anal. Chim. Acta, 444 (2001) 87.
225
M. Anastassiades and E. Scherbaum 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133
226
G. DiGiamberardino, A. Pontecorvi, L. Trapanese and F. Abballe, In: S. Reynolds (Ed.), EuropeanPesticide Residue Workshop, York, UK, 2000. S.B. Hawthorne, Y. Yang and D.J. Miller, Anal. Chem., 66 (1994) 2912. L. Haar, J.S. Galacher and G.S. Kell, NationalResearch Council Steam Tables. Hemisphere Publishing Corporation, Bristol, PA, 1984. R.M. Smith, J. Chromatogr. A, 975 (2002) 31. C. Crescenzi, A. Di Corcia, M. Nazzari and R. Samperi, Anal. Chem., 72 (2000) 3050. L. Wennrich, P. Popp, G. Koller and J. Breuste, J. Assoc. Off. Anal. Chem. Int., 84 (2001) 1194. M.S. Curren and J.W. King, J. Agric. Food Chem., 49 (2001) 2175. L. Wennrich, P. Popp and J. Breuste, Chromatographia,53-S (2001) 380. M.D. Luque de Castro and M.M. Jimenez-Carmona, TAC, Trends Anal. Chem., 17 (1998) 441. T.M. Pawlowski and C.F. Poole, J. Agric. Food Chem., 46 (1998) 3124. X. Lou, D.J. Miller and S.B. Hawthorne, Anal. Chem., 72 (2000) 481. J.J. Jimenez, J. Atienza, J.L. Bernal and L. Toribio, Chromatographia,38 (1994) 395. A. Valverde-Garcia, A.R. Fernandez-Alba, A. Aguera and M. Contreras, J. Assoc. Off Anal. Chem. Int., 78 (1995) 867. A. Valverde-Garcia, A.R. Fernandez-Alba, M. Contreras and A. Aguera, J. Agric. Food Chem., 44 (1996) 1780. S.T. Burford, S.B. Hawthorne and D.J. Miller, J. Chromatogr.A, 657 (1993) 413. R. Stefani, M. Guzzi and R. Grazzi, J. Chromatogr.A, 782 (1997) 123. K.I. Eller and S.J. Lehotay, Analyst, 122 (1997) 429. M.L. Hopper and J.W. King, J. Assoc. Off. Anal. Chem., 74 (1991) 661. S.J. Lehotay and K.I. Eller, J. Assoc. Off. Anal. Chem. Int., 78 (1995) 821. S.J. Lehotay, N. Aharonson, E. Pfeil and M.A. Ibrahim, J. AOAC Int., 78 (1995) 831. A.L. Howard, C. Braue and L.T. Taylor, J. Chromatogr. Sci, 31 (1993) 323. S.J. Lehotay, J. Chromatogr.A, 785 (1997) 289. D.R. Gere, E.M. Derrico and R.E. Majors, LCGC Int., 7 (1994) 370. M.E. McNally, J. AOAC Int., 79 (1996) 380. R.K. Juhler, Analyst, 123 (1998) 1551. T.M. Fahmy, M.E. Paulaitis, D.M. Johnson and M.E. McNally, Anal. Chem., 65 (1993) 1462. S.J. Lehotay and C.H. Lee, J. Chromatogr. A, 785 (1997) 313. M. Anastassiades, E. Scherbaum and W. Schwack, Deutsche LebensmittelRundschau, 97 (2001) 176. J.W. King and J.E. France, In: B. Wenclawiak (Ed.), Analysis with supercritical fluids, extraction and chromatography. Springer Verlag, New York, 1992, p. 32. C. Turner, C.S. Eskilsson and E. Bjorklund, J. Chromatogr. A, 947 (2002) 1. S.B. Hawthorne, C.B. Grabanski, E. Martin and D.J. Miller, J. Chromatogr.A, 892 (2000) 421. E. Stahl, K.W. Quirin and D. Gerard, Verdichtete Gase zur Extraktion und Raffination. Springer Verlag, New York, 1987. M. Anastassiades and W. Schwack, J. Chromatogr. A, 825 (1998) 45.
Sample handling and clean-up procedures II-new developments 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166
R.J. Argauer, J. Agric. Food Chem., 45 (1997) 180. Y.C. Ling and H.C. Teng, J. Chromatogr. A, 790 (1997) 153. J.E. France and J.W. King, J. Assoc. Off. Anal. Chem., 74 (1991) 1013. E.G. Alley and L. Guozhen, J. Assoc. Off. Anal. Chem. Int., 78 (1995) 1051. B. Murugaverl, A. Gharaibeh and K.J. Voorhees, J. Chromatogr.A, 657 (1993) 223. M.L. Hopper, J. Assoc. Off. Anal. Chem. Int., 80 (1997) 639. R.J. Maxwell and A.R. Lightfield, J. Chromatogr. B Biomed. Sci. Appl., 715 (1998) 431. M. Danaher, M. O'Keeffe and J.D. Glennon, J. Chromatogr.B Biomed. Sci. Appl., 761 (2001) 115. N. Motohashi, H. Nagashima and C. Parkanyi, J. Biochem. Biophys. Method, 43 (2000) 313. E. Anklam, H. Berg, L. Mathiasson, M. Sharman and F. Ulberth, Food Addit. Contam., 15 (1998) 729. T.L. Chester, J.D. Pinkston and D.E. Raynie, Anal. Chem., 70 (1998) 301. M. Valcdrcel and M.T. Tena, Fresenius J. Anal. Chem., 358 (1997) 561. M.T. Tena, A. Rios, M. Valcarcel and M. Sanchez-Alarcon, Chromatographia,46 (1997) 524. K. Yoshii, M. Okada, Y. Tsumura, Y. Nakamura, I. Susumu and T. Yasuhide, J. AOAC Int., 82 (1999) 1239. S. Nemoto, K. Sasaki, M. Toyoda and Y. Saito, J. Chromatogr. Sci, 35 (1997) 467. B. Ohlin, K. Norman, A. Valverde-Garcia and B.-G., Osterdahl, In: A. Di Muccio (Ed.), EuropeanPesticide Residues Workshop, Rome, 2002, 234p. K. Yoshii, Y. Tsumura, Y. Nakamura, I. Susumu, Y. Tonogai, T. Tsuchiya, M. Kimura and Y. Sekiguchi, J. Food Hyg. Soc., 40 (1999) 68. N. Aharonson, S.J. Lehotay and M.A. Ibrahim, J. Agric. Food Chem., 42 (1994) 2817. Y.Y. Wigfield and M. Lanouette, J. Agric. Food Chem., 41 (1993) 84. S.U. Khan, J. Agric. Food Chem., 43 (1995) 1718. Z.V. Skopec, R. Clark, P.M. Harvey and R.J. Wells, J. Chromatogr. Sci, 31 (1993) 445. C. Nerin, R. Batle and J. Cacho, J. Chromatogr.A, 795 (1998) 117. J. Poustka, K. Holadova and J. Hajslova, Eur. Food Res. Technol., 216 (2003) 68. D.H. Kim, G.S. Heo and D.W. Lee, J. Chromatogr.A, 824 (1998) 63. B. Ohlin, K. Norman, A. Valverde-Garcia and B.-G. Osterdahl, In: A. Di Muccio (Ed.), EuropeanPesticide Residues Workshop, Rome, 2002, p. 236. K.N. Norman and S.H. Panton, J. Chromatogr. A, 907 (2001) 247. A. Kaihara, K. Yoshii, Y. Tsumura, Y. Nakamura, S. Ishimitsu and Y. Tonogai, J. Health Sci., 46 (2000) 336. Y.C. Ling, H.C. Teng and C. Cartwright, J. Chromatogr.A, 835 (1999) 145. S.J. Lehotay, J. AOAC Int., 85 (2002) 1148. R.M. Smith, J. Chromatogr. A, 856 (1999) 83. M.D. Luque de Castro and M.M. Jimenez-Carmona, TrAC, Trends Anal. Chem., 19 (2000) 223. V. Camel, Trends Anal. Chem., 19 (2000) 229. C. Sparr Eskilsson and E. Bj6rklund, J. Chromatogr. A, 902 (2000) 227.
227
M. Anastassiades and E. Scherbaum 167
168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
228
C. Sparr Eskilsson, Thesis: Recent Extraction Techniques with Emphasis on Supercritical Fluid Extraction and Microwave-Assisted Extraction, Lund University, 2003. R.C. Richter, D. Link and H.M. Kingston, Anal. Chem., 73 (2001) 30A. J.L. Luque-Garcia and M.D. Luque de Castro, TrAC, Trends Anal. Chem., 22 (2003) 90. B. Erickson, Anal. Chem., 70 (1998) 467A. L.E. Garcia-Ayuso and M.D. Luque de Castro, TrAC, Trends Anal. Chem., 20 (2001) 28. M. Ericsson and A. Colmsjo, J. Chromatogr. A, 964 (2002) 11. U. Pilcher, A. Haase and G. Knapp, Anal. Chem., 71 (1999) 4050. W. Vetter, M. Weichbrodt, K. Hummert, D. Glotz and B. Luckas, Chemosphere, 37 (1998) 2439. Y. Wang, M. Bonilla and H.M. McNair, J. High Resolut. Chromatogr., 20 (1997) 213. V. Pino, J.H. Ayala, A.M. Afonso and V. Gonzalez,Anal. Chim. Acta, 477 (2003) 81. Y.I. Chen, Y.S. Su and J.F. Jen, J. Chromatogr.A, 976 (2002) 349. S.P. Frost, J.R. Dean, K.P. Evans, K. Harradine, C. Cary and M.H.I. Comber, Analyst, 122 (1997) 895. H.M. Pylypiw, T.L. Arsenault, C.M. Thetford and M.J.I. Mattina, J. Agric. Food Chem., 45 (1997) 3522. V. Lopez-Avila, Crit. Rev. Anal. Chem., 29 (1999) 195. V. Camel, TrAC, Trends Anal. Chem., 19 (2000) 229. C. Sparr_Eskilsson and E. Bjorklund, J. Chromatogr. A, 902 (2000) 227. V. Lopez-Avila, J. Assoc. Off. Anal. Chem. Int., 66 (1999) 217. M. Weichbrodt, W. Vetter and B. Luckas, J. Assoc. Off. Anal. Chem. Int., 83 (2000) 1334. W. Vetter, M. Weichbrodt and E. Stoll, Environ. Sci. Technol., 37 (2003) 840. J.F. Stout, A.R. daCunha, G.L. Picard and M.M. Safarpour, J. Agric. Food Chem., 44 (1996) 3548. A. Bouaid, A. Martin-Esteban, P. Fernandez and C. Camara, Fresenius J. Anal. Chem., 367 (2000) 291. R.G. Diagne, G.D. Foster and S.U. Khan, J. Agric. Food Chem., 50 (2002) 3204. K.K. Chee, M.K. Wong and H.K. Lee, Anal. Chim. Acta, 330 (1996) 217. J.L. Luque-Garcia and M.D. Luque de Castro, TrAC, Trends Anal. Chem., 22 (2003) 41. K. Martinez and D. Barcelo, Fresenius J. Anal. Chem., 370 (2001) 940. M. Lyytikainen, J.V. Kukkonen and M.J. Lydy, Arch Environ. Contam. Toxicol., 44 (2003) 437. C. Sanchez-Brunete, E. Miguel and J.L. Tadeo, J. Chromatogr.A, 976 (2002) 319. R.J. Bushway, B.E. Young, L.R. Paradis and L.B. Perkins, J. AOAC Int., 77 (1994) 1243. A. Therdteppitak and K. Yammeng, Sci. Asia, 23 (2003) 127. F.J. Schenck and J. Casanova, J. Environ. Sci. Health B, 34 (1999) 349. R. Boussahel, S. Bouland, K.M. Moussaoui, M. Baudu and A. Montiel, Water Res., 36 (2002) 1909.
Sample handling and clean-up procedures II-new developments 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230
A. Bouaid, L. Ramos, M.J. Gonzalez, P. Fernandez and C. Camara, J. Chromatogr.A, 939 (2001) 13. IUPAC, Pure Appl. Chem., 37 (1974) 447. I. Liska, J. Chromatogr. A, 885 (2000) 3. M.C. Hennion, J. Chromatogr.A, 856 (1999) 3. V. Pichon, J. Chromatogr. A, 885 (2000) 195. H. Sabik, R. Jeannot and B. Rondeau, J. Chromatogr.A, 885 (2000) 217. G. Font, J. Manes, J.C. Molto and Y. Pico, J. Chromatogr.,642 (1993) 135. J.S. Fritz, P.J. Dumont and L.W. Schmidt, J. Chromatogr. A, 691 (1995) 133. E. Forgacs, J. Chromatogr. A, 975 (2002) 229. E.M. Thurman and M.S. Mills, Solid-PhaseExtraction:Principlesand Practice. Wiley, New York, 1998. J.S. Fritz, Analytical Solid-Phase Extraction. Wiley, New York, 1999. N.J.K. Simpson, Solid-Phase Extraction: Principles, Techniques, and Applications. Marcel Dekker, New York, 2000. V. Pichon, C. Cau-Dit-Coumes, L. Chen, S. Guenu and M.-C. Hennion, J. Chromatogr.A, 737 (1996) 25. M.C. Hennion, C. Cau-Dit-Coumes andV. Pichon, J. Chromatogr.A, 823 (1998) 147. V. Pichon, M. Charpak and M.C. Hennion, J. Chromatogr. A, 795 (1998) 83. M.E. Le6n-Gonz6lez and L.V. Per6z-Arribas, J. Chromatogr.A, 902 (2000) 3. N. Masque, M. Galia, R.M. Marce and F. Borrull, J. Chromatogr. A, 803 (1998) 147. M.S. Young, D.J. Phillips, P.C. Iraneta and J. Krol, J. Assoc. Off. Anal. Chem. Int., 84 (2001) 556. M.S. Young, M.F. Early, C.R. Mallet and J. Krol, J. Assoc. Off. Anal. Chem. Int., 84 (2001) 1608. T. Cairns, K.S. Chiu, D. Navarro and E. Siegmund, Rapid Commun. Mass Spectrom., 7 (1993) 971. A. Di Corcia, S. Marchese and R. Sampieri, J. Chromatogr.A, 642 (1993) 163. N. Furusawa, K. Okazaki, S. Iriguchi, H. Yamaguchi and M. Saitoh, J. AOAC Int., 81 (1998) 1033. C.E. Green and M.H. Abraham, J. Chromatogr.A, 885 (2000) 41. R.E. Majors, LCGC North America, 20 (2002) 1098. J.S. Fritz and J.J. Masso, J. Chromatogr.A, 909 (2001) 79. Y. Saito and K. Jinno, J. Chromatogr.A, 1000 (2003) 53. D.A. Wells, LCGC, 17 (1999) 808. G.R. van der Hoff, R.A. Baumann, P. van Zoonen and U.A.T. Brinkman, J. High Resol. Chromatogr., (1998) 222. D.T. Rossi and N. Zhang, J. Chromatogr. A, 885 (2000) 97. M. Hiemstra, J.A. Joosten and A. de Kok, J. Assoc. Off Anal. Chem. Int., 78 (1995) 1267. S. Guenu and M.-C. Hennion, J. Chromatogr.A, 737 (1996) 15. J. Slobodnik, O. Oztezkizan, H. Lingeman and U.A. Brinkman, J. Chromatogr. A, 750 (1996) 227. S.A. Senseman, T.C. Mueller, M.B. Riley, R.D. Wauchope, C. Clegg, R.W. Young, L.M. Southwick, H.A. Moye, J.A. Dumas, W. Mersie, J.D. Mattice and R.B. Leidy, J. Agric. Food Chem., 51 (2003) 3748.
229
M. Anastassiades and E. Scherbaum 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262
230
A.R. Fernandez-Alba, A. Agiiera, M. Contreras, G. Pefiuela, I. Ferrer and D. Barcel6, J. Chromatogr. A, 823 (1998) 35. F. Van Hoof, P. Van Wiele, F. Acobas, J.L. Guinamant, A. Bruchet, I. Schmitz, I. Bobeldijk, F. Sacher, F. Ventura and R. Boleda, J. AOAC Int., 85 (2002) 375. A. Di Corcia, M. Nazzari, R.t. Rao, R. Samperi and E. Sebastiani, J. Chromatogr. A, 878 (2000) 87. M.J. Wells and L.Z. Yu, J. Chromatogr. A, 885 (2000) 237. D. Stajnbaher and L. Zupancic-Kralj, J. Chromatogr. A, 1015 (2003) 185. S. Barrek, O. Paisse and M.F. Grenier-Loustalot, Anal. Bioanal. Chem., 376 (2003) 157. P. Cabras, C. Tuberoso, M. Melis and M.G. Martini, J. Agric. Food Chem., 40 (1992) 817. P.T. Holland, D.E. McNaughton and C.P. Malcolm, J. Assoc. Off. Anal. Chem. Int., 77 (1994) 79. J.J. Jimenez, J.L. Bernal, M.J. del Nozal, L. Toribio and E. Arias, J. Chromatogr. A, 919 (2001) 147. A. Kaufmann, J. AOAC Int., 80 (1997) 1302. G.J. Soleas, J. Yan, K. Hom and D.M. Goldberg, J. Chromatogr. A, 882 (2000) 205. E. Matisova, L. Kakalikovd, J Lesko and J. de Zeeuw, J. Chromatogr. A, 754 (1996) 445. A. Prieto, G. Ettiene, D. Medina, I. Buscema, G. Gonzalez and L. Araujo, Food Addit. Contam., 16 (1999) 57. J.W. Wong, M.G. Webster, C.A. Halverson, M.J. Hengel, K.K. Ngim and S.E. Ebeler, J. Agric. Food Chem., 51 (2003) 1148. G. Pavoni and C. Sandirocco, Boll. Chim. Igien., 44 (1993) 41. C. Jansson, J. AOAC Int., 83 (2000) 714. J.C. Chuang, M.A. Pollard, M. Misita and J.M. Van Emon, Anal. Chim. Acta, 399 (1999) 135. G. Pavoni, Boll. Chim. Igien., 43 (1992) 171. G. Pavoni, Boll. Chim. Igien., 43 (1992) 45. P. Branca and P. Quaglino, Boll. Chim. Igien., 40 (1989) 71. W.H. Newsome and P. Collins, J. Chromatogr., 472 (1989) 416. J.A. Casanova, J. AOAC Int., 79 (1996) 936. K. Nordmeyer and H.-P. Thier, Z. Lebensm. Unters ForschA, 208 (1999) 259. M. Battista, A. Di Corcia and M. Marchetti, Anal. Chem., 61 (1989) 935. A. Lagana, G. D'Ascenzo, G. Fago and A. Marino, Chromatographia,46 (1997) 256. G. Niessner, W. Buchberger and G.K. Bonn, J. Chromatogr.A, 737 (1996) 215. G. Niessner, W. Buchberger and R. Eckerstorfer, J. Chromatogr.A, 846 (1999) 341. F.J. Schenck andV. Howard-King, Bull. Environ. Contam. Toxicol., 63 (1999) 277. Y. Yamazaki and T. Ninomiya, J. AOAC Int., 82 (1999) 1474. T. Pihlstrom and B.G. Osterdahl, J. Agric. Food Chem., 47 (1999) 2549. V. Pichon, M. Bouzige, C. Miege and M.-C. Hennion, TrAC, Trends Anal. Chem., 18 (1999) 219. N. Delaunay-Bertoncini, V. Pichon and M.C. Hennion, LC-GC Europe, (2001) 1.
Sample handling and clean-up procedures II-new developments 263 264 265 266 267 268 269 270 271 272 273 274
275 276 277 278 279 280
281 282 283 284 285 286 287 288 289
N. Delaunay, V. Pichon and M.C. Hennion, J. Chromatogr.B Biomed. Sci. Appl., 745 (2000) 15. M.C. Hennion and V. Pichon, J. Chromatogr.A, 1000 (2003) 29. V. Pichon, M. Bouzige, C. Miege and M.-C. Hennion, TrAC, Trends Anal. Chem., 18 (1999) 219. V. Pichon, H. Rogniaux, N. Fischer-Durand and S. Ben Rejeb, F. Le Goffic B., and Hennion, Chromatographia,45 (1997) 289. I. Ferrer, V. Pichon, M.C. Hennion and D. Barcelo, J. Chromatogr.A, 777 (1997) 91. A. Martin-Esteban, P. Fernandez, D. Stevenson and C. Camara, Analyst, 122 (1997) 1113. S. Herrera, A. Martin-Esteban, P. Fernandez, D. Stevenson and C. Camara, Fresenius J. Anal. Chem., 362 (1998) 547. J.F. Lawrence, C. Menard, M.C. Hennion, V. Pichon, F. Le Goffic and N. Durand, J. Chromatogr.A, 732 (1996) 277. J.F. Lawrence, C. Menard, M.-C. Hennion, V. Pichon, F. Le Goffic and N. Durand, J. Chromatogr.A, 752 (1996) 147. E. Watanabe, Y. Yoshimura, Y. Yuasa and H. Nakazawa, Anal. Chim. Acta, 433 (2001) 199. B. Sellergren, Anal. Chem., 66 (1994) 1578. B. Sellergren, Molecularly imprintedpolymers, man-made mimics of antibodies and their application in analytical chemistry. Elsevier Publishers, Amsterdam, 2001. B. Sellergren, TrAC, Trends Anal. Chem., 18 (1999) 164. K. Ensing, C. Berggren and R.E. Majors, LCGC Europe, 2002, 2p. N. Masque, R.M. Marce and F. Borrull, TrAC, Trends Anal. Chem., 20 (2001) 477. A. Martin-Esteban, Fresenius J. Anal. Chem., 370 (2001) 795. F. Lanza and B. Sellergren, Chromatographia,53 (2001) 599. B. Sellergren and F. Lanza, In: B. Sellergren (Ed.), Molecularly Imprinted Polymers, Man-Made Mimics of Antibodies and Their Application in Analytical Chemistry. Elsevier Publishers, Amsterdam, 2001. 0. Ramstrom, K. Skudar, J. Haines, P. Patel and 0. Bruggemann, J. Agric. Food Chem., 49 (2001) 2105. M.T. Muldoon and L.H. Stanker, Anal. Chem., 69 (1997) 803. J. Matsui, Y. Miyoshi, O. Doblhoff-Dier and T. Takeuchi, Anal. Chem., 67 (1995) 4404. J. Matsui, K. Fujiwara and T. Takeuchi, Anal. Chem., 72 (2000) 1810. J. Matsui, K. Fujiwara, S. Ugata and T. Takeuchi, J. Chromatogr. A, 889 (2000) 25. I.I. Ferrer, F. Lanza, A. Tolokan, V.V. Horvath, B. Sellergren, G. Horvai and D. Barcelo, Anal. Chem., 72 (2000) 3934. B. Bjarnason, L. Chimuka and 0. Ramstrom, Anal. Chem., 71 (1999) 2152. A. Martin-Esteban, E. Turiel and D. Stevenson, Chromatographia,53 (2001) 434. C. Cacho, E. Turiel, A. Martin-Esteban, C. Perez-Conde and C. Camara, Anal. Bioanal. Chem., 376 (2003) 491.
231
M. Anastassiades and E. Scherbaum 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318
232
K. Haupt, A. Dzgoev and K. Mosbach, Anal. Chem., 70 (1998) 628. E. Baltussen, C.A. Cramers and P.J. Sandra,Anal. Bioanal. Chem., 373 (2002) 3. C.G. Zambonin, Anal. Bioanal. Chem., 375 (2003) 73. H. Lord and J. Pawliszyn, J. Chromatogr. A, 885 (2000) 153. J. Pawliszyn, J. Chromatogr. Sci, 38 (2000) 270. J. Pawliszyn, Solid Phase Microextraction, Theory and Practice. Wiley-VCH, New York, 1997. J. Pawliszyn, Applications of Solid Phase Microextraction.The Royal Society of Chemistry, 1999. J. Hinshaw, LCGC Europe, 16 (2003) D.A. Lambropoulou, V.A. Sakkas and T.A. Albanis, Anal. Bioanal. Chem., 347 (2002) 932. J. Dugay, C. Miege and M.C. Hennion, J. Chromatogr. A, 795 (1998) 27. C. Goncalves and M.F. Alpendurada, J. Chromatogr.A, 1026 (2004) 239. J. Beltran, F.J. Lopez and F. Hernandez, J. Chromatogr.A, 885 (2000) 389. M. Volante, M. Cattaneo, M. Bianchi and G. Zoccola, J. Environ. Sci. Health B, 33 (1998) 279. C. Falqui-Cao, Z. Wang, L. Urruty, J.J. Pommier and M. Montury, J. Agric. Food Chem., 49 (2001) 5092. C. Falqui-Cao, A. Sanusi and M. Montury, In: A. Di Muccio (Ed.), European Pesicide Residues Workshop, Rome, 2002. C.G. Zambonin, A. Cilenti and F. Palmisano, J. Chromatogr. A, 967 (2002) 255. Z. Wang, B. Hennion, L. Urruty and M. Montury, Food Addit. Contam., 17 (2000) 915. R. Hu, B. Hennion, L. Urruty and M. Montury, Food Addit. Contam., 16 (1999) 111. J.J. Jimenez, J.L. Bernal, M.J. del Nozal, M.T. Martin and A.L. Mayorga, J. Chromatogr. A, 829 (1998) 269. M. Volante, M. Pontello, L. Valoti, M. Cattaneo, M. Bianchi and L. Colzani, Pest Manag. Sci., 56 (2000) 618. R. Rial Otero, C. Yague Ruiz, B. Cancho Grande and J. Simal Gdndara, J. Chromatogr.A, 942 (2002) 41. M. Correia, C. Delerue-Matos and A. Alves, FreseniusJ. Anal. Chem., 369 (2001) 647. M. Vitali, M. Guidotti, R. Giovinazzo and 0. Cedrone, FoodAddit. Contam., 15 (1998) 280. A.L. Simplicio and L.V. Boas, J. Chromatogr.A, 833 (1999) 35. http://www.richrom.com/html/ricappnotes.html, Research Institute for Chromatography. E. Baltussen, Technische Universiteit Eindhoven, New Concepts in Sorption Based Sample Preparation for Chromatography, 2000. P. Sandra, B. Tienpont, J. Vercammen, A. Tredoux, T. Sandra and F. David, J. Chromatogr.A, 928 (2001) 117. P. Sandra, B. Tienpont and F. David, J. Chromatogr.A, 1000 (2003) 299. Y. Hayasaka, K. MacNamara, G.A. Baldock, R.L. Taylor and A.P. Pollnitz, Anal. Bioanal. Chem., 375 (2003) 948.
Sample handling and clean-up procedures II-new developments 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350
C. Bicchi, C. Cordero, C. Iori, P. Rubiolo, P. Sandra, J.H. Yariwake and V.G. Zuin, J. Agric. Food Chem., 51 (2003) 27. R. Eisert and J. Pawliszyn, Anal. Chem., 69 (1997) 3140. Y. Saito, Y. Nakao, M. Imaizumi, Y. Morishima, Y. Kiso and K. Jinho, Anal. Bioanal. Chem., 373 (2002) 81. H. Kataoka, Anal. Bioanal. Chem., 373 (2002) 31. M.A. Jeannot and F.F. Cantwell, Anal. Chem., 69 (1997) 235. Y. He and H.K. Lee, Anal. Chem., 69 (1997) 4634. T. Ligor and B. Buszewski, Chromatographia,52 (2000) 279. B. Buszewski and T. Ligor, LCGC Europe, 2 (2002) 2p. J.A. Jnsson and L. Mathiasson, LCGC North Am., 21 (2003) 424. S. Pedersen-Bjergaard and K.E. Rasmussen, Anal. Chem., 71 (1999) 2650. B. Hauser, P. Popp and E. Kleine-Benne, J. Chromatogr.A, 963 (2002) 27. L. Zhao and H.K. Lee, Anal. Chem., 74 (2002) 2486. J. Norberg and E. Thordarson, Analyst, 125 (2000) 673. J.A. Jonsson and L. Mathiasson, J. Chromatogr. A, 902 (2000) 205. B. Moreno Cordero, J.L. P6rez Pav6n, C. Garcia Pinto, E. Ferndndez Laespada, R. Carabias Martinez and E. Rodriguez Gonzalo, J. ChromatogrA, 902 (2000) 195p. J. Tekel and S. Hatrik, J. Chromatogr.A, 754 (1996) 397. S.J. Lehotay, In: R.A. Meyers (Ed.), Encyclopedia of Analytical Chemistry. Wiley, Chichester, 2000, p. 6344. E. Hogendoorn and P. van Zoonen, J. Chromatogr.A, 892 (2000) 435. G.R. van der Hoff and P. van Zoonen, J. Chromatogr.A, 843 (1999) 301. P.L. Buldini, L. Ricci and J.L. Sharma, J. Chromatogr.A, 975 (2002) 47. C.W. Huie, Anal. Bioanal. Chem., 373 (2002) 23. C.M. Torres, Y. Pico and J. Manes, J. Chromatogr. A, 754 (1996) 301. F.E. Ahmed, TrAC, Trends Anal. Chem., 20 (2001) 649. R.M. Smith, J. Chromatogr. A, 1000 (2003) 3. F.M. Lancas, J. Braz. Chem. Soc., 14 (2003) 183. J.R. Dean, Methods for Environmental Trace Analysis. Wiley, New York, 2003. J.R. Dean, Extraction Methods for Environmental Analysis. Wiley, Chichester, 1998. A.J. Handley, ExtractionMethods in OrganicAnalysis. Sheffield Academic Press Ltd, Sheffield, 1999. J. Pawliszyn, Sampling and Sample Preparationfor Field and Laboratory. Elsevier Health Sciences, Amsterdam, 2002. J. Pawliszyn, Anal. Chem., 75 (2003) 2543. T. Cairns and J. Sherma, Emerging Strategiesfor PesticideAnalysis. CRC Press, Boca Raton, 1992. G.W. Fong, A.H. Moye, J.N. Seiber and J.P. Toth, Pesticide Residues in Foods, Methods, Techniques and Regulations. Wiley, New York, 1999.
233
Chapter 5
Sample introduction techniques Silvia Lacorte and Amadeo R. Ferndndez-Alba
5.1
INTRODUCTION
The purpose of this chapter is to provide an overview of the different sample introduction techniques for both gas (GC) and liquid chromatography (LC), as applied to the analysis of pesticide residues in fruits and vegetables. It includes traditional injection techniques such as split/splitless or on-column injection and more recent ones such as the programmable temperature vapouriser, large volume injection (LVI) and automated coupling to a sample preparation unit. Solid-phase microextraction (SPME) is also discussed for the analysis of pesticide residues in food. Information is given on the functioning principle of such techniques, what pesticides can be best analysed and advantages and limitations of each. In this perspective, the most appropriate injection procedure to be used for a reliable quantification of pesticides of different physico-chemical properties is discussed. The chapter describes effective injection methods that provide precision of the injection volume, no carry-over effects and high versatility and how they can be used for routine analysis of pesticides in food matrices. Automated procedures are specially highlighted due to the ever increasing need for routine analysis of pesticides in food with minimum sample handling. 5.2
OUTLINE OF THE ANALYTICAL APPROACH
Monitoring of pesticides in fruits and vegetables has increased during recent years since most countries have established maximum residue levels (MRLs) for pesticides in food products [1]. To ensure compliance with regulatory requirements and directives, analytical methods have been developed to determine multiple classes of pesticides in raw materials and also in processed food. The most frequently used methods employ solvent extraction [2] and GC, Comprehensive Analytical Chemistry XLIII Fernandez-Alba (Ed.) C 2005 Elsevier B.V. All rights reserved
235
S. Lacorte and A.R. Ferndndez-Alba usually coupled with mass spectrometry (MS) [3]. More recently, LC has been used to determine more polar and less volatile pesticides. Official methods are directed to screen, confirm and quantify pesticide residues in almost all types of fruits and vegetables [4]. However, new analytical tools have been developed to minimise sample preparation and increase sensitivity, which is needed to meet the continuously decreasing MRLs imposed by the European Union. Many emerging methods involve the optimisation of the sample introduction technique, where it is possible to inject large sample volumes or minimise sample preparation. Analytical methods for pesticide analysis require a high identification potential, which permits the analyst to unequivocally determine compounds with varying chemical properties. Commonly, multi-residue methods (MRMs) are generally preferred because they permit the determination of several pesticides in a single run. Several reviews concerning the determination of pesticide residues in fruits and vegetables indicate recently developed methods for the extraction and quantification of pesticides [5,6]. These methods involve (i) chopping and homogenisation; (ii) extraction; (iii) clean-up of analytes from the sample with adequate solvents; (iv) concentration; and (v) analytical determination. The clean-up step is the most laborious and time-consuming and is crucial for pesticide food analysis. If not performed accurately, problems related to the presence of matrix interferences make compound identification difficult. Clean-up should be performed to avoid losses of more volatile analytes but should be extensive enough to (i) eliminate coextracted matrix; (ii) avoid false positives; and (iii) permit analyte identification, quantification and confirmation. The injection technique may be one of the sources of erroneous detection and quantification. To avoid this drawback, which is due to the fact that pesticides are found in fruits and vegetables at very low concentrations and immersed in a complex matrix where the presence of carbohydrates, plant pigments, endogenous acids and cuticular waxes may interfere both with extraction and analysis, there have been significant improvements in sample introduction techniques, both in GC and LC. The present chapter tries to cover the latest developments in injection techniques such as the programmable temperature vaporising injector (PTV), SPME coupled to GC and LC, on-line solid-phase extraction (SPE) and on-line gel permeation chromatography (GPC) coupled to GC and LC. The applicability, advantages and limitations of each will be discussed and attention will be paid to how sample introduction can improve selectivity and sensitivity in food analysis. In addition, due to the routine character of food analysis for quality control, the latest developments in automation will be described.
236
Sample introduction techniques 5.3
INJECTION TECHNIQUES FOR GAS CHROMATOGRAPHY
GC is a powerful separation technique that is widely used for trace analysis of organic compounds. The application of GC in food analysis has been reviewed by Lehotay and Hajslovd, who indicate the state-of-the-art and new trends [6]. As part of the system, the injection port has been constantly optimised to achieve high accuracy in terms of retention time and response area. The development of new sample introduction techniques has been specially relevant for food analysis since the problem associated with matrix interferences occurs in both the injection port and the detector [7]. Matrix effects often produce an overestimation of the analyte concentration if calibration has been performed in pure solvent. This effect can be minimised by improving the injection technique. Apart from the classical split/splitless injections used in the majority of applications, other options such as on-column, PTV and SPME are also adequate to remove sample interferences and thus provide a higher specific response. The definitions of the different sample techniques used in GC are specified in Table 5.1, according to Hinshaw [8]. The use of each for the introduction of food samples will be described in the following sections, as well as their performance and specific advantages for different chemical families of pesticides. The applicability of each is discussed with regards to robustness in real quality control surveys. 5.3.1
Split/splitless injection
Kurt Grob introduced splitless injection in 1969. The reader is referred to the work written by the same author in 1988 to learn about the interesting start and evolution of one of the most widely used injector types [9]. In 1988, 20 years after the injection system was developed, the success of the injector was still uncertain since many authors claimed poor injection reproducibility, discrimination far beyond split injection and poor sample transfer in dirty samples. However, all this resulted due to unsuitable injection conditions and poor handling. Nowadays, 30 years after, the successful outcome of split/ splitless injection is demonstrated by its widespread application. Some technical aspects that should be considered in split/splitless injection are: Injectorvolume. Sample evapouration takes place in milliseconds, and transfer of resulting vapours into the column is slow. Therefore, the vaporising chamber must be capable of storing these vapours along with vapours accompanying the carrier gas. Therefore, to achieve desired sensitivity in 1-2 /l injection, the vaporiser chamber must have a volume of 1 ml.
237
S. Lacorte and A.R. Ferndndez-Alba TABLE 5.1 GC injection techniques for the analysis of semivolatile compounds Type of injection
Description
Cold injection
An injection that occurs at temperatures lower than the final oven temperature, usually at or below the solvent boiling point Occurs when the sample enters an inlet and is swept into a column by carrier-gas flow. No sample splitting or venting occurs during or after injection The sample enters the column directly from the syringe and does not contact other surfaces An inlet system designed to perform large volume injection for gas chromatography A sample plug is placed between two solvent plugs in the syringe to wash the syringe needle with solvent and obtain a better sample transfer into the inlet A sample extraction and clean-up that uses a removable sorptive micro-extraction device that can be introduced into the GC The sample size is adjusted to suit capillary column requirements by splitting off a major fraction of sample vapours in the inlet so that as little as 0.1% enters the column and the rest is vented A derivative of split injection. During the first 0.5-4 min of sampling, the sample is not split and enters the column. Splitting is restored afterwards to purge the sample remaining in the inlet. As much as 99% of the sample enters the column
Direct injection
On-column injection PTV Sandwich technique
SPME
Split injection
Splitless injection
·
·
238
Carriergasflow rate.In splitless injection, virtually the whole sample material injected should reach the column. The carrier flow rate depends on the injector type. For conventional splitless injectors, flow rates are 2-4 ml/min, using narrow bore analytical columns. With lower gas flow rates, insufficient transfer of sample vapours occurs, even after long splitless periods. Gases used are typically nitrogen or helium. Purge gas flow rates. An increase in the purge flow rate increases solvent elimination.
Sample introduction techniques *
*
·
·
·
.
·
·
Splitless period. The splitless period is the time of vapour transfer into the column. A typical example was depicted by Grob [10] using a Carbo Erba injector (80 mm x 3.6 mm I.D. vapouriser chamber) at a carrier flow rate of 4 ml/min. A splitless period of 45 s was needed to transfer a volume of 3 ml (or three times the injector volume). Split ratio(s).The split ratio is the ratio of the sample amount that is vented to the sample amount that enters the column during split injection. A higher split ratio places less sample on the column. It is usually measured as the ratio of total inlet flow to column flow. Normal rates are 1:10 to 1:25. Length of the microsyringe needle. The major source of sample discrimination is produced by selective evapouration from the syringe needle when it is inserted into the hot injection. The length of the syringe needles should be adapted to the geometry of the injector so that the release of the sample liquid should be in the centre of the liner. In a few papers, the type and length of syringe needles are specified. Typical lengths are 50 and 70 mm. 7ype of liner. The liner can be selected according to its internal diameter, shape and coating. Larger I.D. liners are advantageous because of the higher solvent capacity. Too little volume limits the sample size and increases the possibility of discrimination and sample loss [11]. Use of liners packed with an adsorbent. The use of packed liners minimises losses of volatiles during solvent elimination [12]. Other authors indicate that the use of an adsorbent in the liner strongly reduces matrix effects [13]. There are various types of packing material, such as VolaspherA-2, Chromosorb W, glass wood, PTFE wool, Dexsil and Tenax, among others. It has been suggested that the adsorbent distribution in the liner may influence performance of the analytical technique [14]. Injection volume. The injection volume affects the retention power in the injector. In splitless mode, injection volumes are generally 1-2 l. Such sample volumes produce a relative standard deviation of peak areas of 1% and analyte discrimination. Injection time. It is possible to set the time the needle is inside the liner before injection and the time the needle is in the liner after injection. These values are generally 1 s. Too much injection time gives risks of sample loss and poor reproducibility. Injector temperature. Injection temperature should be selected according to the characteristics of the chemical compounds and according to the boiling point of the solvent. Excessive temperatures in the injector produces decomposition of thermolabile compounds. Sample introduction using cold injectors, such as cold on-column or PTV (see sections below), improve this situation. 239
S. Lacorte and A.R. Ferndndez-Alba Split/splitless injection can be operated in different ways. Hot splitless injection is by far the most commonly used technique for sample introduction in GC. For the analysis of pesticides, a typical configuration is 1-2 Al injection volume, 200-250°C injection temperature, splitless period of 0.5-2 min and starting oven temperature of 70-90° C for 1 or 2 min. Specific applications for the analysis of pesticides of agricultural concern using GC-FID and GC-MS indicate that residue levels can be detected in real crop samples down to 10 tAg/kg using 1-3 ldinjection volume [15,16]. Under such conditions, some adverse effects can arise such as losses of the low volatile compounds, sorption within the port and degradation of thermally unstable compounds (e.g., trichlorfon is degraded to dichlorvos) [17,18]. These effects can be significantly reduced by using pulsed splitless injection (or injection with surge) [19,20]. A pressure is applied during the splitless period, which enables a quicker transfer of the analytes. Pulsed splitless injection has the additional advantage that adsorption and degradation effects are reduced and consequently minimise matrix effects [21,22]. The applications of split/splitless injection have been demonstrated for a number of pesticides from fruit and vegetable matrices. Some references are summarised in Table 5.2. One of the main problems in the analysis of food residues in GC is the presence of matrix-induced effects. These effects are especially relevant in split/splitless injection and, therefore, extensive clean-up is needed prior to injection. Hajslov6 et al. indicate an increase of relative detector response depending on the concentration of the analyte and the character of the matrix [22]. Their study reports an MRM for the analysis of multi-class pesticides in orange, wheat, cabbage and other fruits and vegetables by GC-NPD equipped with a split/splitless injector. Purified extract of 1 l was injected in splitless mode, purge off 120 s and injector temperature 250°C. As soon as the splitless injector became contaminated after injection of a large series of matrix-containing samples, a decrease of relative responses of pesticides was observed. This was dependent on the (i) pesticide character; (ii) matrix type; (iii) analyte/matrix concentration; and (iv) state of the GC system. It is indicated in this study that the type and geometry of the injector seem to be most important in relation to matrix-induced effects. These findings are in good agreement with existing literature. Chuang et al. report the presence of interferences and poor quantitative recoveries (< 50%) using supercritical fluid extraction (SFE) and GC-MS and indicate the need for sample clean-up using accelerated solvent extraction (ASE) and ENVI-Carb SPE to detect 0.3-100 jug/kg of pesticides in baby food [23]. To remove sample interferences, Kristenson et al. developed a miniaturised automated solid-phase dispersion extraction followed by GC-MS to 240
Sample introduction techniques zt
C
SCC oC Q
CC
C>
cC Ce
N9
C
C) Pa
C 0'
C, CL -
CC
2~
Cv
C)C
CCI -
b
w
.4$
C 2W
°
CC
C
CC
o
0c).,
5E
2~
l
CC ~'
O
3
C
A
V
r
CC)m
CC
C
C
CC
CCe
C Cq
0i
0T
QQ
0
0
0 00
0
003
0
0~~
a
CC
-o .0 0
~
O
00
o
o
a) cs
9t IC)
n.
0
C 'Sj .)
0
--
CL
ACC
-C CCC 'CC>CC
3
a)
w
C'C m
C
~
a~ CC)
mm~
C C C)ii
a
CC)C-CC
a CB
CCCC
,433
a;~~C
C
~
0CC
~" CC)
m~~~~~~~-
2
'
ZCC) CC5 3C
0 O
C
CCr
C)B
CCC
CC-
O CCC CCC
30
C
u
O
C C
CCC,-
Cl ;"
m
41'
C'
o
S
9 CCC)C' cC)f .> % CC~ )
CC>CCC
-C)
" C) C
vt: XD ~~~~~~C C-CC CCCCC) ' C CC
04
'$P-
I
C
-
CC ccCC51
o
N C)
7CC
0
-CC'C
aC
3
C C C -'
CC
--
P,
C
I+. a)
CC C~a) CC Lo
C)
c'zP .ua CCC CC 0
b
241
S. Lacorte and A.R. Fernandez-Alba A 0 1 a
iO
9
bn bO
a m
C
CC I v
tC)
i
I
i
C) 0
m
0
cc I) w
C)
14
a:
z
0
O 0
ci"I
o
:4
Ca m
4.5g.,
c C 9~ C
I
m
C)
CC m
oo
C)
C)
C)I
O mn
I)) 11I c-
E~
CCi
C
I -
u:
V
a
~
*~~ o~~~~~~~~~~~
CC
I
0
P.,
x 0
C uI U
uI
u 0
V
co
Q
c
C C)
Sp
Ea~~~~~~~~C
toO
-~ 57
I~~
aj
0
U
~
t
C ccd
.~~~ ".
~cc CCo ~~·-" ~S~ ~-SOSa~
0 § 5
4-
0 .:C)C
,
41
g~
.5B t
C)
~
*"~ CC
C~c
a~
cc C
cc
5
)
)
)
a~h
c5-COC'
C
C)
C)·
C)
Z
CDoCCOF$C$SCCZ u .
O, C'd
0
2
-V 0
0
010 C) C
CCCC CC
C)
-
C .
6.·
C)P
C)
C) C
CCW
242
F
-a o
CC C) Co
~
C·SC
)C
)4
a-
-s
C)
nm
-~-
C)g
C
C)C)
:tC)
CC
C 6
0 C
C )
m 'n ) Q
0 CZ 00 tic
O m .. 0 00
o o
ccE
OaoC
aa a c; c
5
CC )C 0 C
-w~~-C
C)
t I IT
cc'
0
C)
~
o
~
C)
C)C)d
a· CC~
C
5
s0
CC
A;,
cc
ci
C) ·
C)m"a
M ul Pc
CCs
CC
c;~~~~~~~~~~~~c CC : O) C)
C)
n
)
CCCCo
ao
·C
-C),-C)
C)
-
>
0
0-C)
0 0
Sample introduction techniques analyse organophosphorus (OP) pesticides and pyrethroids [24]. One microgram of the extract that corresponded to 250 /xg of sample was directly analysed by GC-MS in the cold splitless mode (splitless time 1 min, initial temperature 65°C increased at 16°C/s to 240°C). The method presented excellent recoveries for 11 compounds in oranges, pears, grapes and apples with LODs of 4-90 g/kg. An alternative way to remove matrix interferences involves a statistical treatment to check the stability of calibration curves, compare solvent/matrix calibrations and obtain a correction function [26]. This has been performed using GC-ECD to analyse multiple pesticides in vegetables using 1 Al of a liquid-liquid extract. It was concluded that the procedure could save both costs and time since blank samples are not needed and quantification could be performed using solvent calibration. Another problem related to split/splitless injection is the sensitivity achieved. Accurate identification and quantification of pesticide residue levels are only possible with multi-residue extraction methods that provide LODs 5-10 times below MRLs. Pre-concentration can help to reduce LODs, but then time-consuming clean-up is introduced to avoid contamination of the GC system. An elegant alternative for improving analyte detectability without drastic modifications in the multi-residue extraction methods is the use of injection volumes larger than typically 1-2 Al. Aguera et al. [26] applied a conventional split/splitless injector in splitless mode to analyse 10 organophosphorus and organochlorinated pesticides in vegetables. An empty liner was filled with 0.5 cm Carbofrit placed at 3.6 cm from the upper part of the liner. The injector temperature was 250°C, the initial pulse pressure was set at 30 psi (1.5 min) with a split flow of 50 ml/min and split time of 1.5 min. Figure 5.1 shows the SIM chromatogram obtained from a pepper sample spiked at 0.01 mg/kg. In this case, 10 l of the extract was injected using a conventional split/splitless injector provided with electronic pressure programmer (EPP). Good sensitivity and peak shapes were obtained. However, it should be taken into consideration that injection of a large volume of sample can cause losses of the analytes because of the rapid expansion into a large gas volume, causing part of the sample to be blown into the gas lines filling the injection port. In order to increase the capacity of the injector and improve the efficiency of sample transfer, the carrier gas inlet pressure can be increased just before the beginning of a run (pulse pressure) and returned to the normal value after a specified amount of time. Parameters such as the injection pulse pressure, time of the pulse, split vent time and initial oven temperature have to be optimised when this injection mode is applied. In such work it was concluded that the use of LVI reduced the pre-concentration step and reduced 243
S. Lacorte and A.R. Ferndndez-Alba r, 4500004000003500004,5
a)
O 300000v 250000-
I
C
0
1
7
21
200000150000
-1
100000 -
50000-
I
11 L- -II
n 4.00
6.00
i.
8.00
C'. x,
?IL,
10.00
9 10
1·~~~-/ 12.00
14.00
Time/min
Fig. 5.1. GC-MS chromatogram under SIM mode obtained by the injection of 10 ul of a pepper extract spiked with (1) methamidophos; (2) diazinon; (3) chlorothalonil; (4) vinclozoline; (5) parathion-methyl; (6) pyrimiphos; (7) chlorpyriphos; (8) procymidone; (9) endosulfan I; and (10) endosulfan II at 0.01 mg/kg. Reproduced from Ref. [27]. the analysis time in relation to "classical" methods, thus increasing sample throughput. Recoveries obtained were higher than 80% and limits of detection <10 Ag/kg. Nevertheless, it was recommended to prepare the calibration standards in blank sample extracts to avoid matrix effects. 5.3.2
On-column injection
Another injection technique that arouses interest for food analysis is oncolumn injection. It permits automated LVI and is a technique used in trace determination of pesticides in food for the high sensitivity it achieves. Its main advantages are the possibility of introducing the sample in a cold injector where losses of the more volatile compounds are minimised. In addition, oncolumn injection permits the non-discriminative transfer of sample components into the GC system. However, contamination of the column inlet with non-volatile sample materials is frequent and thus the number of samples that can be analysed without distortion is dependent on the matrix. As a result, and due to its inability to separate target analytes from the matrix, on-column injection has been commonly applied for the analysis of relatively clean water samples [28,29]. A way to overcome this problem is to use a replaceable retention gap prior to the analytical column. However, Nissner et al. [29] have developed a multi-residue screening method for the determination of pesticides in plant material using on-column injection 244
Sample introduction techniques and GC-ECD and injecting 2-3 ,tl of a solvent extract. Recoveries obtained were in the range 85-110% with a standard deviation below 7%. The method could be used to analyse all types of plant materials, including oils, at concentrations down to 10 ng/g. 5.3.3
Programmable temperature vaporiser
The main problem related to contamination of the column inlet using oncolumn injections was overcome by the introduction of PTV injection since non-volatile compounds are retained in a vaporiser chamber and do not reach the analytical column [10]. This injection technique was introduced by Vogt and co-workers in 1979 [30,31] and consists of the injection of the sample into a cold liner (temperature below or near the solvent boiling point) followed by a quick rise of temperature and subsequent transfer of analytes. No thermodegradation [32,33] and no loss of low volatile compounds occurs using this technique [34,35] and the main advantage of PTV is the possibility of LVI. This technique potentially decreases the detection limits [36]. In solvent split mode, 1 ml of sample can be introduced into the GC system, which obviously enhances the sensitivity of the overall analytical method and makes the PTV injector applicable for the on-line coupling of GC with enrichment techniques [37]. This type of injector is highly versatile and can be operated with different configurations (Table 5.2). With PTV in solvent split mode, otherwise called programmed splitsplitless (PSS), the slip vent is open when the sample is introduced into the cold liner. After eliminating the solvent, the valve is closed (solvent venting time) and then fast warming of the liner causes vaporisation of the analytes, which are transferred to the oven when the valve is opened again (transfer time) [38]. Vaporisation efficiency depends on heating rate, split vent flow, solvent venting time, transfer time, initial PSS temperature and type of liner (empty or filled with wool, carbofrit, etc.). Statistical techniques such as Tauguchi experimental design [39], simplex optimisation [40] or PlackettBurman design can be employed to find the best injection conditions. This latter technique has recently been used for the optimisation of PSS in GC-MS for several organochlorine pesticides [41]. In that work, the maximum volume assayed was 20 ul and no peak splitting was observed. A 50-ul1 syringe with a needle length of 7 cm was used and injection took place in 1 s. A PSS quartz liner with 2 mm I.D. can be employed [42]. Conditions for splitless GC-MS were: injector temperature program 80°C (0.1 min) increased at 200°C/min to 290°C (11 min) and then decreased at 200°C/min to 80°C. The split-vent program was: valve initially closed and was opened at 1 min, split-vent flow
245
S. Lacorte and A.R. Ferndndez-Alba 50 ml/min and transfer time at 4.5 min. The liner packed with glass wool provided better injection reproducibility. With PSS injection, LODs for several organochlorine pesticides were 2- 7 ~g/l, much lower than those obtained with splitless injection [43], proving its better performance for trace-level analysis. Stan and Linkerhagner report the injection of 12.5 l volume with a 25-udl syringe using PTV with solvent venting as an on-line pre-chromatographic sample preparation that permitted high reproducibility, complete recovery and no thermodegradation of pesticide residues over a wide volatility range [43]. Other works relate the use of larger sample volumes, which are injected with multiple injections of small volumes (e.g., 40 tl in eight injections of 5 l [44] or 100 Ml in 10 injections of 10 gl [45]) to prevent losses of sample in the split. The performances of PTV splitless, hot splitless and on-column injection were compared for organophosphorus and carbamate pesticides in spinach samples and it was found that all three injection techniques were suitable for multi-residue analysis of a series of food samples [46]. Thermolabile pesticides were best analysed in on-column injection and the best long-term stability was observed with PTV injection technique [47]. The optimisation of PTV parameters for the injection of multiple pesticide residues in pure solvent standards has been reported by Godula et al. [47]. In a following work, PTV injection was optimised for the analysis of 26 difficult pesticide residues in plant matrices [481 with the objective of determining long-term stability responses and the extent of matrix-induced response enhancement. After GPC of a 25-g wheat extract, the purified fraction was analysed by pulsed splitless injection, on-column, PTV solvent split injection and PTV splitless injection. The findings indicated that polar compounds (e.g., carbaryl) presented highly distorted peaks or even non-detectable peaks after 14 injections using on-column, whereas with pulsed splitless injection, approximately 90 samples could be injected and 136 for PTV solvent split injection (Fig. 5.2). It was concluded that PTV and solvent split injection techniques provided superior stability and protection of the GC column from matrix components. Another approach for removing matrix interferences is the use of more selective detection systems such as tandem MS. Using a temperatureprogrammed splitless injection where the injector was held at 50°C (split mode 50:1) for 0.3 min, switched to splitless mode and ramped to 290°C at 180°C/min and, after 0.4 min, switched back to split mode (7 min) with 5 /l injected using the solvent flush method [49]. Selectivity and sensitivity were enhanced by optimising the MS-MS conditions of the ion trap and few interferences were found from food extracts spiked with 19 organochlorine, 246
Sample introduction techniques (A) \ ,
(B) '-'pA 26
25 24
23 22
(C).
14
16
18
20
22
24
26
min
Fig. 5.2. Long-term stability of the GC system using PTV solvent split injection chromatograms (NPD signal) of 30 dl of standard in cyclohexane-ethylacetate at concentration level 2 (STD2B) after (A) 10 injections of wheat sample; (B) 87 injections of wheat samples; (C) 136 injections of wheat samples. Peaks of troublesome analytes are marked with arrows. Peak assignment: (1) methamidophos; (2) dichlorvos; (3) acephate; (4) propham; (5) omethoate; (6) dimethoate; (7) etrimfos; (8) tolclfos-methyl; (9) carbaryl; (10) pirimiphos-methyl; (11) malathion; (12) chlorpyriphos and (13) methidathion. Reproduced from Ref. [48].
247
S. Lacorte and A.R. Fern6ndez-Alba organophosphorus and triazine herbicides. GC-MS-MS with a triple quadrupole has been applied with similar objectives [50]. Thirty-one multiclass pesticides were analysed from green beans, cucumber, pepper, tomato, eggplant, watermelon, melon and marrow using extraction with dichloromethane and 10-A1 injection volume in a split/splitless programmed temperature injector operated in the LVI mode and an electronic flow-control system [50]. In this case, clean-up was avoided by inserting a Carbofrit (Restek, Bellefonte, PA, USA) in the gas liner. Carbofrit reduces the amount of low volatile and interference substances in the MS-MS system and selectivity is enhanced. In addition, maintenance of the instrument is decreased and column life increased. A recent work describes the use of an OPTIC2 programmable injector with a multi-capillary liner (ATAS, Veldhoven, The Netherlands) in two-dimensional chromatography with time-of-flight (TOF) MS for the determination of pesticides in food extracts [51]. In order to achieve better analyte detectability than with 1-l cold splitless injections, 10 Al of the fruit extract was introduced into the GC system using a PTV injector in solvent vent mode. For ethyl acetate extracts, the system was operated with a vent time of 55 s at 40°C injector temperature, 70 ml/min vent flow and 7.2 psi vent pressure. Although at present there is still no integrated software available to perform deconvolution, the system provided excellent analyte detectability and it greatly simplified the cumbersome sample preparation procedures via analyte/matrix pre-separation. Another approach used for the analysis of multiple pesticide residues in a food commodity is the use of low-pressure GC-MS [52]. No special techniques for injection and detection are required but a mega-bore analytical column of 10 m x 0.53 mm I.D., 1-Lm film thickness coupled with a 3 mm x 0.15 mm I.D. restriction capillary at the injector end is used. In this way, the analytical column is kept under low-pressure conditions but the inlet remains at usual column-head pressures in GC and common injectors can be used with conventional injection methods. Due to the faster flow rates, thicker film and low pressure in the analytical column, some benefits versus a conventional GC-MS method include: (i) three fold gain in the speed of chromatographic analysis, (ii) substantial increased injection capacity in toluene, (iii) heightened peaks with 2 s peak widths for normal MS operation, (iv) reduced thermal degradation of thermally labile pesticides, and (v) due to larger sample loadability, lower detection limits for compounds not limited by matrix interferences. An additional advantage is that the restriction column serves as a retention gap in the analysis of relatively dirty samples [52].
248
Sample introduction techniques 5.3.4 On-line LC-GC (solid-phase extraction and gel-permeation chromatography) Hyphenated chromatographic methods, such as on-line coupling of LC with GC, combine the large sample capacity of LC and the efficient separation and high sensitivity of GC. As a result, the use of LC as sample pre-treatment for GC minimises time and costs and has gained considerable attention in recent years. Coupling LC-GC systems on-line have been used in food analysis, mineral oil characterisation [53] and, in particular, environmental monitoring [54,55]. The trace enrichment is performed on a small pre-column, which is placed at the loop position of a six-port valve. After pre-concentration of the water sample, the valve is electrically switched from the pre-concentration position to the elute position and the pesticides are desorbed by the mobile phase and directed to the analytical column where separation will take place. The most common fully-automated devices include the Prospekt (Spark Holland, The Netherlands) and the OSP-2 (Merck, Germany). The two systems differ in the fact that, while the former has only one position for each pre-column, the OSP-2 can be operated in such a way that, while one sample is pre-concentrated in the preparation position, another pre-column is being eluted. This option is very interesting since it saves time. The two systems have their own pre-column design, which covers a large variety of packing materials, and include the possibility to elute in normal and backflush mode. Interfacing SPE with GC is the most critical part of the system and it includes a retention gap and a retaining column prior to the analytical column. On a first attempt, analytes adsorbed upon the pre-column were desorbed with 50-75 l of ethyl acetate without performing any drying step and with SPE-GC and a flame ionisation detector (FID), it was possible to quantify nitrobenzene and m-cresol at the 0.1-10 ug/l level using only 1 ml of water. Even though it was possible to carry out 140 analyses without changing any part of the system, it was found that the water (from the pre-column) dissolved in ethyl acetate destroyed the retention gap by hydrolysis. To solve this problem, two different approaches have been undertaken. The first one was drying the pre-column with nitrogen during 10-15 min at ambient temperature. The second attempt to remove the excess of water from the pre-column was to insert a short drying cartridge containing anhydrous copper sulphate, sodium sulphate or silica between the SPE and GC parts of the system [56,57]. When coupled to MS, SPE-GC needs a 2 m x 150 ,-m I.D. deactivated fused silica restriction capillary in order to avoid large quantities of solvent entering the MS. Column et al. developed an automated SPE system coupled to GC-MS for clean-up and pre-concentration of chlorinated fungicides in fruits [57]. 249
S. Lacorte and A.R. Fernandez-Alba A 1-Al aliquot of pesticide extract permitted a clean chromatogram to be obtained and 0.1-ng/g pesticides could be detected with high precision [58]. The extract was injected in split mode (1:25 ratio) and to achieve enough sensitivity, the amount of raw material extracted was 20 g. Hy6tylainen developed a reverse phase liquid-chromatography (RPLC) coupled to GC to analyse pesticides in wine and sample preparation was minimised [58]. The RPLC was used for sample enrichment and clean-up and GC for final separation. Coupling RPLC and the GC was done through a vaporiser/precolumn solvent split/gas discharge interface (PTV injector) that allowed direct introduction of the aqueous fraction of the LC into the GC [59]. The fraction of interest was vaporised in the heated vaporising chamber under high flow rate. The solvent vapours were removed through the retaining pre-column via a solvent vapour exit (SVE) while the analytes of interest were retained on the stationary phase of the pre-column. Automated on-line GPC - GC was developed for the analysis of chlorinated pesticides in lettuce and oil using small exclusion chromatography columns [60]. A 250 mm x 3 mm I.D. column was used and 0.3 mg of fat could be injected. Detection limits for pesticide in fat were less than 10 ng/g. However, De Paoli found the system unsatisfactory for the analysis of organophosphorus pesticides because of interfering peaks in GC-FID [60]. Therefore, an LC step using silica gel was inserted between GPC and GC to remove interferences. In that work, on-line GCP-LC-GC-FPD was used to determine several pesticides in apples, grapes, etc., and recoveries of 95% and detection limits of 1 ng/g were obtained. The proposed method starts with clean-up of the crude dichloromethane extract by SEC on a polystyrene column followed by a second clean-up on a silica column; the fraction containing the pesticides is transferred to a GC system by concurrent solvent evapouration using a looptype interface. GPC has been coupled on-line with GC-FID with dual column set-up for the analysis of organophosphorus pesticides in edible oils [62]. The automated method involved isolation of target analytes followed by LVI using the on-column interface with co-solvent trapping. In that work, 70 mg of oil was solved in 1.4 ml of cyclohexane containing 7 g of bromophosmethyl, which was used as an internal standard. This solution (20 Al) was injected in the GPC system equipped with a low I.D. column (250 mm x 4.6 mm), which was efficient in separating triacyl glycerols with a molecular weight of 900 from organophosphorus pesticides with molecular weights ranging from 200 to 300 Da. Mono- and diacyl glycerols or free fatty acids co-eluted with pesticides. A pressure and flow-regulated on-column injector was used to introduce the 1.3-ml fraction from the GPC into the GC. 250
Sample introduction techniques To avoid losses of more volatile compounds (e.g., dichlorvos), the GPC-GC transfer temperature was set at 70°C, the helium flow rate at 4.5 mlUmin and the solvent vent exit (SVE) was 10 s. In these conditions, the solvent introduction rate is higher than the solvent evapouration rate and a wetted zone is built up in the retention gap, avoiding losses of high vapour pressure compounds. The LODs for most compounds were 2 tg/kg and although interferences were present, no co-elutions were observed. The systems permitted the analysis of 120 samples without degradation of the system performance.
5.3.5
Solid phase micro-extraction
Most analytical instruments cannot handle the sample matrices directly and sample preparation is needed. In contrast, SPME is a relatively new sample preparation technique using a fused-silica fibre that is coated on the outside with an appropriate stationary phase [63,64]. Analyte in the sample is directly extracted to the fibre coating. SPME can be routinely used in combination with GC and LC. The process detailed by Kataoka [65] is as follows: the sample is placed in a vial, which is sealed with a septum-type cap. The SPME needle pierces the septum, the fibre is extended through the needle into the sample and target analytes partition from the sample matrix into the stationary phase. Extraction can be performed in two different ways: headspace (HS)-SPME and direct immersion (DI)-SPME. In HS-SPME, the fibre is exposed in the vapour phase above the gaseous, liquid or solid sample. In DI-SPME, the fibre is directly immersed in liquid samples. Agitation of the sample is needed to increase the rate of equilibration. After extraction, the fibre is withdrawn from the needle, the needle is removed from the septum and then inserted directly into the injection port of the GC or the desorption chamber of the SPME-LC interface. In GC, desorption of analytes is performed by heating the fibre and then the analytes are transferred directly in the column for analysis. SPME has important advantages such as its higher sensitivity, its versatility and the fact that no solvents are needed and no sophisticated apparatus are required. Moreover, contrary to classical SPE, sample preparation is minimised although salting out is usually needed. SPME has additional advantages such as minimum time and cost, avoidance of some of the risks of SPE (generation of waste, sorbent clogging, etc.) and the option of full automation. However, optimisation is required in the function of the type 251
S. Lacorte and A.R. Ferndndez-Alba of analytes to be analysed and the type of matrix. Optimisation in SPME-GC is described in detail by Kataoka [641 and, in short, involves: (i)
Selection of extraction modes. DI-SPME is recommended for the extraction of semi- or less volatile analytes in liquid samples. On the other hand, HS-SPME is suitable for the extraction of more volatile compounds in gaseous, liquid and solid matrices. DI-SPME is more sensitive for analytes present in liquid, whereas HS-SPME shows a lower background and has a higher life. (ii) Selection of fibre coatings. Several types of coating fibres are available nowadays. Polydimethylsiloxane (PDMS) is preferred for the analysis of apolar compounds. It is highly rugged and withstands temperatures of 300°C. More polar polyacrilate (PA) fibre is especially suitable for more polar compounds. Other coatings are divinylbenzene, templated resin, Carboxen and a combination of these. Film thickness affects extraction efficiency. (iii) Optimisation of extraction. Several parameters such as extraction time, extraction temperature, concentration of target analytes in the sample, agitation and addition of soluble salts in the sample (salting-out effect), and equilibration time should be optimised for achieving accurate and precise results. (iv) Optimisationofdesorption conditions.Efficient thermal desorption in GC is dependent on analyte vapour pressure, type of fibre, injection depth, injector temperature and exposure time. Most modern GC instruments are suitable for direct introduction of the fibre. A narrow-bore GC injector is required to ensure high linear flow. The liner volume affects the shape of the chromatographic peaks. Split/splitless injectors should be operated in splitless mode. Optimal desorption temperature is equal to the boiling point of the least volatile compound and the GC column temperature should be kept low or with cryofocussing. As pointed out in a recent review [65], prior to SPME, food samples have to undergo a preparatory step. This involves homogenisation and extraction with a high-speed blender using acetonitrile-water or water. Liquid samples such as fruit juice and wine can be extracted directly, although dilution is recommended. SPME has been widely applied in food analysis for the evaluation of volatile organic compounds responsible for food flavour, to monitor adulteration, additives and other toxic contaminants such as pesticides, as indicated in Ref. [65]. Table 5.2 shows that current trends have evolved towards the use of SPME for pesticide analysis in food. Boyd-Boland et al. analysed 22 nitrogencontaining herbicides in wine using GC-MS, GC-FID and GC-NPD [66]. 252
Sample introduction techniques Simplicio et al. developed a method consisting of DI-SPME-GC-FID for the determination of organophosphorus pesticides in fruits and fruit juices and validated it in terms of repeatability (0.4-7.3%), linearity (0.25-25 ,ag/l), limits of detection and quantification (0.003-0.014 [g/l) and accuracy (recoveries > 70%) [67]. The presence of interferences was one of the main drawbacks, but this was solved by a simple dilution of the sample addition of an internal standard or otherwise MS was recommended. Another study revealed poor reproducibility of SPME-GC-FID for the analysis of pesticides from honey samples due to the complexity of the sample [68]. Although the method was suggested as being "semi-quantitative", it was greatly superior to classical extractions in terms of analysis time and selectivity obtained. Figure 5.3 shows a chromatogram of spiked and non-spiked honey where 22 compounds could be identified despite the great front with a tail that decreased progressively until disappearing at 50 min. Little reproducibility linked to matrix effects can largely be solved by the use of SPME-GC-MS for the identification of pesticides in fruit and vegetables. MS, as discussed in other chapters, has the advantage of confirmation capability and identification of unknowns besides the low detection limits, which can be achieved in most instruments. Zambonin et al. have used SPME-GC-MS to determine triazole fungicides (triadimefon, propiconazole, myclobutanil and penconazole) in wine and strawberries [69]. For strawberry samples, sample preparation involved homogenisation of 50 g of strawberries and centrifugation for 30 s at 10,000 rpm. A 25-g aliquot was mixed with 40 ml of water and centrifuged again. The aqueous phase was brought to 100 ml of water with 0.2 g/ml of NaCl and 5 ml of this solution was subjected to SPME. For wine samples, wine was filtered through a 0.45 pum filter and diluted 1:2 with water with 0.2 g/ml of NaCl; 5 ml was also extracted. Detection limits for compounds studied in both matrices were below the MRL fixed by European Legislation. In this work, it is concluded that the procedure can be used as a rapid screening method for contamination assessment. HS-SPME-GC-MS was used to determine pyrimethanil and kresoximmethyl in green groceries [71]. After optimisation of pH, ionic strength, extraction and desorption times and extraction temperature, a linearity between 12.5 and 250 ng/g and detection limits of 2-3 ng/g were achieved. The procedure also involved optimisation of the type of fibre. For SPME-GC interface, a silanized narrow-bore injector liner (0.75 mm I.D.) was installed and the fibre was inserted into the injector using a splitless mode with the split closed for 3 min. The method was used to analyse grapes, strawberries, tomatoes and ketchup and, since matrix effects were observed, the standard addition method was suggested for quantification. Problems associated with quantification have also been stated by other authors, who indicate 253
S. Lacorte and A.R. Fernandez-Alba (A) 11
Time (min) (B) 2.5x1 05
2.0x10
5
1.5x10 5 1.0x1 05 5.0xl 04
Time (min)
Fig. 5.3. SPME-GC-ECD using (A) 7 m polydimethylsiloxane fibre on a spiked honey sample and (B) 100 ,um polydimethylsiloxane fibre on a non-spiked sample. Peak assignment: (1) demethon-S-methyl; (2) a-HCH; (3) lindane; (4) vinclozolin; (5) aldrin; (6) chlorpyriphos; (7) malathion; (8) parathion; (9) chlorfenvinphos (Z isomer); (10) endosulfan; (11) 4,4'-DDE; (12) captan; (13) TDE; (14) endrin; (15) ethion; (16) 4,4'-DDT; (17) acrinathrin; (18) methoxychlor; (19) tetradifon; (20) phosalone; (21) fluvalinate 1 and (22) fluvalinate 2. Reproduced from Ref. [69].
254
Sample introduction techniques that external standard calibration using ultra-pure water is not feasible and quantification should be performed either with the standard addition method or by using an appropriate surrogate and internal standard [72,73]. As proven by the great amount of recent applications of SPME-GC(MS), the technique is expected to be much used for the routine monitoring of pesticides from fruits and vegetables, which represent a complex matrix for which the standardised analytical methods are tedious and time-consuming. In addition, the advent of SPME-LC will permit the number of pesticides that can be handled with that technique to broaden. 5.4
INJECTION TECHNIQUES FOR HIGH PERFORMANCE LIQUID CHROMATOGRAPHY
As indicated in the previous section, GC is a powerful technique in food analysis due to the separation efficiency and the high availability of different detectors with a further advantage ofthe easy coupling with MS. However, one of the limitations of GC for the analysis of polar or thermolabile pesticides is the need of a derivatisation step [8], which increases sample manipulation and time of analysis and introduces new sources of errors. For such reasons, there is a general tendency to switch to LC, which can overcome the aforementioned limitations. LC instrumentation is nowadays a rugged option for the analysis of pesticides in food samples. Compounds that are LC-amenable are weakly volatile, thermolabile and polar compounds. Most LC-based methods use ultraviolet (UV), diode array, fluorescence, electrochemical or mass spectrometric detection, which can be combined with post-column treatment. However, present trends for pesticide analysis in food are focused on the use of mass spectrometric methods, which permit highly reliable identification. A recent overview on pesticide residue determination in fruit and vegetables by LC -MS indicates the capacity of different interfaces available (particle beam, thermospray and atmospheric pressure ionisation) for identification and confirmation of target and non-target analytes [74]. There are different options for sample introduction in LC, which are aimed at automated analysis, minimum sample preparation and selectivity enhancement through the injection port. In addition, the injection methodology will be primordial to achieve the sensitivity necessary for pesticides survey in food. Different injection systems will be discussed below. 5.4.1
Loop injection
HPLC systems, equipped or not with automated samplers, achieve excellent injection reproducibility because of the well-controlled total or partial loop-fill 255
S. Lacorte and A.R. Ferndndez-Alba injections with volumes in the order of 5-100 ld.The injection precision is generally of 1% RSD. When automated injection is available, the system benefits from high sample throughput. The LC analysis of food extracts is generally performed using a 20 l injection, which provides enough sensitivity at the levels required (generally [Lg/ml). This is typically performed using a 20 l loop. Carabias Martinez et al. describe a sensitive method for the determination of organophosphorus pesticides in fruits based on LC with UV detection [74]. Extraction of 3 g of sample with benzene and solvent replacement with methanol provided extracts clean enough to avoid cleanup and allowed determination of pesticides at 50-100 fAg/kg levels with recoveries ranging from 83 to 118% and relative standard deviation below 6%. LC with diode array detection (DAD) and LC thermospray MS as the confirmation technique was proved satisfactory for the analysis of imidacloprid in pepper, tomato and cucumber and at a spiking level of 0.25 mg/kg and 20 Al injection; the method provided recoveries higher than 95% and determination of residues at levels > 0.01 mg/kg [75]. Blasco et al. [76] have developed a method for the simultaneous determination of imidacloprid, carbendazim, methiocarb and hexythiazox in peaches and nectarines using LC-MS with atmospheric pressure chemical ionisation (APCI). At a concentration of 0.1 mg/kg and injecting 20 l of a methanol extract, the recoveries were between 64 and 108% with a standard deviation below 14% and a limit of quantification of 0.02 mg/kg. The system was reported as simple and reliable and was used for the quantitative analysis of 159 samples to estimate daily intakes from these samples. Several applications of LC for the analysis of pesticides in fruit and vegetables are summarised in Table 5.3. In most works, 2-25 ,ul of an extract is injected in the LC system. Some works indicate the need to perform a clean-up to remove sample interferences [78-80] and in the cases where complex matrices have to be analysed, such as peanuts or grapes, immunoaffinity extraction is recommended [79,80]. Other works performed an extraction and a single filtration of the extract through 0.45 m filters before sample injection 81-84]. To obtain a reliable identification and avoid false positives, most works use MS as the detection technique. Nowadays, modern injectors have the capability to inject from 5 to 500 j/l of extract, thus increasing the sensitivity of the method. In this case, LVI is especially suitable for the trace-level analysis of pesticide residues in food. In order to avoid peak broadening and double peaks, LVI should be performed in solvent conditions equal or equivalent to the initial mobile phase. Hogenboom et al. [84] combined the extraction of a small volume of sample (2 g) with an organic solvent (3 ml) with 900 ptl LVI and LC-MS-MS to determine nine pesticides at 0.5-2 ,ug/kg. Figure 5.4 shows a typical result 256
Sample introduction techniques
c
Z: '4 C) m
E~~~F;
Nl~
N
CO
c
N
c4
co
CO
N
.
c,-,
'4
4),-
~~~$ ~~~~~~~n~~~~ ~d ~~~2 ~~~~~
'O
44 o
ab I-'
0
AE
Id
H
00~ , cc
I~~
44
~ ~
~
N~ ~
C
44 NiF
~ ~~
C4
00
V
1
C
cc
'44 o
co-S
r
~~~~~~~ I ~
N
V~~9$P6 C)
4) B~,
t-~~~~~~~~~~~~~~Z c 6
I
-
'7
c
a
V
V
S·
~~ ~8g-e~~~~e~~~ ~15m~ ~~'c~ p~~P;
c
cc
3
~~BJ9 o
C)~
,4)c
0
4-
a CO
:O
I
44
o 04
--'44
o 4)0
ar
3~
-
cc· 00
00
40
C* 00 00
ma
a"
a
aF
~$ ~
-,~~~8
04 44
440044
~
cc
,E4
- 5 4 -da-
~
a
Ct
a
cc C
444O
CR cc o
cc-c 4)0
cc-
04)
444
oS 4)4
-"-
-0c
4
4
S-c9 04
00 44
Jb
.2
-cC .5 ccJ
257
S. Lacorte and A.R. Ferndndez-Alba
C
t
.
'
c
-
>
v
v
4 A
4
0
03
v a)
~ I-
a)
C
.
..
S o>0 I
W
O003
o
O
IC
.-
bD
a0 >
N
co m o
.5 >0
tss
,,
t
C ,p~~~~~~~~~~~~~~~ d210 Ca),
-"03
>0
1n 0
C cL
N
EtC
258
N
~~
~
cq
c
S' C*
Hx
ds
003003
~
~
0
b
Sample introduction techniques /A
\
(A)
90210M16 100
(B) AAS
n Chn-ol-
I LAPKA f In
TIC 3.41e4
r6
-- --
C-
TIC 2e4
50 2.00 4.00 6.00 8.00 10.00 12.00 14.00Time
2.00 4.00 6.00 8.0010.00 12.0014.00 Time
Fig. 5.4. LVI-LC-MS-MS (PI mode; SRM) of (A) a potato extract spiked at the 5 g/kg level and (B) a non-spiked carrot extract. Peak assignment: (1) dimethoate; (2) metoxuron; (3) carbofuran; (4) atraton; (5) atrazine; (6) diuron; (7) linuron; (8) metolachlor, (9) diazinon and (10) internal standard 2H5 diazepam. Reproduced from Ref. [85].
obtained after LVI-LC-MS-MS of a spiked potato extract with 5 g/kg and a non-spiked carrot extract where 16 min was enough to identify all compounds. The main advantages of the technique were its simplicity, high precision and little organic solvent consumption. 5.4.2
Capillary electrophoresis coupled to LC
Capillary electrophoresis (CE) is becoming an attractive alternative to chromatographic techniques for the analysis of pesticides in fruits and vegetables. As indicated in a recent review, it offers high separation efficiency, fast analysis and easy operation at low costs [86]. The problems related
259
S. Lacorte and A.R. Ferndndez-Alba to inadequate limits of detection and a lack of selective detectors have been recently overcome by the development of off- and on-column trace enrichment schemes to improve method LODs. Off-column pre-concentration is achieved by liquid-liquid extraction (LLE) or SPE and on-column concentration using on-line SPE or stacking methods as a sample preparation method to be used in CE. LLE has been used to determine thiabendazole in fruits and vegetables using methylene chloride, and LODs of 400 ppb were obtained [87]. Malik et al. [88] extracted dimethyldithiocarbamate from grains using LLE with trichloromethane and LODs were of 700 ppb using a pre-concentration factor of 5. Several fungicides have been extracted from grain with water-acetone using SPE 0.5 mg C1 s and elution with methylene chloride and with a pre-concentration factor of 10; LODs were between 100 and 1000 /,g/l [89]. LODs of 0.05 ppb have been reported for the analysis of urea-derived herbicides in fruits and vegetables using also SPE with 0.5 mg C1 8 glass cartridge [90]. Thiabendazole and procymidone were analysed in fruits and vegetables using stacking methods with matrix removal and capillary zone electrophoresis [91]. LODs for the different compounds vary according to the detection system. However, injection precision performance is probably the most critical part in CE. In CE, injection volumes are typically of 5-50 nl and loop injectors for these tiny volumes are not available for current CE instruments [92]. Injections in CE are achieved by inserting the capillary into a sample solution vial and then pressurising the vial to force the sample solution into the capillary. The volume injected is directly related to the pressure difference and the time that the pressure has been applied. In any case, external factors such as siphoning, surface tension of the sample solution, viscosity and temperature affect the injection volume, which reduces detector linearity and leads to poor accuracy. In a standard solution, CE can give acceptable precision for 5-10 replicate injections [93] but it is impossible to maintain reproducibility in long sequence injections or the analysis of complex samples. To improve injection precision, the use of an internal standard has been suggested [92]. In CE, the internal standard should migrate reasonably near the solute peak of interest and it can also be used for quantification purposes. Coupling CE with MS is gaining interest due to the general trend to use MS for pesticide residue analysis in food and it is anticipated that it will be used as a routine tool in many food laboratories. In addition, as indicated by Rodriguez et al. [90], future trends will be for the adaptation of CE to microchips, which will allow extremely rapid separations that consume only picolitre-sample volumes and raise the possibility of integrating sample preparation and analysis in a single device.
260
Sample introduction techniques
5.4.3
SPME coupled to HPLC
A new SPME technique known as in-tube SPME has been developed for combination with LC or LC-MS using an open tubular fused-silica capillary column as SPME device instead of a SPME fibre and a desorption chamber utilised for solvent desorption prior to HPLC [94,95]. With in-tube SPME, organic compounds from the aqueous phase are extracted from the sample into a capillary column (with coatings similar to SPME fibres) and then analytes are desorbed by introducing a moving stream of mobile phase or static desorption solvent (instead of thermal desorption as was used in GC) depending on the chemistry of the compounds. The capillary column is placed between the injection loop and the injection needle of the HPLC autosampler. As in a normal injection, the injection syringe repeatedly draws and ejects sample from the vial, the analytes partition from the sample matrix to the stationary phase. Afterwards, extracted analytes are desorbed from the capillary coating by the mobile phase. The desorbed analytes are transported to the HPLC column for separation and posterior detection using any detection system (UV, DAD, MS). In-tube SPME coupled to LC has been applied to the determination of pesticides in water samples but sensitivity was limited by the UV detector and the commercial capillary used for extraction [96,97]. Although the applications of in-tube SPME for pesticide analysis in food are very rare, Wu et al. [97] applied it to determine polar pesticides (phenyl urea and carbamates) in water and wine samples and demonstrated that the extraction efficiency and method sensitivity can be increased by combining a polypyrrole-coated capillary and the use of LC-ESI-MS detection. Limits of detection were in the range of 0.01-1.2 ng/ml and a linearity in the range of 0.5-200 ng/ml. In-tube SPME can be automated and can continuously perform extraction, desorption and injection using a standard autosampler. Overall, shorter analysis times are achieved and the method provides better accuracy and precision.
5.4.4
On-line solid-phase extraction coupled to LC
As indicated in a previous section, there is a high tendency to use hyphenated techniques for the automated analysis of pesticides in food. As with on-line SPE-GC, the procedure is based on the use of an adsorbent material placed in a six-port valve. Samples (e.g., food extract reconstituted in water) are pumped through the pre-column, which can retain the pesticides from 261
S. Lacorte and A.R. Fernandez-Alba the aqueous solution. After percolation, the pre-column is rinsed, typically with water, and afterwards the position of the six-port valve is changed so that pesticides are selectively eluted with the HPLC mobile phase to the detector. In this case, elimination of water through the use of a retention gap is not necessary. In general, LODs at levels of ng/l and better reproducibility values can be obtained by using an on-line approach because the entire sample is transferred to the analytical column and losses during sample manipulation are minimised. At the same time, on-line methodologies are more sensitive since it is possible to analyse pesticides at a level of ng/l with only 100 ml of sample. The technique is especially recommended for the analysis of polar pesticides. Physical parameters of the sorbent material, such as pore diameter, particle size and its distribution, amount and type of sorbent, solvents used for extraction, washing and elution, volume of aqueous sample preconcentrated, etc., determine the extraction efficiency, which will vary depending on the pesticides. Breakthrough volumes according to the capacity and type of cartridge and chemistry of the compound should be calculated to avoid low recoveries. Cartridges are available from 30 mg to 2 g and packing materials are commonly C18, C8, polymeric or immunosorbent, and each material will have a different affinity for the problem compound. However, in order to achieve optimal performance with on-line SPE, the sorbent of the pre-column should be as close as possible to the analytical column packing in terms of type of packing, particle size, etc. Band broadening can be minimised by using a suitable gradient, which causes peak compression on the top of the analytical column. The size of the pre-column is also of importance because the elution profile of the analytes should be as narrow as possible, especially at the beginning of the separation where the high water content tends to cause peak distortion. For a classical analytical column of 15 cm x 0.46 cm I.D., common sizes are 2 mm long and 2-3 mm internal diameter packed with 10-60 /im sorbent material, which efficiently traps the analytes. SPE coupled on-line with LC has been successfully applied to the analysis of pesticides from food matrices. The SPE sorbent in this case basically acts as a clean-up step, which otherwise has to be performed in many cases on adsorption columns [98] or using disposable SPE cartridges [99]. De Kok and Hiemstra optimised an SPE clean-up method coupled on-line with LC with fluorescent detection for the detection of N-methylcarbamate pesticides in fruits and vegetables [99]. The automated clean-up step was performed on an ASPEC (Gilson, France) apparatus, which executes complete SPE clean-up automatically, followed by on-line injection of 100 l cleaned-up extract into 262
Sample introduction techniques the LC system. The limits of detection obtained were in the 5-50 g/kg range for 13 carbamates and 12 metabolites on 12 different food products (see Table 5.3). The system was validated and found suitable for the routine analysis of pesticide residues. Recently, Riediker et al. developed a method for the determination of chlormequat and mepiquat in pear, tomato and wheat using on-line SPE with the Prospek (Spark Holland, The Netherlands) coupled to LC-ESP-MS-MS [100]. The sample preparation consisted of extracting 10 g of sample with methanol and water (1:1) and, after the supernatant was filtered through 0.2 /im, 30 ldof the extract was transferred to the SPE cartridge. A strong cation-exchange resin was used and the whole procedure was controlled by the use of deuterated internal standards. The method was fully automated and enabled the quantitative and confirmatory determination of two quats in fruits and vegetables in routine quality control operations. Although one of the limitations of the system is the potential overload of the SPE cartridge when injecting highly concentrated extracts, the method is very versatile and can be adapted to different pesticide concentration values. With on-line SPE-LC, it is recommended to use MS as the detection system in order to avoid sample interferences due to carbohydrates, proteins, etc., which are pre-concentrated along with target analytes. As a precaution, it should be mentioned that on-line pre-columns can be easily clogged if the sample contains small food pieces. Therefore, the main advantages of on-line SPE-LC-MS can be summarised as: (i) no need to evapourate the final extract, therefore losses due to recomposition of the extract are avoided; (ii) elimination of the sample matrix by choosing an appropriate SPE sorbent and cleaning/elution solvent composition, which is especially relevant in food analysis where the matrix can produce interfering ions that produce a distorted spectra, which could not be used for analyte confirmation; (iii) inhibition of ion suppression due to the fact that a cleaner chromatogram is obtained; (iv) lower LODs obtained even when analysing small amounts of sample since all the sample is transferred to the HPLC system; and (v) capacity to trap very volatile, water-soluble pesticides. Even though in routine food analysis there is a tendency to replace off-line methods with automated methods, on-line SPE does not avoid sample extraction as it does for water analysis. Although at present there are still not many applications dealing with on-line SPE -LC -MS, this is more related to the fact that GC-MS remains the preferred approach for the survey of pesticides in food. However, the benefits are clear, as indicated by Torres et al. in a complete review on determination of pesticide residues in fruit and vegetables [4].
263
S. Lacorte and A.R. Ferndndez-Alba 5.5
CONCLUSIONS
Although pesticide residue analysis is well established, there is still some need for fast, cost-effective and automated methods to satisfy the routine export/import survey of a large number of pesticides in fruit and vegetables. Modern trends are directed to minimum sample preparation and implementing high-throughput automated injection using equipment, which high sensitivity to be achieved as well as sample throughput and the possibility of analyte confirmation. Mass spectrometric detection fulfills such requirements provided the injection technique is automated for an upgrade method performance. By using GC techniques, pesticide residues in foods can be analysed with split/splitless injection on column injection or PTV. The selection of one type or another depends basically on the chemistry of the target analytes. While the former is especially suitable for volatile compounds, on-column is preferably employed for thermolabile pesticides. On the other hand, PTV permits LVI, making the technique especially suitable for achieving low method detection limits. As a novel sample introduction technique, coupling SPME with GC has proven to be very suitable for the extraction and quantification of pesticides from different types of fruit and vegetables and results are very promising as regards recoveries, precision, LODs, sample throughput and costs. SPME can also be coupled to LC, the main advantage being the possibility to determine polar, thermolabile and non-volatile pesticides. The applicability of this technique will probably replace the typical protocol of sample preparation, extraction and analysis by the traditional 20 /l loop. However, it is envisaged that in the near future online SPE coupled to LC-MS will also be applied to analysing pesticides in food with minimal sample preparation and automated clean-up, thus achieving high sensitivity. Most of the above-mentioned injection techniques are already widely used for pesticide analysis, some are under development and their applicability is to be demonstrated and others, especially hyphenated techniques, have a high potential in pesticide quality control laboratories due to the reduction of costs and analysis time. All the techniques described are meant to facilitate the analysis of an increasing number of pesticides in fruits and vegetables.
Acknowledgements The authors thank Ana Aguera for providing some figures and Roser Chaler for her useful comments on the manuscript. 264
Sample introduction techniques REFERENCES 1 2 3 4 5 6 7 8 9 10 11 12 13
14 15 16 17 18 19 20 21 22 23 24 25 26
W. Specht, S. Pelz and W. Gilsbach, Fresenius J. Anal. Chem., 353 (1995) 183. J. Fillion, R. Hindle, M. Lacroix and J. Selwyn, J. Assoc. Off. Anal. Chem. Int., 78 (1995) 1252. N. Motohashi, H. Nagashima, C. Pdrkanyi, B. Subrahmanyam and G. Zhang, J. Chromatogr.A, 754 (1996) 333. C.M. Torres, Y. Pic6 and J. Maiies, J. Chromatogr.A, 754 (1996) 301. J.L. Tadeo, C. Sanchez-Brunette, R.A. Perez and M.D. Ferndndez, J. Chromatogr. A, 882 (2000) 175. S.J. Lehotay and J. Hajslovd, TRAC, 21(9/10) (2002) 686. D.R. Erney, T. Pawlowski and C.F. Poole, J. High Resolut. Chromatogr., 20 (1997) 375. J.V. Hinshaw, LC-GC Europe, March (2003). K. Grob, Th. Laubli and B. Brechbuihler, J. High Resolut. Chromatogr., 11 (1988) 462. R.L. Grob, Modern Practice of Gas Chromatography.Wiley, New York, 1995. J. Staniewski and J.A. Rijs, J. High Resolut. Chromatogr., 16 (1993) 182. M. Abdel-Rehim, K.A. Svensson, Y. Askemark and K.J. Pettersson, J. Chromatogr.B, 775(1/2) (2001) 253. M. Mifiones Vazquez, M.E. Vdzquez Blanco, S. Muniategui Lorenzo, P. L6pez Mahia, E. Fernandez Ferndndez and D. Prada Rodriguez, J. Chromatogr.A, 919 (2001) 363. A. Aguera, M. Contreras and A.R. Fernindez-Alba, J. Chromatogr.A, 655 (1993) 293. A.R. Ferndndez-Alba, A. Valverde, A. Aguera and M. Contreras, J. Chromatogr. A, 686 (1994) 263. H.J. Stan and H.M. Muller, J. High Resolut. Chromatogr., 11 (1988) 1405. H.M. Miiller and H.J. Stan, J. High Resolut. Chromatogr., 13 (1990) 759. P.-L. Wylie, R.J. Phillips, K.J. Klein, M.Q. Thompson and B.W. Hermann, J. High Resolut. Chromatogr., 14 (1991) 649. P.L. Wylie, K.J. Klein, M.Q. Thompson and B.W. Hermann, J. High Resolut. Chromatogr., 15 (1992) 763. P.L. Wylie and K. Uchiyama, J. Assoc. Off Anal. Chem., 79 (1996) 57. M. Godula, J. Hajslova and K. Alterova, J. High Resolut. Chromatogr., 22(7) (1999) 395. J. Hajslovd, K. Holadovd, V. Kocourek, J. Poustka, M. Godula, P. Cuhra and M. Kempny, J. Chromatogr.A, 800 (1998) 283. J.C. Chuang, K. Hart, J.S. Chang, L.E. Boman, J.M. van Emon and A. Reed, Anal. Chim. Acta, 444 (2001) 87. E.M. Kristenson, E.G.J. Haverkate, C.J. Slooten, L. Ramos, R.J.J. Vreuls and U.A.Th. Brinkman, J. Chromatogr.A., 917 (2001) 277. F.J. Egea, M.E. Hernandez Torres, E. Almansa L6pez, L. Cuadros-Rodriguez and J.L. Martinez-Vidal, J. Chromatogr.A, 9666 (2002) 155. A. Aguera, L. Piedra, M.D. Hernando, A.R. Fernindez-Alba and M. Contreras, Analyst, 125 (2000) 1397.
265
S. Lacorte and A.R. Ferndndez-Alba 27 28 29 30 31 32 33 34 35 36 37 38 39 40
41
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
266
J. Beltran, F.J. Lopez, M. Focarda and F. Hernandez, Anal. Chim. Acta, 356 (1997) 125. J. Beltran, F.J. Lopez, M. Focarda and F. Hernandez, Chromatographia,44(5/6) (1997) 274. G. Nissner, W. Buchberger and R. Eckerstorfer, J. Chromatogr.A, 846 (1999) 342. W. Vogt, K. Jacob and H.W. Obwexer, J. Chromatogr., 174 (1979) 437. W. Vogt, K. Jacob, A.B. Ohnesorge and H.W. Obwexer, J. Chromatogr., 186 (1979) 197. Z.E. Penton, J. Assoc. Off: Anal. Chem., 74 (1991) 872. G. Schomburg, H. Husmann and F. Schulz, J. Chromatogr., 279 (1983) 689. F. Poy, S. Visan and F. Terrosi, J. Chromatogr., 217 (1981) 81. R.S. Sheridan and J.R. Meola, J. Assoc. Off. Anal. Chem. Int., 82 (1999) 982. W. Engewald, J. Teske and J. Efer, J. Chromatogr.A, 856 (1999) 259. H.G.J. Mol, M. Althuizen, H.-G. Janssen and C.A. Cramers, J. High. Resolut. Chromatogr., 119 (1996) 69. J. Villen, F.J. Sefiorans, M. Herraiz andJ. Tabera,J. Chromatogr.Sci., 36(1998) 535. F.J. Sefiorans, J. Tabera, J. Vill6n, M. Herraiz and G. Reglero, J. Chromatogr., 648 (1993) 407. E. Concha-Grafia, M.I. Turnes-Carou, S. Muniategui-Lorenzo, P. L6pez-Mahia, E. Ferndndez-Ferndndez and D. Prada-Rodriguez, J. Chromatogr.A, 958 (2002) 17. M. Mifiones-Vazquez, M.E. Vdzquez-Blanco, S. Muniategui-Lorenzo, P. L6pezMahia, E. Fernandez-Fernandez and D. Prada-Rodriguez, J. Chromatogr.A, 919 (2001) 363. H.J. Stan and M. Linkerhagner, J. Chromatogr. A, 727(2) (1996) 275. M. Hada, M. Takino, T. Tamagami, S. Daishima and K. Yamagushi, J. Chromatogr.A, 874 (2000) 81. P.L. Wylie, application note, Agilent Technologies, USA, September 1997. H.M. Miller and H.J. Stan, J. High Resolut. Chromatogr., 13 (1990) 697. M. Godula, J. Hajslova, K. Alterova and J. Krivankova, J. Sep. Sci., 24(5) (2001) 355. J. Zrostlfkov6, J. Hajslova, M. Godula and K. Mastovska, J. Chromatogr.A, 937 (2001) 73. S. Schachterle and C. Feigel, J. Chromatogr. A, 754 (1996) 411. J.L. Martinez Vidal, F.J. Arrebola and M. Mateu-Sanchez, J. Chromatogr.A, 959 (2002) 203. J. Dalluige, M. van Rijn, J. Beens, R.J.J. Vreuls and U.A.Th. Brinkman, J. Chromatogr. A, 965 (2002) 207. K. Mastovska, S.J. Lehotay and J. Hajslova, J. Chromatogr.A, 926 (2001) 291. K. Grob and M. Bronz, J. Microcol. Sep., 7(4) (1995) 421. U.A.Th. Brinkman, J. Slobodnik and J.J. Vreuls, Trends Anal. Chem., 13 (1994) 373. S. Lacorte and D. Barcel6, Anal. Chem., 68(15) (1996) 2464. A.J.H. Louter, C.A. van Beekvelt, P. Cid Montanes, J. Slobodnik, J.J. Vreuls and U.A.Th. Brinkman, J. Chromatogr.A, 725(1) (1996) 67. E.R. Brouer, S. Kofman and U.A.Th. Brinkman, J. Chromatogr. A, 703(1/2) (1995) 167.
Sample introduction techniques 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
A. Column, S. CArdenas, M. Gallego and M. Valcarcel, J. Chromatogr. A, 882 (2000) 193. T. Hyotylainen, K. Jauho and M.L. Riekkola, J. Chromatogr.A, 813 (1998) 113. K. Grob and I. Kalin, J. Agric. Food Chem., 39 (1991) 1950. M. De Paoli, M.T. Barbina, R. Mondini, A. Pezzoni, A. Valentino and K. Grob, J. Chromatogr., 626 (1992) 145. B. Jongenotter and H.G. Janssen, LC-GC Europe, June (2002). C.L. Arthur and J. Pawliszyn, Anal Chem., 62 (1990) 2145. Z. Zhang, M.J. Yang and J. Pawliszyn, Anal. Chem, 66 (1994) 844A. H. Kataoka, H.L. Lord and J. Pawliszyn, J. Chromatogr.A, 880 (2000) 36. J. Beltran, F.J. L6pez and F. Fernandez, J. Chromatogr.A, 885 (2002) 389. A.A. Boyd-Boland and J.B. Pawliszyn, J. Chromatogr. A, 701 (1995) 163. A.L. Simplicio and L.V. Boas, J. Chromatogr. A, 833 (1999) 35. J.J. Jim6nez, J.L. Bernal, M.J. del Nozal, M.T. Martin and A.L. Mayorga, J. Chromatogr.A, 828 (1998) 269. C.G. Zambonin, A. Cilenti and F. Palmisano, J. Chromatogr.A, 967 (2002) 255. A. Naval6n, A. Prieto, L. Araujo and J.L. Vilchez, J. Chromatogr. A, 975 (2002) 355. A.A. Boyd-Boland, S. Magdic and J. Pawliszyn, Analyst, 121 (1996) 929. R. Batlle, C. Sanchez and C. Nerin, Anal. Chem., 71 (1999) 2417. Y. Pic6, G. Font, J.C. Molt6 and J. Mafies, J. Chromatogr.A, 882 (2000) 153. R. Carabias Martinez, E. Rodriguez Gonzalo, M.J. Amigo Moran and J. Hernandez Mendez, J. Chromatogr., 607 (1992) 37. A.R. Fernzindez-Alba, A. Valverde, A. Agiiera, M. Contreras and S. Chiron, J. Chromatogr.A, 721 (1996) 97. C. Blasco, M. Ferndndez, Y. Pic6, G. Font and J. Mafies, Anal. Chim. Acta, 461 (2002) 109. J. Hau, S. Riediker, N. Varga and R. Stadler, J. Chromatogr.A, 878 (2000) 77. E. Watanabe, Y. Yoshimura, Y. Yuasa and H. Nakazawa, Anal. Chim. Acta, 433 (2001) 199. S. Ben Rejeb, C. Cleroux, J.F. Lawrence, P.-Y. Geay, S. Wu and S. Stavinski, Anal. Chim. Acta, 432 (2001) 193. E. Lacassie, M.F. Dreyfuss, J.L. Daguet, M. Vignaud, P. Marquet and G. Lachatre, J. Chromatogr. A, 830 (1999) 135. M.C. Peeters, I. Defloor, J. Coosemans, J.A. Delacour, L. Ooms, R. Deliver and D. De Vos, J. Chromatogr. A, 920 (2001) 255. M.J. Taylor, K. Hunter, K.B. Hunter, D. Lindsay and S. Le Bouhellec, J. Chromatogr. A, 982 (2002) 225. M. Hiemstra and A. de Kok, J. Chromatogr. A, 972 (2002) 231. A.C. Hogenboom, M.P. Hofman, S.J. Kok, W.M.A. Niessen and U.A.Th. Brinkman, J. Chromatogr.A, 892 (2000) 379. Y. Pic6, R. Rodriguez and J. Maiies, Trends Anal. Chem., 22(3) (2003) 133. D.T. Eash and R.J. Bushway, J. Liq. Chromatogr.Relat. Technol., 23 (2000) 261. R. Rodriguez, Y. Pic6, G. Font and J. Mafies, J. Chromatogr A, 924 (2001) 387. A.K. Malik, B.S. Seidel and W. Faubel, J. Chromatogr.A, 857 (1999) 365. R. Rodriguez, Y. Pic6, G. Font and J. Mares, Electrophoresis, 22 (2001) 2010. R. Rodriguez, Y. Pic6, G. Font and J. Mafies, J. Chromatogr.A, 949 (2002) 359.
267
S. Lacorte and A.R. Fernandez-Alba 91 92 93 94 95 96 97 98 99 100
268
K.D. Altria, LC-GC Europe, September (2002). B.R. Thomas, X.G. Fang, X. Chen, R.J. Tyrrell and S. Ghodbane, J. Chromatogr., 657 (1994) 383-394. R. Eisert and J. Pawliszyn, Anal. Chem., 69 (1997) 3140. H. Kataoka, H.L. Lord and J. Pawliszyn, Anal. Chem., 71 (1999) 4237. Y. Gou, R. Eisert and J. Pawliszyn, J. Chromatogr.A, 873 (2000) 137. Y. Gou, C. Tragas, K.L. Lord and J. Pawliszyn, J. Microcol. Sep., 12 (2000) 125. J. Wu, C. Tragas, H. Lord and J. Pawliszyn, J. Chromatogr.A, 976 (2002) 357. T. Cairns, M.A. Luke, K.S. Chiu, D. Navarro and E. Siegmund, Rapid Commun. Mass Spectrom., 7 (1993) 1070. De Kok, M., Hiemstra, J. Assoc. Off. Anal. Chem. Int., 75(6) (1992) 1063. S. Riediker, H. Obrist, N. Varga and R.H. Stadler, J. Chromatogr. A, 966 (2002) 15.
Chapter 6
GC-MS. I: Basic principles and technical aspects of GC-MS for pesticide residue analysis Hans-Jiirgen Stan
6.1
INTRODUCTION AND SCOPE
Mass spectrometry has gained a position of outstanding importance in many areas of organic chemical analysis. The technique can be combined on-line with the most important chromatographic separation techniques applied in trace analysis of biological, environmental and food samples: capillary gas chromatography and high-performance liquid chromatography. Both these "hyphenated techniques", GC-MS and LC-MS, are nowadays indispensable for sensitive positive structural identification of pesticides and other pollutants in our environment. These techniques are extremely valuable for the development of analytical methods to meet the low maximum tolerance values as set by the European Union and other legislative bodies for pesticides in foodstuffs. Therefore, a brief review of the principles of mass spectrometry is presented to survey the basics and to emphasize what makes this technique the unrivaled detection method in pesticide residue analysis. To date, virtually no analytical result can be considered as reliable if it does not include a mass spectrometric confirmation!
6.2
THE MASS SPECTRUM
The mass spectrum is a plot of the intensity as a function of the mass-tocharge ratio (Fig. 6.1). The peak with the highest intensity in the spectrum is called the base peak. Generally, the spectrum is normalised to the intensity of the base peak, resulting in relative intensities. Comprehensive Analytical Chemistry XLIII Ferndndez-Alba (Ed.) © 2005 Elsevier B.V. All rights reserved
269
H.-J. Stan Basepeak Ah llnrInrn IUUl -- I I.G
1 1 I
175
100 90-
80 Fragments
70 60 50
Fragme lts
75
40
7
99
30
127
20 101 I m/z--> u
....
8,t100I X1 80 n
..
120
Isotopic Peak
140
160
,...
180
.. I I.
200
.
302 I
220
240
260
280
300
Fig. 6.1. The mass spectrum.
When a molecule is ionised, a molecular ion M +' is produced and this may contain sufficient internal energy to fragment by ejection of a neutral particle N with the formation of a fragment ion A' + or A+ . The original analyte molecule gives a radical-cation as the molecular ion, and the fragment ion may be a cation or a radical-ion. The ejected neutral particle N may be a radical or a neutral molecule: M + e
M'+
-A
M'+
-A'
+
+ N'
or M + e
+N +
If the fragment ion has sufficient internal energy, then further fragmentation may occur with the formation of a whole series of fragment ions: M'+-N -
A + -NA -
B
-NB -
C+-
or M '+ -N - Z '+ -N
z
- Y'-Ny - X + ..
Such a series of decompositions when elucidated from a mass spectrum is called a fragmentation pathway. As shown, the molecular ion M' + may decompose by more than one pathway. The various fragmentation pathways together compose a fragmentation pattern characteristic of the compound under investigation.
270
GC-MS. I: Basic principles and technical aspects of GC-MS Ions
10
20
30
40
50
60
70
80
90
Electron Energy (eV)
Fig. 6.2. Total ionisation current as function of ionisation energy.
The extent to which fragmentation takes place along the individual pathways is determined by the amount of internal energy originally given to the molecular ion M' and its structure. Hence, the mass spectrum is not simply a fragmentation pattern but its appearance depends upon the energy of the ionising electrons and also upon the temperature at which ionisation occurs.
Ion formation as a function of the energy of ionising electrons in the electron impact process is illustrated in Fig. 6.2. The electrons are provided by a heated filament in the evacuated ion source, accelerated through a potential and directed across the chamber where they may hit the analyte molecules. The potential is continuously variable between 0 and over 100 eV. Molecular ions begin to appear around 10 eV (ionisation energy or appearance potential) but at low intensities. Between 10 and 15 eV, fragment ions also begin to appear. Both types of ions increase in abundance following individual abundance curves. Standard mass spectra are obtained at 70 eV because maximum ion yield (total ion current) is observed at this value and mass spectra were found to be reproducible and characteristic of the molecule ionised almost independent of the type and make of instrument. 6.3
STRUCTURAL INFORMATION
Although the structural identification of a pesticide in a pesticide residue analysis is usually performed by means of a reference spectrum of a standard
271
H.-J. Stan compound, a basic understanding of the interpretation of mass spectral data is certainly necessary to produce sound results. This holds true in particular if new compounds or derivatives with unknown mass spectra are to be inserted in the user's own mass spectral library. 6.3.1
The molecular ion
In a mass spectrum, several major kinds of general structural information are available. The molecular weight is probably the most valuable piece of information a mass spectrum can give. The molecular weight is calculated from the integer masses of the most abundant isotopes of the atoms present in the molecule and thus in the molecular ion. The molecular ion, also called the parent ion, is the peak that usually corresponds with the highest mass isotope cluster in the spectrum. However, identifying that peak with certainty can be rather difficult in some cases. In such cases, soft ionisation methods can be applied to produce ions indicative of the molecular weight. In pesticide residue analysis, chemical ionisation is the soft ionisation method mostly used for the production of "quasi-molecular" ions as the protonated molecule [M + H]+ is frequently referred to. In an electron ionisation (EI) mass spectrum, the fragment ions should be consistent with the molecular ion; peaks like [M - 1] + , M - 15] + , [M - 18] + and [M - 20] + confirm the assignment of the molecular ion because they represent the losses of H', CH', H 2 0 and HF, respectively, from the parent ion. Such "small-neutral loss" peaks are of major significance in deducing the molecular structure. Lists of common, small, neutral fragments lost in the formation spectral peaks are given in textbooks on mass spectrometry [1,2]. On the other hand, losses of 3-14 and 21-25 amu generally are not consistent with fragments formed from the parent ion and indicate an incorrect assignment or the presence of impurities. With mass spectra obtained with GC-MS or LC-MS analysis, the recognition of ions originating from impurities (background or not resolved chromatographic peaks) is generally facilitated by observing the relative ion abundances in the chromatographic peak. This method is also used in an automated form as background subtraction. As well as the molecular ion, two other types of peaks are observed in the mass spectrum: fragment and isotopic peaks. 6.3.2
Isotopic peaks
The isotopic peaks are the result of natural isotope abundances of the individual elements, which can be highly indicative. For instance, natural 272
GC-MS. I: Basic principles and technical aspects of GC-MS TABLE 6.1 Natural isotopic abundances of common elements Element
Mass
%
Mass
%
H C N 0 F Si P S Cl Br I
1 12 14 16 19 28 31 32 35 79 127
100 100 100 100 100 100 100 100 100 100 100
2 13 15 17
0.015 1.1 0.37 0.04
18
0.20
29
5.1
30
3.4
33
0.79
34 37 81
4.4 32.0 97.3
Mass
%
Type A A+ 1 A+ 1 A+ 2 A A+ 2 A A+ 2 A+2 A+ 2 A
chlorine exists as 75% 3 5 Cl-isotopes and 25% 3 7 C1-isotopes and consequently each parent or fragment ion containing chlorine can be easily identified by its typical chlorine isotopic cluster. The abundances of isotopic peaks at unit resolution from elements occurring in pesticides are given in Table 6.1. There are mono-isotopic elements such as fluorine, iodine, phosphorus and also hydrogen, which are referred to as "A"-elements and others with typical additional isotopic peaks in the spectrum such as chlorine, bromine and sulphur that arise at two mass units higher and therefore are designated as "A + 2" elements. Molecular and fragment ions containing more than one chlorine or bromine atom therefore give rise to very characteristic patterns, as shown in Fig. 6.3. The isotope patterns to be expected from any combination of elements can readily be calculated and provide a useful test of ion composition. Furthermore, in compounds containing C, H, O and the heteroatoms listed in Table 6.1, the molecular weight must be even. Thus, if a molecule contains one or an odd number of nitrogen atoms, the molecular weight will be odd. This generalisation applies to all stable even-electron molecules (the "nitrogen rule"). 6.3.3
Fragmentation reactions
Molecular ions are generated in the El ion source with a wide range of internal energies with a significant proportion being above the threshold for fragmentation. 273
H.-J. Stan I LU -
100 -
_
i.ILi LiL.
80 60 -
L
40 20 0 ClI
1I
C2
CI
CI
C155
C6 C
-
100 -
O
I
,11 I ,i .l , ,l. Br
Br 2
Br 3
Br 4
I_
.1
CIBr
CI 2Br
1zu 100806040200.
I LL ,
CI 3Br
1Ub.,
CIBr2
,
,
CI 2 Br2
,
CIBr2
LiI L ,
CIBr 3
I
CI2 Br3
Fig. 6.3. Isotopic clusters. Mass spectral reactions are unimolecular; the sample pressure in the El ion source is kept sufficiently low to avoid collision reactions. The mass spectrum reflects the results of a series of competing and consecutive reactions occurring in the ion source. The reactions are thought to be initiated at the favoured site for the unpaired electron and for the positive charge in the parent ion. The most favoured radical and charge site in the molecular ion is assumed to arise from loss of the molecule's electron of lowest energy. Favourability for ionisation generally is in the order of o- < IT < n-electrons from sigma bonds, double bonds (olefinic or phenyl) or non-bonding electron pairs, respectively.
274
GC-MS. I: Basic principles and technical aspects of GC-MS Sigma-bond dissociation is typical for alkane fragmentation. The electron lost in the ionisation comes from a saturated bond. The more abundant fragment will be the one better able to stabilise the positive charge. In unbranched alkanes, the sigma-bonds are nearly equivalent in bond strength. The resulting mass spectra can have many peaks of regularly varying abundances with only a small molecular ion, which can often not be identified. The alkane spectra are easily recognised by their typical ion series with mass differences of 14 (CH 2 ) and therefore were called "picket fence" spectra. They were observed in nearly all chromatograms from environmental samples. R-CH 2 -R' -· R' + +CH 2 -R'
R- CH 2 - R' - R- CH2 + +R' Reaction initiation at the radical site arises from its strong tendency for electron pairing. The electron is donated to form a new bond to an adjacent atom. This is accompanied by cleavage of another bond. This reaction is commonly called "a-cleavage". A well-known example is the allylic cleavages with the electron lost in the ionisation from an allylic double bond: R-CH 2 -CH'+-CH
2
- R' + CH2 =CH-CH+
Another characteristic example is the formation of the benzylium or tropylium ion from alkyl substituted aromatic compounds: H rr.-RR
_R
°
R
CH 2
+
H
H H
0-R~H H
H
Since ionisation by loss of an electron from a non-bonding electron of a heteroatom is favoured, fragments resulting from such cleavage reactions are abundant. The cleavage reaction is initiated by the positive charge which attracts an electron pair. The tendency for the formation of R + from R-Y parallels the inductive effect of Y. Therefore, it is called "inductive cleavage" with halogens > O, S > N. As well as simple bond cleavage reactions, rearrangement reactions are observed. In particular, hydrogen atom rearrangements initiated at a radical site are an important class of reactions. Such hydrogen rearrangements through six-membered ring intermediates are usually referred to as the "McLafferty rearrangement". For compounds containing an unsaturated 275
H.-J. Stan functionality such as the carbonyl group, the y-hydrogen atom is transferred by a sterically favourable transition state: H O
I C-H CH2
/R C
I
-
-R-CH=CH2
,CH2Z
2
H
/H 0+ -I CCH2
0+ -Z
CH
2
Summarising, the most important factor affecting the abundance of a product ion is its stability, which is caused by resonance stabilisation (allyl or benzyl cation) or electron sharing involving a non-bonding orbital of a heteroatom, such as in an acyl ion R- C'=O - R-C=O'+ . Another important driving force is the formation of small, stable, neutral products such as H 2 0, C 2H 4 , CO, CH 3OH, HCI and CO 2 with production of a more stabilised radical ion. The significance of small, neutral loss peaks for the identification of the molecular ion in a mass spectrum has already been emphasised [1,2]. 6.3.4
Interpretation
The principles reviewed are now illustrated with a few examples. Chlorinated pesticides are presented in Figs. 6.4-6.6 in order to demonstrate how to apply the basic knowledge to check the mass spectra and their assignment to a chemical structure for plausibility. Let us start with the methyl esters of 2,4-D and dichlorprop, two herbicides of very similar chemical structure; dichlorprop is derived from 2,4-D by simply substituting one hydrogen in the side chain by a methyl group. Both compounds exhibit abundant molecular ions with that of dichlorprop 14 amu higher than that of 2,4-D, as expected. The initially formed molecular ions are sufficiently stable, as with many aromatic compounds observed, to yield high abundances; they are the second largest peaks in the spectra. Both molecular ions exhibit even numbers and show the characteristic isotopic cluster that indicate compounds containing two chlorine atoms in their molecules, as can be drawn from Fig. 6.4. The fragments with the highest mass are [M - 35] + with 2,4-D and [M - 59]+; with dichlorprop representing the loss of chlorine from 2,4-D and a methoxycarbonyl from dichlorprop both easy to interpret, they confirm the molecular ions. No small fragment losses are observed. The base peak m/z 199 in 2,4-D exhibits the expected isotopic cluster of one chlorine confirming the formation of that fragment by loss of one chlorine: [M - 35] + . Note that the other fragments all contain two chlorine 276
GC-MS. I: Basic principles and technical aspects of GC-MS Ahbundnce.
90 80 70 60 50; 40 30 20 10 m/7_
n
199
CI
M.+
Cl2 Cl2
175
C2
73
ill 1 80
100
175
133145
II
II 120
234
CI2
11
140
161
;Ij
38
II~~~~~~~~~~~ 160
180
200
220
240
234
Fig. 6.4. Mass spectrum of the methyl ester of 2,4-D and the fragmentation pattern. atoms and therefore support the fragmentation pattern given in Fig. 6.4. This also holds true for the fragmentation pattern given for the mass spectrum of dichlorprop in Fig. 6.5. The base peak in this spectrum is m/z 162, which can only be explained by a hydrogen rearrangement. The formation of the peak may include a radical site rearrangement with transference of a hydrogen from the branched methyl group to the phenolic oxygen. Note the different fragmentation pattern in the two similar molecules; the dichlorophenolic ion m/z 161 is formed from both compounds with low abundances by inductive cleavage. The possibility of undergoing a rearrangement with hydrogen migration, however, makes the formation of the dichlorophenol ion
at m/z 162 so favourable that it constitutes the base peak in the mass spectrum. The small difference in the chemical structure between two closely related compounds leads to mass spectra of completely different appearance. Thus, these two "homologous" pesticides back up the statement that mass spectra are indicative of individual compounds and can reflect small changes in the chemical structures.
277
H.-J. Stan Abundance
162
Cl2
90 80 70 60 50 40 30 20 10 _/_
~59
Cl
1 60
133
100
189
248
191
250
120
FL
15
1 1, 1~~1 41II.
80
M° +
CI 2
109
n
CI2
140
160
.. h ...... 180
200
220
240
260
248 189-
145 0 H3 C-OCH 3
Cl Cl -co
133 -161 + H
Fig. 6.5. Mass spectrum of the methyl ester of dichlorprop and the fragmentation pattern.
The third example shown is the mass spectrum of folpet, a chlorinated fungicide with a chlorine substituted in a methylthio group (Fig. 6.6). The active compound contains the heteroatoms nitrogen and sulphur in addition to the three chlorines. With only one nitrogen in the molecule, the molecular ion has an odd number and is observed at a relative abundance of 30% due to its aromatic structure. The base peak ion is formed by a favourable loss of one single chlorine from the trichloromethylthio group by inductive cleavage, as expected. Only a small proportion in this reaction retains the positive charge at the trichloromethyl part (m/z 117). There is only one other chlorinecontaining fragment ion at m/z 232, which is formed by small, neutral loss of CO. Other fragments can be explained by successive decomposition of the thiophthalimide moiety, as partly indicated. Fragments retaining the aromatic acid structure are C 6 H 4 CO+ at m/z 104 and C6H4+ at m/z 76, as characteristic for a substituted benzene ring. The only fragment difficult to
278
GC-MS. I: Basic principles and technical aspects of GC-MS Abundance
26n
90 80 70 60 50 40 30 20 10 lII/--U ' U
CI2
M.+
C13
CI3
104 76
130 117
CI2 178
~ 11 80
100 120
-Co 232 -C-
X~
140
160
295
232
I II . . . . . .... . .... .. 180 200 220 240 260 280
300
295
260
-S--C
O
178
Cl
ICI
117
Fig. 6.6. Mass spectrum of folpet and fragmentation pattern.
interpret is that at m/z 130, which must be formed through a complex rearrangement from the phthalimide moiety. The interpretation seems to be plausible with the chemical structure because all other major peaks can be arranged in a consistent fragmentation pattern.
6.4
CHEMICAL IONISATION
It became clear in the description of the basics of mass spectrometry and the interpretation of EI mass spectra that with several compounds the structural information is limited. In particular, the molecular weight should be unequivocally determined. Chemical ionisation (CI) is the method of choice that can be easily applied using the mass spectrometer as a chromatographic detector. In LC-MS, CI is
279
H.-J. Stan the main ionisation technique (see chapter 7). CI is very useful in that most molecules that do not yield molecular ions by EI can produce ions with CI indicative of the molecular weight. Furthermore, CI conditions produce abundant thermal electrons that form highly efficient negative ions from molecules with high electron affinity by electron capture, a process familiar to the pesticide residue analyst from the electron capture detector. For CI, a reagent gas is introduced into the ion source at a concentration in large excess to that of the analytes (104:1). The reagent gas is usually ionised by electron bombardment as in EIl. The formation of primary ions is followed by ion molecule reactions between those primary ions and the gas neutrals, producing the chemical ionisation reagent ion or a variety of such reagent ions as well as the thermal electrons [3]. 6.4.1
Positive ions
Methane is employed for chemical ionisation as the reagent gas most frequently because almost all organic molecules are ionised. The reactive species are formed by the following reactions: CH 4 + e -- CH'+, CH+, CH", etc. CH'+ + CH 4 - CH+ + CH' CH+ + CH 4
-
C 2H+ + H 2
CH' + CH 4 - C2H+ + H 2 + H' C 2H+ + CH4 - C3 H + + H2 At pressures around 1 Torr, more than 90% of the ion population consists of the ions CH +, C 2H and C 3 H with m/z 17, 29 and 41, respectively. CH+ reacts exothermically with almost all organic molecules behaving as a Bronsted acid to yield a protonated molecular ion: M + CH+ - [M + H] + + CH 4 Other reactions that can be observed are M + CH+ -- [M - H] + + CH 4 + H2 M + C 2H M + C 3H
[M + C2 H5 ] +
- [M + C 3H 5] +
The latter two equations show bimolecular association reactions, which are generally classified as solvation processes in the gas phase. 280
GC-MS. I: Basic principles and technical aspects of GC-MS TABLE 6.2 Proton affinity of reactant gases Gas
Reactant ion
Proton affinity (kJ/mol)
CH 4
CH5 C2H~5 H30 + CH 3OH2 t-C4 H4 NH +
527 665 706 761 807 840
H 20 CH 30H i-C 4Hlo NH 3
Other popular reagent gases are isobutane and ammonia which are "softer" reagent gases because they do not ionise all organic molecules and induce less fragmentation. When chemical ionisation occurs by proton transfer to the analyte from an acidic reagent ion, the exothermicity of the proton-transfer reaction determines the internal energy of the protonated molecular ion and hence the extent of fragmentation: M + [B + HI+ - [M + H]+ + B The exothermicity of the proton-transfer reaction is directly related to the proton affinity. From a series of proton affinities, as shown in Table 6.2 for a few reagent gases, the appearance of CI mass spectra is roughly predictable [4]. For instance, a protonated molecular ion produced via isobutane ionisation is expected to possess less internal energy than that formed with methane. There are two reasons to explain the suitability of CI-MS for the confirmation of the molecular weight of an analyte. It appears that more than about 400 kJ/mol of internal excitation in the "quasimolecular ion" [M + H]+ is uncommon, even when methane is used as the reagent gas. The consequence is that relative abundant ions appear in the molecular ion region. With labile molecules, the exothermicity of the ionisation can be reduced by selecting a softer reagent gas. In addition, the even-electron [M + H] + ions possess an inherent stability compared with the radical M + ions produced with El. 6.4.2
Negative ions
The highly energetic electrons emitted from a filament generate, under El conditions, only small abundances of negative ions. Under CI conditions, 281
H.-J. Stan however, they lose energy by promoting positive ion formation and by colliding with neutral gas molecules. The low-energy electrons produced can interact with a sample molecule AB by three different mechanisms: AB + e -+ AB'AB + e - A' + B-
(resonance capture) (dissociative resonance capture)
AB + e -* A + + B- + e
(ion-pair production)
"Near-thermal" electrons of very low energy (- 0 eV) can undergo resonance capture, assuming that AB possesses a positive electron affinity. With an additional large cross-section for electron capture of AB, such negative-ion spectra can exhibit an increase in sensitivity of orders ofmagnitude above those found with other ionisation techniques. However, it should be noted that the extraordinary sensitivities can only be achieved under most favourable conditions with respect to the chemical structure of the analyte molecule. On the other hand, this source of information is available with all CI measurements provided the instrument is capable of detecting negative ions. The most favourable equipment allows the alternate measurement of positive and negative ions in one chromatographic analysis virtually simultaneously. Electron capture NCI (ECNCI) generates negative molecular ion radicals with low internal energy but with the inherent instability of an odd-electron ion. Therefore, the abundance of the molecular ion depends on the overall resonance stabilisation possibilities in the molecule. Often, an abundant stable anion constitutes the whole mass spectrum. In contrast to ECNCI, negative CI may be performed by applying special reagent gases. Br6nsted bases play a role analogous to that played by Bronsted acids in generating positive ions. For instance, CH3 0- can act as a Brdnsted base, producing [M - H - ions by abstracting a proton from the sample molecule. The ionisation technique generates even-electron molecular ions of low internal energy with little fragmentation tendency. Therefore, the [M - H]- quasi-molecular ion frequently constitutes the base peak [5]. Since the reagent gases are not as easy to handle as those for the PCI and ECNCI, the method is not very popular yet although it has obvious merits in many applications. 6.5
COMPLEMENTARY INFORMATION
The information of mass spectra obtained from the same compound with different ionisation methods is of a complementary nature. With EI, a parent 282
GC-MS. I: Basic principles and technical aspects of GC-MS ion is formed in the ionisation process possessing an inherent instability because of being a radical or odd-electron ion. Therefore, subsequent fragmentation is common, providing structural information. The abundance of the molecular ion, however, may be weak. CI is credited with providing molecular weight information because the formation of even-electron parent ions in a gas phase reaction with proton transfer in the form of "quasimolecular" ions [M + HI + is the dominating mechanism. These even-electron ions are mostly so stable that only little fragmentation is observed. The different exothermicity of the proton transfer may, however, lead to a different appearance of the mass spectra. With methane as the reactant gas, two more ions indicative to the molecular weight, namely [M + 29] + and [M + 41]+ , are usually observed, making the identification of the quasi-molecular ion even more reliable. With ECNCI, negative odd-electron molecular ions are formed with subsequent fragmentation, which frequently results in simple mass spectra dominated by a few ions. With organophosphorous pesticides, often only one fragment ion originating from the organophosphate group is observed, indicative of the subclass of organophosphates as diethyl- or dimethyldithiophosphate [6,7]. To demonstrate the complementary nature of the information provided by the three ionisation methods, two examples are chosen from the organophosphate insecticides, the pesticide group systematically investigated [6,7]. In Fig. 6.7, the three spectra of dicrotophos are presented. The EI spectrum is dominated by the base peak indicative of dimethylphosphates, which show either m/z 93 ((CH3 0) 2PO) or m/z 127 ((CH 3 0) 2PO2 + H) if hydrogen rearrangement is favoured with the side chain, as is formed with dicrotophos. The molecular ion at m/z 237 and a fragment ion at m/z 193 (M-(CH 3) 2N) exhibit low abundances. In trace analysis, these two ions may not reliably be observed. With PCI, the mass spectrum exhibits an intense "quasi-molecular" ion [M + H]+ and the two adduct ions [M + 29] + and [M + 41] + at m/z 238, 266 and 278, respectively. Two fragment ions at m/z 112 and 193 are indicative for the structure of the side chain as indicated. With ECNCI, the mass spectrum of dicrotophos contains only a few negative ions with the base peak at m/z 125 dominating and indicative of the dimethylphosphate group. The fragment ion at m/z 222 denotes the loss of a methyl group. All three fragment patterns "puzzled" together obviously give information that is sufficient to identify the pesticide as dicrotophos. A second example is given with the three spectra of bromophos shown in Fig. 6.8. Although it belongs to the parathion group, whose members usually exhibit intense molecular ions due their aromatic structure, M+ cannot be observed with bromophos. The concentration of three halogens at one phenol 283
H.-J. Stan dicrotophos
El
Abundance
127 l
90 80 70 60 50 40 30 20 10
67 193 55
,,
n
40
III/L--
60
.. 4.. .1.I109
80
160.
237
221
100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 PCI
Abundance
ma_ b
93R v
90 80 70 60 50 40 30 20 10 57 7 2 6(0
80
112 266
193
98
1L
140
100
120
140
1278
I 160
l
Am
180 200
-
r
220 240 260
r
7
7
-
'
.
.
280 300 320
-
I
'
340
ECNCI Abundance
125
90 80 70 60 50 40 30 20 10 IILZ-->
u·
79 6C)
80
1
100 120
141 222 .
L
i I
140 160
180
200 220 240
260 280 300 320 340
Fig. 6.7. Mass spectra of dicrotophos measured with different ionization conditions. Top: EI, middle: PCI, bottom: ECNCI. moiety facilitates the expulsion of a chlorine atom radical, as already seen with 2,4-D in Fig. 6.5. The M-CI fragment constitutes the base peak in the EI spectrum and shows the typical CIBr isotope cluster (see Fig. 6.3). The other intense fragments from m/z 125 to 63 all arise from the dimethylthiophosphate moiety with (CH30) 2 PS at 125, (CH30) 2PO (after rearrangement!) + at 109, (CH30) 2 P at 93, etc. In the PCI spectrum, the [M + H] at m/z 365/367/369/341 exhibits the expected isotopic cluster of CI 2Br as well as 284
GC-MS. I: Basic principles and technical aspects of GC-MS bromophos
El
Abundance
331
90
80 70 60
so50
125
40
30 20 10
79 63
93 143
60
2413
1
3166
80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380
Abundance 90 80 70 60 50 40 30
125
l l 93~~.w. IILU
20 10 mFz--
PCI
367
100
V50
395 287
I·L 150
L · ·
··
200
331
LII · 311~1
· ·I 250
300
350
400 ECNCI
Abundance
257
90 80 70 60 50 40 30 20 10 M/z-->
0 60
270 79 2222
316 330 351
80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380
Fig. 6.8. Mass spectra of bromophos measured with different ionization conditions. Top: EI, middle: PCI, bottom: ECNCI. the two satellite peaks IM + C 2H5]t and [M + C 3H ]5 + . Loss of bromine results in m/z 287, loss of HCI in a small cluster at m/z 329 (base peak in the EI spectrum!). The fragments at m/z 125 and 111 (CH 3 0) 2 PO + 2H] originate
from the dimethylthiophosphate group. The ECNCI spectrum consists of only
285
H.-J. Stan two abundant ions. The base peak is formed by a rearrangement, common with the parathion group, resulting in the thiophenolate with the complete halogen substitution retained, as can be drawn from the isotopic cluster. The other ion is obviously formed from the M- by loss of bromine and methyl, as can be deduced from the m/z 270 and the isotopic cluster. Noticeable is the tiny peak at m/z 141 indicative of the dimethylthiophosphate group. This fragment is usually base peak with other members of this class not belonging to the parathion group but containing an alkyl side chain comparable with dicrotophos, for example. Summarising, the molecular weight information is provided with PCI, the assignment to the dimethylthiophosphates with EI and the affiliation to the parathion group with ECNCI. The presence of two chlorine and one bromine atoms is indicated by the isotopic clusters of the ions formed with all three ionisation methods. The application of ECNCI-MS in combination with other detection methods in GC parallel to GC-MS is described in section 6.13. 6.6
HIGH-RESOLUTION MASS SPECTROMETRY (HRMS)
HRMS enables the measurement of the exact mass of an ion and thus an unequivocal identification of its elemental composition. This high resolution can be achieved with double-focusing mass spectrometers or FT-ICR instruments. The usefulness of elemental composition information increases with increasing mass and this requires also an increase in mass-measuring accuracy. The technique is of great importance in basic research when unknown chemical structures are to be elucidated. In environmental analysis, the combination GC-HRMS is applied to ultra-trace analysis of polychlorinated dioxins (PCDDs). In Table 6.3, possible interferences for the most important chlorinated dioxin, namely 2,3,7,8-tetrachlorodibenzo-p-dioxin TABLE 6.3 Application of HRMS for separation of possible interferences of 2,3,7,8-TCDD Compound
Formula
Mass of interfering ion
2,3,7,8-TCDD Heptachlorobiphenyl Nonachlorobiphenyl Tetrachloromethoxybiphenyl DDT DDE
C1 2H40 2C14 C12H3C1 7 C12HC1 9 C1 3H8OCl 4 C14HgC 5 C14H8 C14
321.8936 321.8678 321.8491 321.9299 321.9292 321.9292
286
Resolution needed
13,000 7300 8900 9100 9100
GC-MS. I: Basic principles and technical aspects of GC-MS (2,3,7,8-TCDD) are compiled. When using the common low-resolution mass spectrometry (LRMS) with GC coupling, it would not be possible to distinguish 2,3,7,8-TCDD from the other compounds listed in Table 6.3 if the principal ion m/z 322 was monitored in selected ion monitoring (SIM) mode [8]. When applying LRMS, it is definitely necessary to resolve these interferences by chromatography. Otherwise, if any of these compounds coelutes with 2,3,7,8-TCDD, false positive identification may be obtained. The other problem is to avoid false negative results that may arise from the coeluting interference that makes the mass to intensity ratio of the indicative ions m/z 320, 322, and 324 incorrect. Since the residue analysis of PCDDs is carried out to monitor femtogram amounts, GC-MS in SIM mode is the only method for achieving the detection sensitivity needed. Positive or negative results cannot be proved by another analytical technique and the results are of great public concern. This is the reason why official methods demand the application of GC-HRMS. 6.7
TANDEM MASS SPECTROMETRY (MS/MS)
The structural information provided by mass spectrometry can be further enhanced with the combination of two mass spectrometers in one instrument adding a new dimension. This technique allows the measurement of the fragmentation of a selected peak in a mass spectrum producing the product (daughter) mass spectrum of that selected (parent) ion. The first mass spectrometer is used as a separating device for mixtures (such as unresolved peaks in GC-MS or LC-MS); after separating one particular ion, energy is added to yield dissociation product ions that are then separated in the second mass analyser. This mass spectrum is then used for structural characterisation of the selected (parent) ion. While in theoretical research this method is used to investigate the structure and stability of a molecule's fragment ions, in residue analysis the technique can be applied to the molecular species produced by soft ionisation methods such as CI. As described, PCI produces [M + H]+ usually with little fragmentation, which is useful in molecular weight determination but provides no structural information. In the instrument first used for MS/MS measurements, three quadrupoles are combined to the so-called "triple quad" with the first quadrupole as the separating device for the [M + H] + ions and the third as the mass analyser to monitor the products of the dissociation process. This dissociation is induced by collisions with a target gas that takes place in the central quadrupole and is referred to as collision activation (CA) or collision-induced dissociation (CID). Such CID mass spectra are as indicative for the structure or identity of 287
H.-J. Stan a compound as common EI mass spectra. The MS/MS instrument can in the same way be combined with GC and LC just as a normal mass spectrometer, making it the most sophisticated tool in pesticide residue analysis. Since the instrument is fully under computer control, it is possible to perform special techniques. Tandem MS can be carried out principally in two ways: consecutive in space by using two separate spectrometers (multiple-sector or multiplequadrupole instruments) or consecutive in time by using the same mass resolving system twice (ion traps or less frequently in pesticide residue analysis in FT-IRC) [9-121. Consecutive separation in space is easier to understand and is schematically shown in Fig. 6.9. A mixture of compounds is ionised and separated in the mass analyser MS 1. Only one (parent) ion is transmitted into the collision cell where the CID takes place. The resulting fragment ("daughter" or "product") ions are separated in mass analyser MS 2. Note that by selecting monoisotopic ions only monoisotopic daughter ions are produced. The daughter ion spectrum is, therefore, devoid of isotopic clusters. The basic equation describing the formation of a daughter ion md+ from a parent ion mp+ by loss of a neutral mn is mp +
md+ + mn
The parent ion is selected in MS 1, the daughter ions are detected in MS 2, and mn is inferred from the difference. Each of these three species can be designated as the independent variable in MS/MS measurements; the relationships are summarised in Table 6.4. The most simple reaction in MS/MS is the dissociation of a parent ion into daughter ions and neutral fragments. This is referred to as product (daughter) ion scan and provides, with a full product ion spectrum, the greatest information. To achieve the lowest possible detection limits for a target compound in product ion MS/MS, single-reaction monitoring (SRM) is performed. In this technique, all variables, as shown in Table 6.4, are fixed. This means that the first mass analyser is set to transmit the parent ion and the second mass analyser is set to transmit a specific daughter ion. This SRM technique is analogous to single-ion monitoring used in GC/MS when asking for the lowest possible detection limit in SIM mode. SRM in GC-MS/MS and LC-MS/MS is a means of eliminating "chemical noise" in the MS/MS spectrum. This technique is of outstanding importance for the analysis of pesticides with LC-MS in real food samples, as described in chapters 8 and 9. Although it is apparent that SRM offers less information than a complete MS/MS spectrum, it nonetheless provides a considerable increase in 288
GC-MS. I: Basic principles and technical aspects of GC-MS Al A2
Mixture of Analytes
A3 A4
1
I P3 P2 P1
P4 Mass spectrum of Parents
I Collision Cell
I P3+
Daughter Fragments of P3
Fl F2 F3 F4 F5
I
P3 F3 F2
F5 F4 Daughter mass spectrum
Fig. 6.9. Schematic of the principle of tandem MS (MS/MS).
specificity over a single-stage mass spectrometric analysis. Notice that the generation of a peak in a GC-MS/MS analysis in SRM mode requires that three independent criteria be met: retention time, mass of the parent ion, and mass of the product ion must satisfy the selected values. In pesticide multiresidue analysis, not just one such SRM measurement has to be performed in 289
H.-J. Stan TABLE 6.4 Parameter setting in MS/MS measurements Scan
mp+
md+
mn
Product (daughter) ion scan Precursor (parent) ion scan Neutral loss scan Single-reaction monitoring
Fix Vary Vary Fix
Vary Fix Vary Fix
Vary Vary Fix Fix
one retention time window but many of them can be repetitively carried out, which is referred to as multiple reaction monitoring (MRM). This is the most effective and popular way of performing pesticide multi-residue analysis in food samples with LC-MS/MS. A unique analytical aspect of MS/MS is the ability to screen rapidly for compound classes. Referring to the basic MS/MS reaction, any of the three species can be designated as the independent variable in an MS/MS experiment, as summarised in Table 6.4. If the product ion, md+ , or the neutral fragment, mn, is specified as the independent variable, new information is available that is provided by no other analytical technique. This technique, however, is not of great importance for pesticide residue analysis because the target compounds, namely pesticides, are known and belong to a multitude of compound classes. Measurements in which md+ is the independent variable are known as parent (precursor) ion scans. If a compound class includes a particular substructure which forms a very stable ion, parent ions derived from members of this class tend to produce a common daughter ion with this substructure. With MS 2 fixed to that daughter ion and MS 1 scanned, all parent ions can be detected. This technique also is of limited use in pesticide residue analysis. MS/MS experiments as described are performed with triple quadrupole instruments where, after separation of a certain parent ion in a first quadrupole, the CID takes place in a second quadrupole or octopole which serves as a collision cell. The resulting product ions are finally measured in the third quadrupole. This arrangement of tandem MS is mostly described as MS/MS in space. MS/MS can also be carried out with ion-trap instruments where all these experimental steps occur in the same space of the ion trap but one after another, and this is therefore designated as MS/MS in time. In the first step, the precursor ion is isolated and accumulated in the trap while all the others ions are ejected, then the isolated precursor ion is accelerated and collides with the helium gas in the trap and fragments to generate the product 290
GC-MS. I: Basic principles and technical aspects of GC-MS ions which then are ejected to generate a mass spectrum. The difference between ion-trap instruments and triple quad instruments is that, with ion traps, only product ion scans are possible but not parent ion or neutral loss scans. However, this must not be considered as a major drawback, these two techniques having no real value in pesticide residue analysis. On the other hand, ion-trap instruments are capable of performing MSn experiments or multi-stage MS, which means that a product ion can be retained in the trap and again allowed to collide to obtain another set of product ions. This process can be sequentially automated so that the most abundant ion from each stage of MS is retained and fragmented by collision. This is a very powerful technique for determining the structure of molecules such as peptides but until now it has rarely been applied to pesticide residue analysis.
6.8
MULTI-RESIDUE SCREENING FOR PESTICIDES APPLYING GC-MS
6.8.1 Introduction to multi-residue screening for pesticides with GC-MS Over the last few decades, multi-residue screening procedures for more than 400 thermostable pesticides in food samples have been based on gas chromatographic determinations. Results obtained with popular selective detectors such as ECD, NPD, FPD or the element-specific AED required confirmation by GC-MS [13-16]. GC-MS has dominated confirmatory analysis in the pesticide field since the early days. This technique has greatly benefited from the development of fused silica capillary columns and the development of small, relatively inexpensive mass spectrometers as dedicated gas chromatographic detectors. GC-MS is now readily available to residue chemists and the ease of operation and maintenance make specialists in MS no longer a prerequisite for GC-MS operation as with the more complex instruments of earlier generations. Positive identification of low-level residues in a food sample presents the analyst with a number of problems. Full-scan spectra should be obtained wherever possible. The high sensitivity and selectivity of modern GC-MS instruments enables this in almost all situations to below 0.01 mg/kg depending on the matrix and, in particular, on the chemical structure of the pesticide. With most instruments, full-scan spectra can be evaluated at the low ng level, i.e., 1 or 10 pg analyte injected into the GC-MS system with the sample. This can be achieved with extracts from food samples applying
291
H.-J. Stan a minimum clean-up. Spectral averaging and background subtraction facilities provided by the data system are generally used to remove contributions from matrix background or partially resolved contaminants. However, with very weak spectra, these data-processing procedures may lead to corrected mass spectra of dubious validity. This is the point where the analyst has to change from full spectral scanning to selected ion monitoring using the reduced number of mass channels with the considerably improved detection limits for the specified target compounds ions. In the following section, the application of GC/MS for the screening for pesticides in food with full scan as well as target compound analysis applying SIM is reviewed. The same methods are also applied for the confirmation of positive results from screening methods using less selective detectors. 6.8.2
The GC-MS instrument
Capillary GC is the analytical method with the greatest separation power. MS is the most sensitive method of molecular analysis with the potential to yield information on the molecular weight as well as the structure of an analyte. When these two methods are directly combined into one GC/MS system, the capabilities of that system are not merely the sum of the capabilities of the two outstanding analytical methods; the increase in analytical information is exponential. Extreme selectivities can be obtained, which are of utmost importance in screening analysis of target compounds in food as well as in environmental samples. The enormous amount of data generated by the GC-MS system in one single analysis makes a dedicated computer an integral part of the instrument. Automated analysis is routine in GC with food samples. Autosamplers carry out automatic injection in splitless, programmed temperature vaporiser (PTV), large volume injection (LVI) or any other mode fully controlled by builtin software [15,16]. Recently, difficult matrix introduction (DMI) injectors and special on-line sample preparation interfaces have been introduced, which appear very promising with respect to the reduction of the time necessary for clean-up [17]. An example is given in section 6.13. The enormous amount of data generated in each GC-MS analysis is stored in data files, usually on a hard disk. In this way, sample throughput can be maximised by round-theclock instrument operation. The analyst is no longer dedicated to instrument operation but confronted with a vast amount of analytical data. In screening analysis, it is highly desirable at least to be able to select positive or possibly positive samples from those certainly free of residues of pesticides or other 292
GC-MS. I: Basic principles and technical aspects of GC-MS target compounds. This is achieved by dedicated software programs for automated evaluation of full-scan as well as SIM analyses. 6.8.3
The mass spectrometer
When a molecule is ionised in a vacuum, a characteristic group of ions of different masses is formed. When these ions are separated, the plot of their relative abundances versus mass constitutes a mass spectrum. The emergence of such a mass spectrum and the information that can be drawn from it have been described in the preceding section. Mass spectrometry can be divided into two fundamental processes: ionisation and mass separation or filtering with subsequent recording of the ions formed. The recorded ions are finally subject to data processing by means of computers. The mass spectrometer is nowadays a highly sophisticated instrument under full computer control. It basically consists of five parts: sample introduction, ionisation, mass analysis, ion detection, and data processing. 6.8.3.1 Sample introduction Sample introduction in capillary GC-MS is simply performed nowadays by conducting the end of the fused silica column directly into the ion source through a heated transfer line. Modern mass spectrometers are equipped with efficient pumps to cope with the flow of up to 20 m/min carrier gas from the column, values commonly encountered with wide-bore capillary columns. Narrow-bore columns, however, are usually operated with a carrier gas flow of less than 2 ml/min. 6.8.3.2 Ionisation The analytes may be ionised in a number of ways but, for automated screening analysis, only electron ionisation is in common use although special applications of target analysis with other ionisation techniques are possible. The various ionisation methods employed in pesticide residue analysis are reviewed in the preceding sections. 6.8.3.3 Mass analysers After their production in an ion source, ions are analysed according to their mass-to-charge ratio (m/z) in a mass analyser. Five types of mass analysers are currently available: the magnet sector, quadrupole mass filter, ion trap, time-of-flight (TOF) and ion cyclotron resonance instruments. Over the last three decades, quadrupole and ion-trap instruments have dominated 293
H.-J. Stan the pesticide residue analysis field. Recently, however, TOF instruments have been successfully combined with gas chromatography enabling, in particular, with the appropriate columns, much faster GC-MS analyses. Magnetic sector instruments All the early work in organic MS as well as the pioneering work in GC-MS by coupling packed columns to an ion source by means of special interfaces, which preferentially removed carrier gas molecules and transferred the analyte molecules to the ion source, were performed with magnetic sector mass spectrometers. An electromagnet is used to separate ions for subsequent mass detection. In a single focusing sector instrument, the ions with mass m and z elementary charges are accelerated towards the source exit slit with a great deal of energy by means of the accelerating voltage in the source and fly through the magnetic field, which focusses ions of a particular m/z ratio into a narrow beam at a slit just prior to the detector. By variation of the magnetic field (or the accelerating voltage), ions of different m/z values pass through and can be detected by a detector at a fixed position as being separated in time. The most common way of scanning is by an exponential magnet scan allowing equal dwell times for all individual masses within the scan. The resolution of the mass analysis can be improved by means of an electrostatic analyser, which provides an additional focus to the ions. Instruments where both a magnetic and an electrostatic sector are coupled are called double-focussing mass spectrometers. These are capable of high-resolution mass determination separating different ions with the same nominal masses and are mainly used for elucidation of chemical structures of unknown compounds. In food analysis and in particular in routine pesticide residue analysis, these instruments are rarely in use. They are, however, state of the art in the trace analysis of polychlorinated dibenzodioxins and dibenzofuranes. Quadrupole instruments The quadrupole mass analyser is actually a mass filter. It consists of four hyperbolic rods that are placed parallel in a radial array. Opposite rods are charged by positive or negative DC voltage upon which an oscillating radio frequency is superimposed. Ions are introduced into the quadrupole field by means of a low accelerating potential of typically 10-20 V. They start to oscillate in a plane perpendicular to the rod length. When the oscillations are not stable, the ions do not pass the filter because the amplitude of the oscillations becomes infinite. When stable trajectories are made, the ions are transmitted towards the detector. The quadrupole filter thus acts 294
GC-MS. I: Basic principles and technical aspects of GC-MS as a band-pass filter, usually set to transmit ions of one particular m/z ratio ("unit-mass resolution"). To obtain a mass scan, the DC and radio frequency voltages are varied while their ratio is kept constant. The mass permitted to pass through is linearly related to the amplitude of the voltage. This simplifies GC/MS operation as well as computerisation. The linear relationship between mass and voltage makes control and calibration by computers easy. Quadrupole mass spectrometers have a reputation for high sensitivity and the ability to scan rapidly at millisecond intervals. These qualities made them well suited for coupling with capillary GS to scan the narrow peaks produced. At the present moment, the quadrupole mass filter is the most widely applied mass analyser in GC/MS as well as in LC/MS. Ion-trap detectors The ion trap was developed as a quadrupole-related detector for capillary GC. The unique feature of the ion trap compared with conventional mass spectrometers is that the ion source and analyser region are the same. In recent years, instruments with a separate ion source have also been developed. Ion traps are operated at relatively high pressures (0.1 Pa of He). Molecules entering the trap are ionised by conventional electron impact. Ions over the entire m/z range of interest are not allowed to leave; they are trapped by a quadrupole field, which is formed between end-cap electrodes and a ring electrode by applying a radio-frequency voltage. By raising the RF potential, the trajectories of ions of successive m/z values are made unstable. Unstable ions will rapidly depart the trapping field region in the direction of the end-cap electrodes, and since the lower end cap is perforated, a significant percentage will be transmitted through and are detected by an electron multiplier. Detection limits reached with the ion-trap detector have been reported to be better than with any other mass spectrometric detector in fullscan mode but there is not the increase in detection sensitivity in SIM mode that is observed with quadrupole or magnetic sector instruments. Mass spectra generated by an ion trap in earlier instruments were not always identical to those from conventional quadrupole mass spectrometers, although differences were generally not great. The reason was that the pressure in the ion trap is higher than in a conventional ion source forming (M + 1)+ ions from addition of H+. This ion-molecule reaction resembles production of pseudo-molecular ions in chemical ionisation. Therefore, under certain conditions, EI mass spectra might have contained a few additional ions resulting from the chemical ionisation process. Improvements in the computer control of the ionisation process have reduced the ion-molecule reactions such 295
H.-J. Stan that "mixed EI/CI mass spectra" no longer are recorded under conditions of routine GC/MS analysis. Time-of-flight (TOF) instruments In a TOF mass spectrometer, a pulsed beam of ions is accelerated by a potential into a flight tube and the time needed to reach a detector is measured. The ion source is pulsed in a way that a full mass spectrum is recorded before the first ions of the next pulse arrive at the detector. A rate of 5-40,000 pulses per second are usual. Depending on the acceleration voltage, 100-200 jts are necessary to record a complete spectrum. At least 10 of the acquired transients are summed prior to storing, which brings the number of mass spectra stored at present to a maximum of 500 per second. At higher data-acquisition rates, however, the apparent detection sensitivity of the instrument decreases due to the ion statistics. If the TOF-MS system is pulsing ions into the flight tube at the same rate of 5000 transients per second for the acquisition of 10 spectra per second, 500 transients are summed for each spectrum while, for the acquisition of 100 spectra per second, only 50 transients are summed for each stored spectrum. The greater number of transients summed at lower acquisition rates improves the signal to noise (S/N) ratio and, therefore, the sensitivity. One important characteristic of all TOF instruments is high ion transmission. Consequently, detection limits of TOF-MS are expected and reported to be better than that of quadrupole MS. The resolution has long been limited, although impressive improvements in this respect have been achieved recently by using reflectrons. This type of TOF mass spectrometer has been extensively used in studies with plasma and laser desorption techniques on large molecules such as proteins. Recently, it has also been interfaced to gas chromatography and liquid chromatography. Two aspects have been emphasized: improved resolution with the capability of exact mass measurement providing elemental composition data for both molecular and fragment ions and also fast data acquisition with high-speed data-collection systems capable of obtaining up to 500 full-range mass spectra per second. In fast gas chromatography, such a detector allows handling of narrow peaks of a width of 100 ms at the base, adequate for the generation of a pattern of sufficient data points for accurate recognition of the retention time and precise peak-area calculation. The new possibilities to automatically deconvolute full mass spectra with new algorithms to provide clean mass spectra from co-eluting compounds on the basis of minimum retention time differences appear even more promising. These algorithms were developed by Stein [18] at the National Institute of Standards (NIST) and incorporated 296
GC-MS. I: Basic principles and technical aspects of GC-MS into a Microsoft Windows program called AMDIS (automated mass spectral deconvolution and identification system). The principles of AMDIS are described in section 6.9.7.
Fourier-transformion cyclotron resonance (FT-ICR) instruments In a Fourier-transform ion cyclotron resonance mass spectrometer (FTICR/MS), the mass analysis is performed in a cubic cell placed in a magnetic field. The cell consists of two opposite trapping plates, two opposite excitation plates, and two opposite receiver plates. Ions are trapped in the cell in cyclotron motions and can be excited by means of a radio-frequency pulse to move them in phase on increased circle radii. The coherent movement of the ions generates an image current in the receiver plates that finally can be transformed by applying Fourier transformation into a regular mass spectrum. An important feature of FT-ICRMS is the extremely high resolution and sensitivity that can be achieved. The cell must be placed in relatively high vacuum (10 - 7 Pa). Although interfacing to a gas chromatograph has been reported, the domain of the high-cost instrument is basic research rather than analysing real-life samples. The choice of the analyser depends on the application. In practice, most GC-MS instruments have been developed on quadrupole including ion-trap technology. This choice is mainly determined by the simplicity of construction and vacuum technology and consequently the cost and space requirements. The situation, however, is changing and TOF instruments will play an increasing role.
6.8.3.4 Ion detection All mass spectrometers that are easily interfaced to a gas chromatograph are nowadays equipped with an electron multiplier. In such an electron multiplier, the ion beam is converted to an electron beam that is subsequently amplified through a cascade effect. In analogue detection, the signal of the multiplier is converted to a voltage, further amplified, and finally converted into a digital signal that can be processed by a computer. Usually, the electron multiplier is constructed to detect positive ions but, by placing a conversion dynode in front of the electron multiplier, negative ions can be detected, too. Upon impact of negative ions, the conversion dynode produces positive ions, which are amplified as described. TOF instruments are equipped with microchannel plate detectors working on a similar principle but with a very high time resolution. 297
H.-J. Stan
6.8.3.5 Data acquisition and processing (handling) Modern GC-MS systems produce an enormous amount of data that is acquired using standard personal computers. The operation of the mass spectrometer, data acquisition and processing is fully executed and controlled by the computer. Additionally, the interpretation of the data is also to a considerable extent carried out with the computer, especially in the form of automated library searches against reference spectra compiled in dedicated libraries. The provision of a powerful macro language with some software packages allows the creation of individual software solutions for the support of automated screening procedures. Two modes of operation are in common use and are applied to automated screening analysis of pesticide residues: repetitive scanning, also described as cyclic scanning or full-scan mode, and SIM. In full-scan mode, data are acquired by continuous repetitive scanning of the GC column eluate over the full analysis time starting after the solvent peak has been passed. The rate of scanning is predetermined by the operator; usually values in the range of 0.5-1 s per scan are used with capillary columns but, with shorter columns and faster gas chromatography, higher scan rates may be necessary to obtain accurate peak profiles from narrow fast gas chromatographic peaks as a prerequisite of reliable quantification. With modern quadrupole instruments, scan rates up to 20 per second are possible but, at higher scan rates, the apparent detection sensitivity of the instrument decreases because the S/N ratio depends on the dwell time of the acquisition of the individual ions. Therefore, a scan rate of 10 per second seems the acceptable limit in pesticide trace analysis. The dependence of apparent detection sensitivity on the acquisition rates is also valid with TOF instruments, but at a different level as described. The greater number of transients summed at lower acquisition rates improves the S/N ratio and therefore the sensitivity. Independently of the way the data are collected, each scan results in a full mass spectrum that is stored separately in the computer memory. Basically, a three-dimensional data array is generated by repetitive scanning with time, m/z and ion intensity as the three dimensions. This data array can be processed in various ways. A section in the plane of m/z is called a mass or ion chromatogram. When the intensities of all ions in each spectrum are summed and plotted as a function of time, a total ion current (TIC) chromatogram is obtained. In GC/MS, this plot is used as the nonselective chromatogram to see all compounds in the sample amenable to gas chromatographic analysis. This chromatogram is often compared with those obtained with the universal flame ionisation detector.
298
GC-MS. I: Basic principles and technical aspects of GC-MS Mass or ion chromatograms are here referred to as reconstructed ion chromatograms (RIC) in order to emphasize the fact that they are produced by reconstructing chromatogram-like ion intensity plots from separate mass spectra acquired in repetitive scanning and to distinguish them clearly from SIM.
6.8.3.6 Tuning and calibration The information that can be obtained from a mass spectrum fully relies on proper tuning and calibration of the instrument. Tuning is performed to achieve a high sensitivity over the whole mass scanning range and a proper mass resolution. The former tedious task of iterative adjustment to obtain the desired performance is nowadays automated employing software algorithms, which optimise the interactive lens potentials. The calibration of the m/z axis of the mass spectrum in EI is performed with reference compounds of which perfluorokerosine (PFK) and heptacosafluorotributyl amine (PFTBA) are in general use over the mass range relevant for GC/MS.
6.9 6.9.1
COMPOUND IDENTIFICATION Mass spectral libraries
Mass spectra obtained under standard conditions may be considered as a fingerprint of the molecule reflecting its chemical structure. They have therefore been collected in various mass spectral libraries. These libraries are commercially available for computer searching and identification of unknown compounds provided that a clean mass spectrum can be produced with the analytical procedure [19-24]. Excellent search methods for computerised libraries are available but the usefulness of these methods must not be overestimated with respect to elucidating the identity of unknown compounds in a food sample because only a minor portion of all known organic compounds are compiled in the universal mass spectral collections. In target analysis such as pesticide residue analysis, however, the situation is much better because all the peaks in a chromatogram are compared by means of their mass spectra with the entries of a limited mass spectral library containing only the target compounds, which means pesticides and their metabolites in this case.
299
H.-J. Stan 6.9.2
Background ions
A major problem in identification of sample peaks by means of their mass spectra is background ions. These may cause confusion in the interpretation of mass spectra. In electron impact MS, there are background peaks in the lower mass region at m/z 18, 32, 40, and 44 due to residual air. In GC/MS analysis, background ions may arise from column bleed of the separation phase and from the carrier gas. Since these background ions appear constantly over the whole gas chromatogram, they can easily be eliminated by background subtraction. Additionally, impurity peaks may arise from the sample preparation and clean-up; phthalates and other plasticisers are ubiquitous and practically unavoidable in trace analysis. The main problem with background ions in mass spectra of gas chromatographic peaks, however, arises from incomplete separation of the analytes from matrix compounds.
6.9.3
Background subtraction
Fortunately, computer background correction enables the removal of background ions from the analyte spectrum in most cases. The easiest way to clean up mass spectra is to subtract another mass spectrum which contains only background ions. Background ions are common to a larger number of mass spectra scanned at the base line of the gas chromatogram while ions due to a sample component exhibit abundances following chromatographic peak shapes with a maximum at the apex of the peak. The simplest type of subtraction involves a mass-by-mass subtraction of ion abundances of a background spectrum from the ion abundances at corresponding masses from the mass spectrum at the apex of the peak. Improved results can sometimes be obtained by averaging two or three spectra taken across the top of the GC peak, and subtraction of averaged spectra from both the leading and trailing edges of the peak. This method is of particular value in resolving overlapping peaks. Automated background subtraction methods can be used to remove interfering ions from overlapping matrix peaks as well as from non-separated target compounds. Automated background subtraction procedures are based on an algorithm that was introduced by Biller and Biemann as early as 1974 [25]. The procedure identifies all ions that maximise at each scan number and strips away all other ions from that scan. This stripping procedure not only effectively removes background ions from the column bleed and common matrix but also removes ions in a mass spectrum that originate from closely eluting unresolved GC peaks.
300
GC-MS. I: Basic principles and technical aspects of GC-MS Modern GC/MS instruments have such a software routine for automated background subtraction at the operator's disposal. On the other hand, other methods of background subtraction, as described for manual evaluation, can be easily designed using macro language facilities. The effect of background subtraction in producing a clean pesticide spectrum from overlapping matrix compound ion peaks is demonstrated in section 6.9.5. A more computationally intensive approach to extract weak ion peaks by explicitly considering S/N values for the identification of those weak peaks has been elaborated recently and is described in sections 6.9.6 and 6.9.7. 6.9.4
Library search
The most important step in checking for pesticides or other target compounds is the possibility of an automated library search. When measured under standard conditions, the mass spectrum of a molecule is very indicative (like a fingerprint). By comparing the mass spectrum of the analyte with others in a reference file, the identity of its nature can be recognised. A useful feature of all computerised comparison algorithms is the calculation of factors which are used to distinguish between good, average and poor matches. A human would generally stop searching at the first good match, but a computer is usually programmed to find all matches above a given threshold of match factor and to report them in a rank list. A very efficient search method for the retrieval of good matches from a mass spectral library is probability based matching (PBM), first developed and refined by McLafferty and his group [26,27]. PBM incorporates the features of data "weighting" and "reverse search". The "weighting" involves the two principal parameters in mass spectra: masses and abundances. The probability of occurrence of most mass values varies in a predictable manner. The larger fragments tend to decompose to give smaller fragments. According to McLafferty and Stauffer [28], the probability of higher masses decreases by a factor of two approximately every 130 mass units. More important for the identification of components in mixtures or incompletely resolved chromatographic peaks is the second feature of PBM: by means of this, PBM ascertains whether the ions of the reference spectrum are present in the unknown spectrum, which may be a spectrum containing extraneous ions. The "reverse search" approach ignores ions in the unknown that are not in the reference spectrum, since these could originate from other components of the mixture. In a TIC chromatogram, all peaks can be recognised by means of an automated integration procedure provided by the instrument's software. Each peak consists of a number of full mass spectra, which can be individually 301
H.-J. Stan called up and applied to the library search with or without manual background subtraction. The library search can also be run fully automated, as will be described later. The performance of a library search routine should not be checked by theoretical considerations but only by its application to standard mixtures and spiked samples. Analysing standard mixtures with decreasing concentrations spiked to the variety of food matrices gives the analyst a measure of the instrument's detection sensitivity. In other words, he or she will learn which amount of a particular pesticide must be injected to obtain a positive identification with a full-scan spectrum. The limits of detection vary with the target compounds depending on their fragmentation behaviour. This includes both the abundances of the molecular ion and of fragment ions in the high mass region as well as the presence of isotopic clusters. The detectability, however, also depends considerably on the food matrix and on the chromatographic properties of the compound: good GC is a prerequisite for reliable results in trace-level residue analysis with GC-MS. 6.9.5 Manual verification: use of RIC with background subtraction Library search results can show poor hit quality but excellent correlation in the retention times for the peak searched and the suggested library compound. In this case, the target compound may be overlapped by a co-eluate from the matrix and, therefore, a manual evaluation must be performed. In many cases, such a manual verification procedure allows the confirmation ofthe identity of a compound generating a peak overlapped by the peak of a matrix compound. The prerequisite is that the target compound ions and the ions belonging to the overlapping matrix compounds do not elute exactly at the same time. This emphasizes again the importance of good chromatographic resolution in GC-MS. An example is given in Figs. 6.10-6.14. Shown in Fig. 6.10 is the TIC chromatogram obtained in full scan from an extract of oranges exhibiting a great number of large peaks all resulting from the matrix. Zooming in on a small portion of the TIC chromatogram provides the display in Fig. 6.11 where a number of peaks of co-eluting substances of different signal intensities are seen. Two positions on the chromatogram are indicated. The small peak at retention time 12.30 was recognised as a peak by the peak-finding algorithm and therefore it is a target of library search of all peaks in the pesticide library; it was identified as the internal standard used in our laboratory for pesticide residue analysis, namely ALDRIN. The second 302
GC-MS. I: Basic principles and technical aspects of GC-MS Abundance .41rn~n TlR ,. MI .-
5e+07
n
4.5e+07 4e+07 3.5e+07 3e+07 2.5e+07 2e+07
I {I. ,kj
1.5e+07 le+07 5000000 0 Time-->
U kk~I
5.00
I 1
10.00
y.
~~ ,-.
15.00
1.1
1I .id
A.1iALWUIIW1A44m44Nwl . I
20.00
. .
......
25.00
.
I
.
30.00
.
.
.
35.00
.
IA1 WW -
.
40.00
Fig. 6.10. TIC chromatogram of an extract of oranges. arrow points to the portion of the TIC chromatogram at retention time 13.24 where no peak can be spotted. At this retention time, parathion and chlorpyrifos are expected. RIC with three ions indicative for chlorpyrifos produce peaks which have their apex exactly at the same retention time, thus hinting at the presence of chlorpyrifos (Fig. 6.12). The mass spectrum in the apex looks like a typical mixed spectrum as found with matrix and gives, as expected, no positive result in a library search (Fig. 6.13). After background subtraction, however, a good match between the unknown and the library spectrum of chlorpyrifos was found with a quality of 70. As can be seen in Fig. 6.14, the mass spectrum from the extract is by no means free of ions originating from the co-eluting matrix but visual inspection confirms the presence of the molecular ion cluster and the relevant fragment ions in the sample spectrum. Note that the concentration level of chlorpyrifos was finally determined at 0.03 g/kg. As shown in the example, manual evaluation of peaks that exhibit a poor hit quality to relative proposed library compounds, but a good correlation in
303
H.-J. Stan Abundance
4200000 4000000 3800000 3600000 3400000 3200000 3000000 2800000 2600000 2400000 2200000 2000000 1800000 1600000 1400000 1200000 1000000 800000 600000 400000 200000 0
12.00
12.20
12.40
12.60
12.80
13.00
13.20
13.40
13.60
13.80
Time-->
Fig. 6.11. Zoomed portion of the TIC chromatogram of an extract of oranges. retention time, can provide excellent results when using RIC of appropriate selected ions. A disadvantage of this technique is that it is very timeconsuming when applied to a great number of peaks and should only be necessary when identifying unknown contaminants. Therefore, the macro program AuPest Level 2 was developed to execute automatically all the steps described above when monitoring target compounds [29,301]. The characteristics of this program and its application are described in section 6.10.1. 6.9.6
Screening for pesticides with full scan
Since a maximum tolerance of 0.01 mg/kg for almost all pesticides in at least one of the food commodities was established in the EU guidelines, this concentration level has become the standard for the evaluation of all analytical methods in the field of residue analysis. Recent improvements in mass spectrometric detection sensitivity allow for the presence of most of the more than 400 pesticides amenable to GC to monitor at this low concentration level with full-scan data acquisition when applying a suitable clean-up and extract concentration. 304
GC-MS. I: Basic principles and technical aspects of GC-MS Abundance 50004000 3000 2000 1000 t
Ion 314.00 (313.50 to 314.50): 412G08.D
Time-->
12.20
12.40
12.60
12.80
13.00
13.20
13.40
13.60
13.80
14.00
14.20
14.00
14.20
1400
14.20
Abundance Ion 316.00 (315.50 to 316.50): 412G08.D
5000 t 4000 3000 2000 1000 Time-->
12,20
12.40
12.60
12.80
Abundance
13.20
13.40
13.60
13.80
ion 197.00 (196.50 to 197.50): 412G08.D I
500
2000
Time-->
Time-->
12.20
12.40
12.20
12.40
12.60
12.60
12.80
12.80
13.00 13.00
13.20
13.20
13.40
13.40
13.60
13.60
13.80
13.80
14.00
14.20
Fig. 6.12. RIC chromatograms of three indicative ions of chlorpyrifos (m/z 314, 316, 197) in the zoomed portion of the TIC chromatogram of an extract of oranges.
The prerequisite for the recognition of an analyte in full scan is the appearance of an analyte peak in the chromatogram to start with the library search and background subtraction as described. Pesticides at trace concentrations completely overlapped by matrix compounds may be missed R."-n IRfi
flq 947
in\' 1?(.rR n
700 650 600 550 500 450 400 350 300 250 200 150 100 50 40
60
80
100
120
140
160 180
200 220
240 260
280 300 320
340
Fig. 6.13. Mass spectrum at the apex of the RIC chromatograms.
305
H.-J. Stan Abundance
Scan 2564 (13.247 in): 412G08.D (-)
9000 8000 7000 6000 5000 4000 3000 2000 1000 m/z->
..... 40
60
80
i , 100
....,1" 120 140
J_
160 180
200 220 240 260 280
300 320
#66: CHLORPYRIFOS e Abundance 9000 8000 7000 6000 5000 4000 3000 2000 1000 m/z-->
.
. 40
60
80
100
11 120 140
. 160
180 200 220 240
1,L,
260
280 300
L 320
Fig. 6.14. Mass spectrum at the apex of the RIC chromatograms from Fig. 6.12 after background subtraction as described in the text. when relying only on peaks arising from the TIC chromatogram. However, full-scan analysis provides the only database to recognise pesticides not expected but included in the mass spectral library. An additional dedicated search can be performed by applying RIC of selected pesticides at the expected retention times as described. This approach can also be carried out automatically with special software routines, as will be described later with AuPest Level 2. 6.9.7
AMDIS from NIST
As demonstrated throughout this chapter, GC-MS is the method of choice for the identification of volatile (thermostable) pesticides in complex matrices from a wide variety of foodstuffs. The method can fail when acquired spectra are contaminated with extraneous ions, which commonly arise from co-eluting matrix compounds. The extraneous ions can pose a serious problem for automated target-compound identification methods where they can cause false negatives by reducing the spectrum match factor below some pre-set identification threshold. This uncertainty in the origin of ions in a mass spectrum may lead to a general loss of confidence in the reliability of making identifications of trace components such as pesticides in complex matrix extracts by GC-MS. 306
GC-MS. I: Basic principles and technical aspects of GC-MS In earlier methods, such as those of Biller and Biemann [251 and Colby [31], the extracted spectra are composed of all mass spectral ions that maximise simultaneously. These and other methods process an abundance data matrix consisting of ion/elution time pairs. Sets of ions whose abundances are correlated with one another are extracted but this approach is not able to make use of peak-shape information. A recent approach of Stein began with noise analysis for the recognition of component peaks at a very low signal level [18]. The algorithms were incorporated into a Microsoft Windows program called AMDIS (automated mass spectral deconvolution and identification system). The procedures include an integrated set of methods for first extracting pure component spectra (and retention data) from complex chromatograms and then using this information for the identification of target compounds in reference libraries. The methods were developed for a specific application, the automated identification of chemical weapons and related compounds, but they are expected to be applicable to any application requiring extraction of spectra from noisy chromatograms such as those obtained with pesticide residues in foodstuffs and the identification of target compounds by full spectrum matching. The overall data-analysis process involves four sequential steps: noise analysis, component perception, spectrum deconvolution and finally compound identification. The first step extracts signal characteristics from the whole data file of the chromatogram for later use in noise processing and threshold setting. The second step perceives the individual chromatographic components and determines a model peak shape for each component. The third step extracts purified spectra from the individual ion chromatograms using the model-shape approach, explicitly subtracting nearby components when necessary. The final step computes match factors for the extracted "deconvoluted" spectra with spectra in the reference libraries. In short, in the first step for each ion chromatogram, a noise factor is estimated which is then applied to indicate any possible peak which surpasses a calculated threshold. In the second step, components are perceived when a sufficient number of different masses maximises together. A precise maximisation time is computed by fitting a parabola to the maximum and its two adjacent scans. The measure of peak sharpness is computed for use in component detection. The model shape for each perceived component is then used for deconvolution. Finally, a hit list of library spectra ranked in similarity to the target compound spectrum is produced with a computed match factor, which ideally should reflect the similarity of the mass spectrum of the extracted compound and the reference compound from the library. 307
H.-J. Stan The application of AMDIS to deconvolute peaks in a chromatogram obtained with GC-TOF-MS is described later in section 6.13. 6.9.8
Confirmation and quantitative determination with SIM
SIM is a measurement method which changes the mass spectrometer into a highly selective detector tuned to monitoring of a small number of mass channels. The high gain in detection sensitivity with quadrupole instruments more than compensates for the reduction of structural information. The reduction of structural information caused by measuring only three ions instead of whole spectra is sometimes overestimated because mass spectral identification is based on various criteria. Reproducibility of retention times of better than 0.1 min is easily achieved with capillary columns that provide the highest chromatographic separation power of all chromatographic techniques, and can be additionally checked with internal standards. All three ions must give rise to a peak at the retention time corresponding within the reproducibility margin with the reference compound. Furthermore, the appearance profiles should be uniform and clearly resolved from other sample compounds where they have ions in common. The three selected ions must also match in relative intensity. Correspondence at trace-level concentrations is considered as established if the maximum difference intensity ratio of indicative ions relative to the reference is less than 20% [32]. The relative ratios can be determined either as peak heights or more frequently as areas of the ion chromatogram peaks. The use of isotope peaks of chlorine for this comparison is also acceptable for confirmation of chlorinated pesticides. Ions selected for SIM confirmation must be intense in the mass spectrum and indicative; that means all ions prominent with the mass spectrometric background from column bleed and common environmental contaminants as phthalates and hydrocarbons should be avoided. Generally, ions with higher masses are to be preferred because of their statistically lower abundance in other compounds and consequently greater significance. Ions of lower mass can arise by fragmentation from many compounds with higher molecular weight. A molecular ion of sufficiently high intensity is usually the best-suited indicative ion as applied in the confirmation. The proof of suitability, however, is always the appearance of the ion trace in the appropriate time window. If the confirmation criteria are fulfilled, any of the three ion traces can be applied for quantification. Frequently, one ion trace is used as quantifier and the other two as qualifiers. Quantitation is best carried out with two 308
GC-MS. I: Basic principles and technical aspects of GC-MS spiked food matrix samples "bracketing" the estimated concentration level of the target pesticide and run before and after the target sample. Through this process, confirmation and quantification are performed in one analytical sequence. 6.9.9
Target compound analysis with SIM
The combination of high separation efficiency provided by modern capillary columns with tunable selectivity and high sensitivity provided by mass spectrometric detectors under SIM conditions has in recent years gained the reputation of being the most powerful tool in ultra-trace analysis. This is the analytical method frequently used for monitoring baby food or any other kind of produce grown under the various "bio" conditions with respect to the low maximum tolerances of 0.01 mg/kg established for these kinds of foodstuffs. The method takes advantage of SIM time window programming and the high reproducibility of retention times. More than 100 pesticides can be monitored and determined in one run. The application of target-compound analysis with SIM, however, exhibits an inherent limitation to those pesticides selected as targets for the monitoring. Whilst capable of detecting the residue of one pesticide at the low ttg/kg concentration level, since the method is transparent to all other contaminants, another heavy contamination in the mg/kg level is missed because the particular pesticide is not included in the analytical method. Another critical point is the possible shift of retention times causing target compounds to leave the retention time window which would produce false negatives. Therefore, the reliability of the chromatographic conditions must be carefully checked by running standard mixtures of the target pesticides together with each sample sequence including spiked food samples. Although the SIM chromatograms appear almost transparent to coextracting matrix compounds, it is a severe mistake to apply a SIM peak to quantitation of an analyte without having carefully checked the peak shapes and peak-area ratios of all indicative ions. Quantitation must be performed with the ion least interfered with by matrix compounds or with all three ions independently. The latter method additionally provides a good indication of interferences should one ion trace give a different result. An early multi-residue method based on SIM was developed and evaluated with recovery data for 189 pesticides in fruit and vegetables by Fillion et al. [33]. Residues were extracted from food samples with 309
H.-J. Stan acetonitrile and co-extractives were removed by a clean-up step on a charcoal-Celite mini-column. SIM analysis was performed time-programmed with retention time windows containing one target ion and two qualifiers for each target pesticide. Two injections were required per sample to cover all compounds. In the first group, 35 retention time windows and, in the second group, 20 retention time windows were programmed over an analysis time of more than 60 min to cover all the target pesticides. The method demonstrated acceptable performance for the analysis of the number of crops investigated, exhibiting limits of detection from 0.02 to 0.2 mg/kg depending on the compound. An equivalent of 4 mg of food sample was injected onto the gas chromatographic column. The drawback of this method, however, is the well-known fact that a few relevant pesticides cannot be monitored because they are completely retained by the charcoal treatment. Such pesticides are chlorothalonil, dicloran, diphenylamine, HCB and propanil. The method was later miniaturised and modified by substituting the charcoal-Celite mini-column with an activated carbon membrane, but the problem of retaining pesticides extracted from the food matrix and amenable to GC in the charcoal clean-up could not be convincingly solved [34]. With regard to GC-MS, however, the method demonstrated the capabilities of this kind of target pesticide residue analysis because recognition and quantification can be carried out in one analysis if a proper calibration is performed. This type of trace analysis has become very popular in environmental research and is supported by the manufacturers of GC-MS instruments by dedicated software packages. The same food sample can be analysed in parallel with a spiked one. The spiked food sample is used to check the GC-MS instrument's performance in the same sequence of analyses and allows calibration and quantification with the corresponding matrix. An example is given in Fig. 6.15 where chlorpyrifosmethyl is detected with three characteristic masses, namely 286, 288, and 125, of which the latter mass is found with many organophosphates such as tolclofos-methyl, parathion, and pirimiphos-methyl eluting immediately after chlorpyrifos-methyl. Although one mass is common to these pesticides, they can easily be distinguished by their characteristic masses, as shown with chlorpyrifos-methyl. A special approach is target pesticide analysis developed with SIM and time window programming but using an ion trap instrument applying full scan. Ion trap instruments do not provide the increase of detection sensitivity observed with quadrupole instruments because they always produce the full spectrum of ions. An example of such an analysis is presented in 310
GC-MS. I: Basic principles and technical aspects of GC-MS RT:27.00 -32.00 NL: 2.50E7 mlz= 285.5286.5 MS K6sO430b
25000000 20000000
3004
15000000 10000000 5000000 28.97 29.11 29.26 29.49
3057 30.97 NL: 2.50E7 = m/z 287.5288.5 MS K6s0430b
25000000.
'
20000000. 15000000;
30.04
1 0000000 5000000; C)30.57 31.00
28.97 29.12 29.24 29.54 25000000 30.05
20000000
31.11
15000000 10000000\ 5000000
\
27.02 27.027.55 27.0
27.5
28.03 28.44 28.0
28.5
NL: 2.50'E7 31.70mz 124. 55 MS 0430b
28 81 29.14 29;54 29.77 29.0
29.5
30.0
30.5
31.0
31.5
32 .0
Time (min)
Fig. 6.15. Analysis of a mixture of pesticide standards in SIM with time window programming. Target pesticide: chlorpyrifos-methyl (m/z 286, 288 and 125).
Fig. 6.16 showing the whole computer screen as produced by the Enhanced Data Analysis software of the HP ChemStation (Agilent). In window #6, which fills the upper part, the SIM ion traces are displayed selected for penconazole in this case. The right window shows a table with all the target pesticides in the SIM method. Those pesticides found by the search algorithm are marked with "x" in front of the name. A click on a pesticide name calls up the upper retention time window with the corresponding SIM traces and the name, retention time at the apex, the concentration according to the calibration performed, and the relative peak areas related to the quantifier ion, which is set to 100%. These peak area relations are automatically calculated with a corresponding calibration run. The parallel display of calibrated peak area relations and those in the sample together with the match of the retention time is the measure of the identity of the compound in the sample. In this example, however, the GC-MS data were generated with an ion trap instrument running in full scan, as already explained, and then transformed for this software developed for quadrupole instruments. This 311
H.-J. Stan
ww~~tK:J rn.i |
XI
~l niguI KK
Melhamidopho xDidhlornin AKephate
:
K 2-Phenlphenol lhiolpropha x Dicloan DiatelhoaIe Elhoxyquin
:
Calhofulan
Lrdane PopyzPmide sPylehanil Diulfoton xDiazinon
Chloolothlonil : Pimicadb PaaNhon -ethyl
Chlrpydlo-ethyl Vinlozolin Toloolos-melhyl xCalbalyl xaMelalaxyl Y Fenilothih x Melhincdrb I
Y Aldin
DichIrflanid Pilinmphs-methyl x Malathion x Fenlhion ChloWpyrqio, Palathon i Diethofencarb Chohal-dimelthyl x T6dineeon x Fenson -Chlolthion Teiacenazol x Captean
Toffiuanid : Pfleno
I
: Folpet ruinalphs I Mecalbal
x
Chlz-nate Pacymidone Tliadimenol I ThdadifenolK 1
Fig. 6.16. Analysis of a mixture of pesticide standards in full scan applying SIM Enhanced Data Analysis software of the HP ChemStation (Agilent). results in a complete mass spectrum, as is presented in the left windows, with all the capabilities known from full-scan data processing including a library search from these windows where the mass spectrum is displayed and manual background subtraction in the window at the top right where the SIM traces are displayed, which are in reality RIC traces here. The result of such an analysis is shown in Fig. 6.16. A similar method was reported in 1995 by De Kok et al. [35,36], who applied the ion trap instrument GC-ITD (Varian Saturn), with software packages processing the data along the same lines as described, for the automated screening of a total of 320 pesticide residues in fruit and vegetables. At that time, the method had been evaluated for 2 years in the Food Inspection Service in Alkmaar (The Netherlands) with more than 2000 samples of fruits and vegetables. 312
GC-MS. I: Basic principles and technical aspects of GC-MS 6.10 AUTOMATED SCREENING APPLYING FULL-SCAN ACQUISITION Traditional screening analysis was always carried out with selected detectors such as ECD, NPD, and FPD followed by dedicated confirmation with GC-MS in full scan or SIM mode. The other method was target compound analysis with time programmed SIM window setting with the naturally imposed restriction on the number of pesticides monitored. Either method poses severe disadvantages. The combination of screening with selected detectors and additional confirmation of suspicious peaks is time-consuming. The application of target compound analysis with SIM exhibits the inherent limitation to these pesticides selected as targets for the monitoring method. Since more than 400 pesticides can be analysed by GC-MS and since most of them may be extracted and cleaned up with good or medium recovery applying standardised procedures, all these pesticides can be detected in a gas chromatogram if a suitable detection method is applied. Such a universal detection method is undoubtedly EI mass spectrometry when operated in full scan. Therefore, in recent years, the mass spectrometer, which had been used only in confirmatory analysis for decades, has also gained popularity in screening analysis. This new application has become the domain of quadrupole or ion trap mass selective detectors but they may be joined in the future by TOF instruments. In principle, any problem that can be solved using pen and paper or the keyboard of a computer in a finite amount of time by following logical rules can be performed by a computer. First, one needs to define clearly a set of rules for GC-MS data analysis. The key to the flexibility necessary for creating automated methods is to break down the data analysis problem into a number of small sequential tasks, each of which has associated software routines. These routines built up with a special macro language can then be linked together as needed to customise data analysis for individual samples with respect to target compounds. Considerable improvements in available software have been introduced in recent years for qualitative automated data analysis handled by powerful macro programming language, which enables links with standard text and spreadsheet programs for both processing and output. Since the optimum automated evaluation programs are developed by analysts familiar with the daily routine in a laboratory dedicated to pesticide residue analysis, the clear documentation of the macro commands and their capabilities are the basis for developing a powerful userfriendly macro program.
313
H.-J. Stan 6.10.1 Automated evaluation of full-scan acquisition data applying AuPest Screening analyses in the author's laboratory in the early 1990s, using the HP 5970 mass selective detector (MSD) with cyclic scanning and searching in a designated mass spectral pesticide library, gave surprisingly good results. Manual data evaluation still remained very time-consuming, although the search was only carried out in a designated library. Therefore, the macro programAuPest was developed in our laboratory to simulate automatically all the steps usually applied in manual data evaluation [29,30]. The program and its predecessors have been used successfully over the last 10 years or so for pesticide residue analysis in food and also various kinds of environmental analysis in ground and surface water as well as soil samples. AuPest, taking full advantage of WINDOWS M , follows the line an analyst would take in evaluating the mass spectral data acquired. Such an evaluation includes autointegration with automated peak recognition, background subtraction and library search in designated pesticide libraries and also in universal mass spectral libraries. The decision concerning the presence of a pesticide is supported by quality factors but needs final inspection by the analyst with a direct visual comparison of the mass spectrum of the suspect with that found in the library with the search routine. A very important feature is the use of actual or corrected retention times as a very important independent piece of information in pesticide recognition. This enables the recognition of target compounds overlapped by matrix compounds, which produce poor library search results. On the other hand, any similarity between the mass spectrum of the sample peak and the reference spectrum must be considered as purely coincidental if the retention times of the reference target compound and sample peak are significantly different. AuPest provides, when operated on its first level, a complete analysis report with all integrated peaks listed with their retention times and search results. In a second result table, called important peak list, only those results of the library search that have met user-defined thresholds for hit quality and retention time windows are compiled. A third result table contains the integration results of all peaks with such details as peak area, peak width, resolution, peak start and peak end. Together with the TIC picture, the integration results table presents an overview of all compounds detected in the sample, as shown in Fig. 6.17. Data processing is completed in the time needed by the GC/MSD system to cool down after a run with a temperature program and then equilibrate before the next start. A second advantage of AuPest Level 1 is that the analyst can check the results first on the screen and decide later what he wants to print out. 314
GC-MS. I: Basic principles and technical aspects of GC-MS
P;
r.
TS
"HaeuPesScreening _
I-
-Cre- Ued Al. 07 14:27:07 2002 Jsng methd C:\ IPCHEN I\IEN I ODISOII IST1 le
atE :
DATAFILE EAMPLEDESCRIPTIO: ACQUIBITIONDTE NETHODFILE TIERITORFI0 MI5C ItII!:RSATIMN __ -_ _ -_ -_ _ -_ -_ _ -_ _
4q10G16.D :
:
DEC 11
21:1
AN.I 1-1 AGNM: 1.01-lG0
_-_-_ _-______ -_ -_ __ -_ _ -_-_ _ -_ -
Fig. 6.17. AuPest results of a tomato sample after automated evaluation. Right window: HP ChemStation Standalone Data Analysis with TIC chromatogram in window #2 and AuPest integrated in the task line with AuPest pull-down menu open. Left window: AuPest results with Level 1 and Level 2 results and the corresponding important peak lists.
This saves printing costs and reduces paper consumption enormously. Finally, the automatic comparison of search results with and without background subtraction always guarantees the best search results. As mentioned above, the AuPest Level 1 results table may contain some search results with a poor hit quality but excellent correlation in the retention times for the peak searched and the suggested library compound. In this case, the target compound may be overlapped by a co-eluate from the matrix and manual evaluation, therefore, is then required. Such a manual verification procedure, performed to confirm the identity of a compound generating a peak overlapped by the peak of a matrix compound, can provide excellent results when using RICs of appropriate selected ions as described in section 6.9.5. A disadvantage of this technique is that it is very time-consuming when applied to a great number of peaks. Therefore, AuPest Level 2 was developed to execute automatically all the steps described above.
315
H.-J. Stan The user has to create so-called "control files" that define two or three indicative ions and the time window for the target compounds. Level 2 begins to check for the first target compound by redrawing the specified ion traces in a user-defined time window, usually 1 min around the expected retention time of the target compound. Only if Level 2 has found peaks in the traces of the indicative ions will it proceed to check whether peaks appear at the same retention time in these ion traces. If their difference in retention is smaller than 0.015 min, Level 2 assumes that these ions originate from the same target compound and continues by sampling a scan at the apex of this peak followed by background subtraction. This is performed by subtracting the scans taken at peak start and peak end. A dedicated pesticide library is searched to find a match for the resulting spectrum. It can usually be seen when looking at the RIC traces that the apices of the target compound ion traces do not fit exactly the apices of the matrix compound peak. Therefore, the raw spectrum of the scan selected by Level 2 generally shows good correspondence to that of the target compound sought. Further background subtraction at peak start and peak end of the target compound may eliminate the interfering ions almost completely, so that the library search now results in a better hit quality. The library search is also performed without background subtraction. Only the better result is reported. Level 2 then continues to search for the next target compound listed in the control files that include a total search capacity of up to 500 target compounds. One particular feature of Level 2 is based on the RIC technique. Level 2 can find those peaks that are normally overlooked through being hidden in noise. These peaks are naturally not integrated by the integration software and, as a consequence, no search is carried out. Since the S/N ratio is, with most of the ion traces, orders of magnitudes better than with TIC, the presence of compounds can be spotted at very low concentration levels depending, of course, on the overall abundance of the fragment ions selected. An example is given in section 6.9.5 with the manual verification procedure. The development of AuPest was carried out using an older GC/MSD system. It turned out that the limits ofAuPest were bound by the instrumental limits of detection. With the newer generation of GC-MS systems having an increased detection sensitivity, automated evaluation of full-scan GC-MS analysis of food samples applying AuPest became the established procedure for screening analyses for pesticide residues in our laboratory. In the following, the application of AuPest to a sample of tomatoes with a few pesticide residues detected is described briefly. The whole procedure and the total number of pesticides included have been recently published [30]. 316
GC-MS. I: Basic principles and technical aspects of GC-MS 412L
;:~ ~' 6 87
E
<.i: p;>f: ? ! T~[IONAlZl 1
1l.790
FENPROPIDIN t
11.832
11.853
12.316 13153
s9O ALDRI e 95
LINURO5t
102 HEXAZINONE
113 116 12i
14 25
a
>
5.30Z
11
359
1
R
199
1
12.360
59
215
1
5Z. 31
2
18
1
13.970 23 47.Z 14
156
14.93
14.d49
70
294
1
5LLETRIN e
15.S47
15.821
9
204
1
DINOCAP
1S,948 16.933
428
I
PACLOBUTRAZOL u
1.220
16.686
9
100
1
DINO P
16.573
156.933
1
28
pHOXI t
16.735
16.686
7
12
1
528
1
1
428
1
126
DINOCAP
16761
16,933
127
DINOCAP
16.869
16.933
1
I
l 1
151 131
Ina03LI0L 0e 17.955 17.786 1,2,4 TRIC LOROACETOPlENO 17.B55 5596
91 25
3250 1 440 t
I
131
CLORFLUREC OL-ITHYL
1
322
1
1 1
17.85S
13.349
133
FLUDIOXONIL e
18.295 18.157 96
318
1
135
PROCIDOIE
18.52
136
FENQ~UCRD
140
PYRETHRIIII
C
' ?~'
15.697
3Z
Z16
1
18.660
7.020
27
507
1
19.q87
18.66q
7
272
1
: .... :Or
Fig. 6.18. Important peak list Level 1 with zooming in the peak of cyprodinil at RT 14.49 in the right top window #2 of the standalone data analysis and the mass spectrum of the peak apex in the right bottom window #1. Note, the recognition of the ISTD ALDRIN and the pesticide imazalil with Level 1.
How an analyst works out the evaluation of results produced with AuPest can also be followed in detail via the internet site http://www.AuPest.de. AuPest is a subprogram integrated in the task line of the Standalone Data Analysis of the HP ChemStation software. When opening the Results from the AuPest pull-down menu, the display is divided into two parts with all the automatically generated results in tables on the left side, as demonstrated in Fig. 6.17. Usually, the Important Peak List Level 1 is opened and the positive results are checked. By mouse clicking on a pesticide, the corresponding peak from the TIC chromatogram is zoomed in and presented in window #2 on the right side together with the mass spectrum from the apex of the peak without background subtraction in window #1, as seen in Fig. 6.18. The peak of cyprodinil shown is well separated and a double mouse click starts the library search, resulting in a good match of the sample peak to the cyprodinil spectrum from the pesticide library, as can be seen in Fig. 6.19. The quality may be improved by manual background subtraction. With an icon or pull-down command, the result is copied into the "Summary Report", 317
H.-J. Stan :
<
86
TEIONAZINIt
11.790
5.302
359
1
87
FENPROFIDIN 5
1l.632
11853
4
199
1
90
ALDRIN e
12.316
12.360
58
215
1
95
LINURONt1
0.153
12.731
HEXAZINO1EL
102
j
'",,,
__4~0 113
l
I
;
0LLETHRIN e
i
2
la
I
13.970 23.472
15
156
1
14.49
14.449
70
29
1
15.547
15.&21
9
204
1 1
118
DNOC P
15-948
16.933
1
428
121
PACLOBUTRIOL b
16.208
16.686
9
J100 1
124
VINOCAP
16.573
16.933
1
428
125
PHOXI
16.735
16.686
7
12
16
DINOCAP
16.7161 16.933
1
428
1
127
DINOCAP
16.869
16.933
1
428
1
131 131 131
IMkZALIL e 1,2,4-TRICLOROCETOP1No CHLORFLURECOL0ETYL
17.855 17.855 17.855
17.708691 5.896 25 13.349 14
250 440 322
1 l 1
133
FLUDIOXONILe
18.295
10.157
96
318
1
135
PROCIIDONE e
18.526 11.697
32
216
1
136
FENOBUCARB
18.660
27
507
1
ig._ 6.
t
9
.b
7.020
l
enc
S
: I
-~,Si
1 1
Dou l3ickingon the 5 pe
mass 1 spet~ilrof
l
ne ri eot
Fig. 6.19. Double clicking on the mass spectrum of the peak apex in the right bottom window #1, as shown in Fig. 6.18, starts the library search in the PEST.L and confirms cyprodinil.
which is laid out only for internal use in the laboratory and mainly to support the following quantification (Fig. 6.20). This is the only part of the whole data collection where comments or suggestions can be made; all other data are protected against manipulation. After the important peak list Level 1 has been checked, the analogue procedure is carried out with the results provided in the important peak list Level 2. Zooming in is demonstrated in Fig. 6.21 for the three indicative ions of imazalil with all three exhibiting the same peak form. In this sample, three pesticide residues were eventually determined, namely 0.12 mg/kg cyprodinil, 0.4 mg/kg imazalil and 0.08 mg/kg fludioxonil and, of course, the ISTD, as can be seen in Fig. 6.22. Another important feature of the macro program is demonstrated in this figure, namely the possibility of tracing back from the final report to the mass spectral data. 318
GC-MS. I: Basic principles and technical aspects of GC-MS
~
5~i~ 'I~ '~ -,
Fv4os
I%
~~~~~~ I
I I -
, M
a
4,
~
D
~"-1
I :'i
"I-
0
ca:E;£ Pjsn~ S: C
4e.i
can44~~n~~·~~----Y
Cl~nf1: e 15 Tt 14493 CPR0ODNI
1x
7717Th+b MAGPEST L
294
11357774
It~
J
DI"
tU4
i tJ
J
DilJ r
Xi tj
f
Fig. 6.20. The confirmation of cyprodinil is copied into the Summary Report.
6.10.2
Retention time locking (RTL)
Retention time is an important piece of information about the possible identity of an unknown compound in a gas chromatogram. Shifts in retention time may occur for several reasons, column trimming being just one. Usually, retention times for each instrument will differ from each other, even when run under nominally identical conditions. This also holds true when replacing a column by a nominally identical one in the same instrument. RTL is a recent concept in gas chromatography used to make analytical runs on different instruments more comparable. RTL allows recalculation of calibrated retention times and transfer of calibration tables from one instrument to another [37,38]. In pesticide residue analysis, it was first applied by Hewlett Packard, now Agilent, in order to narrow an analyte's identity to a few possibilities by calculating the retention time with high accuracy to enable a good match to reference compounds in a retention time table. In pesticide residue analysis, it was applied to the parallel analyses of the same sample on a gas chromatograph with an atomic emission detector (AED) and with GC-MS equipped with nominally the same column and run under identical conditions. 319
H.-J. Stan
Target: C~kNAZINEt 37
Z .600 14.49
CYPRODINILe
Target: CPROOINIL e 56 CYPRODIIIe Tziuew:
14.549 14.449 4.600 3.819
DE5EIIION
6UTRICHLOR¥ON t 70077: 66
DINOU0ON 0 p60XI t
14.494
70 294
S.4~5
70
4.573
7
i5.800 16.686 ]5.712
7
12
DINOSEBt CARB0FUR0 Ne
a.500 7.?87
B.4Z5
1Z
7T6ret: FEN0ONe 79 CYPRODINILe
13.5s0 14.q49
q.49Z
64
Z94
Thr0e0:FLUDIO0ONILe 88 0 'LUDIOXONIL e
18.700 18.157
18.292
9s
318
% Target: HEPTACHLOR ALDRINe 96 ALDRINe 97
11.400 12.60 12.60
12.292 12,$18
76 58
Z15 215
5.120 3.8t9
4.575
17.750 17.766
17.841
90
250
17.700 17.786
17.a52
60
176
Tare: 67
Target: 98
HEPTENOPO5 e TRIC6LORFONt
Tage:IIkzIL e
Targer: iG3
IMAZALILrip e IlMAZALIL
Target: ISOpROPaLIM t 106 CYPRQDINILe 106 IS0METIOZIN t
I
14.700 14.149 14.4~6 i4.446 13.472
V
91 9
Fig. 6.21. Zooming in of the indicative ions of imazalil by RIC by means of the icon in the task line or pull-down menu, as shown in Fig. 6.22. The intention was to obtain a compound's calculated element ratio with GC-AED and confirm its identity by GC-MS with EI in full scan and library search. RTL should ensure that peaks originating from the same compound are investigated in both systems. A problem arose, however, from the fact that the detection sensitivities of both techniques were not compatible. The AED turned out to be much less sensitive than the GC-MS [14]. Detection sensitivity, however, is the most important feature in pesticide residue analysis. Therefore, RTL is now applied as already mentioned and is also used to improve the quality of retention data in daily routine work. The information provided by a mass spectrum measured with EI is, in most cases, however, sufficient for an unequivocal identification of a target compound independent of small retention time differences between the "unknown" in the sample and the reference pesticide laid down in the database. 6.11
ANALYSIS OF PESTICIDES WITH GC-MS/MS
Analysts in many fields, including pesticide residue analysis, have long been thinking about methods to eliminate the time-consuming and laborious 320
GC-MS. I: Basic principles and technical aspects of GC-MS File
cleanup
Edit Summary
hemstation
Window
Ax ~'i;y 1SL'r ATE
hep
i::
_
1
ec Due
TIC 12315ALDRINe 65
MAGPESTL 215
745159
12318ALDRNe
MAGPESTL 215
511502 11357774
TIC 14493CYPRDINIL
MAGPEST1 294
224 1445 CPROINILe
MAGPEST L
215 17241IMA4LIL
4MAGPESTL250
1017615
11C17 5 IMA2ALIL
MAGPESTL 250
18566450
MAGPEST L
11438317
TIC 125 244
FLUWDIXONIL e
IB22 9FLUDIXONILe
I
294
314
',^,
Remove ROW Reset Columns:
3384884
13472
Show Formla
Show Orlpi
A
Find Text...
_J iEatfAt on hro agiams eectedit Axl ,'tsCi:g A:rs4P11i3Dle 544 5440110455551144
-
0
Fig. 6.22. Final Summary Report with all confirmed pesticides and tracing back from the entry of fludioxonil to the three indicative ions, which enabled this residue with Level 2 to be found. Note that all pesticides found with Level 1 are indicated in the left column with TIC and those found with Level 2 with the ion used as quantifier. clean-up by applying more selective and specific identification methods. The invention by Yost and Enke in 1978 of GC-MS/MS, using two quadrupole mass analysers in line in combination with a gas chromatograph provided a new perspective to achieving this goal [39]. As described in section 5.7, the first quadrupole analyser is adjusted to select for individual precursor ions, which are then energised to yield dissociation product ions, which are then separated in the second mass analyzer. This mass spectrum is then used for structural characterization of the selected (parent) ion. MS/MS can also be carried out with ion-trap instruments where all these experimental steps occur in the same space of the ion trap but one after another [40] and this is therefore designated as MS/MS in time, as described in section 6.7. The invention of GC-MS/MS was welcomed as the breakthrough everyone had been waiting for. MS/MS was considered as an additional dimension in separation power. The most optimistic analysts thought of chromatography in the mass spectrometer itself. Early applications of GC-MS/MS and direct 321
H.-J. Stan probe introduction with tandem MS demonstrated the possibilities but all applications published appeared more of academic interest only, because it turned out in daily routine work that the detection sensitivity and performance decreased rapidly with the increase of matrix deposits in the ion source. In GC-MS/MS, the chromatography additionally deteriorated with the burden of deposits in the injector and at the beginning of the capillary column. Recently, a new method of sample introduction has been developed and made commercially available: direct sample introduction or "dirty sample introduction". It seems to improve the applicability of GC-MS/MS with complex food samples, which underwent no further clean-up after extraction. A portion of the raw extract is placed directly into a disposable microvial and the injector is first heated gently to evaporate the solvent and then heated rapidly to thermally desorb semivolatile sample components, including many pesticides amenable to gas chromatography. A major benefit in this direct sample introduction approach is that non-volatile matrix components no longer contaminate the insert liner remaining in the microvial, which is discarded after injection. The complex extracts require a very selective detection technique to determine the target analytes among the many matrix components. GC-MS/MS has been demonstrated to be capable of detecting many of the pesticides in such complex extracts and the instrument commonly applied for this approach is the ion trap [17,41,42]. A similar analytical approach applying an automated system for direct sample introduction is one in which a TOF-MS is used as the very selective detector. At the present time, GC-MS/MS is frequently used for food samples whose gas chromatograms show an overlap of certain pesticides by matrix compounds such as free fatty acids after conventional or reduced clean-up. These pesticides may be detected by applying the higher selectivity of GC-MS/MS or GC-TOF-MS, as described in an example in section 6.13.
6.12
PESTICIDE RESIDUE ANALYSIS BASED ON CI IN NEGATIVE AND POSITIVE MODE
The following unusual method for the screening of pesticide residues in foodstuffs is used in the Berlin-based laboratories of the analytical specialists SOFIA. It also relies on mass spectrometry but manages almost completely without EIMS. EIMS is only applied for confirmatory analysis in the form of MS/MS.
322
GC-MS. I: Basic principles and technical aspects of GC-MS The development of this method began with the task of analysing for pesticide residues in very difficult matrices such as tea down to the 10 ppb concentration level with special attention to pyrethroids. These pesticides are not credited with high detection sensitivity in EIMS. Organophosphorous insecticides and chlorinated hydrocarbon pesticides were known as compounds that respond with good sensitivity to electron capture negative chemical ionisation (ECNCI). After a successful start with tea, this method was applied to all other matrices and worked in the hands of their creators more effectively than other more standard methods. The effectiveness with respect to cost and time is also a result of the deliberate creative linking of all acquired data by dedicated software programs created by J. Lipinski in their laboratories. After clean-up with a modified DFG S19 procedure, the gas chromatographic analysis is carried out on one single gas chromatograph equipped with two identical 15-m capillary columns (SE-54) operated in parallel. One column is connected to an MSD operated in CI mode with methane as reactant gas, the other is connected with effluent splitting to nitrogen-phosphorus and phosphorus selective flame photometric detectors. The sample is introduced into both injectors in close sequence using a programmable autosampler with the oven held at the low initial temperature applied for splitless injection. The sample aliquots are thus trapped at the beginning of both columns and the temperature programme is then started. The retention times of all analytes are kept absolutely equal on both columns by controlling the gas flow and additionally with RTL calculated from added internal standards. In the cleanup procedure, a fractionation is carried out on a silica mini-column resulting in two fractions, one non-polar to medium polar and one polar. Both fractions are applied to the gas chromatographic system, measuring the non-polar and medium polar pesticides in negative CI and the polar in positive CI full-scan modes. The group of polar pesticides includes important organophosphorous insecticides such as acephate, methamidophos, dimethoate and others that exhibit no useable mass spectra in NCI mode. This is the reason for applying PCI mode. Included in the group of 30 polar pesticides, however, are also azole fungicides, which exhibit good NCI spectra. These fungicides also respond to the NPD and therefore cannot be missed in the analysis. The reason not to run the fraction additionally in NCI mode is simply a question of sample throughput. If, however, a critical concentration of one of these pesticides is found, analysis under conditions optimum for the target pesticides is always an option. The ion traces m/z 35 and 79 are displayed from the NCI full scan by means of RIC, allowing screening for chlorinated and brominated compounds. 323
H.-J. Stan These traces are displayed together with the TIC and the recordings of the signals from NPD and FPD, respectively. The presence of chlorinated, brominated and phosphorous-containing compounds can thus be recognised at a glance and nitrogen-containing compounds are also easily spotted. Additionally, a printout is created for a selected number of target pesticides considered actually relevant, presenting in retention time window two or three reconstructed ion traces indicative of these corresponding pesticides. The way the screening analysis is carried out in the SOFIA laboratories is demonstrated with a sample of grapes from a recent monitoring. The sample is spiked with four internal standards, namely bromobiphenyl (ISTD 1), tris(dichloropropyl)phosphate (ISTD 2) and the PCB congeners 206 and 209. The first display of chromatograms is presented in Fig. 6.23. The TIC chromatogram shows a few peaks of high intensity but not the great number of matrix compounds typical for a TIC chromatogram obtained with EIMS, demonstrating the more selective ionization in ECNCI. Brominated compounds can be excluded as only ISTD 1 is seen in the bromine trace at m/z 79. A few chlorinated compounds are present, three of which represent the internal standards ISTD 2 and the two PCB congeners. The peak pattern of the NPD and FPD(P) traces appear similar, indicating the presence of organophosphates. The retention times of the two FPD peaks at about 8 and 8.6 min hint at the presence of parathion-methyl and chlorpyrifos, which may be confirmed by the ion chromatograms of the indicative ions at m/z 263 for parathion-methyl and m/z 313 for chlorpyrifos. Since the mass spectrometer was operated in full scan, the complete ECNCI mass spectra are available. After background subtraction, clear spectra are ready for the library search in a dedicated NCI mass spectral pesticide library. The two spectra are shown in Fig. 6.24. This is in agreement with the automated recognition procedure shown in Fig. 6.25. Further investigation of the chlorine trace shows the presence of endosulfane with two isomers and procymidone, which were confirmed by their full mass spectra. Finally, a clearly separated peak in the TIC chromatogram has been identified as quinoxyfen. Note the good detection sensitivity of the ECNCI mass spectrometric method. There was no clear indication of the two nitrogen-containing pesticides procymidone and quinoxyfen in the NPD trace! Note that all the windows not showing corresponding peaks in their retention time windows have not been selected for presentation. The results of this pesticide monitoring of a grape sample from the market are compiled in Table 6.5. 324
GC-MS. I: Basic principles and technical aspects of GC-MS
Uv
_A 1
.
11
R."
4.00
6.00
8.00
4.00
6.00
8.00
4.00
6.00
8.00
I
Pk
I ID
10.00
[
12.00
.
4
Aq~r- lr· 16.00 14.00
.
,[
10.00
12.00
14.00
16.00
10.00
12.00
14.00
16.00
FPD
,....
4.00
6.00
8.00
. ,
1,....,.
10.00 12.00
.........
14.00
16.00
Fig. 6.23. Parallel display of TIC, RIC ion traces at m/z 35 and 79 from full-scan NCI measurements and signal recordings from NPD and FPD(P) detectors. (Courtesy of J. Lipinski.)
325
H.-J. Stan
Wi
501
'
'chforpyrifos
parathion-methyl
a1 7.90
.80
8.00
8.10 154 parathion-methyl
[263
393 427 449 1282 300 329 351 , 15 206 232 71 95 117 13 . . . I . I . . ., . . ., 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 41
chlorpyrifos
169 212
4.
35 249 286 267, i , , ,9 gy·-cr~~P L 54 73 9 124143 367 400 433 332 .:.,....Itr ·... '. '. ... , , ,! !..,, u. .. A . . 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440
1
Fig. 6.24. Parallel display of TIC (extract of the chromatogram) and NCI mass spectra of parathion-methyl and chlorpyrifos called up from the scans as indicated. (Courtesy of J. Lipinski.)
326
GC-MS. I: Basic principles and technical aspects of GC-MS 22a222aa a
I~ 143 0(14270 io 14370)
432WD318 D
lon 283o0(22 70t 26270) D 432WDa18
1 20000 18000:
cooo olryio
322°
parathion-methyl
procymidone
2000
aoQ
-O 002
t
11202 02 2 327 2 Z? n oes
0 24
9
7
Swon 90 91 92 93 g4 9
ouoo
T) 7617798081
B283B4B5~687888990
>bunance 25 Us
BI
9a 9 3
o
8080PO
l 10000
'OO()D
D 432WD318
aooo
'iaoao~~~~~
som2
9
s 9o99 .> .,<.ez:
soo2 43202 49990
o1457) Ion 145001447D t
8
""mono r 20° nooD* 2
3~5705?O
"""
ZSON
, gots
l5400( 4
98 97 98 99 101011 02
Time bun.nc
s9 / 1 ·
·f~ '~
9
2
999991
~
·
~
·
~
ISDI
ooo
922~
25-
9222 9000
76T777879807 1 828384858687888990 TLme 00 0 0 O00 0
AbUdanse
i
K
1U00
·
11232(2700 o22170)432223183
----
o 90 91 92 93 94 95 98 97 98 99100101102 o 0 D 0 O O Tilme-D0 o
~nn
F n40007t4087DI:43ZWD319D
lo
soaM 3-ow sa-w
quinoxyfen
10801o
11C 1120
14g 118011B9D 120D1220 1240
11.e A-udac lon 27300(2270 to 223 72): 432WD318
lon4080 (40770to4087) 432W2312 D 200
I-.w 20000
-Woo ___.
3.-o
I.O. 5000
v,
19C i109 100
1120 11i40ii8i80
120
10
124C
940
960
U
k 98g
1000
1020
1040
1060
1080
1100
Fig. 6.25. Automated search in reconstructed indicative ion traces for pesticides in the corresponding retention time windows. (Courtesy of J. Lipinski.) Summarising, the combination of CI-MS with the two selective detectors, NPD and FPD, allows fast residue analysis for a great number of relevant pesticides with very good limits of detection all on one instrument. Applying ECNCI mode for the non-polar and medium polar pesticides, almost all peaks in the TIC chromatogram could be identified as well as in the selective detectors or the selective ion traces with the exception of an unknown chlorinated compound, as shown in Fig. 6.26. This is in contrast to results usually obtained with EIMS. A similar approach applying GC-ECNCI-MS together with conventional methods in pesticide residue analysis has been reported recently [42].
327
H.-J. Stan TABLE 6.5 Summary of the results of pesticide monitoring of a grape sample RT (min)
Pesticide
mg/kg NCI TIC
6.10 11.50 14.25 14.65 7.90 8.60 9.50 9.63 10.63 11.30
ISTD 1 ISTD 2 PCB 206 PCB 209 Parathion-methyl Chlorpyrifos Procymidone Endosulfane-alpha Endosulfane-beta Quinoxyfen
0.020 0.020 0.020 0.020 0.020 0.075 0.025 0.010 0.005 0.030
6.13
+ + + + + + + + + +
NCI RIC + + + + + + + + + +
NCI m/z 35
NCI m/z 79
NPD
FPD (P)
+
+
+ +
+ +
+ + + + + + + +
GC-TOF-MS
As described, two important characteristics of all TOF instruments are high ion transmission, a prerequisite for high detection sensitivity and fast data acquisition with high-speed data collection systems capable of obtaining up to 500 full-range mass spectra per second. The LECO system applying AMDIS, for example, allows accurate deconvolution of shared masses. As long as any co-eluting analyte in a group has one ion unique to its mass spectrum, the Pegasus III software CHROMATOF can provide an accurate peak height and area for any of the shared masses. This is convincingly demonstrated by the identification of 21 analyte peaks eluting within 13 s from a gas chromatographic column, as shown in Fig. 6.27. After deconvolution of the co-eluting substances, the most intense ion of each of these is used to provide the best accuracy and precision. The ability to deconvolute the peak shapes of shared masses can also be applied to calculate accurate peak heights and areas of the individual substance masses. Another important feature of this deconvolution algorithm is to track down trace compounds overlapped by a matrix peak with a several hundred thousand-fold concentration difference. The following example, provided by M. Linkerhagner (Eurofins, Specht & Partner, Hamburg, Germany), was from a series of preliminary experiments to introduce the described technique into the routine work of a commercial analytical laboratory that specialised in pesticide residue analysis. 328
GC-MS. I: Basic principles and technical aspects of GC-MS
8.00
8.50
9.00
950 10.00 10.5
11.0
11.'0 12.00 12.50 13.00 13.50 14.00 14.50
m/z 35
__
ISTD 2 chlorpyrifos
ane betaunknown
'9 I
7.50
8.00
8.50
9.00
~~~unkow
9.50 10.00 10.50 11.00 11.50 12.00 12.50 13.00 13.50 14.00 14.50
Fig. 6.26. Parallel display of TIC and reconstructed ion trace m/z 35 from full-scan NCI measurements with labeling of the peaks identified in the analysis. (Courtesy of J. Lipinski.)
329
H.-J. Stan
Seconds
498
500
502
504
506
Fig. 6.27. Unique masses for 21 analyte peaks located by the "Peak FindAlgorithm" in a 13-s portion of the reconstructed total ion chromatogram of a complex sample.
(Reproduced with permission, courtesy of LECO.)
The analysis was carried out with a Leco Pegasus III combined to an Agilent gas chromatograph HP 6890 equipped with an ATAS Focus Direct Automated Thermal Desorber as injector and a 25 m x 0.25 mm DB-5 capillary column. A wheat sample of 10 g was extracted with ethyl acetate/cyclohexane (1 + 1) applying accelerated solvent extraction (ASE) to give 10 ml extract of which 5 pl were injected without further clean-up. As expected, huge peaks of fatty acids such as palmitic and linoleic acid dominate parts of the TIC chromatogram. Linoleic acid elutes between 980 and about 1150 s, ending in a long tail. The wheat sample has been spiked at a concentration level of 40 ng/g, which means 200 pg of each pesticide were injected together with an equivalent of 5 mg wheat matrix. The question arose as to whether the pesticides co-eluting with linoleic acid can be identified when buried under this huge peak. The answer was convincingly positive, as will be demonstrated with one example: between retention times 1037.46 s, where bupirimate was identified, and 1040.06 s, where fluazifopbutyl was identified, two more pesticides were found, namely myclobutanil at 1038.36 s and flusilazole at 1038.68 s. This means that, in the middle of the huge matrix peak of about 170 s width, four pesticides could be identified within a region of 2.6 s using a scan rate of 10 scans per second. This is in agreement with the statement that deconvolution is successful if the compounds differ in their retention times at least by two scans. To illustrate 330
GC-MS. I: Basic principles and technical aspects of GC-MS
5e+097 4e+007~:~:: 3e± 087i-----: te+007:-:-·:: 213x
22x~D0 -179x500
I3,50
ITiC
Fig. 6.28. Extract from a TIC chromatogram of a wheat sample spiked with a mixture of pesticides at a concentration level of 40 ng/g. TIC chromatogram is zoomed into the linoleic acid peak, RIC of the base peaks of four pesticides are enlarged 500-fold. (Courtesy of M. Linkerhiigner.)
the power of the combination of GC/TOF-MS with the dedicated deconvolution software, some figures are presented. In Fig. 6.28, the TIC chromatogram is zoomed into the linoleic acid peak and overlaid with recon-structed ion chromatograms of the base peaks of the four pesticides, which have had to be enlarged 500-fold to make them visible. In Fig. 6.29, three mass spectra are shown. The mass spectrum 7282 at 1038.36 s shown at the bottom is obviously a mixed spectrum; the deconvoluted mass spectrum at the top, however, exhibits sufficient similarity with the library spectrum of myclobutanil shown in the middle to be identified. In Fig. 6.30, the same comparison is presented for the mass spectrum 7387 at 1038.85 s. The raw mass spectra at the bottom of both figures shows a very similar overall appearance but small differences can be seen in the masses 273 and 208, which originate from the earlier eluting bupirimate, and masses 383, 282 and 254, which originate from the later eluting fluazifop-butyl. Recently, Thermo Finnigan introduced a GC-TOF-MS instrument TEMPUS, which produces in EIMS mode standard library searchable spectra at high speed and with good sensitivity and correct isotope ratios. The instrument is now also equipped with CI with integrated digital flow control of the CI gas. According to Thermo Finnigan, the TEMPUS can switch between El, positive CI (PCI) and negative CI (NCI) on sequential injections electronically with no manual interaction required. This facilitates full and fast characterisation of 331
H.-J. Stan PeakTrue-sampe "ezsen ASEdot N-hat Q,04_", peak604, at 103,36 seconds(Spec 7382)
~;
200th
f6e70)
500
i
:
:2
,I 0
j 20600
80: -i
40
0
10
H= safffple 064zen,A i 0do0N-hal'O,04..1 _i _j
:
::::;:8
,1001:: :
s,
I
Spectrum 7382 at 1038.36s after deconvolution
7L 10
r[ Calip
:I
I
:
60
80:: t
2
6
0
20
7
Li;b '00 rl
lpec ; 7062
90622 1
::
9 211
ii i:
. 1 .7
e
i
170
S:
1:406:
':7:
~:i Spectrum 7382 at 1038.36s
i_:
120:
0
0
7777 :7
2i30
los o 106i~: 100 '. ,I'
0
0026,266ecood2 to 15134,2b45
-
-1
11
2
00
. I .
1
~~
, I I,
0 200: 222
220
36 ~ ; 3015203~~~~~8 3 6:::0 8 0
. I, ,. I ..
l0
02 :
240: 24026
I
I
-
I
-
I
l
420 440 460 480 500
40
Fig. 6.29. Deconvolution of the mass spectrum of myclobutanil. TIC and RIC of masses 179, 233, 273 and 282 are presented in Fig. 6.28. (Courtesy of M. Linkerhagner.)
PeakTIue- asioe 'tWebenASEdo
;
000-
i 56
: ;? ::::i :;::iUi:
t-
0,04 "1'. peak60., at 1036 second& (Spec #7386
::;:: ; i : ;
~ ~:
:M :
:
:
:
: <
Spectrum 7387 at 1038.85s after deconvolution
-
647261
020
~:14e::~f-i ~
100 120 14,0160
60 :0 L~lbras:;Hlt:-s:r'iil:S:i:
60i 60 10 :acr-1~ 0
-
160
'i·::::
60+0:if000:0:X
I
4--
~
;0:
5 ::00i;
20260300203406 0604
-': i:::i:if:
0
j
&t
:0
::::
:)S::;:? _ _00
-
300U,140
6 06 726
7367 tj '
|Wni0S-: 0 itiA
; 100 120 160 100 w220 10 60
500
Library spectrum Flusilazole
:i -' t00|
::: :::~~~~~~~6
-:; i:
40 460
: j
--
00
46 46 ;
iS :2>
;$Rei2<X
:zo :0
:
S 00 21 20 2600 0 10 6 c1 t 0( Ic
0qi420 440 : :
; -: : 'i-r00ii:; ::::: 0- 0i039 80; t
063:
:-:::::
;i 'vgj:~i~~i i:. i:-ifi 1000-::i4(0:p::'i'-'. : ':: ;'"'; :i::~:::ii:: -;;22 ':;~::::i--:::: 50::: : :5:0
:;ii:::00:: :: :::i:
,
041:
o
:60 :020 : . 24
~
f10 140 0 r it0 5 ;:
2
170 1220 lL 4c,,,,
,l th
2 02
Spectrum 7387 at 1038.85 s
:,:i~ 2~~~~~4I 01 ~ ~ 212:~i~: *; ::~ - :~ii::
060 00034
:7)r:0i ::i -. ;0
0~~~~~~--'
- 063 E:i:
.:
..- ...
i:4W 00000029
:::: ::)
060 066:06ti0 420440 40 460 50
Fig. 6.30. Deconvolution of the mass spectrum of flusilazole. TIC and RIC of masses 179, 233, 273 and 282 are presented in Fig. 6.28. (Courtesy of M. Linkerhagner.)
332
GC-MS. I: Basic principles and technical aspects of GC-MS GC peaks and confirmation, EI for library searching, PCI for molecular weight confirmation and NCI for traditional ECD-amenable compounds such as pesticides.
6.14 6.14.1
NEW APPROACHES IN GC-MS Resistively heated GC-MS
Another approach to pesticide residue analysis is applying modern quadrupole MS to fast GC, as recently described by the group of Vreuls and Brinkmann [44]. They combined resistively heated GC with a quadrupole mass spectrometer from Agilent (MSD 5973). This MSD can be operated at a maximum scan rate of 14.7 scans of 300 amu per second. A resistively heated capillary over a mass range of column enables fast temperature-programmed GC in a conventional gas chromatograph by upgrading it with an assembly called EZ Flash, which is commercially available. The capillary column is placed inside a metal tube, which can be heated and cooled much more rapidly than any conventional GC oven. The EZ Flash assembly generates temperature ramps up to 1200°/min and cools from 300 down to 50°C in some 30 s. By applying a voltage over the tube, both the tube and the column were resistively heated. Owing to the low thermal mass of the assembly, the column could be cooled in a very short time. The host GC oven was kept at a constant temperature of 93°C. The GC analyses were performed on a 5 m x 0.10 mm ID. DB-5-column with a film thickness of 0.1 Am. The pesticides were extracted with ethyl acetate and injected at 97°C initial column temperature to obtain an optimal small peak width. A few pesticides were spiked to apples and extracted to demonstrate the applicability of the method. To give an idea of the speed of the analysis, chlorpyrifos appeared in the chromatogram at a retention time of 1.20 min. Limits of detection were reported in the low picogram range. The use of fast temperature programming and short columns results in a substantial loss of separation. With a mass spectrometer, coeluting analytes can be resolved using the different masses in their spectra. The extraction of pure mass spectra from co-eluting compounds was achieved by the computer program AMDIS, demonstrating again its merits in automated peak deconvolution. This method offers a perspective for high-speed screening in pesticide residue analysis applying conventional hardware with an affordable commercially available extension of the instruments and the software package 333
H.-J. Stan AMDIS downloadable from the internet without charge. The method is, however, a far from routine application in a pesticide residue laboratory. 6.14.2 Comprehensive two-dimensional gas chromatography with TOF-MS One of the most sophisticated techniques available today is comprehensive twodimensional GC with TOF-MS detection. In the technique of comprehensive two-dimensional GC, two capillary columns are connected in series through an interface called a "thermal modulator". This device transforms the effluent from the first capillary column into a series of sharp, injection-like pulses suitable for high-speed chromatography on the second column. Dramatic increases result in the resolving power, detection sensitivity and speed of the gas chromatographic analyses [45]. This technique was first successfully applied to the separation of complex fractions of crude oil and gasoline. The combination with TOF-MS makes this technique much more powerful [46]. It is now being used by the group of Vreuls and Brinkmann [47] to demonstrate its merits in pesticide residue analysis in foodstuffs. They report that the separation provided by conventional GC (1D-GC) could be significantly enhanced by using comprehensive two-dimensional GC (GC x GC) instead. Combination with MS detection is desirable for unambiguous confirmation of target pesticides and the provisional identification of unknowns. A GC x GC system using a cryogenic modulator was coupled to a TOF-MS detector. With the detection of pesticides in vegetable extracts as an example, it was demonstrated that GC x GC improves the separation dramatically. All 58 pesticides of interest could be identified using their full-scan mass spectra, which, according to their observation, was not possible when using 1D-GC -TOF-MS. By using the computer program AMDIS, the high scan speed of the TOF-MS allowed the deconvolution of pesticides from matrix compounds partly co-eluting in GC x GC. The method described must be considered as applying the most sophisticated hardware available today and is still far from being introduced into a laboratory dealing with routine pesticide residue analysis. 6.14.3
Fast Supersonic GC-MS
Another new instrumental approach, termed Supersonic GC-MS, was developed by Amirav's group [48]. The new method achieves fast, sensitive, confirmatory and quantitative analysis of a broad range of pesticides in food 334
GC-MS. I: Basic principles and technical aspects of GC-MS samples. The Supersonic GC-MS system is a modification of an Agilent HP 6890 GC and HP 5972 MSD with a supersonic molecular beam (SMB) interface and fly-through EI ion source. The group found enhanced molecular ion intensities in the resulting mass spectra. The M+ was observed in all 88 pesticides that were studied using the Supersonic GC-MS. At the same time, the degree of matrix interference was found to be exponentially reduced. The enhancement of the M+ combined with the reduction in matrix background noise permitted rapid full-scan analyses of all pesticides amenable to GC. Supersonic GC-MS is described as exceptionally suitable for fast GC-MS with high carrier-gas flow rate. Furthermore, with Fast Supersonic GC-MS it was possible to analyse thermally labile pesticides, such as carbamates that are difficult or impossible to analyse with conventional GC-MS. Applying direct sample injection with large volume injection using a ChromatoProbe, pesticides at a trace concentration level of 20 g/kg in a spice matrix could be analysed within 6 min for one analytical run. REFERENCES 1
F.W. McLafferty and F. Turecek, Interpretation of Mass Spectra. University
2
Science Books, Mill Valley, CA, 1993. F.W. McLafferty, Interpretationof Mass Spectra. University Science Books, Mill Valley, CA, 1980.
3
A.G. Harrison, Chemical Ionization Mass Spectrometry. CRC Press, Boca Raton,
4 5 6 7
FL, 1992. J.E. Bartmess and R.T. McIver, The gas phase acidity scale. In: M.T. Bowers (Ed.), Gas Phase Ion Chemistry. Academic Press, New York, 1979, p. 88. K.R. Jennings, Chemical ionization mass spectrometry. In: M.T. Bowers (Ed.), Gas Phase Ion Chemistry. Academic Press, New York, 1979, p. 123. H.-J. Stan and G. Kellner, Biomed. Mass Spectrom., 9 (1982) 483. H.-J. Stan and G. Kellner, Biomed. Environ. Mass Spectrom., 18 (1989) 645.
8
F.W. Karasek and R.E. Clement, Basic Gas Chromatography-Massspectrometry: Principlesand Techniques. Elsevier, Amsterdam, 1988.
9
K.L. Busch, G.L. Glish and S.A. McLuckey, Mass Spectrometry/Mass Spectrometry: Techniques and Applications of Tandem Mass Spectrometry. VCH,
10
Weinheim, Germany, 1988. K.R. Jennings, Int. J. Mass Spectrom., 1 (1968) 227.
11
R.E. March and R.J. Hughes, QuadrupoleStorage Mass Spectrometry. Wiley, New
12 13 14 15 16
York, 1989. E. de Hoffmann, J. Mass Spectrom., 31 (1996) 129. H.-J. Stan, J. Chromatogr.A, 467 (1989) 85. H.-J. Stan and M. Linkerhagner, J. Chromatogr. A, 750 (1996) 369. G.R. van der Hoff and P. van Zoonen, J. Chromatogr. A, 843 (1999) 301. S.J. Lehotay and J. Hajslova, Trends Anal. Chem., 21 (2002) 686. 335
H.-J. Stan 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
36 37
38 39 40 41
42 43
336
S.J. Lehotay, A.R. Lightfield, J.A. Harman-Fetcho and D.J. Donoghue, J. Agric. Food Chem., 49 (2001) 4589. S.E. Stein, J. Am. Soc. Mass Spectrom., 10 (1999) 770. The Wiley/NBS Registry of Mass Spectral Data. NIST/EPA/NIH NIST'02, National Institute of Standards and Technology, Gaithersburg, MD. K. Pfleger, H.H. Maurer and A. Weber, Mass Spectral and GC Data of Drugs, Poisons, Pesticides, Pollutants and Their Metabolites. Wiley, New York, 2000. Dr Ehrenstorfer Laboratories mass spectral library. H.-J. Stan and J. Lipinski, HP PesticideLibrary. Hewlett-Packard, Palo Alto, CA, 1989. http://www.AuPest.de/ mass spectral library of pesticides. J.E. Biller and K. Biemann, Anal. Lett., 7 (1974) 515. G.M. Pesyna, R. Vengkataraghavan, H.E. Dayringer and F.W. McLafferty, Anal. Chem., 48 (1976) 1362. F.W. McLafferty and S.Y. Loh, J. Am. Soc. Mass Spectrom., 2 (1991) 438. F.W. McLafferty and D.B. Stauffer, Important Peak Index of the Registry of Mass Spectral Data. Wiley-Interscience, New York, 1991. H.-J. Stan and F. Schwarzer, J. Chromatogr., 653 (1993) 45. H.-J. Stan, J. Chromatogr. A, 892 (2000) 347. B.N. Colby, J. Am. Soc. Mass Spectrom., 3 (1992) 558. Criteria for identification of an analyte by GC-LRMS, Off J. Eur. Commun., No. L 223/26 number 11 (1987). J. Fillion, R. Hindle, M. Lacroux and J. Selwyn, J. Assoc. Off. Anal. Chem. Int., 78 (1995) 1252. L.E. Sojo, A. Brooke, J. Fillion and S.M. Price, J. Chromatogr.A, 788 (1997) 141. A. De Kok, C.P. Vreeker, E.W. Besamusca, A.A. Toonen and M. Hiemstra, In: G. Sontag and W. Pfannhauser (Eds.), Current Status and Future Trends in Analytical Food Chemistry, Proceedings of the 8th European Conference on Food Chemistry, Vienna, Sept. 18-20, 1995, Austrian Chemical Society, Vienna, Austria, 1995, p. 415. A. De Kok, C.P. Vreeker, A. Toonen and E.W. Besamusca, Poster Presented at the 2nd European Pesticide Residue Workshop, Almeria, 1998. V. Giarrocco, B. Quimby and M. Klee, Retention Time Locking: Concepts and Applications, Application Note 228-392, Publication (23) 5966-2469E, December 1997, Hewlett-Packard. H. Prest and P. Cormia, Retention Time Locking Advantages in GC/MS SIM Analysis, Application Brief (23) 5967-379E, www.agilent.com.de. R.A. Yost and C.G. Enke, J. Am. Chem. Soc., 100 (1978) 2274. J.N. Louris, R.G. Cooks, J.E.P. Syka, P.E. Kelly, G.C. Stafford and F.F.J. Todd, Anal. Chem., 59 (1987) 1677. S.J. Lehotay and J.A. Harman-Fetcho, Book of Abstracts, 218th ACS National Meeting, New Orleans, Aug. 22-26, 1999, American Chemical Society, Washington, DC, 1999. S. Schachterle and C. Feigel, J. Chromatogr.A, 754 (1996) 411. E. Jover and J.M. Bayona, J. Chromatogr.A, 950 (2002) 213.
GC-MS. I: Basic principles and technical aspects of GC-MS 44 45 46 47 48
J. Dallige, R.J. Vreuls, D.J. van Iperen, M. van Rijn and U.A.Th. Brinkman, J. Sep. Sci., 25 (2002) 608. J.B. Phillips and J. Beens, J. Chromatogr.A, 856 (1999) 331. P. Marriott and R. Shellie, Trends Anal. Chem., 21 (2002) 573. J. Dalluige, M. van Rijn, J. Beens, R.J. Vreuls and U.A.Th. Brinkman, J. Chromatogr. A, 965 (2002) 207. M. Kochman, A. Gordin, P. Goldshlag, S. Lehotay and A. Amirav, J. Chromatogr. A, 974 (2002) 185.
337
Chapter 7
GC-MS. II: Applications for pesticide analysis in food Ana Agiiera and Andre de Kok
7.1
INTRODUCTION AND SCOPE
Since the presence of pesticide residues in agricultural products became evident, and as a consequence of the potential risk these pose for human health, a multitude of public health protection laws have been passed by numerous countries. Such interest, not only by governments but also that reflected in public opinion, has led to the intensification of actions to assure the safety of the food supply and to protect humans from harmful residues. In this context, analytical chemistry has an essential position because it plays an important role in a variety of fields: (i) setting "safe levels", as well as providing additional information for the establishment of the basic mechanisms of toxicity; (ii) tracing these compounds in the food chain; (iii) providing the support for the establishment of regulatory levels or "maximum residue limits" (MRLs); and (iv) determining the presence of suspected pesticides and/or their metabolites in the food supply. With respect to the last point, the analysis of pesticides in foods presents serious difficulties as a consequence of the large concentration difference between the food components and the pesticides present in the samples. Each time more legislation appears on, e.g., baby food, or since the emergence on the market of "eco-food"-that claims to be essentially pesticide-free commodities-there is a need for far more sensitive methods, applying larger concentration factors. Both the sensitivity requirements and difficulties with the food samples make it necessary to apply analytical procedures which are sensitive and selective enough to provide unambiguous results. The confirmation of analytical results, which is a problem as old as pesticide residue analysis, is still essential, and analytical methods are continuously incorporating new advances to improve the reliability of the results. Comprehensive Analytical Chemistry XLIII Fernindez-Alba (Ed.) C 2005 Elsevier B.V. All rights reserved
339
A. Aguiera and A. de Kok Traditionally, gas chromatography (GC) has been the analytical technique of choice, and advances in this area have been linked closely to developments of the technique. During the 1960s, the early investigators used packed-column gas chromatography and selective detectors to make residue determinations. Electron-capture (ECD)-, nitrogen-phosphorus (NPD)-, and flame photometric (FPD)-detection provided high sensitivity but scarce information for unequivocal identification, and so errors were not uncommon. Second-column confirmation was introduced to alleviate this problem, but errors continued and costs were increased. At that time, mass spectrometry (MS) was a relatively small and mysterious part of the average analytical laboratory, owing to the expense and complexity of the analysis. The use of these instruments to generate the data and determine the level of confidence necessary was left to specialists. Coupling of MS as a gas chromatographic detector became feasible with the introduction of capillary columns [1]. Capillary chromatography reduced the carrier-gas flow to that necessary for optimum functioning using direct coupling of GC and MS. The first benchtop GC-MS systems, based on quadrupole mass analysers, were introduced in the early 1980s. The ability of this detector to obtain full mass spectral scans of pesticide residues attracted a great deal of attention, and numerous published reviews provided specific case histories illustrating the power of MS to provide identification and confirmation [2,3]. However, the sensitivity of these (at that time) expensive and tedious instruments was not competitive with selective GC detectors, rendering them inapplicable as routine detectors. The opportunity to use full mass spectra was often impractical at trace levels, and new detector designs and techniques (selected ion monitoring (SIM), chemical ionisation (CI)) could be applied to improving the sensitivity and selectivity of quadrupole systems. The introduction of ion-trap detectors (ITDs), in the late 1980s, represented an important improvement in pesticide residues analysis. This was a consequence of its excellent sensitivity-at picogram levels, typically, in full-scan mode-high mass-range and sufficient resolution, and the ability to manipulate ions during storage to effect ion-dissociation or reaction in an easy way, and all of this with equipment having relatively low cost and size. In recent years, both ITDs and quadrupole instruments have been improved in the design of equipment and of operating and acquisition software, leading to the widespread routine use of bench-top mass spectrometers in pesticide residue laboratories.
340
GC-MS. II: Applications for pesticide analysis in food 7.2
QUADRUPOLE VERSUS ION TRAP
The two benchtop instruments most commonly used in pesticide residue analysis are the quadrupole mass spectrometer (QMS) and ion-trap mass spectrometer (ITMS). Both systems have yielded good results in the identification and quantification of pesticides in food samples, and analytical methods using both detectors are currently being used in routine analysis [4-6]. Traditionally, the main deficiency imputed to QMS systems has been the lack of sensitivity compared with the ITMS. Although the sensitivity of modern GC-MS instruments has increased in recent years, differences in sensitivity between the two principles of MS are still present as a consequence of their characteristic operating mode. The ITMS stores a large number of ions at a specific mass-to-charge range by trapping them inside the trap [7]. This efficiency in storage of the ions is responsible for the higher sensitivity of the ITMS, in contrast with the QMS where the majority of the ions never reach the detector. The exhaustive control of the number of ions present in the trap at each moment is a critical issue that can affect both the sensitivity and identification capability. One disadvantage traditionally imputed to the ITMS has been that the electron impact spectra were not consistent with those obtained by classical QMS. Space-charging and charge-exchange were responsible for these variations in the spectra, in both cases as a consequence of the presence of high concentrations of ions stored in the trap [8]. Since 1992, the instruments have incorporated an automatic gain-control function, which is a pre-scan of approximately 200 gs, to determine how many ions are allowed to form. The ionisation time is adjusted to avoid the saturation of the trap, and distortion in the spectra has been reduced. In this sense, recent work has confirmed the applicability of mass spectral libraries created with QMS instruments for ITMS instruments [9]. However, despite this improvement, variations in the intensity of cluster ions of chlorinated compounds are not uncommon and, although the identification of target compounds is feasible, this variation can be inconvenient in the identification of unknown compounds, such as metabolites. As to the precision of measuring ion ratios, both instruments yield similar results in the full-scan mode. However, under SIM data acquisition, ITMS provides lower precision compared with the QMS [8]. Additionally, SIM on the ITMS does not lead to increased sensitivity, as is usually observed with QMS instruments-on the contrary, in the presence of interfering ions coming from the sample matrix, a reduction in signal can be observed [10]. 341
A. Agiiera and A. de Kok An important advantage of benchtop ITMS is that it offers routine CI and tandem MS (MS/MS) capabilities without the high cost and added complexity of multi-sector instruments. A GC-ITMS system can easily be turned from EI to CI ionisation mode, or from MS to MS/MS mass analysis, even during the same run. This feature allows selection of the best conditions for each individual compound, thus improving the efficiency in multi-residue methods. Next, the application of GC-MS systems to the analysis of food samples will be discussed, focusing on relevant practical aspects, such as identification and quantification of pesticides in the samples, and reviewing the main methods currently applied (Table 7.1). 7.3
PESTICIDE IDENTIFICATION
Despite the improvement in confirmation capability achieved by the introduction of MS into pesticide residue analysis, this technique cannot be considered as being error-free, without identification mistakes [24]. In any GC/MS method, the ever-present question is whether the observed chromatographic peak does or does not correspond with the target analyte. As a consequence, each time regulatory standards, and the economic and legal consequences of positive findings, become more restrictive, so the demand increases for valid criteria for reliable confirmation [25-27]. The criteria for pesticide confirmation at residue levels in foods have been changing until a general consensus is reached. The rigor of such criteria depends on the purpose of the analysis. Thus, the reporting of false positives by regulatory agencies must be minimised, with very strict confirmation requirements, while the reporting of false negatives near the reporting limit is more acceptable. The GC/MS confirmation criteria must take into account the great variety of instrumentation with different operational modes. In the more simple and ideal approach, the retention time and full-scan mass spectrum match (Ž 80%) between samples and standards, working under capillary GC/EI/MS, could be an unequivocal confirmation criterion. Nevertheless, the complexity of the matrices in food analysis, and the low concentration levels of pesticides present in the samples, have made these criteria insufficient. Matrix interference constitutes the bottleneck that limits the identification of trace-level pesticides in complex food matrices. Variable amounts of matrix components are unavoidably present in sample extracts as a consequence of the application of the unspecific extraction methods required by most of the laboratories involved in the monitoring of a wide range of pesticide residues in foods. The application of multi-residue methods reduces the time, labour, and cost, but increases the presence of co-extracts if a wide polarity range 342
GC-MS. II: Applications for pesticide analysis in food 2
a) a) L
F
T F7 -
71 -1
36 -
F
-1 -1 S -1 a -1
Cf
CX
a
CS
a
C
C
Cl
I
0
m
0
O
O
CO
a
O 6So O A' °
.
000 0
a,
10
o
0 Oxr
I I
1-
Q AE-WE-EE4
~-qr
-
--
E5E~ ,~ ,aS
-
.,a~~~!~ EE I I
~
I
O
o
6
V
I
V
I
V
4
~
9a
x
P
(..2
PC
F414P2
Co
a
Co
o
Co
4 O 4343 a) 0) a)
l Co
4
O1
0)
Co
4
O
a)
EIti
Co
Co
Co
Co
bD
a
a
CO
>
c
a)
'a w C)
X L-
-
(..2 P4 U) pc 02 I II
C.)
9 a
x X
UD
a
a) Co
°X x V 1
,i, 5i11uu-U U H ~~ *T~ SnS
-2
43
Cq
I0
0
0 O O
v00vo
o X O C.
rddd:: gI
ro
L'0
00
-q 0
:~
o
0 0
10
0
a)
>> 4 1O
C
a) v
a)
>
cO
CtO~EC.CI)Co
PLO
Crt
a)
>
da
U a a,~~~~~EJa a a>v a a -a~aS
x0
CoIoF o
a
a)>
aa
a
o~C
C r
PMO
pr
Co
0
C
c
Co
a a
o C
Co
W
a)
a)
0) o
,
o
'
a)
*CC.)H o OC
m Co C.)
Co
.
-
'
aaa ' a
. Co, Co
O
,
'a
Co
a)
~
4a) C.)
Co
.C0') a.o0 Co
-
CO Co
rE
,
0021
'ac.
'a
Co
Co
C7 UD
O
CO
M
U
Co
0'0 rS
.o
'aC
-a
0o
'a Co
'a)
~ 4 O ~C: -U ~ C.5 ) 53 $Co Co ~aaaCo Co 0Co Co Cl Co O 1> l m'C 'a ca a
a)
'a
10 3010
0
0
-0
343
A. Agiera and A. de Kok of analytes is included. Matrix components, usually present in a large range of concentrations and compositions, can interfere in the determination of analytes, from both the identification and quantification points of view. Interferences, mainly resulting from co-eluting peaks or from background noise, can induce false interpretation of the mass spectra. Co-eluting peaks yield overlapping mass spectra, which are difficult to interpret, and the relative ratios of the diagnostic ions can be altered by the presence of background ions in the matrix, leading to false negative results. As a consequence, additional confirmation requirements have to be established, such as: (i) the response of interfering ions in the spectrum lower than 25% of the base-peak intensity, (ii) intensity ratios for the main ions within approximately + 20% of those obtained on the same day from the reference standard, and (iii) diagnostic ion chromatograms (minimum three data points, signal-to-noise (S/N) ratio 3:1) showing peaks of similar retention time and peak shape. The presence of high concentrations of co-eluting molecules in the extracts can cause other negative effects. Especially relevant is their influence when an ion-trap instrument is used, because it can affect the ionisation time of the analytes and, consequently, their detection limit. Other negative effects caused by matrix co-extracts are the incorrect quantification (as will be commented on later) and the progressive contamination of the chromatographic system that makes more frequent maintenance necessary. To avoid all of these problems associated with the presence of matrix interferences, analysts have developed clean-up procedures to reduce the introduction of co-extracts into the chromatographic systems. These procedures include partitioning with organic solvents, adsorption chromatography, gel permeation chromatography (GPC) or solid-phase extraction [28]. However, the introduction of a clean-up step causes, in all the cases, an increase in the analysis time, an extra consumption of organic solvent, and a source of losses in the analyte recoveries. As an option, a reduction in the sample size analysed is an easy and effective approach but it implies an urgent demand for alternative techniques exhibiting improved sensitivity and selectivity [14]. 7.3.1
Selected ion monitoring (SIM)
The SIM mode represents a good example of enhanced methods, and has been applied widely in GC-MS systems based on quadrupole mass analysers [6,29-31], in which the increase in sensitivity can amount to several powers of ten. However, the undeniable gain in detection sensitivity is overshadowed by 344
GC-MS. II: Applications for pesticide analysis in food the extra uncertainty of analyte confirmation, owing to the lack of full-scan spectrum checks (see Fig. 7.1). With SIM, a limited number of ions are monitored during a selected time interval of the chromatogram. The presence of the analyte is determined by the presence of these "diagnostic" ions, so reducing the structural information available for the identification. An additional question arises when one has to fix the confirmation criteria relative to the number of diagnostic ions that must be considered and to the tolerances in the variation of their relative intensities. It seems clear that the selection of two ions is not enough to avoid detection of false positives, as can be observed in Fig. 7.2 for methamidophos, where the presence of ion fragments only at m/z 94 and 95 can lead to the identification of a false positive. On the other hand, the consideration of four or more ions can lead to an increase of false negative results. A good compromise, which the analysts involved in pesticide residue analysis accept, is the selection of three diagnostic ions. However, the suitability of the three-ion method has been discussed extensively [32,33], and it is inapplicable for many pesticides that give only one or two strong ions in MS. Guidelines for residues monitoring in the European Union [34] establish the minimum requirements as two ions of m/z > 200 or three ions of m/z > 100. With regards to the tolerances in the ions' relative intensities, a + 20% variation can be accepted at high concentration levels. However, it is well established that, in many cases, the relative intensities of the ions do not remain sufficiently constant when the analyte concentration decreases. So, when the presence of matrix interferences is high, the application of very restrictive criteria can lead to an increased chance of false negative results, especially at low concentrations. As a consequence, the margin on the relative intensities must be increased to 30%, or higher, with an increasing chance of false positives. For this reason, results close to the limit of determination and MS identification must be considered on a case-by-case basis. Comparison of the known sample with a blank matrix spiked with the pesticide at the estimated concentration is the best guarantee of avoiding false positive identification. Another important decision is the selection of the most appropriate diagnostic ions from those available in the full-scan mass spectrum. Generally speaking, the selection of the three most abundant ions could be the best option because it will provide better sensitivity. However, other criteria have to be taken into account for some particular cases. For example, ions with higher masses are generally preferred because of the statistically lower probability of their occurrence in other compounds, and consequently of their 345
A. Aguera and A. de Kok --
a) o hD
$
C)
tD
0 0
0 o LO
-o
o
o
·,
. C)
a a
oo
o
CDC o
Q
o
a)
gr
$
,
CD
"atia
o
t z
o
C
CDC
0 -o :0
d
E
a, 5
o
0
~ o*
=o 06 ;
-
CD 0)hn
Pr
346
0
oa) O
0
m
U
o o
a)
C6
CD o
GC-MS. II: Applications for pesticide analysis in food 0 oa ) 11
E
tQ
F ._
Co
00
00
.
o
w
00°
S
m
=l
co bB a o
4d o
o 00
O
"l
~m
c,
k
C o,
I
o C
-' -
0
"
CZ
Es F
00
C
16 :I I-
1: I
--2 8 10~~~~
m
a) t 0
~~2 8
S S
,I 0)8
o
a0 B 0~5~0
2
ssaO8E
.
;8
°Sgooo" tS88'
o
._
O
S coa
wqRW 9-F,
SP888888"
Ci
o8
\i rb of
347
A. Agiiera and A. de Kok greater significance. It is also important that the selected diagnostic ions should not be present in the chromatographic background (from column bleed, phthalates, hydrocarbons) or in the matrix. This therefore requires the analysis of blanks. Another particular difficulty can arise when two or more pesticides co-elute at the same retention time. This problem can be resolved by selecting different qualifier ions for each compound. The use of diagnostic ions belonging to the isotopic cluster can be useful in increasing the qualitative information for halogenated compounds. Despite the uncertainty associated with the technique, we should remember that mass spectral identification is based on various criteria, such as the retention time and the peak shapes in the reconstructed ion chromatograms, that help to identify the analytes. Although the retention time of a chromatographic peak is a poor confirmation criterion by itself, especially in very complex chromatograms, it is essential for recognising the presence of the analytes in the samples. Capillary GC allows a very precise reproducibility of the retention timesusually with acceptance of variation limits less than 5 s (better than 0.1 min) or within a 2% window. Recently, the emergence of the technique called retention time locking (RTL) allows a GC method to be reproduced so accurately that analyte retention times match exactly with those recorded in RTL libraries [35]. By linking the locked retention times to mass spectral data, the reliability of the identification in complex matrices is improved. Furthermore, the RTL simplifies the translation of chromatographic methods to alternative confirmatory chromatographic systems. The use of RTL, GC/MS and database search, in combination with the high selectivity and the elemental information from a GC-AED system, has proved to be a powerful tool for identifying pesticides in complex matrices [35]. From the discussion above, we may conclude that the SIM mode can be considered as a conclusive technique, which is useful for confirmation of trace concentrations of pesticide in vegetable samples when the confirmation criteria including the following requirements are applied: (a) peak retention times within a 2% window, (b) match of m/z values of three selected ions from the mass spectrum of the analyte in the samples and in the standards, and (c) the ratios of the ions' signal intensities for the samples and standards should be within 20-30%. It is undoubtedly true that this technique presents some inherent inconveniences, such as the fact that it is a target-compound technique and allows the simultaneous determination of only a limited number of compounds. However, several multi-residue methods based on SIM have been developed with quadrupole instruments. Of special interest, because of the broad scope 348
GC-MS. II: Applications for pesticide analysis in food of application, is the method applied largely by the Agriculture and Agri-Food Canada (AAFC) (now Canadian Food Inspection Agency) [31] that describes the analysis of 189 pesticides in fruits and vegetables. Residues were extracted from food samples with acetonitrile, and the co-extractives removed with a miniaturised charcoal-Celite column clean-up. However, two injections, with an analysis time of around 1 h, were required per sample to cover the total number of compounds analysed. The method that had been applied largely in routine analysis demonstrated acceptable performance for the analysis of the samples investigated, exhibiting limits of detection (LODs) from 0.02 to 0.20 mg/kg. Recent modifications of this procedure [6], including the use of commercial solid-phase extraction cartridges for clean-up, have increased the scope of the method to cover 239 pesticides and degradation products. Useful confirmation, additional to the GC-EI-MS data, can be obtained from parallel analyses by GC or HPLC with proven selective detection (e.g., GC-ECD, NPD, FPD or HPLC-DAD or fluorescence) or by CI-MS. 7.3.2
Positive and negative chemical ionisation
The use of GC-MS in the CI mode, with both positive- and negative-ion recording (PCI and NCI), can offer great advantages in pesticide residue analysis as a consequence of its high specificity and sensitivity [36]. However, this technique has not been applied very often for routine analysis [11,37], but mainly to confirm the identity of the analytes in conjunction with El ionisation [38]. Under mild and soft ionisation conditions, the CI mode usually generates intense base-peaks with high masses-frequently the quasi-molecular ion-in the positive mode, and group-specific fragments of high intensities in the negative mode (as is the case for organophosphorus compounds) that provide additional structural information about the molecule. Despite the utility of this complementary information, the scarce fragmentation obtained for some compounds can be considered insufficient for an unequivocal identification by itself. Another inconvenience in routine screening analysis is the absence of commercial spectral libraries as a consequence of the high dependence of the spectra on the ionisation conditions-especially with the reagent gas used [39]. Both the mass spectrum and the analytes' sensitivity in CI are highly dependent on the reagent gas used. Methane has been employed most frequently as the CI reagent gas, because of its low proton affinity (550 kJ/mol), which allows ionisation of almost all organic molecules. Additional confirmation of the molecular ion is obtained by the presence of [M + C 2H 5] + and [M + C 3H 5] + cations observed at M + 29 and M + 41, 349
A. Agiiera and A. de Kok respectively. Other reagent gases such as iso-butane or ammonia are softer and do not ionise all organic molecules [40]. These gases also induce less fragmentation, so reduce the structural information in a screening analysis. Important advantages have, however, been reported recently with the use of ammonia regarding the detection and quantitation limits and the lower dependence of the sensitivity on the degree of chlorine substitution of the compounds [39,41]. The use of acetonitrile as reagent gas has been applied to pesticide multi-residue analysis [5]. Acetonitrile is easy to use as a reagent gas and its routine use in automated GC-ITD multi-residue methods has been proved. As advantages of the use of acetonitrile PCI mode we can enumerate: (i) a higher sensitivity for most pesticides with detection limits in the ppb range, (ii) fewer matrix interferences, and (iii) the presence of the quasi-molecular ion [M + 1]+ in many cases. This last capability is especially useful for pesticides whose fragmentation obtained under the EI mode yields intense fragment ions at very low m/z. This is the case for methamidophos, which exhibits a base peak at m/z 94 under EI conditions, and at m/z 142 [M + 1] + under acetonitrile PCI. Besides the increase in sensitivity obtained by PCI, the use of m/z 142 also provides a more accurate identification and quantification. Despite these advantages, the routine use of acetonitrile PCI also presents unavoidable limitations, such as the absence of response for some compounds, e.g., chlorothalonil, or the sparse fragmentation obtained that, in many cases, does not provide enough specific ions for an unambiguous identification. The dependence of the chromatographic response upon the analyte structure is especially significant under NCI conditions (Fig. 7.3). The high sensitivity provided by NCI (of one or two powers of ten greater than EI or PCI) is well known for the analysis of compounds with electron-capturing groups. The halogen and sulphur atoms in the molecules form very stable anions, and consequently provide the good detection sensitivity of NCI for halogenated alkane, organometallic, and polynitroalkane pesticides, some carbamates, and organophosphorus and chlorinated hydrocarbon pesticides. Other pesticides of interest, e.g., methamidophos or pyrimiphos-methyl, do not exhibit any response in NCI, so limiting a broader scope of application of this ionisation technique. Most of the NCI applications described in the literature refer to the analysis of a specific group of target compounds, especially halogenated pesticides in complex matrices, for which the use of ECD does not provide enough selectivity [42-44]. An important impetus to the application of CI for pesticide analysis was given by the advent of the ion-trap technology, owing to its high versatility and the easy operation of the instruments without modifications in the hardware, thus allowing a simple and rapid change in the ionisation conditions even 350
GC-MS. I: Applications for pesticide analysis in food -----360000
Chlorpyrifos Vinclozoline
320000 280000 240000
Chlorothalonil
200000 160000 120000 80000 40000
El
.
=
_.........
8.20
8.40
=
/
_
Pirimifos-Methyl
..... 8.60
8.80
9.00
920
9.40
9.60
9.80
10.00 10.20 10.40 Time
Fig. 7.3. GC-MS analysis of four selected pesticides (0.02 mg/kg) under EI and NCI conditions. during the same run. Several experimental difficulties were, however, observed in the CI analysis of pesticides. It was noticed that the full-scan methane CI spectra produced by the ion trap under automatic reaction control (ARC) conditions were hybrid in nature, and strongly dependent on the pesticide's concentration [45]. The spectra produced during the elution profile of various pesticides indicated a large percentage of an EI-spectral component superimposed on the CI spectrum. This fact represented an important inconvenience for confirmation purposes, hindering comparisons with previously recorded mass spectra from quadrupole and magnetic-scanning instruments. Identification of chlorinated compounds is especially affected by this effect, because the presence of molecular (via EI) and protonated molecular ions (via CI) alters the molecular-ion-cluster composition, making the assignment of the chlorine number difficult. Improvements in ion-trap CI performance have contributed to minimising this effect [46]. A new tendency in pesticide analysis is the combined use of EI and CI modes during the same run. This option allows one to take advantage of the differences in sensitivity obtained for some pesticides in the two ionisation modes by selecting the most favourable ionisation conditions for each compound. Some multi-residue methods have made use of this option [21,47]. Conventional iontrap systems that successfully perform ionisation, fragmentation and analysis in the trap, allow the combination of PCI and El modes. Problems associated with ITD functioning hamper the application of NCI, as the pressure of the reagent gas necessary for working in NCI is too high for a good ion-trap 351
A. Aguera and A. de Kok performance. The use of external-source IT mass spectrometers, on the other hand, requires the change of the ion source volume from El to CI, but combinations between PCI and NCI are feasible [19]. As a consequence of the sparse fragmentation obtained for many pesticides under CI conditions, this ionisation mode has been used in combination with tandem MS/MS fragmentation, so increasing the selectivity of the analytical method and also the structural information available for pesticide identification. 7.3.3
Tandem mass spectrometry
Gas chromatography-tandem mass spectrometry (GC/MS/MS) is a technique recently applied in the multi-residue analysis of pesticides in foods. MS/MS is a powerful tool for obtaining enhanced analytical selectivity and accuracy. Although this is not a new technique, its application in routine analysis had been limited by the high cost of instruments and the requirement of specially trained personnel for multi-sector instruments. The emergence of bench-top ion traps has greatly simplified the operation, and many applications have been developed [13,16,17,20,21,48]. Some authors have compared the pesticide analysis results obtained with selective detectors or standard GC-MS and with MS/MS [48], showing that GC/MS/MS can be used in routine analysis with few difficulties and good reliability. The technique can provide a virtual elimination of background interferences, thereby inducing an increase in S/N ratio and usually also in sensitivity, which is dependent on the efficiency with which daughter ions are created relative to the reduction in noise. Simultaneously, confirmation of pesticides is obtained with a high degree of certainty thanks to the obtainment of very characteristic product-ion mass spectra. The MS/MS operation mode includes two additional steps between the formation and detection of ions-the isolation of a single parent ion and further fragmentation into characteristic product ions to form a complete spectrum. MS/MS is therefore a target-compound technique with each compound requiring its own conditions, that is, the excitation voltage and the excitation storage level. The software programs that are used facilitate the optimisation of these operational parameters (which can be tedious and timeconsuming), to provide the best MS/MS conditions for many compounds with only a few injections. A systematic procedure has been proposed for the development of the MS/MS analytical method [17]. Pesticide confirmation at low levels in complex matrices, based on the product-ion spectrum, is greatly improved by MS/MS methods. This spectrum is unique for each pesticide and it is not affected by the sample-matrix ions; these are excluded during 352
GC-MS. II: Applications for pesticide analysis in food the isolation step and do not affect the product's spectrum or the sensitivity of the analysis. Figure 7.4 illustrates this statement by representing the analysis of the same pepper sample containing chlorpyriphos under GC/MS and under GC/MS/MS conditions. In the first case, the presence of chlorpyriphos (s/n 8) can be suspected but cannot be confirmed by the EI full-scan mass spectrum as a consequence of the background ions present. Under MS/MS analysis, chlorpyriphos gives a higher response (s/n 71) and
100
8 90
GC-EI-MS (FULL SCAN)
B 80 70
6
< . 9 rC
60
so 50 40 30 20 1n lU
.':./N X
n u
9.3
o
9.6
' 9.7
.98 Time (min)
^
99
1
0.
.'--, 102
0.1
10.3
.4
97
100 c
.5
94
7'.4
80
93
3 60
3135
<
40 20
m ,m
i
0 50
IIUIIIIIIOI~~~~~~~~~~~hllllllYIIIYIIIIIIIIIIYIIIIII...~ 11~.,..-,.,,~"...
m
100
150
200
250
300
100 §
350
400
450
500
RT: 9.48 AA: 11034 SN: 71
80 60
40
550
600 m/z
GC-EI-MS/MS
20 ....... F~~~~' :E1, -
6.5
7.0
.5
80
8.5
9.0
_
S' I
9.5 100 Time (min)
110
10.5
12.0
12.5
258 ...
100 I
11.5
80
60
259
., 40 286
G~ 20 - I n -
Im
..
40
60
80
100
120
140
160
180
200
I.
I,
220
240
260
280
300
320
340 mlz
Fig. 7.4. Analysis of a pepper sample containing chlorpyriphos, under GC/MS and GC/MS/MS conditions.
353
A. Aguera and A. de Kok the absence of spectral interferences allows a reliable confirmation. The criteria required for confirmation by MS/MS include the presence of two or more product ions and an ion ratio for each parent/daughter peak combination falling within 20% of established references. Studies on the stability of the spectra of the pesticides in samples have shown that any dependence of the masses' intensity with the concentration is not significant. This is because, under MS/MS conditions, only a narrow band of masses is kept in the trap, so reducing ion-ion interactions [16]. At this time, there is no MS/MS spectral library for identification of the secondary spectrum, and different conditions yield different spectra; thus, precautions have to be taken. The choice of the precursor ion is very important-we must be sure that it comes only from the target analyte in order to obtain a product-ion spectrum free of matrix interferences. Usually the base peak in the MS spectrum is the best choice because an intense precursor ion generally gives the largest MS/MS signal, so increasing the sensitivity. In this context, some authors have taken advantage of the lower fragmentation observed in the CI spectra-that gives rise to more abundant base-peaks, which are useful for further fragmentation by MS/MS [19]. This option provides not only higher sensitivity but also an enhanced selectivity. The product-ion spectra obtained generally show enough fragmentation for confirmation purposes. In some cases, however, the selection of the base peak as precursor ion is not the best option. That is the case for endosulfan sulphate, which shows a base peak at m/z 98. MS/MS fragmentation of this low-mass-ion fragment provides a high background and insufficient structural information for identification purposes, so in this case a less abundant ion at high mass (a cluster at m/z 386) is preferred. It can also be useful for identification purposes to isolate a group of ions instead of a single precursor ion-as is the case for chlorinated compounds [49]. The excitation of the entire cluster yields product ions that also show the isotopic pattern according to the number of chlorines remaining in each ion. With regard to fragmentation conditions, these have to be optimised to provide at least two product ions for confirmation purposes. It is a good practice to avoid the complete dissociation of the precursor ion to help analyte identification. An important advantage of MS/MS with respect to other operating modes is the ease of identifying overlapping peaks. Although, ideally, each pesticide should be analysed in a different segment, the presence of co-eluting or closely eluting peaks makes this unviable. In these cases, two or more pesticides are included in the same segment and characteristic parameters selected for each compound are applied in successive scans along the segment. The information 354
GC-MS. II: Applications for pesticide analysis in food obtained is stored in different data channels. In this way, it is possible to resolve co-eluting peaks by obtaining the corresponding product-ion spectra free of interferences. Figure 7.5A shows the case of methamidophos and dichlorvos, where the selected ion chromatograms, at m/z 145 for dichlorvos and m/z 126 (A) GC-IT-PCI-MS/MS 145 145
100
DICHLORVOS
126
100-
METHAMIDOPHOS
75:
K0-
50
25
141 94 157
221
109 127
109 ..In....
75
125
100
1!50
175
200
m/z
75
. '
n100125
------
I
150
'
I
I
' '''
175 200 -1 .
II''
m/7 ""-'
10 \: 145
Ion 126
5:
Ion: 145 .
7.00
7.25
7.75
7.50
8.00
min
(B) GC-IT-EI-MS/MS
.7
30.8
30.9
31.0
min
Fig. 7.5. Extract from the selected-ion chromatograms for the quantitation masses and MS/MS spectra of a pepper sample containing: (A) methamidophos and dichlorvos, and (B) nuarimol and dicofol. In both cases, the correct identification and quantification of the co-eluting peaks can be observed. 355
A. Agiiera and A. de Kok for methamidophos, and the correspondent product-ion mass spectra, are represented. The separation is possible even when the quantification ion is the same for both peaks, as we can observe in Fig. 7.5B for the case of nuarimol and dicofol where the m/z 139 ion is common to both compounds. If we take into consideration all the topics described above, MS/MS, together with full-scan El spectra, can be considered as providing the most definitive evidence of identity and quantity. At present, screening methods based on MS/MS determination are being applied successfully in routine analysis of pesticide residues in foods (see Table 7.1). Scheridan and Meola [16] have optimised a method for the detection, quantitation, and confirmation of more than 100 multi-class pesticides under GC-EI-MS/MS. Two injections per sample are necessary to run each pesticide with its own unique set of parameters. The method can reliably identify and confirm all the pesticides simultaneously at the low parts-perbillion (ppb) level. One limitation, however, is observed in the analysis of some organophosphorus pesticides, such as methamidophos or acephate, that show a limited sensitivity compared with selective detectors analysis. Gamon et al. [20] proposed the use of methanol-CI to increase the sensitivity of these and other similar compounds. Clean and reproducible CI spectra, showing intense [M + 1] ions at m/z higher than those obtained in EI, permit an increase in the sensitivity and avoid spectral interferences which are frequent in the low mass range. Each sample is analysed in two different injections, one in EI/MS/MS and the other in methanol CI/MS/MS, so covering the analysis of 80 compounds, including organochlorine, organophosphorus, organonitrogens, and pyrethroids. The high selectivity of MS/MS analysis allows the injection of a relatively large injection volume (5 ul). To minimise the amount of sample matrix that enters the column, an empty liner filled with Carbofrit (Restek, Bellefonte, USA) is used. Carbofrit is a porous carbon plug that retains sample co-extractives. The efficiency of this packing material has been proved in previous work [50,51] and it is currently used in pesticide residue analysis [21,47,52]. The plug is carefully placed just below the end of the needle, in such a way that the discharge of the sample into the injector takes place over the Carbofrit surface, as shown in Fig. 7.6. In this way, interactions between the sample and the glass liner surface are also avoided, reducing matrix effects in the injector. The Carbofrit must be changed after about 30 sample injections in order to prevent sorbent saturation. Another variation published recently [21] includes a single analysis per sample by combining PCI and EI GC/MS/MS with an ITMS. The change of the ionisation mode takes place during the sample analysis, so reducing 356
GC-MS. II: Applications for pesticide analysis in food
)ofrit
Fig. 7.6. Scheme of a split/splitless injector fitted with a liner filled with 0.5 cm Carbofrit. the analysis time per sample. MS/MS conditions have been adjusted to obtain a parent ion and a minimum of three product ions, allowing a reliable identification at low ppb levels in most of the cases. The method is being applied routinely, with good results, in an accredited laboratory for the determination 357
A. Agiiera and A. de Kok of 55 multi-class pesticides commonly used in crop protection. A TIC chromatogram obtained in the GC-PCI/EI-MS/MS analysis of a pepper sample spiked at a 0.50 mg/kg concentration level is shown in Fig. 7.7.
7.4
PESTICIDE QUANTIFICATION
Another important aspect in pesticide residue analysis concerns the quantification of the analytes. This is a very important task, especially when a violative result is reported. Errors in the quantification, especially in concentrations close to the MRL of the pesticides, can lead to serious consequences. Quantitative GC-MS methods include a series of requirements relating to the sensitivity (establishment of limits of determination), precision (reproducibility and repeatability), and concentration range (dynamic range of the detection system). Quantification is currently performed by relation to external standards. Calibration curves generated for each pesticide will contain at least seven data points within the linear range for that pesticide. Nowadays, it is generally accepted in many laboratories that pesticide calibration standards must be prepared in a matrix extract ("matrixmatched") instead of in pure solvent, unless the matrix effect has been proved to be insignificant. Comparison of the calibration curves obtained under both procedures has, in many cases, given evidence of the presence of important matrix effects that can lead to serious errors in the quantification (Fig. 7.8). The influence and extent of these matrix effects has been studied extensively [31,53-571. In addition to the adverse effects for pesticide identification already discussed (masking of residue peaks, occurrence of false positives), the presence of matrix components could also lead to inaccurate quantification. The matrix-induced enhancement phenomenon has been encountered widely in the gas chromatographic analysis of pesticides in foods. This effect is explained by the presence of active sites in the GC system, mainly in the injector liner, which adsorb and/or induce thermal degradation of certain pesticides in the absence of matrix components [57]. When a sample matrix is also present in the injected solution, its components block the active sites, so increasing the injection efficiency for the pesticides, which give a greater response and an erroneously high calculated concentration if the standards in pure solvent solutions are used for calibration. Hajslova et al. [56], in their study of the influence of matrix-induced effects in the analysis of pesticide residues in foods, have established
358
GC-MS. II: Applications for pesticide analysis in food w
E
a
(1 :
"
m
k
a,
0
Ct o
>)
_o (()
m
°
LOC C'O
+ o
U
U be
w
S
'-C
@no Ca P o
C E o
)
E
., o
c4
E
C LT
0
0
o o
0) o0
CD
to
0
0
p
m
0 o
0
0 o
0
O
a 0 o
0
_ b
N
O 0
359
A. Agiiera and A. de Kok 75000000
Spiked pepper matrix 099 2
60000000
R = 0.9995
45000000 30000000Solvent
15000000
R2 = 0.9978
0 0
0.2
0.4
0.6
0.8
1
Concentration (ppm) Fig. 7.8. Comparison of methamidophos calibration curves prepared from a matrix extract, and in pure solvent.
the experimental factors that may give rise to the enhanced gas chromatographic responses. These factors include: the nature of the pesticides, the nature of the matrix, the pesticide-to-matrix ratio, and the state of the GC system. Thus, matrix effects can be very variable and unpredictable in their occurrence. Among the precautions that can be taken to overcome or reduce the matrix effects, the following can be cited: the use of matrix-matched standards, the use of alternative quantification methods (method of standard additions, deuterated pesticides as internal standards), the use of on-column or other means of injection, application of extensive clean-up procedures, priming of the GC system, and the use of compensation factors. The most common option, followed by many laboratories and recommended in the guidelines for residues monitoring in the European Union [58], is the use of standards in blank extracts. Although this option has certain limitations [59], it represents an easy and effective approach for reducing the quantification errors and improving the accuracy of the calibration [31]. The US federal regulatory agencies (FDA and EPA) do not allow the use of standards in a matrix and have opted for performing extensive clean-up to remove matrix components [57]. Together with the well-known matrix-enhancement effect, the presence of co-extractives from the sample also produces decreased responses. Effects on the MS response may be produced by co-eluting matrix components influencing the efficiency of ionisation or ion collection [52,60]. Irrespective of the procedure chosen to minimise these matrix effects, a careful selection of the quantification ion(s) has to be made to avoid coincidences with the matrix ions that can also lead to significant errors. 360
GC-MS. II: Applications for pesticide analysis in food Usually, the most abundant ion that shows no evidence of chromatographic interference, and the best S/N ratio, is used as the quantification ion. In some cases, it is better to select a group of ions in order to obtain more sensitivity or better reproducibility (when the group selected corresponds with an isotopic cluster) [21]. The selected ion(s) will present masses as high as possible to avoid interferences at low m/z. Under MS/MS, masses below 100 amu can be selected as quantification ions because the only masses in the product spectrum are those resulting from fragmentation of the precursor ion, and no interferences are observed coming from the matrix. This is the case for acephate, whose daughter ion with m/z 42 has been used successfully for quantification purposes [16]. An advantage of MS detection over classical detectors is that, as has been discussed above, it is not necessary to completely separate the targeted analytes unless co-eluting components have key mass spectral ions that also overlap. Overlapping pesticides can be quantified accurately by selecting specific quantification ions that are not present in the targeted pesticide. The simultaneous identification and quantification of pesticides in samples is generally provided by automatic sub-routines in the equipment's software within only a few seconds of the sample's analysis. Some laboratories have developed their own programs for automatic evaluation of sample chromatograms in screening analysis [5,9]. In these programs is stored information about reference mass spectra and the retention times of many pesticides and metabolites. An example of the information given by this method is presented in Fig. 7.9, where oxadixyl has been detected in a pepper sample. Pesticides are searched for, in their respective retention-time windows, by comparison with their reference spectra. If some peak is found which matches the corresponding reference spectrum, the single-ion chromatogram for the selected quantitation masses is obtained and the peak is quantified by using the calibration equation stored previously. The stored calibration data have to be verified frequently by the injection of selected calibration solutions, preferably within each batch of samples. The fit of the calibration must be inspected visually to ensure that it is satisfactory in the region relevant to the residues detected. Despite the advantages of these automatic methods, the results have to be submitted to further review by the analyst in order to verify the absence of errors. LODs are usually calculated as the average analyte concentrations that give an S/N ratio of 3. The S/N ratios are calculated by dividing the integrated analyte peak area by an estimated area for the noise. Noise is estimated by averaging the peak area of several background peaks near 361
A. Aguera and A. de Kok
0
E
E
N
C, U)
o
$ U) (D
m
erI$
E co
0) 0•
c0 'IT~~W
O
*-
~-
81:5:A( ,s-
ddl
C_
$ CI
................. i
-S
i
°
/ w0 n). ~
a) o
z1
W0 _0
U)
')
U) F
0
+
U)
'I)
0
2
;
U
U Q
00 0)
E Co
b
1
z
N
E
0
0
u
0
11 0
Co
6
soqdop!weqlaj
m x
I
a cCo -a)
0
0•
m CL
U)
362
:
3
0
)
E C
P a)
U)
o
U 0
aU) Ca r r
co
.
.
E 4)
A
a)
E
+,
0
I
I
co
0O O_ U.
0E1
0 4-
U) 0•0o 0.•
-u
C~~
0•
C <-'
GC-MS. II: Applications for pesticide analysis in food the analyte peak. The system's software usually provides an estimation of the S/N ratios, but not very accurately. The best way of determining a realistic limit of quantitation for a GC-MS residue method is via acceptance of the lowest validated spike level with a relative standard deviation not exceeding 20%. LODs reported by the literature are highly dependent on the mass spectrometer and the operating mode selected in the analyses. They can vary from the low ppb to the ppm range, as can be seen in Table 7.1. Lower LODs correspond with the use of ITMS and especially with the MS/MS approach. LODs also depend on the pesticide/matrix combination. Polar pesticides and pyrethroids usually provide higher LODs. In a similar way, complex matrices such as citrus, or leafy vegetables, also reduce the detectability of the analytes. 7.5
NEW TRENDS IN GC-MS PESTICIDE RESIDUE ANALYSIS
The coupling of new extraction techniques with GC-MS systems represents one of the most recent trends in pesticide food analysis. Sample-preparation techniques based on sorptive extraction, such as solid-phase microextraction (SPME) and stir-bar sorptive extraction (SBSE), are easily coupled to GC-MS systems-provided that the thermal desorption of the analytes can be carried out directly in the injection port of the GC (SPME) or with the help of an automated thermo-desorption unit placed on the top of the GC (SBSE). The simplicity, speed, high sensitivity and easy automation, together with the reduction or total elimination of the use of organic solvents, have made these techniques attractive alternatives to liquid extraction. Both techniques have been applied for the determination of pesticide residues in aqueous food samples, such as drinking water, fruit juices, and beverages [61-67], and in fruits [68,69]. SBSE in combination with the thermal desorption-retention time-locked GC-MS method (RTLcapillary GC-MS) has been described as a versatile and cost-effective method for the elucidation and quantification of over 350 pesticides in different foodstuffs down to sub-ppb levels [70]. With the aim of reducing the degree of matrix interferences, a new instrumental approach termed Supersonic GC-MS has been developed [71]. Its design involves minimal modifications of a commercially available GC-MS system to include a supersonic molecular beam (SMB) interface [72]. This technique is based on the obtaining of full-scan mass spectra presenting an enhanced molecular ion (M+). This feature improves analyte identification by using the M+ and a second major ion. The replacement of two low-mass ions with the M + (supersonic two-ions method)
363
A. Aguera and A. de Kok 28.7
297>79 297>81 Smo: 5
15.0
25.0
Ret. time
Fig. 7.10. GC-NICI-MS/MS (triple-quadrupole) analysis of a hot chilli pepper sample spiked with deltamethrin at 0.05 mg/kg. results in a significant reduction of matrix interferences by a factor of up to 90. Although this approach requires further exploration and validation, it represents an interesting attempt to provide better pesticide analysis with improved confidence. Other advances in instrumentation include the recent introduction of a GC-triple-quadrupole MS/MS system, which offers extra possibilities for increasing both the sensitivity and selectivity (in all MS/MS modes), as compared with ITMS and single QMS systems. A nice example is given in Fig. 7.10 for the detection of 0.05 ppm of deltamethrin hot chilli pepper sample extract, which is known to be very difficult to analyse. The negative CI mode is an especially good alternative for those pesticides that are usually detected more sensitively by a GC-ECD than a GC-ITD or GC-QMS system. Despite the improved detection limits provided by the new GC-MS systems, and their proven capability of identifying and quantifying pesticide residues in food samples [4,12,73,74], many laboratories still prefer to use conventional detectors, as is revealed in the more recently published reviews relating to pesticide residue analysis [75-80]. In many cases, GC-MS is used in parallel with selective detectors [5,81], or its application relies on the confirmation of identity of pesticide residues and identification of unknown analytical responses [29,82-85]. Certainly, GC-MS is nowadays an essential tool in pesticide residues analysis. 364
GC-MS. II: Applications for pesticide analysis in food REFERENCES 1 2
3 4 5 6 7 8 9 10 11 12 13 14
15 16 17 18 19 20 21 22 23 24 25 26 27
J. Abian, J. Mass Spectrom., 34 (1999) 157. T. Cairns and E.G. Siegmund, In: G. Zweig and J. Sherma (Eds.), Analytical Methods for Pesticides and Plant Growth Regulators, Vol. XIV. Academic Press, New York, 1986, pp. 193-253. J. Gilbert, J.R. Startin and C. Crews, Pesticide Sci., 18 (1987) 273. T. Cairns, K.S. Chiu, K.S. Navarro and E. Siegmund, Rapid Commun. Mass Spectrom., 7 (1993) 971. A. De Kok, K. Vreeker, A. Toonen and E. Besamusca, Poster, Presented at the 1st European Pesticide Residue Workshop, Alkmaar, 1996. J. Fillion, F. Sauv6 and J. Selwyn, J. Assoc. Off. Anal. Chem. Int., 83 (2000) 698. R.E. March and J.F.J. Todd (Eds.), Practical Aspects of Ion Trap Mass Spectrometry, Vols. I-III. CRC Press, Boca Raton, FL, 1995. R.L. Fitzgerald, C. O'Neal, B.J. Hart and D.A. Herold, J. Anal. Toxicol., 21 (1997) 445. H.-J. Stan, J. Chromatogr. A, 892 (2000) 347. L.D. Bowers and D.J. Borts, Clin. Chem., 43 (1997) 1033. G.C. Mattern, G.M. Singer, J. Louis, M. Robson and J.D. Rosen, J. Agric. Food Chem., 38 (1990) 402. L.G.M.Th. Tuinstra, P. Van de Spreng and P. Gaikhorst, Int. J. Environ. Anal. Chem., 58 (1995) 81. S. Schachterle and C. Feigel, J. Chromatogr. A, 754 (1996) 411. P. Van Zoonen (Ed.), Analytical Methods for Pesticide Residues in Foodstuffs, 6th ed., General Inspectorate for Health Protection. Ministry of Public Health, Welfare and Sports, The Netherlands, 1996. S.J. Lehotay and A. Valverde-Garcia, J. Chromatogr.A, 765 (1997) 69. R.S. Scheridan and J.R. Meola, J. Assoc. Off. Anal. Chem. Int., 82 (1999) 982. S.J. Lehotay, J. AOAC Int., 83 (2000) 680. G.J. Soleas, J. Yan, K. Hom and D.M. Goldberg, J. Chromatogr.A, 882 (2000) 205. M.D. Hernando, A. Agiera, A.R. Ferndndez-Alba, L. Piedra and M. Contreras, Analyst, 126 (2001) 46. M. Gam6n, C. Lle6, A. Ten and F. Mocholi, J. Assoc. Off Anal. Chem. Int., 84 (2001) 1209. A. Agiiera, M. Contreras, J. Crespo and A.R. Fernandez-Alba, Analyst (Lond.), 127 (2002) 347. F.J. Arrebola, J.L. Martinez-Vidal, M. Mateu-Sdnchez and J. Alvarez-Castell6n, Anal. Chim. Acta, 848 (2003) 167. D. Stajnbaher and L. Zupancic-Kralj, J. Chromatogr. A, 1015 (2003) 185. T. Cairns, E.G. Siegmund, T.L. Barry and G. Petzinger, J. Assoc. Off. Anal. Chem. Int., 82 (1999) 982. T. Cairns, E.G. Siegmund and J.J. Stamp, Mass Spectrom. Rev., 8 (1989) 127. T. Cairns and E.G. Siegmund, Crit. Rev. Food Sci. Nutr., 30 (1991) 397. F. Andr6, K.K.G. De Wasch, H.F. De Brabander, S.R. Impens, L.A.M. Stolker, L. van Ginkel, R.W. Stephany, R. Schilt, D. Courtheyn, Y. Bonnaire, P. First, P. Gowik, G. Kennedy, T. Kuhn, J.-P. Moretain and M. Sauer, Trends Anal. Chem., 20 (2001) 435.
365
A. Agiiera and A. de Kok 28 29 30 31 32 33
34
35 36 37 38 39 40
41 42 43 44 45 46 47 48 49 50 51 52 53
366
T. Cairns, M.A. Luke, K.S. Chiu, K.S. Navarro and E. Siegmund, Rapid Commun. Mass Spectrom., 7 (1993) 1070. A. Gelsomino, B. Petrovicova, S. Tiburtini, E. Magnani and M. Felici, J. Chromatogr. A, 782 (1997) 105. P.D. Johnson, D.A. Rimmer and R.H. Brown, J. Chromatogr. A, 765 (1997) 3. J. Fillion, R. Hindle, M. Lacroix and J. Selwyn, J. Assoc. Off. Anal. Chem. Int., 78 (1995) 1252. R.A. Bethem and R.K. Boyd, J. Am. Soc. Mass Spectrom., 9 (1998) 643. R. Baldwing, R.A. Bethem, R.K. Boyd, W.L. Budde, T. Cairns, R.D. Gibbond, J.D. Henion, M.A. Kaiser, D.L. Lewis, J.E. Matusik, J.A. Sphon, R. Stephany and R.K. Trubey, J. Am. Soc. Mass Spectrom., 8 (1997) 1180. A.R.C. Hill, Quality Control Procedures for Pesticide Residue Analysis, Guidelines for Residues Monitoring in the European Union, European Commission Document 78261VI/97, (1997). J. Cook, M. Engel, P. Wylie and B. Quimby, J. Assoc. Off. Anal. Chem. Int., 82 (1999) 313. G.C. Mattern, J. Louis and J.D. Rosen, J. Assoc. Off. Anal. Chem. Int., 74 (1991) 982. G.C. Mattern, C.H. Liu, J. Louis and J.D. Rosen, J. Agric. Food Chem., 39 (1991) 700. H.R. Yoon, S.Y. Cho, J.M. Kim, I.B. Yoon, M.K. Park and J.H. Park, Chromatographia,49 (1999) 525. L. Amendola, F. Botre, A.S. Carollo, D. Longo and L. Zoccolillo, Anal. Chim. Acta, 461 (2002) 97. T. Miyoshi, Y. Hirahara, M. Narita, K. Okamoto, M. Miyata, S. Koiguchi, M. Hasegawa, K. Kamakura, T. Yamana and Y. Tonogai, J. FoodHyg. Soc. Japan, 36 (1995) 274. R. Chaler, R. Vilanova, M. Santiago-Silva, P. Fernandez and J. Grimalt, J. Chromatogr. A, 823 (1998) 73. G. Niessner, W. Buchberger and G.K. Bonn, J. Chromatogr.A, 737 (1996) 215. G. Niessner, W. Buchberger and R. Eckertorfer, J. Chromatogr.A, 846 (1999) 341. H. Obana, K. Akutsu, M. Okihashi and S. Hori, Analyst (Lond.), 126 (2001) 1529. T. Cairns, K.S. Chiu and E. Siegmund, Rapid Commun. Mass Spectrom., 6 (1992) 331. T. Cairns, K.S. Chiu, E. Siegmund and M. Weber, Rapid Commun. Mass Spectrom., 6 (1992) 449. J.L. Martinez, F.J. Arrebola and M. Mateu-Sanchez, J. Chromatogr.A, 959 (2002) 203. S. Schachterle, R.D. Brittain and J.D. Mills, J. Chromatogr. A, 683 (1994) 185. I. Moret, R. Piazza, A. Gambaro, P. Traldi and F. Guidugli, J. Mass Spectrom., 34 (1999) 1389. S. Hong and A.T. Lemley, J. Chromatogr.A, 822 (1998) 253. A. Aguera, L. Piedra, M.D. Hernando and A.R. Ferndndez-Alba, J. Chromatogr. A, 889 (2000) 261. A. Agiiera, L. Piedra, M.D. Hernando, A.R. Ferndndez-Alba and M. Contreras, Analyst, 125 (2000) 1397. D.R. Erney, A.M. Gillespie, D.M. Gilvydis and C.F. Poole, J. Chromatogr., 638 (1993) 57.
GC-MS. II: Applications for pesticide analysis in food 54 55 56 57 58
59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
75 76 77 78 79 80 81
D.R. Erney and C.F. Poole, J. High Resolut. Chromatogr., 16 (1993) 501. D.R. Erney, T. Pawlowski and C.F. Poole, J. High Resolut. Chromatogr.,20 (1997) 375. J. Hajslova, K. Holadova, V. Kocourek, J. Poustka, M. Godula, P. Cuhra and M. Kempny, J. Chromatogr.A, 800 (1998) 283. F.J. Schenck and S.J. Lehothay, J. Chromatogr. A, 868 (2000) 51. A.R.C. Hill, Quality Control Proceduresfor Pesticideresidue Analysis, Guidelines for Residues Monitoring in the European Union, 2nd ed., 1999/2000, European Commission Document No. SANCO/3103/2000. V. Kocourek, J. Hajslova, K. Holadova and J. Poustka, J. Chromatogr. A, 800 (1998) 297. A.B. Deger, T.J. Gremm and F.H. Frimmel, Acta Hydrochim. Hydrobiol., 28 (2000) 292. J. Beltran, F.J. L6pez and F. Hernandez, J. Chromatogr.A, 885 (2000) 389. A.A. Boyd-Boland, S. Magdic and J. Pawliszyn, Analyst, 121 (1996) 929. K.-W. Yang, R. Eisret, H. Lord and J. Pawliszyn (Eds.), Applicationof Solid Phase Microextraction. Royal society of Chemistry, Cambridge, UK, 1999, p. 435. C.G. Zambonin, M. Quinto, N. De Vietro and F. Palmisano, Food Chem., 86 (2004) 269. M. Vitali, M. Guidotti, R. Giovinazzo and 0. Cedrone, Food Addit. Contam., 280 (1998) 280. L. Urruty, M. Montury, M. Braci, J. Fournier and J.M. Dournel, J. Agric. Food Chem., 45 (1997) 15. P. Sandra, B. Tienpont, J. Vercammen, A. Tredoux, T. Sandra and F. David, J. Chromatogr.A, 928 (2001) 117. D.A. Lambropoulou and T.A. Albanis, J. Chromatogr. A, 993 (2003) 197. R. Hu, B. Hennion, L. Urruty and M. Montury, Food Addit. Contam., 16 (1999) 111. P. Sandra, B. Tienpont and F. David, J. Chromatogr. A, 1000 (2003) 299. M. Kochman, A. Gordin, P. Goldshlag, S.J. Jehotay and A. Amirav, J. Chromatogr. A, 974 (2002) 185. A. Amirav, A. Gordin and N. Tzanani, Rapid Commun. Mass Spectrom., 15 (2001) 811. S.J. Lehotay and K.I. Eller, J. Assoc. Off. Anal. Chem. Int., 78 (1995) 821. G.-F. Pang, Y.-Z. Can, C.-L. Fan, J.-J. Zhang, X.-M. Li, J. Mu, D.N. Wang, S.M. Liu, W.B. Song, H.P. Li, S.S. Wong, R. Kubinec, J. Tekel and S. Tahotna, J. Chromatogr. A, 882 (2000) 231. C.M. Torres, Y. Pic6 and J. Maries, J. Chromatogr. A, 754 (1996) 301. N. Motohashi, H. Nagashima, C. Parkanyi, B. Subrahmanyam and G.W. Zhang, J. Chromatogr.A, 754 (1996) 333. Z.-M. Chen and Y.H. Wang, J. Chromatogr. A, 754 (1996) 367. J. Tekel and S. Hatrik, J. Chromatogr. A, 754 (1996) 397. G.R. Van der Hoff and P. Van Zoonen, J. Chromatogr. A, 843 (1999) 301. J.L. Tadeo, C. Sdnchez-Brunete, R.A. Perez and M.D. Fernandez, J. Chromatogr. A, 882 (2000) 175. J. Cook, M.P. Beckett, B. Reliford, W. Hammock and M. Engel, Rapid Commun. Mass Spectrom., 4 (1990) 58.
367
A. Agiiera and A. de Kok 82 83 84 85
368
A. Andersson and H. Palsheden, PesticideAnalytical Methods in Sweden, 1 (Part 1) (1998) 9-41. Rapport 17/98. G. Blomkvist and B. Hellkvist, Pesticide Analytical Methods in Sweden, Part 1 (1998) 98-104. Rapport 17/98. Pesticide Analytical Manual, Vol. I, U.S. Food and Drug Administration, Washington D.C., USA, 3rd ed. 1994, Revised October, 1999. J. Oliva, A. Barba, N. Vela, F. Melendreras and S. Navarro, J. Chromatogr.A, 882 (2000) 213.
Chapter8
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis E. Michael Thurman, Imma Ferrer and Amadeo Fernandez-Alba
8.1
INTRODUCTION AND SCOPE
Many-polar, volatile pesticides can be determined efficiently by GC methods. Different detection systems, including FID, ECD and MS in the electron impact or chemical ionisation modes, are available for GC to cope with the broad range of pesticide classes that can be found in food and in the environment. However, polar, thermally labile, low-volatility pesticides are often not directly analysed by GC. In these cases, chemical derivatisation may be used to yield derivatives suitable for GC determination. Most pesticides, including those not easily analysed by GC, can also be separated by high-performance liquid chromatography (LC) without the need for troublesome chemical derivatisation. Carbamates, phenylureas, organophosphorus pesticides, triazines, quaternary ammonium compounds and chlorinated phenoxy acids are examples of pesticides submitted to LC analysis. LC methods take advantage of the wide selectivity range offered by chromatographic supports. Conventional LC detectors such as the UV detector are, however, not selective enough for pesticide analysis in complex matrices. Moreover, selective detectors such as fluorescence or electrochemical detection can only be applied when the pesticide or its derivatives fulfill certain specific requirements. The potential of on-line coupling of LC with MS to provide LC with a universal, sensitive detector was recognised nearly 40 years ago, but only recently has it become a routine practice [1-16]. The use of a mass spectrometer for liquid chromatographic detection of pesticides offers several special advantages. The most important one is that it can provide unique information about pesticide chemical composition. This information is much more specific than that obtained using detectors that generate absorption Comprehensive Analytical Chemistry XLIII Fernandez-Alba (Ed.) ( 2005 Elsevier B.V. All rights reserved
369
E.M. Thurman, I. Ferrer and A. Fernandez-Alba bands, such as the UV-Vis sensing diode array detector or the infrared absorption detector. Furthermore, mass spectrometers show high sensitivity for semivolatile and non-volatile compounds and, because they are mass flow sensitive, the detector response can be used for quantitative purposes [2-5]. GC-MS was an established technique when the first practical attempts at LC-MS coupling succeeded in the early 1980s [1]. This slowness in developing LC/MS was due to the technical problems that derive from the introduction of liquids into a mass spectrometer where a high vacuum has to be maintained for proper analysis. Consequently, the use of on-line LC-MS was preceded by several off-line approaches, mainly based on the MS analysis of chromatographic fractions transferred to solid introduction probes [1-5]. For on-line LC-MS coupling, it was necessary that some kind of an interface or restrictor was available to keep the total mass (solvent and gas) flow entering the mass spectrometer low enough to be compatible with the pumping capacity of the vacuum system. The main strategies adopted for this purpose were based on the use of (a) transport devices with concomitant elimination of the solvent (i.e., the moving belt interface), (b) molecular separators (particle beam interface), (c) direct introduction methods with very low liquid flow rates (direct liquid introduction (DLI) and continuous flow FAB (CF-FAB) interfaces), and (d) direct sampling through small apertures of ions produced in sub-atmospheric (thermospray (TSP)) or atmospheric pressure ionisation (API) sources, such as electrospray ionisation (ESI) and atmospheric pressure chemical ionisation (APCI) [2-6]. The principles and general description of LC-MS interfaces have been described in detail [2-6]. We will restrain the scope of this chapter as far as possible to the coupling of conventional and narrow-bore LC to MS using the most popular sources of ESI and APCI. This is because of the more efficient approaches based on the ESI and APCI ion sources that have improved the feasibility of identification of pesticides of different chemical structures in fruits and vegetables at concentrations comparable with those achievable by GC-MS. Furthermore, the more powerful approaches of mass spectrometry/ mass spectrometry (MS/MS) have been refined for analysis of pesticides in food [17-191. For example, during the decade of the 1990s, instrument manufacturers developed sensitive and reliable methods for liquid chromatography/mass spectrometry/mass spectrometry (LC/MS/MS). A variety of instruments and design types have been marketed with great success, including MS/MS and accurate MS using time-of-flight (TOF). The majority of this work has focused on the new field of proteomics [1], which is the study of proteins using enzyme hydrolysis linked to LC/MS and computer modelling. The field of 370
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis environmental MS of food and water has been following the activities of LC/MS/MS in biochemistry with great interest and there is now a growing group of researchers actively working in environmental MS, especially LC/MS/MS of pesticides in food and water. The recent reviews of environmental MS by Richardson [20] and by Ferrer and Thurman [21-23] point out the importance of MS, especially the latest methods using TOF and ion trap MS, and their ability to focus on pesticides in environmental analysis.
8.2
IONISATION IN LC-MS
The two primary methods of ionisation in LC-MS, ESI and APCI, both involve API. APCI involves the spraying of the solvent from the HPLC through a heated nebuliser, which vaporises the sample (Fig. 8.1). In APCI, chemical ionisation occurs in the vapour state. Neutral solutes must first vaporise from the nebulised droplet and then be protonated in the vapour state to form the [M + HI + ion. The APCI+ source has a corona discharge needle, which creates a stream of electrons that serve to ionise the solvent of the mobile phase, which is typically methanol/water or acetonitrile/methanol/water mixed with the nitrogen nebulising gas. Thus, according to the current theory of APCI ionisation, CH 3 OH2 and H 3 0+ are protonated species present in the vapour state and transfer protons to the weakly basic pesticides that are present in the vaporised state according to their proton affinity. Proton affinity refers to the acidity or basicity of a compound in the vapour state. APCI is most effective for pesticides that are neutral in solution and are difficult to ionise by electrospray ionisation. APCI may be operated in both positive and negative mode. Atmospheric Pressure Chemical Ionization
Liquid Sample flow Nebulizer gas flow
Fig. 8.1. An APCI source that generates gas phase ions for small molecules under conditions harsher than ESI conditions.
371
E.M. Thurman, I. Ferrer and A. Fernindez-Alba For ESI, the current model of ionisation is summarised from several references [11-16]. In positive ion mode, the HPLC effluent is pumped through a nebulising needle that is at ground potential. The spray goes through a semi-cylindrical electrode that is at high potential. The potential difference between the needle and the electrode produces a strong electrical field that charges the surface of the liquid and forms a spray of charged droplets. The charged droplets are attracted toward the capillary sampling orifice where a counter flow of heated nitrogen gas shrinks the droplets and carries away uncharged solvent. Positive ions migrate downfield toward the surface of the microdroplet as the microdroplet moves toward the metal cap of the capillary, and the negative ions migrate toward the other end of the microdroplet. Because the charged droplets from the ESI needle to the metal cap at the capillary tube carry the electrical current, electrons flow in the external circuit of the electrospray power supply. Thus, in positive ion mode, oxidation occurs at the needle and reduction of the positively charged species occurs at the metal cap of the capillary. During the time that migration is occurring, there is also evaporation of the solvent and droplet shrinkage taking place as the heated nitrogen gas (300°C) is blown into the cloud of charged droplets. It is in this stage of droplet shrinkage that fission occurs, which is the process where a large droplet (20-50 gm) expels a small microdroplet of - 1 im. The droplet is thought to be misshapen and elongated toward the metal end of the capillary where the excess positive charge has accumulated by electrophoretic migration (-2% of the original mass, but - 15% of the charge, resides in this microdroplet). The offspring droplets can also go through this same process until its size reaches between 0.08 and 0.03 gm. These smaller-sized droplets are considered to be second-generation offspring. At this size, the surrounding electric field becomes strong enough to lift a solute ion over the energy barrier and to ionise it before the complete evaporation of the solvent, a process called field-assisted desorption (Fig. 8.2).
8.3
THE MASS SPECTRUM
The ions generated by either APCI or ESI are guided into the mass spectrometer and separated by their charge to mass creating the mass spectrum. The mass spectrum is a plot of the intensity as a function of the mass-to-charge ratio (Fig. 8.3). The peak with the highest intensity in the spectrum is called the base peak and is nearly always the protonated molecule in positive ion or the deprotonated molecule in negative ion. Generally, the 372
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis
Electrospray Source
Fig. 8.2. An ESI source.
spectrum is normalised to the intensity of the base peak, resulting in relative intensities. Depending on the cone voltage or fragmentor voltage of the instrument, the mass spectrum shown in Fig. 8.3 will change. The LC-MS spectrum is different from the GC -MS spectrum in three ways. First, is the difference in intensities of the molecular ion. The molecular ion is rarely the base peak in GC -MS and is nearly always the base peak in LC-MS. Second, electron impact ionisation of GC/MS is a high-energy fragmentation and collision-induced dissociation (CID, defined in section 8.4) is a low-energy fragmentation in LC/MS. Third, GC-MS has a mixture of odd and even electron ions that are formed in the source while
Fig. 8.3. The mass spectrum.
373
E.M. Thurman, I. Ferrer and A. Ferndndez-Alba LC-MS has typically even electron ions that are formed in the source, thus giving a simpler spectrum than GC-MS. For example, Fig. 8.3 shows the comparison of the fragmentation of atrazine in both GC-MS and LC-MS. The base peak ion in GC-MS is the m/z 200 ion while the protonated molecule is the base peak in LC-MS (m/z 216). The GC-MS spectrum has the m/z 200 ion from a loss of a methyl group (from an odd electron ion to an even electron ion) and the loss of the isopropyl group (m/z 173 ion, odd electron to odd electron) and the molecular ion (odd electron m/z 215). The LC-MS spectrum has only the even electron ion (even electron to even electron), m/z 174, and the protonated molecule (even electron) m/z 216.
8.4 8.4.1
STRUCTURAL INFORMATION The protonated or deprotonated molecule (molecular ion)
In a mass spectrum, several major kinds of general structural information are available. The molecular mass of the molecule is one of the most valuable pieces of information a mass spectrum can give. The nominal molecular mass is calculated from the integer masses of the most abundant isotopes of the atoms present in the molecule. Because of the soft ionisation used in LC-MS, the ions that are formed are typically the protonated and deprotonated molecule. The protonated or deprotonated molecule (incorrectly but commonly called the molecular ion) is the peak that usually corresponds with the highest mass isotopic cluster in the spectrum. However, identifying that peak with certainty can be rather difficult in some cases because of adducts that may form between ammonium, sodium and dimers of the molecule. CID is the process of the fragmentation of ions either in the source or in the collision chamber of a MS/MS instrument. The ions are accelerated to increase their kinetic energy and pass into a chamber with the collision gas, either Ar for the triple quadrupole or He for the ion trap. In an API interface, CID can be performed in the API source itself. By the application of an additional voltage in the region between the heated capillary and the skimmer, or in the zone between the skimmer and the octapole, the ions are accelerated. Nitrogen from the sheath gas and solvent molecules act as collision gas in a single quadrupole or TOF system. Typically, the increase of the fragmentation voltage leads to the partial or total destruction of the proton or ammonium adducts formed. In the case of a single quadrupole mass spectrometer, this fragmentation "mode" is the only one that is available and is called in-source 374
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis CID. The voltage difference or fragmentor voltage can be changed from low to high settings. In a CID mass spectrum, the fragment ions should be less than the molecular ion and are typically neutral losses, such as the loss of 18 (H 2 0) and 17 (NH 3), 44 (CO2), 46 (H 2CO2), etc. Such "small-neutral loss" peaks are of major significance in deducing the molecular structure of the compound. On the other hand, losses of 3-14 and 21-25 amu generally are not consistent with fragments formed from the parent ion and indicate an incorrect assignment or the presence of impurities. With mass spectra obtained from GC-MS or LC-MS analysis, the recognition of ions originating from impurities (background or unresolved chromatographic peaks) is generally facilitated by observing the relative ion abundances in the chromatographic peak. This method is also used in an automated form as background subtraction. As well as the molecular ion, the other type of peak observed in the mass spectrum is the isotopic peak. 8.4.2
Isotopic peaks
The isotopic peaks are the result of natural isotopic abundances of the individual elements, which can be highly indicative of the number of certain atoms, such as chlorine, bromine and sulphur. For instance, natural chlorine exists as 75% 3 5 Cl-isotopes and 25% 37 Cl-isotopes and consequently each parent or fragment ion containing chlorine can be easily identified by its typical chlorine isotopic cluster. The abundances of isotopic peaks at unit resolution from elements occurring in pesticides are given in Table 6.1 (see Chapter 6 on GC-MS). There are mono-isotopic elements such as fluorine, iodine, phosphorus and also hydrogen which are referred to as "A"-elements and others with typical additional isotopic peaks in the spectrum such as chlorine, bromine and sulphur that arise at two mass units higher and therefore are designated as "A + 2" elements. Molecular and fragment ions containing more than one chlorine or bromine atom therefore give rise to very characteristic patterns, as shown in Fig. 6.3 (see Chapter 6 on GC-MS). The isotope patterns to be expected from any combination of elements can be readily calculated and provide a useful test of ion composition. Furthermore, in compounds containing C, H, O and zero or even amounts of nitrogen, the protonated or deprotonated molecule will be odd mass. If a molecule contains one or an odd number of nitrogen atoms, the protonated or deprotonated molecule will be even. This generalisation applies to all stable even-electron molecules ("the Nitrogen Rule"). The nitrogen rule for LC-MS
375
E.M. Thurman, I. Ferrer and A. Fern.ndez-Alba appears different than for GC-MS because the molecular ions formed are odd electron for GC-MS and even electron ions for LC-MS [2]. 8.4.3
Fragmentation reactions
Protonated or deprotonated molecules are generated in the ESI or APCI ion source with a range of internal energies usually below the threshold for fragmentation. CID fragmentation is required in the lens region of the ion source due to different voltages on the lens plates that guide the ions into the quadrupole region of the mass spectrometer. There are several types of fragmentation that may occur to the even electron ion of the protonated or deprotonated molecule. They are the result of a collision of the protonated or deprotonated species with a neutral (N 2 gas) or water vapour to give the first fragmentation. They are typically even electron atoms (EE) and may involve rearrangements and multiple bond cleavage. A second fragmentation may also occur with charge migration or charge retention. Examples of pesticides are shown later. Odd electron fragmentations have been considered in an earlier chapter (Chapter 6) and will not be discussed here. 8.4.4
Interpretation
The principles reviewed are now illustrated with a few examples. Chlorinated pesticides are presented in Figs. 8.4 and 8.5 in order to demonstrate how to apply the basic knowledge to check the mass spectra and their assignment to a chemical structure for plausibility. Let us start with the protonated molecule of diazoxon and diazinon, an insecticide and its degradation product both with very similar chemical structures; diazoxon is derived from diazinon by simply substituting the sulphur atom with oxygen in the side chain of the phosphate group. Diazoxon is a possible hydrolysis product of diazinon in chlorinated water. The protonated molecule is m/z 289 for diazoxon. The m/z 311 ion is formed as the sodium adduct of the molecule [M + Na]+ . The m/z 261 ion results from the loss of 28 (ethylene, a neutral loss) from the protonated molecule. The m/z 233 ion is the result of a second loss of 28 due to another ethylene loss from the phosphate group. Finally, the m/z 153 ion is the basic structural fragment that results from the loss of the phosphate group. Diazinon has fragmentation products that follow a similar pathway but with different masses. For example, the protonated molecule is m/z 305 but the sodium adduct is not seen. The first loss is of 28 to give the m/z 277 ion followed by a second loss of 28 to give the m/z 249 ion. The last ion is the m/z 376
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis Diazoxon Abund.
3: MS, Time=12.69min (#1341), 100%=14964000arb
100
289.0
M+ =233 M+ = 153
80
28 M+=261
N
-28
NN 60 M + H+ = 289 amu M + Na+=311
40
261.0
232.9 20
311.0 153.1 100
50
150
200
250
300
350
400
m/z
Fig. 8.4. Mass spectrum of diazoxon.
Diazinon
Intense.
Q
305.0
o
M+=249
M+ (O= S)=169
6"
p-a
M+ = 277
II
-28
M+ H+ = 305 amu
2
277.0
153.0 169.0 0 . .
50
.
100
I
150
200
248.9 226.8
. .
'. . . . . . .
250
300
. .
. . . I
. .
350
400
m/z
Fig. 8.5. Mass spectrum of diazinon.
377
E.M. Thurman, I. Ferrer and A. Ferndndez-Alba 153 ion, which is the same as diazoxon. The m/z 163 ion is an interesting structure where the S substitutes for oxygen on the ring and gives a mass of 16 more (S - O = 16 mass units).
8.5
ATMOSPHERIC PRESSURE IONISATION, POSITIVE AND NEGATIVE ION
It should be clear from the description of the basics of MS and the interpretation of LC-MS and GC-MS spectra (Chapter 6) that LC-MS is often more useful than GC-MS because the molecular ion (as the protonated or deprotonated molecule) is typically present and is often the largest ion in the CID atmospheric pressure mass spectrum. In particular, the molecular weight can be unequivocally determined, which does sometimes occur in an electron impact GC-MS spectrum but is not predictable. Thus, chemical ionisation is required in GC-MS (Chapter 6). There are two modes in LC-MS: positive ion and negative ion. This refers to whether the ions being formed are positively or negatively charged. 8.5.1
Positive ions
Positive ions are formed by the addition of a proton [M + 1] + , of an ammonium ion [M + 18] + , or of sodium [M + 23]+ . These three ions are formed frequently in the positive ion mode. Both the addition of a proton and of an ammonium ion are the result of the mobile phase (ammonium formate, for example). Sodium is the result of sodium either in the mobile phase, in the standard, or in the sample that is being analysed. The spectra of these compounds are easily spotted because of the characteristic pattern that exists between the adducts, which are separated by 17 for the proton to ammonium adduct and by 5 mass units from the ammonium to sodium adduct. Figure 8.6 shows the spectrum of fluometuron, which has ions at m/z 233, 250 and 255. In this spectrum, the m/z 255 ion is the major ion of the spectrum and comes from the sodium adduct. Note the odd mass, even mass and odd mass ratio, which indicates the addition of nitrogen in the adduct. 8.5.2
Negative ions
Negative ions are formed by the removal of a proton from the molecule. More rarely, they may be formed by the addition of an electron. This process 378
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis
100
0 01 0, .
I
-
FRAGM.= 80V
[M+23]+
j
80NHCON(CH 3)2
6040-
[M+18 Fluometuron. [M+H] + =: 233
F3 C
20-
N 1..) 01
N) a) 1°
n
So
100
150
200
250
m/z
Fig. 8.6. Examples of proton, ammonium, and sodium adducts to fluometuron.
is more rare because the ion formed is an odd electron ion, which is infrequent in CID fragmentation using LC-MS. It is rare because odd electron ions are reactive in the atmospheric chamber and quickly react and are removed before entering the mass spectrometer. Negative ions typically have a lower response factor than positive ions but, because the background counts are also less, the sensitivity may be nearly equal or more than in positive ion. Fragmentation in negative ion is commonly less than in positive ion because there are less possibilities in a fragmented molecule to remove a proton than to add a proton. Heterocyclic ions are likely places to add a proton (i.e., N and 0), whereas to remove a proton it is generally necessary to have an acidic functional group, such as carboxyl, phenolic hydroxyl, or sulfhydryl group, which are considerably less common in pesticides.
8.5.3
Complementary information
The information of mass spectra obtained from the same compound with positive and negative ionisation methods is of a complementary nature. With a positive ion spectrum, the molecular ion may be more difficult to recognise because of the different adducts that form while, in a negative ion spectrum, typically only the [M - H]- is formed; thus, it is clear what is the molecular mass of the compound. However, doing MS/MS experiments it is often more useful in positive ion because there are more ions formed to give structural information about the compound and its ultimate structure. 379
E.M. Thurman, I. Ferrer and A. Ferndndez-Alba 8.6
LC-MS QUADRUPOLE INSTRUMENTS
The quadrupole mass analyser is actually a mass filter. It consists of four hyperbolic rods that are placed in parallel. Opposite rods are charged by a positive or negative DC voltage upon which an oscillating radio frequency is superimposed. Ions are introduced into the quadrupole field by means of a low accelerating potential, typically of 10-20 V. They start to oscillate in a plane perpendicular to the rod length. When the oscillations are not stable, the ions do not pass the filter because the amplitude of the oscillations becomes infinite. When stable trajectories are made, the ions are transmitted towards the detector. The quadrupole filter thus acts as a band-pass filter, usually set to transmit ions of one particular m/z ratio ("unit-mass resolution"). To obtain a mass scan, the DC and radio frequency voltages are varied while their ratio is kept constant. The mass permitted to pass through is linearly related to the amplitude of the voltage. This simplifies LC/MS operation as well as computerisation. The linear relationship between mass and voltage makes control and calibration by computers easy. Quadrupole mass spectrometers have a reputation for high sensitivity and the ability to scan rapidly at millisecond intervals. These qualities make them well suited for coupling with LC to scan the narrow peaks produced. At the present moment, the quadrupole mass filter is the most widely applied mass analyser in LC/MS. The quadrupole mass spectrometer is typically not sensitive enough in scan mode for LC/MS and is operated in selected ion monitoring. Selected ion monitoring consists of selecting the protonated or deprotonated molecule and at least one major fragment ion. These ions are then used for identification of the unknown. It is a common practice to have at least two fragment ions for identification of pesticides in food samples. SIM allows for sensitive analysis of pesticides in food by single quadrupole analysis but requires the knowledge of what compounds are being monitored in advance, since only selected ions are available and not a full scan of the sample. The single quadrupole is considered the least reliable method for pesticide analysis in food, but is probably the most common method at this time. 8.7 8.7.1
HIGH RESOLUTION MS LC-MS TOF
High-resolution MS is defined as LC-MS TOF analysis at a resolving power of 10,000 and greater (at m/z 1000), which translates to an ability to measure 380
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis the elemental formula of an unknown to 5 ppm or less. These types of resolution and accuracy are needed for total unknown identification [21-23]. This resolving power enables the measurement of an exact mass of an ion and thus an unequivocal identification of its elemental composition. This high resolution can be achieved with double focusing mass spectrometers or more commonly for LC-MS with the TOF mass spectrometer. The usefulness of elemental composition information increases with increasing mass, which also requires an increase in mass-measuring accuracy. The technique is of great importance in basic research when unknown chemical structures are to be elucidated. The TOF/MS instrument represents a powerful tool for identifying nontarget compounds in complex environmental matrices due to three important characteristics: first, the ability to collect data across a wide mass range without a decrease in sensitivity, with a full spectral sensitivity thus achieved; second, the possibility of resolving interferences away from signals of interest with high resolving power; and third, the achievement of mass measurement accuracy adequate for the measurement of elemental composition. Usually, the accurate mass measurements for the target analytes involve a single point correction of the base calibration (to compensate for slight drift of the calibration due to temperature fluctuations in the flight tube and instabilities of the power supplies) utilising a reference compound, or lock mass. In principle, if masses can be measured with sufficient accuracy, it is possible to assign unique elemental compositions to peaks observed during the course of an analysis. In practice, a mass measurement within 1 mDa (in the 300 m/z range) gives rise to a short list of elemental compositions to consider. The TOF/MS system is considered to be a high-resolution instrument capable of 10,000 resolution expressed at full peak width at one-half maximum (FWHM), shown in Fig. 8.7. Resolution is defined as shown in Eq. (8.1), where M is the mass of the ion and AM is the width of the peak at the half height of the peak (Fig. 8.7): M/AM resolution at full peak width at one-half maximum (FWHM)
(8.1)
Because the TOF mass detector collects data in a Gaussian-shaped peak, it is possible to express the resolution in terms of standard deviation units. Thus, the peak width at one-half height is 2.355(r (Fig. 8.7), defined below. The resolution using TOF/MS is expressed as the mass of the analyte, typically reserpine (609 nominal mass), divided by the AM value, which is equal to 2.355o- in mass units. This value for resolution is different from the values used for a magnetic-sector high-resolution GC/MS. The definition for resolution in these systems is shown in Fig. 8.8, where the resolution is 381
E.M. Thurman, I. Ferrer and A. Fernindez-Alba
2)
Fig. 8.7. Definition of FWHM (full peak width at one-half maximum in standard deviation units, which is calculated from the Gaussian distribution function, f(x) = C e( -x2 /2 2 ) and resolution, which is defined in Eq. (8.1) [23].
defined as in Eq. (8.2): M/AM = resolution for a defined value of AH/H = 0.1 or 0.5.
(8.2)
Note here that Eqs. (8.1) and (8.2) are identical, but the definition of AM is different. In Fig. 8.8, AM is the difference between two closely related mass spectral peaks with a valley between them that is defined by AH. Typically, the AH/H value for magnetic-sector high-resolution GC/MS is 0.1. This method is called the doublet method [2] versus the singlet method of TOF [2]. The resolution equation may be calculated at any height using the Gaussiandistribution function and expressing the width in standard deviation units (see Eq. (8.3)). - 2/2 2 f(x) = C e( x ( )
(8.3)
M/AM = Resolutior
ence = Ah
For a defined value AH/H = 0.1, 0.5 etc
Fig. 8.8. Definition of resolution commonly used in magnetic-sector high-resolution
GC/MS [2].
382
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis TABLE 8.1 Calculation of resolution in mass spectrometry Type of instrument
AH
AM (amu)
Difference factor
Defined resolution
Q-TOF MS/MS Sector GC/MS
0.1
2.3o4.9 o
1.0 0.5
20,000 10,000
Note: See Eqs. (8.1) and (8.2) for definition of resolution.
Because the doublet method requires that two peaks overlap at a value of 0.1, the value of H (i.e., the height at the point of intersection of the two massspectral curves) is 0.05 for each of the two peaks. Solution of Eq. (8.3) for a f(x) of 0.05 gives a peak width from the mean to the point of intersection of 2.45w. Because of the definition of AM in Fig. 8.8 (i.e., the difference between the two mass-spectral peaks), AM is equal to twice the 2.45o value, or 4.9o-. Thus, if reserpine were used for a resolution calculation, then the mass of reserpine would be divided by the mass width of 4.9o rather than by the 2.355- value used by the singlet method: Mreserpine/4.9a,
(8.4)
which is approximately one-half the calculated resolution obtained by the TOF method. This calculation means that a singlet resolution of 10,000 # 10,000 resolution by the doublet method. In fact, the difference is approximately a factor of two less resolution with the singlet method versus the doublet method. Table 8.1 shows the effect of calculating resolution by the two methods. Thus, a resolution of 10,000 for a sector GC/MS method is equal to 20,000 for Q-TOF MS/MS. Nonetheless, resolution of 10,000 by Q-TOF MS/MS is suitable for accurate measurements on complex samples. 8.7.2
Q-TOF/MS
The Q-TOF mass spectrometer combines the simplicity of a quadrupole MS with the ultra-high efficiency of a TOF mass analyser. In a Q-TOF instrument, the sample is introduced through the interface, and ions are focused using the hexapole ion bridge into the quadrupole MS. Here, the precursor ion is selected for later fragmentation and analysis with a mass window of approximately three mass units, which is a typical window to preserve the isotope envelopes in the product ion spectra. The ions are ejected into the hexapole collision cell where argon is used for fragmentation. From this point, 383
E.M. Thurman, I. Ferrer and A. Ferndndez-Alba the ions are collected into the TOF region of the MS/MS. The introduction of ions is such that the flight path of the ions changes 90 °, which is called an orthogonal TOF. The purpose of the change in direction is optically to focus the kinetic energy of the ions so that their kinetic energies are as similar as possible. The ions are then accelerated by the pusher and travel down the flight tube 1 m to the reflectron. The purpose of the reflectron is to slow down ions of equal mass but higher kinetic energy and then to focus this beam of ions at the detector such that ions of the same exact mass but slightly different energies arrive at the detector at exactly the same moment. This process results in the mass accuracy of the Q-TOF MS/MS. Thus, the TOF side of the Q-TOF MS achieves simultaneous detection of ions across the full mass range at all operation times. This continuous full-scan mass spectrum is in contrast to the tandem quadrupoles that must scan over one mass at a time, and for this reason the Q-TOF MS/MS is more sensitive when scanning the TOF side of the instrument (estimates are 10-100 times in the product literature) in scan mode than the third quadrupole of the triple quadrupole MS/MS [21-23]. However, it is important to remember that the TOF side of the Q-TOF MS/MS has the same sensitivity in scan mode and in selected-ion mode, which is not true for the triple quadrupole MS/MS, which has increased sensitivity in multiple reaction monitoring (MRM) compared with scan mode. The Q-TOF MS/MS system is considered a high-resolution instrument capable of 10,000 resolution expressed at FWHM. The unique capability of the Q-TOF MS/MS compared with the triple quadrupole and ion-trap MS/MS instruments lies then in its ability to determine accurate mass on the fragment ions generated in the collision cell. Because the quadrupole allows ions of nominal mass to pass through, there may be masses that interfere with the determination of the molecular ion. Interfering ions are much less likely for the fragment ions, which help in the determination of accurate mass by lowering mass interferences and increasing accuracy with the same resolution. Table 8.2 summarises the disadvantages and advantages of TOF/MS techniques compared with other mass spectrometric methodologies in terms of sensitivity, selectivity, accuracy and dynamic range. 8.8
TANDEM MASS SPECTROMETRY (MS/MS)
The structural information provided by MS can be further enhanced with the combination of two mass spectrometers in one instrument adding a new dimension, as we have seen earlier with the use of Q-TOF. The technique of MS/MS allows the measurement of the fragmentation of a selected peak 384
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis TABLE 8.2 Relative comparison of LC/MS systems Sensitivity in full-scan
Selectivity
Accuracy
Dynamic range
Unique features
Triple quadrupole Ion trap MS/MS LC/TOF/MS
Low High High
High High Medium
Low Low High
High Medium Medium
LC/Q-TOF/MS
Medium
High
High
Medium
Neutral loss MS' Accurate mass and sensitivity Accurate mass of fragments
in a mass spectrum producing the product mass spectrum of precursor ion. The first mass spectrometer is used as a separating device for mixtures (such as unresolved peaks in LC-MS); after separating one particular ion, energy is added to yield dissociation product ions that are then separated in the second mass analyser. This mass spectrum is then used for structural characterisation of the selected (parent) ion. While in theoretical research this method is used to investigate the structure and stability of a molecule's fragment ions, in residue analysis the technique can be applied to the molecular species produced by electrospray or APCI, which produces [M + H]+ or [M - HI- ions. In the instrument first used for MS/MS measurements, three quadrupoles are combined to the so-called "triple quad" with the first quadrupole as the separating device for the [M + H] + ions and the third as the mass analyser to monitor the products of the dissociation process. This dissociation is induced by collisions with a target gas that takes place in the central quadrupole and is referred to as CID. Such CID mass spectra are as indicative for the structure or identity of a compound. MS/MS or, as it is sometimes called, tandem MS can be carried out principally in two ways: consecutive in space by using two separate spectrometers (multiple sector or multiple quadrupole instruments) or consecutive in time by using the same mass resolving system twice (ion trap) [13,23]. Three MS/MS instruments are compared in this study. These three instruments represent three different design types, and their comparison shows the unique capabilities of each of these instruments for the analysis of pesticides. Table 8.3 shows the first simple comparison of the three instruments. The discussion will follow the order of Table 8.3, including a discussion of each instrument type, its unique features, advantages and disadvantages on the basis of analysis of pesticides and their degradates. 385
E.M. Thurman, I. Ferrer and A. Ferndndez-Alba TABLE 8.3 Comparison of MS/MS capabilities of Q-TOF, triple quadrupole and ion-trap mass spectrometers Instrument
Unique features
Advantages
Disadvantages
Q-TOF MS/MS
Accurate mass of fragment ions
Most sensitive in MS/MS for quasi-selected precursor ions Always gives full-scan data of the precursor ion Sensitive for quantitation with multiple-reaction monitoring (MRM) Sensitive in scan pathway of fragmentation deduced easily
Most expensive
Triple quadrupole MS/MS
Neutral loss
Quadrupole ion-trap
MS n
8.8.1
Neutral loss not possible No accurate mass data MRM, neutral loss not possible
Triple quadrupole MS/MS
The triple quadrupole MS/MS consists of two single quadrupoles with a collision cell in between (Fig. 8.9). The ions are directed from the Z-spray source into the first quadrupole, where the precursor ion is selected. A hexapole collision cell is followed by the final quadrupole and the photomultiplier detector. This configuration is nearly identical for most triple quadrupole MS/MS instruments within the limits of patented configurations. Standard triple quadrupole MS/MS instruments have four major modes of operation (shown in Table 8.4). They are product-ion scan, MRM, constant neutral loss and the precursor-ion scan. These four modes of operation correspond to the options of running quadrupoles 1 and 3 in either scan mode or selected-ion monitoring. In the product-ion scan, the first quadrupole (MS 1) selects the molecular ion of an unknown or suspected pesticide contaminant and sends only this ion to the collision cell where it undergoes fragmentation to generate the production spectrum. This is the fundamental example of LC/MS/MS and is commonly the first step in the identification of an unknown or the identification of a spectrum for a standard compound, which will be compared with the unknown spectrum. 386
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis M&MUM
ii
IT Y
VMUXTR
i~~~~~~~
amazing" LoIMINOMMIN , .... I~~~~~~~I
_
Za
ItD W>E
16~~~~s~,
4ilPME
6
HeXarE
1
>_;=ou4
OfAWkWOc
e
bM0100
Fig. 8.9. Diagram of triple quadrupole MS/MS. Published with permission of
Micromass Limited, Manchester, UK. The second mode of operation is MRM, which is sometimes called selected reaction monitoring (SRM). In this mode, MS 1 selects the molecular ion of interest and sends it to MS 2 for collision and subsequent fragmentation. A specific product ion is selected and monitored in MS 3. In Fig. 8.10, there is an example of atrazine and its precursor ion of 216 m/z [M + HI+ , which undergoes loss of propylene (- 42) to give the product ion (174). The MRM is written as 216 - 174 m/z. For isotope-dilution measurements, the deuterated atrazine also can be monitored, which gives the d5-atrazine transition of 221 - 179 m/z, and gives excellent quantitation because it equals the effect of matrix suppression. The MRM is a useful technique that is most effective in the triple quadrupole. The MRM experiment, when coupled with deuterated standards, gives both excellent sensitivity and quantitative results because of the fact that any matrix interaction that may occur in MS 1 occurs to both the analyte and its deuterated standard. Furthermore, the low background noise in this mode of operation gives rise to the most sensitive detection limits for pesticides in food. The sensitivity of the various MS/MS systems is now such that trace levels as low as 0.1 pg may be detected with a sensitivity of signal-to-noise ratio of 10:1 with the triple quadrupole in MRM mode. TABLE 8.4 Modes of triple quadrupole MS/MS operation Experiment type
MS 1
MS 2
Product-ion scan Multiple reaction monitoring Constant neutral loss Precursor-ion scan
Static precursor ion Static precursor ion Scanning Scanning
Scanning Static product ion Scanning Static product ion
387
E.M. Thurman, I. Ferrer and A. Ferndndez-Alba CI
CI
"N NH
AtraziNH e [M
N"
-42 NH
Atrazine [M + H+] = 216
NH
CI N
Non N
%
N
I
NH
N ion 174
CI
N"
N
NH
-42
Propazine [M + H+] = 230
NH
N
Ion 188
SCH 3
SCH 3
N
N [ NH L
N
NH
NJ
NH
Prometryn [M + H+] =242
-42
NH 'l
N
N INH
Ion 200
Fig. 8.10. MRM transitions for three triazine herbicides showing the loss of the propylene group with a mass loss of 42 mass units. These examples also show the neutral loss of 42 mass units.
The third method of operation is the neutral loss experiment in triple quadrupole MS/MS (Table 8.4). In this mode of operation, MS 1 and MS 3 scan in synchronisation so that neutral losses from molecular ions detected in MS 1 can be linked to their appropriate spectra. The instrument then calculates the mass loss between MS 1 and MS 3 and finds all peaks of the specified mass loss. An example is shown in Fig. 8.10 where the loss of 42 m/z (propylene) is scanned for several triazine herbicides. The triazines are detected by their common loss of 42 m/z (propylene). This technique may be useful for the detection of unknown compounds from the same family by the neutral loss experiment. Unfortunately, however, the sensitivity decreases when triple-quadrupole MS is operated in this mode, increasing as a consequence the LOD of the analysis.
388
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis MCounts 40-
30-
20-
10I^--
]
"
' ' ' '-''''`-"
2.5
5.0
7.5
10.0
12.5 minutes
Fig. 8.11. Example of the reconstructed ion chromatogram for the 126 m/z ion that is characteristic of the nicotinamide insecticides in food [17]. The last mode of operation of the triple quadrupole is the precursor-ion scan. In this mode of operation, MS 1 is scanned and MS 3 looks at a single ion (selected ion). This operation may be diagnostic when the fragment ion produced in the collision cell is specific for a family of compounds. An example shown in Fig. 8.11 is the fragment ion of 126 m/z (2-chloro-5-pyridylmethyl ion), which is scanned for several nicotinoid insecticides in vegetable samples. The nicotinoid insecticide thiacloprid was detected by a precursor ion scan looking for all peaks that give the 126 m/z ion. In the example in Fig. 8.11, all of the nicotinoid compounds give the 126 m/z ion because of their common structural feature. The reconstructed-ion chromatogram then shows the peaks originating from the 126 m/z ion and draws the chromatogram. Each of these peaks then can be re-examined at full spectrum with the possibility of identifying unknowns. The full spectrum of each of the peaks in the reconstructed 126 m/z ion chromatogram is the precursor-ion spectrum. More information on the operation of the triple quadrupole MS/MS operation may be found in Refs. [2,8,18,23].
8.8.2
Quadrupole ion-trap MS/MS
In quadrupole ion-trap MS/MS, the instrument operates by trapping the full spectrum of ions present in the sample (Fig. 8.12). The isolation in the trap 389
E.M. Thurman, I. Ferrer and A. Ferndndez-Alba PnHtrn pl.rfmraQ
Ring electrode
Ion
detector
Fig. 8.12. Operation of the quadrupole ion-trap MS/MS. is carried out by first focusing the ions into the trap and holding them, by various combinations of RF voltages, in a kind of "electronic beaker" for ions. The usefulness of the quadrupole ion-trap MS/MS lies in several applications. Because it does MS/MS in time rather than in space, scan mode is sensitive, equal to the Q-TOF MS/MS but more sensitive than the triple quadrupole MS/MS in scan mode. The quadrupole ion-trap MS/MS is unable to do a true MRM experiment, which limits its abilities as the most sensitive detection compared with the triple quadrupole MS/MS. However, the quadrupole iontrap has the feature of MS' or until sensitivity disappears by first trapping a specific ion, then fragmenting it, then trapping a new product ion and fragmentating it, etc. This tool in MS/MS is unique to the quadrupole ion-trap MS/MS and is useful for the identification of unknowns such as pesticides in food and water [13,18,19,23]. An example of the use of MSn is shown in Fig. 8.13. Here, the compound is a chlorination degradation product of diazinon, a common insecticide. The structure of the new compound was thought to be diazoxon, whose structure also is shown in Fig. 8.13. By carrying out a MS 4 experiment, it was possible to assign the structural fragments by the losses of ions shown in Fig. 8.13. The two ions at 289 and 311 m/z are assigned as the [M + H]+ and [(M + Na)] + for diazoxon. When the 289 m/z ion is isolated and fragmented in the trap, it gives rise to the 261 m/z ion, which is the loss of 28 mass units or the ethylene group. Isolation of the 261 m/z and MS 3 gives the 233 m/z ion, which is a second loss of 28 mass units (ethylene group). MS4 of the 233 m/z ion gives the 153 m/z ion, which is the pyridinol of diazoxon. The loss of 80 mass units when going from 233-153 m/z shows the loss of the PO3 H group. This examples shows the usefulness of the quadrupole ion-trap at MS4 .
390
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis Abund.
3: MS, Time=12.69min (#1341), 100%=14964000arb +
100-
M = 233 M=153
28
80-
60-
289.0
o
M+=261
+
M + H = 289 amu M+Na+=311
40-
261.0 232.9
20-
311.0 153.1
0 .. 50
, ,,,,. I
''.. ....100 ... 150
.1 ....... 200 250
_j ,1 I., 300
...... 350
400
m/z
Fig. 8.13. An example of MS4 for the determination of diazoxon. The diazoxon was synthesised in the laboratory by treatment of diazinon with a 10 ppm solution of hypochlorite. The diazoxon has been implicated as a toxic degradation product of diazinon that may occur during the chlorination of drinking water. This example shows how the quadrupole ion-trap MS/MS was used to identify the degradation product of diazinon for future pathway and toxicity studies. Further references on the use of quadrupole ion-trap MS/MS can be found in the book on electrospray MS edited by Cole [4]. 8.9
COMPARISON OF MS/MS INSTRUMENTS
Table 8.3 shows the comparison of the three instrument types with the unique features of each. The Q-TOF MS/MS has the unique feature of accurate mass of the fragment ions. The advantages of the instrument are that it is most sensitive in MS scan mode for the product ions of the precursor ion and it gives accurate mass so that each fragment may be given an empirical formula. These empirical formulae are useful for the identification of the ion fragments. The disadvantages of the Q-TOF MS/MS include its cost, which may be prohibitive for routine analysis in many laboratories, and the fact that this instrument is not capable of MRM and the neutral loss experiment. This instrument is a powerful research instrument and less useful for low-level monitoring of pesticides in food. It is best suited for 391
E.M. Thurman, I. Ferrer and A. Ferndndez-Alba identification of unknowns when standards are not readily available and for new compound identification, such as metabolism experiments with pharmaceuticals and pesticides. The triple quadrupole MS/MS has the unique capability of the neutral loss experiment. This feature may be used to identify unknown compounds that are related to one another, a feature that is useful for finding related compounds or metabolites of parent compounds. The triple quadrupole MS/MS is also excellent for quantitation of compounds, especially in difficult matrices, using the MRM feature (Table 8.4). It is the most popular MS/MS instrument for analysis of pesticides in food and water, but it has the weakness of not obtaining full-scan spectra with the same sensitivity of the ion trap and Q-TOF MS/MS instruments. Nonetheless, the triple quadrupole MS/MS is the instrument most commonly chosen for LC/MS/MS analysis of pesticides in environmental samples. Finally, the quadrupole ion-trap MS/MS system is unique in its ability to obtain MS 3 and higher. It does give sensitive full-scan spectra and is a rugged and low-cost instrument. The disadvantages of the quadrupole ion-trap MS/MS are that the MRM experiment is not possible, which raises detection limits as compared with the triple quadrupole MS/MS, especially for difficult matrices, and the neutral loss experiment is not possible. 8.10
8.10.1
OPERATIONAL FACTORS THATAFFECT LC-API-MS RESPONSE AND FRAGMENTATION Source selection
The flexibility that LC-API-MS provides with respect to the physical properties of analytes makes this technique attractive for target and nontarget screening of pesticide residues in food samples. However, one has to consider that different operational conditions of the source may affect the information, structural and sensitivity that are obtained. For example, Pleasance et al. [24] evaluated the N-methylcarbamate pesticides with both ESI and APCI and concluded that an APCI source is often more sensitive than ESI and that the linearity and range of concentration is greater than ESI. These results, however, are different from those obtained by Fernandez et al. [25] where both interfaces are similar in terms of sensitivity and structural information. However, there were differences related to the formation of salt adducts (e.g., M + Na, M + K) with ESI forming adducts of salts. This occurrence of adducts is most common with standards in solvent, especially when doing flow injection. However, real samples will have many fewer
392
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis sodium adducts because of chromatographic separation of sodium from the analyte. In fact, ESI ionisation is considered as preferable because it is a softer technique than APCI and much less prone to thermal degradation as the sample is ionised directly in the liquid phase at quasi-ambient temperature, thus leaving fragile pesticides intact. Also, ESI will work on a broader range of compounds than APCI because of the fact that ionic compounds are easily analysed by ESI but not by APCI. For example, Thurman et al. [26] compared the APCI and ESI interfaces for a group of pesticides and found that the APCI interface works better for compounds that are neutral in charge and contain basic nitrogen that allows positive ion operation. ESI works better in ESI negative, especially for compounds that contain carboxyl and phenolic hydroxyl groups. Furthermore, they used the concept of the ionisation continuum diagram to show the range of pesticides and where the ionisation works best. Figure 8.14 shows an example of the continuum diagram. Pesticides that are positively charged in solution, e.g., diquat, work in ESI+ but not by APCI or APPI. Pesticides such as metolachlor and the chloroacetanilides work in both APCI and ESI positive and negative. Generally, however, the positive is more sensitive. Compounds that are non-polar, such as DDT, do not generally work with either APCI or ESI, but work well when ionisation is by El (such as in GC/MS). The APPI source is a new source that was recently introduced that uses light (i.e., low eV ionisation 10-20 eV) to ionise compounds that are non-polar. Finally, pesticides that are negatively charged, such as the phenoxyacetic acids, work well in ESI negative. Thus, the ionisation continuum diagram is a useful concept to understand ionisation of pesticides and other environmentally important compounds when using LC/MS ionisation techniques. However, it is often necessary to "fine tune" the analysis for maximum sensitivity for pesticide analysis in food. The diagram is but a helpful tool in methods development.
Fig. 8.14. The ionisation continuum diagram for pesticides [26].
393
E.M. Thurman, I. Ferrer and A. Fernandez-Alba Therefore, until now, there is a paucity of information about which type of interface is most suitable. A priori, it is not possible to know which is best. Based on our recent experience and information, the selection of an interface is partly based on the experience of the laboratory and type of instrument. In any case, we can consider that, currently, ESI is more often applied for pesticide screening than APCI probably because of ease of use (less plugging), its ruggedness, more compound selectivity (ions in solution are analysed), and its advantages in negative ion. This said, APCI will remain an important source for maximum sensitivity in positive ion of neutral species, and for unusual compounds in negative ion that require electron capture for ionisation (i.e., nitrated and chlorinated phenols).
8.10.2
Eluent composition
Next comes consideration of eluent composition and how it affects the solvent-analyte reactions. For example, typically, more signals are measured in the positive-ion mode when methanol is used as the organic modifier instead of acetonitrile [27,28]. An explanation for this fact can be found in the proton affinity of methanol, 761 kJ/mol [26], which is lower than acetonitrile, 787 kJ/mol. However, this effect is small, and probably does not represent an important consideration for a selected group of pesticides, especially since acetonitrile is a better chromatographic solvent and the possibility of reactions of analyte with the solvent (methanolysis, for example) is less. By the same token, the solvent also affects the amount of internal energy an ion gains during ionisation, and thus it can be contributed to the degree of the CID [28]. In negative mode, the acidity of the eluent affects the ionisation process. Therefore, when a low pH of the LC eluent is necessary for the separation of acidic compounds, the S/N ratio is better if acetic acid is used to acidify the LC mobile phase instead of the stronger formic acid; however, this difference is small and not of major consideration. The fact that formic acid has a lower pKa makes it more useful for positive ion and also a practical reason is that it is a less biologically active solvent than acetic acid. Another consideration is the contribution from water in the mobile phase and the presence of buffers. Typically, when the organic modifier reaches higher concentrations, especially acetonitrile, there is a drop in response because of the lack of water, which is due to a lower efficiency of ionisation. That is, the ionisation current measured in nanoamperes will drop from -1100 nA (at a flow rate of 0.6 ml/min measuring the endplate offset current on an Agilent ion trap) to less than 394
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis 50 nA with pure acetonitrile. The decline is less with methanol but still important. Thus, when running chromatographic gradients, there will be a major drop in ionisation efficiency and response when the organic modifier is greater than 90% and a minor drop beginning at about 60% organic modifier. The influence of the flow rate entering the MS interface has also to be considered on the MS signal response. It has been pointed out for a selected group of herbicides that there is a decrease from 40 to 100% when the flow rate is moved from 0.2 to 1.0 ml/min [29,30]. This is in contrast to the results obtained by other authors [31], who stated that a mass-flow dependent detector response of high-flow electrospray interfaces when working at flow rates up to 2 ml/min. This trend can be explained by a decreasing efficiency of the ionisation process with increasing amount of eluent sprayed into the ion source. The explanation of the different behaviour of the individual pesticides probably relates to their hydrophilic/hydrophobic properties [29]. In our experience, a flow rate of 0.6 ml/min is an appropriate flow rate with standard LC columns (i.e., 150-250 mm in length and diameters of 3-4.5 mm). In general, the coupling of LC with MS does not allow the use of nonvolatile buffers or modifiers, such as phosphate buffers (commonly used in chromatography). There are no systematic studies about the influence of eluent modifiers on the response obtained in LC-MS analysis of the main groups of pesticides. However, the need for a compromise is clear between various factors with the use of buffers in the LC mobile phase, such as column separation, peak shape, response, etc. Because of the weak acid-basic properties of many pesticides, typically the working LC conditions are best with acidic mobile added at a concentration range of 0.1%. For an ammonium formate or acetate buffer, a 10 mM concentration is typically used for good response, buffer usefulness and sensitivity, especially when an extensive clean-up is not applied in food analysis. We can consider ammonium formate a more useful buffer than ammonium acetate as a consequence of its higher volatility and it being less prone to form sodium adducts as a consequence of the impurities present in the salts or to undergo biological degradation. The main conclusion at this point is that an analyte response may vary widely with the LC eluent composition with results that can show differences in relative responses from 0 to 100% [28]. In this way, we can also consider the spectra affected, especially in those cases with low relative abundance, that could disappear with the background at low pesticide concentration depending on the mobile phase used. Thus, some care should be spent in method development and modification so as not to lose the response of the pesticide of interest.
395
E.M. Thurman, I. Ferrer and A. FernAndez-Alba 8.11 8.11.1
COMPOUND IDENTIFICATION Target screening
API sources share with thermospray the fact that they are soft ionisation techniques that produce primarily M + H or M - H ions. This means that, for a correct pesticide identification, additional processes are required to produce fragment ions. CID is the most common way to do this. The ions are accelerated to increase their kinetic energy and pass into a chamber with the collision gas, either Ar for the triple quadrupole or He for the ion trap. In an API interface, CID can be performed in the API source itself. Typically, it is recommended to avoid a complete fragmentation of the precursor ion and to keep at least a small amount of it in order to improve the identification process. By the application of an additional voltage in the region between the heated capillary and the skimmer, or in the zone between the skimmer and the octapole, the ions are accelerated. Nitrogen from the sheath gas and solvent molecules act as the collision gas in a single quadrupole or TOF system. Typically, the increase of the fragmentation voltage leads to the partial or total destruction of the proton or ammonium adducts formed (Fig. 8.15). In the case of the single quadrupole mass spectrometer, only this fragmentation "mode" is available and is called in-source CID. The voltage difference or fragmentor voltage can be changed from low to high settings. However, the use of in-source CID has important drawbacks, such as the practical difficulty of working in the optimum fragmentor voltage for every compound in a multi-residue method. Owing to the very close or overlapping chromatographic peaks, it is difficult to obtain some or enough fragmentation in many cases, even at high voltages (Fig. 8.15) and the fragmentation of all peaks that co-elute gives fragment ions that confuse the mass spectrum, especially in the food analysis when extensive clean-up procedures are not applied. All these drawbacks can be solved working in MS-MS tandem by using triple quadrupoles or ion trap systems because the voltage used can be low and similar for all compounds and the number of fragments produced can be controlled by a fine tuning of the MS/MS experiment in order to specify the fragmentation and produce simple spectra. There are at least three means of identification of pesticides in food from spectral analysis. First, and most commonly used, is the matching of retention times and selection ions for pure standards. This method looks typically at the molecular ion of a pesticide in a food sample and at least one fragment ion. The analysis of the fragment ion should have the same intensity relative to the molecular ion for identification purposes. It is best to obtain three fragment 396
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis
zalil iVV
V
100:
FRAGMENTOR=40 V I
80:
.. .
80:
60O
,;a,, ;
j
100 1
N
--
IVls/Ii
-
..-
I ..
254.9
60-
20 0.9
ClC
40:
40-
20:
20-
296.9 I
Fragmentatlon
158.9 050
C)
a 0LL 6j
100 150 200
250 300 350 mz
100
150
228.9
00 350 miz
2 00 250
100-
6
100
lit1
50
296.9
C)
N
FRAGMENTOR=80 V
80
80-
254.8
60
60
1o
40
40-
158.9
c
20
20 u
100 150
0
350 m/z
200 250 300
O trr~t
100
Ci Llj
.1
109.1
F+4sr, PI~ . -t_ 150 200 25 0
10 0O
hk-t
300
350 m/z
255.0297.0
=RAGMENTOR=120 V
8
-
E
630
201 .0
050
4
1,
40 0 2
159
C .ii....
,
_.; .
,,,I .. i .. , . j
100 150 200
N
j
i ,-
.
.
..
250 300 350 m/z
v
.
109.1 1 1i .I i..
t
2218.9 rr
..,.,... .... 100 150 200 250
. ._
I.
300
350 mz
Fig. 8.15. An example of imazalil showing how CID fragmentation does not easily provide a fragment ion with various fragmentation voltages for quadrupole/MS. However, fragmentation is seen for the ion trap using MS/MS conditions.
ions for what is considered an unequivocal identification by LC/MS with a single quadrupole. Avariation on this technique is the MRM experiment with the triple quadrupole, which is much more reliable and sensitive, as described previously. 397
E.M. Thurman, I. Ferrer and A. Fern6ndez-Alba 8.11.2 Non-target screening, identification of spectra and libraries, and the future in LC/MS spectra of pesticides in food The technology of LC/MS of pesticides has advanced tremendously over the past 10 years, going from its infancy to a mature analysis system in a decade. Much of this advance was due to the invention of ESI and the development of the orthogonal and off-axis spray chambers. This introduction of sample from the liquid chromatograph gives sensitivity but also allows for the spraying of complicated samples, even with salts, which do not plug the source. Thus, the instruments of today allow trace analysis of pesticides in the complex matrix of food and beverages. However, what lies ahead is the development of software for spectral analysis of food samples. The spectra that arise from both ESI and APCI are quite different from that of GC/MS (as explained earlier in this chapter); thus, there is the need for the development of software and spectral analysis tools for analysis of unknowns. The most important of these tools is the library of spectra to identify unknowns. However, this is a difficult task because of the differences in fragmentation that occur among instruments, source types and pesticides. For example, the fragmentor voltage of the ion source is the critical factor to produce CID spectra. Each pesticide fragments differently with a wide variety of intensities; thus, it is difficult to find the ideal fragmentor voltage for all pesticides (Fig. 8.16). This fact makes it more difficult to generate a typical library that is searchable for unknowns. One approach is that of Hough et al. [32] where library searchable databases are generated at three different fragmentation voltages corresponding to low, medium and high fragmentation energies. This library was found to work well for 16 sulfonylurea herbicides tested against spiked-water samples. Their preliminary work showed that the libraries were independent of flow rate and solvent system. The performance of the library was tested over a concentration range of two orders of magnitude using the sulfonylurea herbicides. All library work by this group was carried out on a single quadrupole LC/MS system. Another example of spectral libraries for pesticides is the work of Schreiber et al. [30]. They also observed the importance of the fragmentor voltage and the generation of CID spectra. They report the importance of doing both positive and negative ion spectra and using different fragmentor conditions. They found that the composition of the solvent for HPLC did not affect the library spectra. Thus, LC/MS library spectra are more difficult to acquire
398
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis
i[M+H]V
10100
H
FRAGMENTOR=40V
8:
80 FRAGMENTOR= 60 V
60
N i M_
N
40
6Cl/
I =
W = 201
20 0
.
I..,
5
.
100
MW 255
2-
,
i::
15
200
25
NO2
N
4.
O
.1
'X
CN
.
300
5
m/z
100
150
200
25
300
m/z
20
25
300
m/z
25
30
m/z
10: 100 8:
FRAGMENTOR=80V
80 60
6FRAGMENTOR = 120V
40
42-
20 0
I
I.... -----
5
100
15
L 20
25
200
25
,%
0-
300
m/z
N
5
100
150
10.
100 FRAGMENTOR=120 V
80 60 4.
40 7
20 _-1 -,,J .-
0 5
100
= _-
150
2.
D
A ------
200
-
25
_ __
300
__-
mhz
O.
5
10
15
20
Fig. 8.16. This diagram shows how different pesticides at different fragmentor voltages yield different spectra, which makes the use of a single library spectrum difficult.
and compare than GC/MS spectra and are a future need for LC/MS analysis of pesticides in food. Although it is sometimes dangerous to predict where a field is going, it is probably safe to say that the analysis of pesticides in food will continue to grow in the area of the application of accurate mass analysis. There are new instruments appearing each year that improve the resolution and accuracy of LC/MS, especially in the area of TOF. Thus, a future trend will surely be the continued use and application of LC/MS accurate mass analysis of pesticides in food. Also, there is the need to know more of the importance of the role of matrix from food, such as vegetables and fruits, in the analysis of pesticides by all LC/MS methods. Finally, our prediction is that the use of accurate mass libraries will be quite useful in the future analysis of pesticides in food using LC/MS methods, especially accurate mass. 399
E.M. Thurman, I. Ferrer and A. Fernindez-Alba REFERENCES 1 2 3 4 5 6 7 8 9
10
11
12
13
14
15
16 17 18 19 20
400
M.A. Grayson (Ed.), MeasuringMass. Chemical Heritage Press, Philadelphia, PA, 2002, p. 149. R. Willoughby, E. Sheehan and S. Mitrovich, A Global View of LCIMS, 2nd ed., Global View Publishing, Pittsburgh, PA, 2002, p. 518. W.M.A. Niessen, Liquid Chromatography-Mass Spectrometry. Marcel Dekker, New York, NY, 1999. R.B. Cole (Ed.), Electrospray Ionization Mass Spectrometry. Wiley, New York, 1997, p. 577. D. Barcelo (Ed.), Applications of LC-MS in Environmental Chemistry. Elsevier, Amsterdam, 1996, p. 543. C.G. Herbert and R.A.W. Johnstone, Mass Spectrometry Basics. CRC Press, Boca Raton, FL, 2003, p. 474. O.D. Sparkman, Mass Spectrometry Desk Reference, Global View Publishing, Pittsburgh, PA, 2000. K.L. Busch, G.L. Glish and S.A. McLuckey (Eds.), Mass SpectrometrylMass Spectrometry. VCH Publishers, New York, NY, 1988, p. 333. M.A. Brown (Ed.), Liquid Chromatography Mass Spectrometry: Applications in Agricultural, Pharmaceutical,and Environmental Chemistry, ACS Symposium Series 420, American Chemical Society, Washington, DC, 1990, p. 298. A.L. Yergey, C.G. Edmonds, I.A.S. Lewis and M.L. Vestal (Eds.), Liquid ChromatographylMass Spectrometry: Techniques and Applications. Plenum Press, New York, NY, 1990, p. 306. C.N. McEwen and B.S. Larsen, Electrospray ionization on quadrupole and magnetic-sector mass spectrometers. In: R.B. Cole (Ed.), ElectrosprayIonization Mass Spectrometry. Wiley, New York, 1997, pp. 177-202, Chapter 5. I.V. Chernushevich, W. Ens and K.G. Standing, Electrospray ionization time-offlight mass spectrometry. In: R.B. Cole (Ed.), Electrospray Ionization Mass Spectrometry. Wiley, New York, 1997, pp. 203-234, Chapter 6. M.E. Bier and J.C. Schwartz, Electrospray ionization quadrupole ion-trap mass spectrometry. In: R.B. Cole (Ed.), Electrospray Ionization Mass Spectrometry. Wiley, New York, 1997, pp. 235-290, Chapter 7. D.A. Laude, E. Stevenson and J.M. Robinson, Electrospray/fourier transform ion cyclotron resonance mass spectrometry. In: R.B. Cole (Ed.), Electrospray Ionization Mass Spectrometry. Wiley, New York, 1997, pp. 291-320, Chapter 8. R.D. Voyksner, Combining liquid chromatography with electrospray mass spectrometry. In: R.B. Cole (Ed.), Electrospray Ionization Mass Spectrometry. Wiley, New York, 1997, pp. 323-342, Chapter 9. J.B. Fenn, M. Mann, C.K. Meng, S.F. Wong and C.M. Whitehouse, Science, 246 (1989) 64. Y. Pico, G. Font, J.C. Molto and J. Manes, J. Chromatogr. A, 882 (2000) 153-173. Y. Pico, C. Blasco and G. Font, Mass Spectrom. Rev., 23 (2004) 45-85. M. Careri, F. Bianchi and C. Corradini, J. Chromatogr. A, 970 (2002) 3-64. S.D. Richardson, Anal. Chem., 74 (2002) 2719-2742.
LC-MS. I: Basic principles and technical aspects of LC-MS for pesticide analysis 21
22 23
24 25 26 27 28 29 30 31 32
I. Ferrer and E.M. Thurman, Liquid Chromatography/Mass Spectrometry, MS/MS and Time-of-Flight MS: Analysis of Emerging Contaminants. Oxford Press, Oxford, 2003, p. 415. I. Ferrer and E.M. Thurman, Trends Anal. Chem., 22 (2003) 750-756. E.M. Thurman and I. Ferrer, In: I. Ferrer and E.M. Thurman (Eds.), Liquid Chromatography/Mass Spectrometry, MS/MS and Time-of-Flight MS: Analysis of Emerging Contaminants.American Chemical Society/Oxford University Press, Washington, 2003, pp. 14-31, Chapter 2. S. Pleasance, M.R. Anacleto, M.R. Bayley and D.H.J. North, Am. Soc. Mass Spectrom., 3 (1992) 378-382. M. Fernandez, Y. Pico and J. Manes, J. Chromatogr.A, 871 (2000) 43-56. E.M. Thurman, I. Ferrer and D. Barcelo, Anal. Chem., 73 (2001) 5441-5449. T. Remmstma, Trends Anal. Chem., 20 (2001) 533-538. A.C. Hogenboom, M.P. Hofman, S.J. Kok, W.M.A. Niessen and U.A.Th. Brinkman, J. Chromatogr. A, 892 (2000) 379-390. A. Asperger, J. Efer, T. Koal and W. Engewald, J. Chromatogr. A, 937 (2001) 65-72. A. Schreiber, J. Efer and W. Engewald, J. Chromatogr. A, 869 (2000) 411-425. J. Abian, A.J. Oosterkamp and E.J. Gelpi, Mass Spectrom., 34 (1999) 244-254. J.M. Hough, C.A. Haney, R.D. Voyksner and R.D. Bereman, Anal. Chem., 72 (2000) 2265-2270.
401
Chapter 9
LC-MS. II: Applications for pesticide food analysis Imma Ferrer, Joaquin Abian and Amadeo R. Fernandez-Alba
9.1
INTRODUCTION AND SCOPE
Currently, around 1000 pesticides (active substances) are sold worldwide. The main applications can be classified as production and post-harvest treatment of agricultural commodities, structural pest control, landscape maintenance and others (e.g., public health, roadside, furniture, etc.). Agricultural production obviously comprises the main category of use and is subject to control requirements and, therefore, legal action levels (e.g., maximum residue limits (MRLs) or tolerances) have been fixed to assess food safety. Since 1993, the global pesticide market value has fluctuated from year to year with no clear increasing or decreasing trend. However, since 1990, the global market has moved from $23,170 million to reach $25,150 million at the distributor level in 2002 [1]. An increase or decrease in use from one year to the next or in the span of a few years as has happened does not necessarily indicate a general trend in use; it simply may reflect normal variations due to agricultural and intervention prices, weather at planting times, international agreements, etc. Short periods of time (3-5 years) may suggest trends, such as the increased pesticide use from 1994 to 1998 or the decreased use from 1998 to 2001 [2]. Nevertheless, global data indicate a stable market during the last decade (see Table 9.1). Agricultural production has noticeably increased during the last decade and therefore we can consider this effect a consequence of a combination of factors such as the introduction of more effective compounds, more efficient use, increasing concern of farmers and agencies in developing plans to reduce their risk, etc. Obviously, this trend is closely related with social demands to ensure the production of better and safe foods in an environmentally sound way. Comprehensive Analytical Chemistry XLIII Fernandez-Alba (Ed.) C 2005 Elsevier B.V. All rights reserved
403
I. Ferrer, J. Abian and A.R. Ferndndez-Alba TABLE 9.1 Pounds of pesticide active ingredients used in California in different categories,
1992-2002 [3] Year Production agriculture 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Post-harvest fumigation
156,664,418 1,811,128 172,492,706 1,703,738 175,408,663 2,004,123 187,577,922 3,770,169 182,375,369 1,847,859 189,796,122 1,608,996 198,568,999 1,655,875 185,457,062 2,019,542 172,220,535 2,134,698 137,276,887 1,425,407 156,509,065 1,869,350
Structural Landscape All others Total pest maintenance pounds control 5,319,391 4,687,296 5,186,253 4,839,368 4,738,168 5,184,905 5,930,988 5,673,321 5,165,170 4,922,554 5,467,113
1,250,624 1,317,791 1,325,560 1,382,563 1,259,332 1,231,788 1,405,312 1,403,635 1,395,493 1,288,100 1,439,222
15,445,580 7,811,172 7,430,770 7,563,928 7,607,753 6,957,906 6,783,731 7,858,042 6,727,099 6,211,940 6,801,540
180,491,141 188,012,703 191,355,369 205,133,950 197,828,481 204,779,717 214,344,905 202,411,602 187,642,995 151,124,888 172,086,290
aThis category includes pesticide applications reported in the following general categories: pest control on rights-of-way; public health, which includes mosquito-abatement work; vertebrate pest control; fumigation of non-food and non-feed materials such as lumber, furniture, etc.; pesticide used in research; and regulatory pest control used in ongoing control and/or eradication of pest infestations.
In the same way, very small changes in the distribution of conventional pesticide types (insecticides, fungicides and herbicides) during the same period of time can be observed. Only an appreciable relative decrease in the insecticide group is noticeable. However, in regional market values, a considerable increase in the importance of the fungicide group with respect to the other two is clear (Fig. 9.1). In general, there is a very inhomogeneous distribution of these group types depending on the regions considered (Fig. 9.1). Furthermore, the relevant change in this last decade is the trend to use new pesticides with a "reduced risk" status and the decline in use of older groups containing chemicals classified as carcinogens, reproductive toxins, ground water contaminants, etc. (Figs. 9.2 and 9.3). This trend is mainly seen in developed countries; therefore, the number of pesticides in use is not likely to decrease and, as a consequence, the scope of their analysis tends to increase. Moreover, new transformation products can be expected from these new or "emergent" compounds and need to be evaluated. These "reduced risk" groups of pesticides are the result of the development of chemicals with a more specific action. They can be manufactured by synthesizing more complex molecules via the replacement and withdrawal of 404
LC-MS applications for pesticide food analysis 60 50 40'
I
30 20
MMF
10
I
0*
World*
L
b
World
I El Herbicides
M Insecticides
h-
Europe
Spain
0 Fungicides
C Others
Fig. 9.1. Percentage of the total conventional pesticide market value in 1993* and 2002 for each pesticide use [1,2,4].
chemical groups with broad non-selective impacts, persistence, etc. As a consequence of this complex structure, these new compounds are currently not GC amenable. An example is shown in Fig. 9.4, where a great difference in molecular complexity between well-known organophosphate or carbamate pesticides (such as methamidophos or methiocarb) and new chemicals currently in use as fungicides and insecticides (azoxystrobin and spinosad) can be observed.
16 14 Z 12 '
C,
E
10
._
8 o
r
-6
0
-44 0 0: 0
a.
2o 0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 U
Pounds A
E
Acres reated
Fig. 9.2. Use trends of cholinesterase-inhibiting pesticides, which includes pesticides with organophosphate and carbamate active ingredients. Reported pounds of active ingredient (Al) applied includes both agricultural and reportable non-agricultural applications. The reported cumulative acreage treated includes primarily agricultural applications. Data are from the Department of Pesticide Regulation's Pesticide Use Reports (California).
405
I. Ferrer, J. Abidn and A.R. Ferndndez-Alba A
0.8 A
4.0
0.7
3.5 -
. 0.6
3.0 .
r
2.5 E 2.0 'V 1.5 2
i
0.2
1.0 Iocs
0.1
0.5 <
0.0
0.0 1994
1995
1996
1997
1998
1999
2000
2001
2002
Pounds A [ Acres treated
Fig. 9.3. Use trends of reduced-risk pesticides. These active ingredients are contained in pesticide products that have been given reduced-risk status by US EPA. Reported pounds of active ingredient (AI) applied includes both agricultural and reportable nonagricultural applications. The reported cumulative acres treated includes primarily agricultural applications. Data are from the Department of Pesticide Regulation's Pesticide Use Reports (California).
The new developments are the answer to the new challenges in the cropprotection industry and the implementation of new laws such as the European Directive 91/414/EEC in Europe, or the Food Quality Protection Act (FQPA) in the USA [5]. Both regulations increase the standards for human health, workers and environmental protection. They also mean re-registration 0 II H3 CHN-C-
0 CH 3 OPSCH 3
H3C/
NH 2 METHAMIDOPHOS CH3 H3C
CH 3 METHIOCARB
n
H3C
H3 CO"
,, 3 H3u
AZOXYSTROBIN
CH3
4 O 'o'
3
H H
O.CH 3 O-CH
H2CH SPINOSAD
Fig. 9.4. Chemical structures for some well-known and new pesticides.
406
3
CH 3
LC-MS applications for pesticide food analysis schemes for older pesticides. Additional measures also promote this trend; for example, EPA gives priority in its registration programme to pesticides that meet the "reduced risk criteria", such as low impact on human health, low toxicity on non-target organisms, low potential for ground water contamination, lower use rates, etc. The review programmes have withdrawn authorisations for many of the crop-protection products currently on the market: 177 compounds in the USA and 320 in Europe. Moreover, in Europe a total of 110 products will be withdrawn in the near future. Therefore, the Review Programme in Europe will have over 450 existing active substances taken off the market in 2008, with around 400 remaining in use (see chapter 2). Furthermore, these regulations have implemented special provisions for infants and children, including additional safety factors. For example, the two Baby Food Directives in Europe set zero detection limits for 11 substances that are being phased out and close to zero limits for another five. The above-mentioned increase of quality standards within the new regulations also includes the re-assessment of the MRLs or tolerances that typically become lower or much lower than the previous ones. In Europe, the new Directive also led to the harmonization of the MRL for each EU country, which is not complete at the moment, and it also maintains individual country MRLs in many cases. As a consequence, in the coming years we can expect an important reduction in the number of authorised substances in the EU and the USA (more reduced in the EU) combined with an important decrease of the MRL or tolerances, the introduction of new pesticides with more complex structure, as well as a reduction in commodity application. This will not be the case for other countries with much lower standard quality criteria in agricultural practices and regulations. Therefore, the reduction in the number and levels of pesticides in food is more regional than global, considering the characteristics of global trade, with a clear trend towards the removal of regulatory impediments to trade agricultural produce. This fact leads to the existence of various MRLs (e.g., CODEX, USA, EU, etc.) with large differences between them in many cases, depending on the origin of the regulation applied. Various examples are shown in Table 9.2 where extreme variations in some MRLs/ commodity combinations can be clearly observed. These variations can be even wider when a pesticide is authorized in the producer country and banned in the destination country. One example is fenthion, a pesticide frequently used on citrus in Spain (authorised with a MRL of 0.5 mg/kg) and banned in the new FQPA US regulation as a consequence of a voluntary cancellation of the registrant company and, therefore, taken out of the re-registration process.
407
I. Ferrer, J. Abian and A.R. Fernindez-Alba TABLE 9.2 Comparison of MRLs (mg/kg) for buprofezin and endosulfan in various vegetables for different countries considering EU harmonised and EU non-harmonised regulations Codex
Spain
Buprofezin MRL (mg/kg) (EU non-harmonised) Pepper 0.5 Cucumber 1 0.2 Tomato 1 0.5 Lettuce 0.01 Marrow 0.2 Green beans 1 Codex EU Endosulfan MRL (mg/kg) (EU harmonised) Pepper 2 1 Cucumber 2 0.05 Tomato 2 0.5 Lettuce 1 0.05 Marrow 2 0.05 Green beans 2 0.05
Germany
USA
0.5 0.2 0.5 0.02 0.02 0.02
0.01 0.5 0.4 13 0.5 0.01
USA 2 2 2 2 2 2
All the aspects commented upon above have an important incidence on LC-MS method development because the interdependence of all these factors with the analytical method performance required is clear. Furthermore, the stress of EU institutions on food monitoring for residues has important effects in the development of new and high-performance pesticide multi-residue methods (Fig. 9.5). As a consequence of the existence of multiple standards for pesticides and the destination or origin of the sample (e.g., local, regional, international), pesticide residue control could need a different approach and this could affect the goals of the analytical procedures applied. However, in any case, this goal requires multi-residue analytical methods to reduce workload and costs. In order to be more effective, it is important to adjust the performance of the analytical methodologies to the analysis of the target compounds in the region/commodity, in this way allowing an exhaustive control in optimum conditions. Nevertheless, it is often very difficult for the food control laboratories to maintain various analytical procedures for the same or a similar group of substances depending on the different goals to be achieved. For this reason, the development of single or very few multiresidue methods taking as a reference the "worst case" (e.g., LC-MS- and GC-MS-based 408
LC-MS applications for pesticide food analysis
Social Pressure
Applioations
.
Fig. 9.5. Interdependence of social and analytical aspects in food regulation. methods, over 100 compounds per run, detection limits close to 0.01 mg/kg, analysis times around 30 min, etc.) on a realistic basis of costs and reliability, is the approach to follow in many cases. This chapter will focus mainly on the need for robustness when performing MRM routine analysis. It will present an overview of the main achievements by LC-MS and LC-MS/MS in food multi-residue analysis. The difficulties encountered when using LC-MS for the qualitative and quantitative analysis of fruit and vegetables after fast and inexpensive extraction will also be discussed. The present strategies and technical developments to solve these problems are also described in relation to identification and quantification aspects.
9.2
ELECTROSPRAY AND ATMOSPHERIC PRESSURE CHEMICAL IONIZATION (LC-API-MS) INTERFACES
The majority of mass spectrometers offer two interfaces: electrospray ionisation (ESI) and atmospheric pressure chemical ionisation (APCI). They have been described in detail in the previous chapter. Recently, a third API interface became commercially available, the atmospheric photoionisation interface (APPI) [6,7]. However, there is not enough information so far to evaluate its effectiveness in pesticide analysis. Due to the higher response for many of the current pesticides analysed, ESI is the first choice in multi-residue methods [8-10]. However, the LC-API-MS 409
I. Ferrer, J. Abidn and A.R. Ferndndez-Alba system has to be considered as a whole and, therefore, mobile phase composition, gradient, buffers, etc., can strongly influence the signal intensity for pesticides. For this reason, taking into consideration only one of the various parts independently from the others can lead to wrong conclusions. As a result, both APCI and ESI are currently used successfully for pesticide multi-residue methods.
9.3
INTERFACE ROBUSTNESS
LC-API-MS-based methods are by far the most widely accepted because of their compatibility with highly polar water-soluble pesticides, reversedphase LC and usual flow rates. In the early 1990s, API ousted TSP as the competitive interface. An advantage of API sources over TSP is that they introduce only a small amount of solvent into the low-pressure region of the MS and in this way render the system more robust by requiring less maintenance. The importance of a multiclass compound capability is obvious, as well as the development of a fast and low-cost analytical methodology in food analysis in order to develop an effective pesticide-residue control and to achieve the specifications previously discussed. For these reasons, current applications in food analysis demand special capabilities for the fast analysis of a high number of moderately or highly complex samples, at the same time avoiding some clean-up procedures, which typically represent an important limitation in LC-UV- or LC-Fl-based methods. Vegetable extracts are typically present, depending on the commodity/process, an important amount of co-extractives (see Chapter 3) that can make more difficult the LC-MS operation. In a conventional API inlet, the initial spray of electrically charged and neutral droplets forms a cone-shaped region. As low molecular solvent droplets evaporate, they diffuse from the line-of-sight trajectory to the skimmer; many neutral molecules tend not to diffuse so quickly because of their much greater molecular mass, allowing them to continue to travel close to the line-of-sight trajectory. As the analyte ions and neutrals approach the skimmer, few solvent molecules remain, so mostly analyte ions and neutrals pass through the skimmer. However, because of the diffusion and the mutual ion repulsion, the ion beam is not closely defined and some of it strikes the edges of the skimmer, causing a build up of material that eventually blocks the orifice [11, 12]. The situation is exacerbated with the increase of neutral molecules, analyte ions and buffering agents as usually happens in food extracts without any previous clean-up stage. This situation has caused an appreciable delay
410
LC-MS applications for pesticide food analysis in the application of LC -API-MS in routine food control compared with other fields such as water or pharmaceutical analysis. Nowadays, system robustness is a need in this field for general acceptance of API LC-MS and much effort has been focused in this regard by commercial companies. Several strategies have been developed to prevent plugging of the sampling orifice or diminished sensitivity due to solid or liquid deposition (see Fig. 9.6). The classical procedure to prevent aperture plugging is to locate the spray probe off-axis with the MS entrance. Ions are focused by the electric field to the mass spectrometer and the bulk of the neutral droplets and vapour hits the chamber or lens surface where solid materials deposit. The ion entrance based on the "pepperpot" device uses a plate with chicane-type tunnels to prevent the solvent from entering directly into the mass spectrometer. The cross-flow device uses the same strategy but with a different design. In the last few years, orthogonal disposition has been implemented in several commercial mass spectrometers to overcome this problem. Accordingly, a first skimmer orifice is moved from the line-of-sight position to one at right angles to the initial spray direction. In this case, neutrals and non-volatile materials follow the gas flow towards the vacuum region of the mass spectrometer. Ions are captured into the mass spectrometer with a one or two 90° extraction angle, or a combined 90 ° off-axis extraction, such as in the aQa disposition. Consequently, in contrast to other conventional API sources, the skimmer does not need to be cleaned frequently, even when no clean-up procedures are applied, and the sensitivity and performance of the instrument remain constant for long periods of time [13].
59012 "_1
I
IF
Fig. 9.6. Strategies used in commercial API sources to increase solvent compatibility and system robustness [12].
411
I. Ferrer, J. Abidn and A.R. Ferndndez-Alba 9.4
IDENTIFICATION AND CONFIRMATION
For pesticides that are not GC amenable, several LC-UV and LC-Fl methods are available [14-17]. However, these methods encounter important difficulties in achieving a reliable identification in multi-residue analysis at the established MRLs. This is a consequence of their insufficient selectivity and/or sensitivity because of the variety and complexity of food samples, as well as the small amounts of residues present [18,19]. Accordingly, there is a need for sophisticated clean-up sample procedures, which could limit the scope of the analysis and enlarge the analysis time [20,21]. The use of mass spectrometry as a detector gives a far superior degree of identification and confirmation of molecular identity than other LC-based methods. Therefore, in recent years, abundant LC-MS-based methods have appeared for the determination of residues such as aryloxyalkanoic acids [22-25], benzoylureas [26], benzimidazoles [27-30], carbamates [31-33], sulfonylureas [34-36] and polar organophosphates [37-40]. However, in most cases, the methods published are only suitable for a small group of compounds or pesticides belonging to the same chemical class. Very few are generally applicable to the determination of a large number of pesticides from different chemical classes after a very fast, or no, sample clean-up [8-10]. Obviously, the identification capability relies on the LC-API-MS combination used and the correct choice for each of the three following components: the LC, the API and the MS, which contribute to the final identification and confirmation level achieved. Moreover, it must be taken into consideration that the selection of one component cannot be made independently from the other two components of the system. This chapter is mainly focused on the suitability of the MS detector because we consider the LC well documented [13] and the API has been already evaluated in Chapter 8. 9.5
FRAGMENT GENERATION IN TARGET AND NON-TARGET ANALYSIS
API sources share with TSP the disadvantage of producing primarily [M + H] + or [M - H]- ions. To overcome this limitation in the identification process, there are additional processes to produce fragment ions depending on the mass spectrometer used. The most simple way is the application of an appropriate voltage difference between two zones of the API source [41-44]. The ions are accelerated to increase their kinetic energy and pass through a chamber with a collision gas that generally induces the fragmentation of the primarily formed ions. This mode of operation is called collision-induced 412
LC-MS applications for pesticide food analysis dissociation (CID) or cone voltage fragmentation (CVF). In most of the multiresidue methods developed by ESI and APCI, after applying CVF it is possible to obtain at least two ions for confirmation of each analyte at a reasonable intensity [45-54]. Fragmentation can thus be induced by varying this orifice voltage. However, there are compounds scarcely affected by this parameter (e.g., imazalil, diazinon), although this can be solved in a different way, as we will see in the next section. The CVF value is crucial for an efficient transmission of the ions to obtain the best compromise between sensitivity and fragmentation. The generation of alternative confirmatory ions happens at the expense of the molecular ion intensity. As an example, Fig. 9.7 shows the spectrum of diflubenzuron obtained at low and high extraction voltages where [M - H] - is the base peak. Higher extraction voltages cause increased fragmentation and, under these conditions, the deprotonated difluorobenzamide ion (m/z 156) is the base peak.
10080
o
CD
O
< 40.>_N
( I,
I
ry 50
100
150
200
250
300
. .
.
350
.
m/z
C)
100 8060-
00
, 50
- ' _",'l',L';-llt _-,'',-'-:I, _
, ',-', 100
150
200
250
,-; ',, 300
, 350
, , m/z
Fig. 9.7. Spectrum of diflubenzuron obtained at (a) low extraction voltages (5 and 10 V, respectively) and (b) higher extraction voltages (30 and 35 V, respectively) [51].
413
I. Ferrer, J. AbiAn and A.R. Ferndndez-Alba Usually, the CVF value cannot be independently fixed for each pesticide because of their proximity to, if not the overlapping of, other target analytes. It is important to note the relatively low resolution capability of the LC system as a consequence of the molecular diffusion originating in the API interface, which is sometimes insufficient to allow complete separations of a large number of compounds (e.g., 50), as is intended for MRM methods, in a reasonable analysis time. Therefore, it is necessary to reach a compromise in the selection of the voltage in the eluted pesticide in different time windows. This would require moving away from the individual optima values selected for each compound and losing either confirmatory ions or sensitivity. This aspect is critical for those pesticides unlikely or highly prone to fragment because either identification or sensitivity can be seriously affected. An example is illustrated in Table 9.3 where the fragments obtained by LCsingle quadrupole MS for oxamyl, methomyl and carbendazim are shown. These compounds present similar retention times, which makes the selection of an intermediate voltage of 80 V necessary, greatly reducing the sensitivity for carbendazim and the confirmatory ions for methomyl. One strategy to overcome this problem, when it is feasible, is to combine positive (PI) and negative (NI) ion modes in the same chromatogram. This change can be done very fast, even within one scan of difference. As a general rule, for many pesticides such as organophosphorus and some carbamate compounds, higher fragmentations were obtained in NI mode with lower TABLE 9.3 Optimisation of the fragmentor voltage in LC-Q-MS for oxamyl, methomyl and carbendazim. The optimum (high sensitivity) voltage is shown in bold and the selected voltage for analysis is shown in italics LC-Q-MS fragmentor voltage 45 Oxamyl MW= 219 R t = 8.11 min Metomyl M, = 162 R t = 8.72 min Carbendazim M = 191 Rt = 9.23 min
414
72 90 220 88 106 163 192
60 (100) (55) (17) (100) (55) (38) (100)
80
100
72 (100) 90 (39)
72 (100) 90 (26)
72 (100) 90 (10)
88 (100) 106 (55)
88 (100) 106 (33)
192 (100) 160 (15)
192 (100) 160 (68)
88 (100) 73 (54) 106 (6) 160 (100) 192 (35)
120 72 (100)
73 88 58 160 192
(100) (52) (38) (100) (7)
LC-MS applications for pesticide food analysis voltages compared with PI mode. Accordingly, fragmentor voltage changes were less influenced in NI mode than in PI mode. For this reason, it is relatively easy to get a good compromise voltage between both modes, thus constituting an important identification tool [11,55]. Many of the limitations commented above are critical when the MS detector selected has low resolution, as in the case of a single quadrupole, but they are much less critical when using higher resolution or better mass accuracy MS detectors, such as time-of-flight mass spectrometer (TOF-MS) or Fourier transform ion cyclotron. In the latter cases, the identification and confirmation criteria are also provided by the exact mass for each fragment, allowing unambiguous identification when they are accompanied by isobaric compounds [56]. Alternatively, the use of triple-quadrupole or ion trap mass spectrometers producing MS-MS or MS' analysis can yield structurally significant ions, greatly reducing the risk of misidentification and reaching confirmation levels without the need to fine tune the fragmentor voltage. In these systems, the second quadrupole or the ion trap acts as the collision chamber after the selection of the precursor ions in the first quadrupole or the trap itself. The extent of the fragmentation can be selected for each compound, depending on the internal energy of the ions gained during ionisation, the voltage difference between the ion source and the collision chamber, or resonant excitation in the case of ion trap, and the pressure and properties of the collision gas (see the previous chapter). In such a way, these instruments allow working at low and similar voltages during the whole analysis, improving the sensitivity and structural information obtained. The improvement reached by these methodologies can be observed in Table 9.4, which shows the comparison between fragmentor voltages corresponding to the analysis of oxamyl, methomyl and carbendazim. Using the ion trap, the MS-MS process can be repeated a number of times, allowing the acquisition of MS' fragmentograms and thus enhancing the identification and confirmation process. However, the development of operation modes higher than MS2 are more complex and time consuming and they are not very well adapted to multi-residue routine pesticide control. In general, the most common way to proceed in multi-residue analysis is to develop methods for selected lists of compounds, taking into consideration all aspects commented upon in the first section of this chapter. However, the situation is much more complicated when the goal is the identification of nontarget pesticides and no selective sample extraction or clean-up procedure is applied, as usually happens. This goal has become more important in recent years with a clear trend towards using new pesticides that are often 415
I. Ferrer, J. Abidn and A.R. Ferndndez-Alba TABLE 9.4 Optimisation of the fragmentor voltage in LC-IT-MS in full-scan operation and the selected fragmentor voltage for MS-MS mode. The optimum (high sensitivity) voltage is shown in bold and the selected voltage for analysis is shown in italics LC-IT-MS/fragmentor voltage
50 Oxamyl Mw = 219 R = 8.11 min Metomyl M, = 162 Rt 8.72 min Carbendazin MW = 191 Rt = 9.23 min
72 90 237 88 106 163 192
(6) (9) (100) (8) (11) (100) (100)
LC-IT-MS/MS fragmentor voltage
60
80
100
72 (7) 90 (13) 237 (100) 88 (17) 106 (20) 163 (100) 192 (100)
72 90 237 88 106 163 192
(18) 72 (22) (100) 90 (93) (39) (100) 88 (98) (99) 106 (100) (20) 185 (63) (100) 162 (40) 192 (100)
120
60
Parent ion
72 (16) 72 (74) 237 90 (34) 90 (100) 148 (10) 88 (100) 65 (34) 163 106 (56) 88 (57) 185 (74) 65 (34) 160 (100) 160 (82) 192 192 (24) 192 (100)
unexpected or not "controlled" by the routine laboratories as a consequence of different speed of introduction and approval of new substances for agricultural practices by the respective authorities. For detecting and identifying unknown pesticides, the single quadrupole mass spectrometer is clearly insufficient due to its low sensitivity in full scan mode and the lack of information when selected ion monitoring mode (SIM) is applied. Triple quadrupole mass spectrometers provide different options of operation: the product ion, the parent ion and the neutral loss mode. However, MS-MS fragmentation may be limited and insufficient to perform a full structure elucidation. A more promising strategy is the screening of similar compounds with selected functional groups. A wide variety of functional groups exhibit characteristic fragmentation properties, which can be used to detect compounds with a specific moiety by different MS-MS approaches. In this sense, neutral loss scans can indicate pesticides losing a specific non-ionic fragment. The selection of a constant neutral loss (CNL) along with the chromatogram combined from parent ion scans provides molecular mass identification and subsequent confirmation. The potential members of a particular compound class in the samples can be detected in this way. This concept has been successfully applied to water and effluent samples for the identification of compounds with characteristic fragmentation [57-591. For example, CNL 416
LC-MS applications for pesticide food analysis of methylisocyanate from N-methylcarbamates (constant m/z difference 57) can screen and identify this class of compounds [60]. Depending on the experimental conditions, some variations can be expected; for example, some N-methylcarbamates are very prone to form adducts [M + NH4 + and the neutral loss value can change. However, this rule has important exceptions with N-methylcarbamates typically used in food samples that do not yield the characteristic moiety such as aldicarb, ethiofencarb, fenoxycarb, etc. As a consequence, due to the relevant structural differences that are typically present even in pesticides of the same family, this procedure has important limitations. Moreover, the sensitivity decreases markedly when the triple quadrupole is operated in neutral loss mode. Another mode of operation is the precursor ion scan that gives typical or diagnostic fragment ions. For example, some of the neonicotinoid insecticides, which are relevant in food analysis, present a diagnostic ion at m/z 126 [9,27]. Ion trap mass spectrometers present higher sensitivity in the scanning mode and the ability to perform MSn experiments is very well suited for the detection of unknown compounds, provided the time requirement and personal skills are available. Until now, there has not been any development in pesticide food analysis in this promising direction reported in the literature, probably as a consequence of the need for highthroughput samples in pesticide food methods that makes this procedure not very suitable for routine analyses-based laboratories. Correspondingly, there are no reports found in the literature that describe any success of non-target screening of food samples by LC-MS methodologies. In this sense, benchtop TOF-MS has important advantages, which make this instrument attractive when screening for unknown compounds. The higher resolution and mass accuracy in full-scan mode can be readily provided by this type of MS. Sometimes, it can be enough to provide a molecular formula and to confirm or deny a suggested structure [56,61]. The new generation of TOF-MS is very well suited for pesticide food-screening purposes (see the previous chapter). The application of TOF-MS for identification of unknowns can be observed in Fig. 9.8. Based on the accurate mass, the elemental composition of the unknown peak of interest is calculated using the elemental composition tool. By using this data, and the information of the appropriate number of chlorines in the molecule determined from the isotopic pattern, the search is performed in a pesticide database, obtaining the unequivocal identification of thiachloprid with a mass deviation of less than 5 ppm. It is clear that TOF-MS can be very suitable for the main analytical requirements of pesticide food analysis. For these reasons, an important increase in the application of these systems to pesticides in food can be expected in the next
417
I. Ferrer, J. Abidn and A.R. Fern6ndez-Alba ._
.0
2.0e4
12.78
0E
M 1.5e4
?4 Thiacloprid
C10H1ON4CIS
Exp. Mass
Exact Mass
253.0302
253.0309
Error (mDa)
Error (ppm)
-0.7223
-2.8549
V 0
o
'
1.0e4
-2 E
5000.0
2.53 13.,59 J..
u.u'2
i 7.12
4
6
8
........... ..... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ It..
I IA
. 1,I 10
12
14
16
18
20
22
24
26
28 Time, min
Fig. 9.8. Identification of thiachloprid, as "unknown", in a tomato sample.
few years. Even more useful in terms of identification capabilities is the
quadrupole-TOF combination (qTOF) as it allows MS-MS performance and FTICR mass spectrometers, although these instruments are not available for pesticide food applications. 9.6
MATRIX EFFECTS
The selectivity of LC-MS is far superior to other detection methods, such as LC-UV, as is shown in Fig. 9.9. As can be observed in this figure, there is a big interference matrix signal in the LC-UV chromatogram, which can easily be solved by selecting the target ion (m/z = 192) for carbendazim. However, this detection capability may be overestimated and this may result in positive or negative findings because the single MS approach cannot make sure that the detected ion is truly the molecular or fragment ion of the compound one wanted to detect. The uncertainty in pesticide food analysis increases in the following cases: (i) when low selectivity is achieved or no clean-up of the sample is applied, (ii) when the complexity of the commodity/pesticide is high, or (iii) when a large number of pesticides have to be detected in a single run. These three aspects are typically present in the current pesticide residue analysis, as was commented upon in the first
418
LC-MS applications for pesticide food analysis mAU 80
LC-UV ( = 285 nm)
60 40 20 0 C
4
6
8
10
12
min
on 192
700 500 300
i--
0
2
4
6
8
10
12
min
Fig. 9.9. Comparison between LC-UV and LC-Q-MS chromatograms corresponding to the analysis of a strawberry sample containing carbendazim.
section of this chapter. Two examples of the difficulties we can expect are shown in Figs. 9.10 and 9.11. In Fig. 9.10, the analysis of the insecticide imidacloprid in an orange sample is shown together with the presence of the ion 175 in the orange matrix at a very close retention time to the pesticide. As a consequence, a hybrid spectrum along the chromatographic
m/z= 175
6000 5000 4000
IMIDACLOPRID 70 ~g/kg
3000
100
M
N-
0
80 E
a° 0
Co
60
2000
M+]
1000.
80
200
220
10
200
22
240 4
nz
0. U
2 2.5
5.
.
7 55
10
12.5
15
.
17.5
20
22.5 22,5
m. min
Fig. 9.10. LC-Q-MS chromatogram corresponding to the analysis of an orange sample containing imidacloprid. Mass spectra are also shown.
419
I. Ferrer, J. Abidn and A.R. Ferndndez-Alba LC-Q-MS
LC-ES-IT/MS-MS
___ I -sn
3000025000-
IntensT. 209.0
STANDARD
209.9
X05
20000 -
STANDARD
4.
15000100005000 -
1'
f
n 100
200
10000
174.9
3: 2
256.0
3005
84.3 50
1280
100
IIIIL
256.1
150
20C
1l 250
300 mit~~~~~~~~~~~/
zuY.U
8000
PEPPER EXTRACT
2
PEPPER EXTRACT
x104
6000
1.00
175.0
4000
0.75
V
0.50
175.0
2000
256.0
0
.
100
.
-.
,
200
.-. .
I
0.25 .
.
300
nnn U.Uu
m/z
50
842 -'
'
127.9 .
100
"
.'
.
150
,
1.9
256.0
,A
....
200
.....
250
300 mz
Fig. 9.11. Comparison of the mass spectra for imidacloprid (Mw = 256) in a pepper extract using LC-Q-MS and LC-IT-MS-MS. peak is obtained. By looking at the spectrum at the top of the peak, an inadequate concordance with the standard can lead to a false positive. In addition to this, by selecting the whole area of this inhomogeneous peak, an overestimation of the concentration for this pesticide would be made for quantitative purposes. The same pesticide is affected by matrix ions from pepper samples, as is shown in Fig. 9.11. In that case, the coincident ion is 209 (which happens to be present in the matrix altering the ion ratio percentage), thus leading to a false negative identification when using LC-Q-MS analyses. Therefore, an intensive matrix study through the injection of blank samples is necessary to detect and avoid interference matrix ions and to resolve them by changing or discarding identification ions, depending on the matrix analysed [55]. However, the matrix effects may vary considerably from sample to sample and from sampling to sampling [8,9,62]. This fact can represent a serious difficulty in the application of LC-MS-based methods, especially when a low number of fragments are generated, leading to poor identification. For these reasons, the applicability of quadrupole mass spectrometer instruments is often restricted to clean matrices or to the determination of a limited number of compounds.
420
LC-MS applications for pesticide food analysis The application of LC-MS-MS-based methods can easily overcome many of these matrix problems. An adequate selection of the precursor ions, typically [M + H] + or [M - H]-, and the specificity of the structurally diagnostic product ions increase the selectivity obtained compared with single MS analyses. Many matrix ion interferences can be removed, as is shown in Fig. 9.11B, where the good concordance of the spectrum of imidacloprid obtained from a sample with respect to the standard spectrum is clear. When using ion trap MS-MS, a selective spectra can be acquired by isolating the m/z = 256 ion and obtaining the characteristic fragment ions. This fact supports the general trend from MS to MS-MS taking place in pesticide food applications. However, false positives or negatives can occur even when using MS-MS detection. Again, matrix interferences are less important when exact mass measurements are performed because the number of coincident ions between matrix and pesticides can be considered negligible for mass accuracy levels lower than 1 mDa. This feature reinforces the usefulness of benchtop TOF mass spectrometers applied to analyses of pesticides in food. Figure 9.12 shows an example of the selectivity achieved by TOF-MS. When a wide amu window is selected in the extracted ion chromatogram for m/z = 256, other interferences might be present in the sample matrix, as is observed in the peak at 17.29 min. When the same window is narrowed down, the interferences disappear, leading to a more selective identification for the target compound and also to an enhanced signal-to-noise ratio. Obviously, retention times can be used as additional identification criteria. Criteria reported in The Netherlands establishes a maximum of 1-0.2% deviation for retention times to be considered as positive findings [63,64]. Superior criteria can be applied by performing two analyses in different elution conditions, interfaces or positive/negative modes but, the larger time and cost expense make them impracticable for routine analysis. Nevertheless, it is clear that, since the potential of LC-MS has become a similar alternative (in costs and performance) to the common GC-MS methods in this area, confirmation based only on MS spectra by unambiguous criteria is desirable. This new trend by the European Union [65] to confirm organic residues and contaminants may become very important in Europe. The new criteria propose a system of identification points establishing that three points for authorised pesticides and four (at least) for banned products are required for a positive finding (Table 9.5). Additionally, the maximum deviation of the relative intensity of the recorded ions with respect to the base peak has to be lower than 20 and 50% (depending on the technique) for relative ion intensities of 10 and 50%, respectively. 421
I. Ferrer, J. Abidn and A.R. FernAndez-Alba 5.0e4 (/
c o
o
(C
4.e4
4.0e4Window + 0.2
o
U)
m/z= 256
3.0e4
a,
2.0e4
n0! o,
1.0e4 0.0
Mu
o
6
T
CO Co
r04
2
4
6
8
10
12
14 16 18 Time, min
20
22 24
26 28 30
o 0
6
4.0e4
Window ± 0.1
U) a
0
3.0e4
m/z = 256
~I Co
a,
2.0e4 CO
1.0e4 0.02
l 2
4
4
AOIOrnwhu
6
8
10
12
14
kP
16
. .. 1. .. 1 . L...
I^.
.1
A"
RM-1w* -
18
20
22
24
26
28
30
Time, min Fig. 9.12. LC-TOF-MS of a tomato sample containing imidacloprid in two different amu windows.
TABLE 9.5 Identification points (IPs) criteria system according to various types of techniques MS technique
IPs earned per ion
Low resolution mass spectrometry (LR) LR-MS n precursor ion LR-MS' transition products High resolution mass spectrometry (HR) HR-MS n precursor ion HR-MS' transition products
1.0 1.0 1.5 2.0 2.0 2.5
422
LC-MS applications for pesticide food analysis In this respect, for LC-single quadrupole MS analysis at low resolution, three or four ions (depending on the commodity/pesticide) are necessary for confirmation. In MS-MS analysis, the selection of one precursor ion and the recording of two product ions at low resolution would result in four points for a safe positive finding, which is enough for whatever combination pesticide/ commodity. In the case of LC-TOF-MS, two fragments with exact mass measurements would also allow a positive confirmation. It is clear that the application of these criteria for pesticide residue analysis should convert many positive findings commonly reported to questionable or negative. 9.7 9.7.1
QUANTITATION Sensitivity and reproducibility
A mass spectrometric detector is by definition a mass flux sensitivity detector [66-68] and so the peak area is theoretically independent of the mobile phase flow rate [69,70]. However, considering the fact that the ionisation process requires nebulisation of the eluent stream into tiny charged droplets, which must then be depleted of solvent by evaporation, it is not surprising that the efficiency of this process (and hence the response factors (RFs)) depend on the rate of the liquid flow into the ion source [71,72]. Furthermore, an increase in pesticide response and sensitivity can be produced when flow rate decreases [67,71]. Obviously, other operational parameters that affect the ionisation efficiency of the LC-MS as a whole [71] are also relevant (see the previous chapter). Although the detection limit can indicate the suitability of a specific LC-MS method for pesticide analysis, the criteria and guidelines used for setting the detection limits and reported in the literature are highly variable and, therefore, difficult to compare on a realistic basis. Moreover, the limits of detection (LODs) are continuously decreasing with the new generation of mass spectrometers. Hence, in general, we can expect that any LC-API-MS system used will be able to perform to the most stringent regulatory levels for pesticides in food (e.g., 200 pg) [73-75]. It is important to point out the correct and suitable instrument to produce reliable results in all agricultural matrices, fulfilling the main goals (low ,ug/k) and practical requirements for pesticide control laboratories. In general, working in MS-MS mode with a triple quadrupole or with ion trap instruments results in lower detection limits as compared with a single quadrupole instrument due to the significant decrease in the signal background noise. 423
I. Ferrer, J. Abidn and A.R. Ferndndez-Alba Another important question is to evaluate the reliability of the LC-MS quantification process, taking as a reference a well-known procedure such as LC-UV. There are few comparative studies with LC-UV and LC-MS [76-81]. The quantitation for various organic compounds resulted in an excellent agreement between the two techniques regarding the relative standard deviations (RSDs) of the peak areas obtained [76-81]. In the same way, Guiochon et al. [71] developed extensive comparative studies for a selected group of compounds commonly used for LC testing, obtaining similar results for chromatographic parameters but obtaining a less reproducible peak area when LC-MS was applied compared with LC-UV. It is important to note that, in the case of LC-MS, differences in peak areas can arise from the number of scans per second selected. Hence, the selection of a specific value can yield important differences in signal percentages (e.g., 20%). These differences can be enhanced with band broadening, bad peaks shapes, interference peaks, etc. 9.7.2
Calibration and robustness
Another point of interest is the linear dynamic range between two "level off' regions in which the signal intensity no longer increases or decreases, with a region of transition found between the two extremes. The extent of this linear dynamic range is case-dependent and it cannot be easily predicted. It is clear there exists an interest to obtain linear calibration curves in multi-residue analysis as a way to simplify and facilitate the automation of the quantitative process. Hence, one should be aware that the linear calibration curve is the exception of the more general case of a quadratic calibration curve [82]. Currently, a linear calibration curve can be reasonably obtained for pesticides in food analysis. Usually, a small range of concentration up to a maximum of two orders of magnitude can be covered (e.g., 5-500 ng/ml) with correlation coefficients greater than 0.995, which is often sufficient in pesticide food analysis. Sometimes, three orders of magnitude are achieved. However, it is noteworthy that for some compounds, there exists a clear trend to reach convex curves showing a decrease in the slope as concentration increases [8,83,84]. Rather than using an arbitrarily chosen non-linear function for calibration for the target pesticides, the best option is to determine linear regression lines in a specified concentration range. Routinely, extracts of samples found to contain over the upper linear limit can be re-injected after dilution in a blank extract to improve the accuracy of determination [85]. This phenomenon is probably related to the ion formation process of electrospray due to the competition of the analytes and matrix components for 424
LC-MS applications for pesticide food analysis
a, a, I c E a:
5 Concentration (mg/kg) -
Flazasulfuron .........Fenhexamid ..........
Metiocarb Iprovalicarb
Concentration (mg/kg) -
- - Imidacloprid ........ Acetamiprid Thiacloprid
Fig. 9.13. Calibration curves obtained for LC-TQ-MS [8] and LC-TOF-MS for some selected pesticides in vegetable extracts. the charges supplied during the ionisation process. For this reason, extensive overlapping of target pesticides in multi-residue methods can affect the linear ranges. However, the mechanisms leading to this non-linearity are unknown and probably other factors can also have an effect. Ion trap mass spectrometer systems, for example, present critical values regarding the maximum accumulation time in which the ions are stored in the trap. A commonly commented upon drawback when using TOF-MS is the narrow dynamic range that is covered, making these systems practically useless for quantitative purposes [82]. Nevertheless, the new improvements in the last generation of TOF-MS present similar ranges to other MS systems, as can be observed in Fig. 9.13. The repeatability and reproducibility of LC-API-MS analyses are typically in the range of 3-20% [74,86], even without the application of any sample clean-up. These precision values, as well as the robustness obtained showing constant responses over long periods of time [74], are sufficient to obtain reliable results in all cases [8,9]. 9.7.3
Matrix effects
Although some of the interferences are typically "invisible" in the chromatograms when using single ion monitoring in quadrupole systems, co-extracts are present along the analysis, frequently causing a poor signal or poor accuracy in the results. This phenomenon was first discussed in detail by
425
I. Ferrer, J. Abidn and A.R. Fern6ndez-Alba Kebarle et al. [87,88], suggesting that organic compounds present in the sample exceeding around 10-5 M may compete with the analyte when accessing the droplet surface in gas phase emission. Another hypothesis refers to a modification of the surface field and, therefore, a modification in the critical radius of the droplets [891. In these cases, either the droplet aerosolforming process or the ionisation potential is changed. As this refers to a chemical process, it is reasonable to assume compound specific enhancement or decrease of the ionisation efficiency. An inorganic load of the samples is considered less relevant than the organic matrix. However, since not only the quantity of the organic compounds but also their quality have a strong influence in signal, no correlation can be considered from the total dissolved organic carbon content [90] with regard to signal suppression or enhancement. A good example showing experimental evidence that matrix effects can severely compromise quantitative data generated by LC-MS is shown in Fig. 9.14, where the response variation of two pesticides in different matrices is shown. Important positive and negative differences in the response can be noted, although the most usual case is a decrease in the response of the analyte
- - Letucce ---- Egg plant - - -Pepper
Acetamiprid ~0.1 mg/kg
/ I
C
ta,)
-
Solvent
.5
a-
$
co
I I 7.75
k,
I
8.00
I~
8.25
8.50
8.75
9.00
min
2i,
C
a,
a,
19
20
21
22
23
24
25
26
Fig. 9.14. LC-Q-MS analyses for acetamiprid and triflumizole in different vegetable matrices.
426
LC-MS applications for pesticide food analysis due to signal suppression. The extension of intensity suppression/enhancement is typically around 0-30% but in some cases it can even be around 100% or higher [8,9] when using the most common extraction solvents [911. As a consequence, the response of an analyte in pure solvent can differ significantly from that in the matrix sample. For this reason, optimisation procedures made on standards in pure solvent by adjusting MS parameters during method development could lead easily to wrong conclusions. Avery efficient method to visualise the signal-suppression effect by matrix components is by continuous post-column infusion of a standard pesticide solution during the injection of pure extraction solvent and blanks of sample matrix extracts. As shown in Fig. 9.15, the selected ion chromatogram obtained showed a drastic enhancement and decrease in response during the early eluting time of the analysis. The response is completely restored after approximately 10-15 min. The general trend observed when working in reverse phase is the shift from strong suppression to weak suppression during the chromatographic run. There is an inverse correlation of the suppression with the amount of organic percentage in the mobile phase [90,92]. Therefore, this way of checking is able to identify potential problems, which can be avoided or diminished by an appropriate LC separation [8].
C
(D
min Fig. 9.15. Effect of the LC effluent on the MS detector response (m/z 189) of post-column infused propamocarb after injection (20 l) of extraction solvent (methanol) and blank sample extract of lettuce [85].
427
I. Ferrer, J. AbiAn and A.R. Ferndndez-Alba The dependence of matrix effects on concentration levels can be carried out by RFs calculated by linear regression of the peak areas as a function of the added analyte concentrations from the standard addition experiments. Typically, RFs are quite constant in a wide range of concentrations and become more variable when concentration approaches the LODs [8,90]. Successful analytical methods are only those that can rapidly solve a problem by providing accurate and precise results. Hence, matrix effects must be eliminated or compensated. There are several approaches to address the problem of signal suppression in quantitative LC-MS analysis [92-96]. All these can be classified in two mayor groups: (a) elimination or reduction of the matrix effects or (b) compensation of matrix interferences.
9.7.3.1 Matrix effects reduction The reduction of matrix interferences can be carried out by selective sample dilution decreasing the amount of sample injected [93] or by decreasing the ratio of mass/solvent volume in the final sample extract. Sometimes, these strategies can be useful but they are not usually a method of choice in trace level analysis due to the decrease in sensitivity. Another approach is the reduction of the amount of matrix interferences entering the MS at the same time as the pesticide. This can be achieved by either a more selective extraction procedure [97] or a more extensive clean-up [86,94]. The first choice has been discussed in chapters 3 and 4. The application of clean-up using size exclusion or other similar approaches [98] can also be a powerful tool to reduce these effects to an acceptable level, as is shown in Fig. 9.16 [85]. However, these results cannot be guaranteed in all cases. In addition, a reduction of matrix effects can be achieved by improving the analytical separation [93-96,99] (e.g., longer gradients, twodimensional LC). All these approaches are typically time- or organic solvent-consuming and the risk of analyte losses or sample contamination is increased. On the other hand, when LC-MS was introduced into the food analysis field, it was expected that these clean-up procedures would become obsolete and, clearly, there is a definite trend to avoid them as much as possible. It was recently pointed out that signal suppression by a co-eluting matrix can be considerably reduced by directing lower flow volumes into the ESI source [100] but more studies are necessary for a full evaluation. The mobile phase composition is known to have a significant effect on the ESI-MS signal response and for this reason it has been widely investigated 428
LC-MS applications for pesticide food analysis 100 2
Thiasulfuron
-
Co
80
-- - Thiasulfuron + Matrix
0
F
60
o a
20
o .
°
o .
0o 1
I
2 3 Conc. (mg/L)
4
5
100O0
C
80
3 r 60-
Thiasulfuron
- -0- Thiasulfuron + Matrix (SEC) . i
.2
a 40 C: 0
a- 20 0
e|l.
0
1
I . . .
I I
2 3 Conc. (mg/L)
' I I .
4
l
5
Fig. 9.16. Dependence of calibration functions dependent on matrix: (a) without cleanup and (b) with clean-up [86].
[101-1051. The addition of low concentrations of additives or buffers into the solution or mobile phase (e.g., ammonium formate, formic, acetic acid, ammonium hydroxide) has been reported to provide a considerable improvement in the LC-MS signal sensitivities. Improvements for LC-MS signal responses up to a factor of ca. 50 have been reported for various analytes [101]. Figure 9.17 shows an LC-MS/MS-extracted ion chromatogram of G-fenozide, hydroxy-fenozide and methoxy-fenozide in the matrix extract using different mobile phase buffers. The results were obtained in the negative ionisation mode using LL matrix-extracted samples. The results from the analysis of standard solution samples are shown for comparison. The signal response acquired from the sample matrix was the lowest relative to that of the standard sample when using a pure acetonitrile: water mobile phase 429
I. Ferrer, J. Abian and A.R. Fernandez-Alba
OCD
A
w
W d
CO
q
~a
ii
m i i .
i -
E 0
==4-1
F
I
E X.
E
E Qo a I
" "I'
I
I
1---
I
0
L
i
Il
o
---
L~~
Oo o
0I
a'
1
8
c
CE b ------
IN
-
,-r I~~
V)
ID
C==Z=
0
IL
z.) 4
0 ·
0
-
0 0
0 0
:z asuods
430
u- p
R-·Mi
Cq- .-' +-
+
eU Iu!S e^AleIa9
I -
'.
a
)°,:
b
·
LC-MS applications for pesticide food analysis (Fig. 9.17A). The signal response from methoxy-fenozide was ca. 10% of that obtained from standard samples. Better signal agreement between matrix and standard signal responses was obtained using mobile phases composed of 0.1% (v/v) formic acid, 0.01% (v/v) NH 4 0H or 1 mM ammonium formate (Fig. 9.17B-D). The signal response of methoxy-fenozide from the matrix sample was ca. 70% of that obtained from the standard sample analysis using a 1 mM ammonium formate buffer. Similar improvements were also observed for PI mode analyses in this study [94]. 9.7.3.2 Matrix effects compensation If matrix effects cannot be eliminated by one of the above methods, appropriate calibration techniques using standard addition are available: (i)
Quantification by standard addition into each sample and with each of the target analytes is a very reliable method. This, however, requires 3-4 times greater sample numbers [82,106] and, although it can correct for sensitivity losses due to matrix compounds, this approach cannot avoid the total loss of sensitivity. Moreover, it cannot be automated due to the need to know the concentration ranges before standard addition, which represents a great difficulty to be dealt with in routine analyses. (ii) An approach commonly used in other fields is the addition of labelled isotope standards [92,107-109]. The use of internal standards is a highly effective approach in order to compensate signal reproducibility or signal instability, but this technique cannot be used to compensate the sensitivity reduction associated with the matrix effects. Moreover, matrix components are also subjected to the chromatographic separation, resulting in a different effect depending on retention times and on each commodity. It is unfeasible to obtain commercially available standards for all pesticides of interest in a multi-residue method. Furthermore, the matrix effects cannot be compensated by the addition of only one internal standard compound. Therefore, this methodology of compensating for matrix effects is only of interest when the goal is a single or limited residue method [92,107-109]. (iii) An instrumental approach to compensate for matrix effects is provided by the so-called echo peak calibration [1101. A standard mixture of all analytes is injected shortly after the sample into the same chromatographic run. Standard signals reach the detector with a delay of one peak width after the respective sample signal. Provided that the matrix peak is broad, which is often the case due to column overloading, the standard signal can be used as an internal standard to compensate for the sensitivity
431
I. Ferrer, J. AbiAn and A.R. Ferndndez-Alba differences due to matrix effects or sensitivity fluctuations [110]. However, this procedure does not show a clear advantage with regard to the quantitation of the analyte peaks present in a multi-residue method, thus adding an extra difficulty in the total analysis time. (iv) A more suitable solution to compensate matrix effects is by means of matrix-matched calibration (standards with the same or similar matrix composition as the analysed sample). However, the variability of the matrix between commodities and series makes it necessary to carry out intense matrix characterisation studies (e.g., water percentage, acid content, dryness, sugar and fat content). A general pre-requisite is the availability to use blank matrix (material free of target analytes) that may not be always available. Usually, it is not possible to test for matrix effects for one pesticide in a specific matrix in order to predict the matrix effect of another pesticide in the same matrix [9]. Furthermore, an adequate study of the stability of pesticides in matrix-matched standards is necessary to apply this approach in food control. Matrix contents also have to be subjected to chromatographic separation, resulting in a different, and in each case unknown, extension of the effect for each analyte in a multi-residue method [94]. However, we cannot assume the only possible matrix effects are those from similar LC retention behaviour or interaction with the stationary phase. Another possible matrix effect is due to the overloading of the LC columns with matrix components. The overloading results in continuous elution of matrix components, which can interfere throughout the analysis depending on the LC gradient applied [94]. This fact is clearly dependent on the sample injection volume and the increase of this volume will increase the extent of signal suppression [94]. Furthermore, matrix components poorly retained on the chromatographic column can also affect a wide range of compounds. For a wide range of commodities, matrix effects are quite small with a relative standard deviation of 23% as average [9]. The general method is to select various representative types of commodities (see chapter 1) and evaluate the calibration curves obtained for all them. Every calibration curve obtained can be used as a representative of a group of vegetables and it can be used for the quantification of the target standards. If a uniform matrix within a series of samples can be obtained, a calibration curve can be performed by standard addition into this matrix representing the whole range of series [110]. This calibration can then be applied to the whole series of samples. Before this approach can be applied,
432
LC-MS applications for pesticide food analysis however, one has to ensure that the matrix of all the samples of a series is sufficiently homogenous. 9.8
FUTURE TRENDS IN LC-MS PESTICIDE RESIDUE ANALYSIS
The application of LC-MS on pesticide food analysis is relatively new in multi-residue routine control and there is a clear trend for it to increase exponentially in the near future to become a comparative option in quantitative terms to GC-MS. Currently, the first option for routine control laboratories is typically triple quad mass spectrometry. However, the new tendency is to use other alternatives, such as ion trap, quadrupole trap, TOF, etc. Considering that the development of these systems is ongoing, there is a clear trend in two aspects that will greatly improve the pesticide food analysis area: sensitivity and mass accuracy. Sensitivity has increased by a factor of 10-100 times with the newer generation of LC-MS systems with respect to the older ones. This improvement could allow great simplification of sample preparation and the difficulties related to matrix effects that are at the moment the most critical limitation for LC-MS applications. We need to take into account that, until just recently, it was necessary to have a ratio around 5 mg of sample per millilitre of extract before injection in order to achieve enough sensitivity and to decrease the amount of derived difficulties due to the presence of co-extractives in the samples. Nowadays, the great improvements in sensitivity obtained with the newer instruments allow the pesticide-control laboratories to achieve a reduction of a factor of five of this ratio. In the same way, a further decrease of this ratio to 0.5 or 0.2 mg/ml would keep enough sensitivity with a much lower amount of matrix effects (dilute and shoot being the best approach). The second point is related to mass accuracy with the new introduction of benchtop TOF-MSs yielding a mass accuracy in the range of 2-5 ppm errors and with a good linear range calibration. These analyses can greatly enlarge the detection scope of the analysis from target to unknown pesticide residues, which will be an important issue in the near future. Furthermore, this aspect will contribute to diminishing the matrix effects as well. Other systems with mass accuracy capabilities will have serious difficulties being implemented in food-control laboratories as a consequence of the higher cost and the personal skills required to run the instruments. Regarding the commodities, we can distinguish various groups with special difficulties, including high fat content matrices (e.g., olive oil). In these cases, it might be necessary to apply specific clean-up strategies previously used in LC-UV (e.g., gel permeation chromatography, etc.) or new ones such as stir bar sorptive extraction (SBSE).
433
I. Ferrer, J. Abi6n and A.R. Ferndndez-Alba These strategies can be more effective in pesticide food analysis because of the way that they can be easily automated. LC improvements, mainly concerning column technology through the introduction of new materials (e.g., polymeric materials, restricted access medium), will undoubtedly improve the pesticide residue analysis area and contribute, to a lesser extent, to the reduction of associated matrix problems. It is clear that the acceptance of narrow columns in LC-MS is a way to decrease the total amount of solvent and organic compounds present in the interface.
REFERENCES 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
434
Summary of Pesticide Use Report Data 2002, http://www.cdpr.ca.gov/docs/pur/ purO2rep/02com.htm. Ten Years of ECPA 1992/2002, http://www.ecpa.be. ECPA REVIEW 2002-2003, http://www.ecpa.be. CROPLIFE Annual Report 2002/2003, http://www.croplife.org. Asociaci6n Empresarial para la Protecci6n de la Plantas (AEPLA) Datos Destacables del Mercado, http://www.aepla.es. J.-P. Rauha, H. Vuorela and R. Kostiainen, J. Mass Spectrom., 36 (2001) 1269. T.I. Kauppila, T. Kuuranne, E.C. Meurer, M.N. Eberlin, T. Tiaho and R. Kostiainen, Anal. Chem., 74 (2002) 5470. J. Klein and L. Alder, J. Assoc. Off. Anal. Chem. Int., 86 (2003) 1015. C. Jansson, T. Pihlstrom, B.-G. Osterdahl and K.E. Markides, J. Chromatogr.A, 1023 (2004) 93. A. Aguera, S. L6pez, A.R. Ferndndez-Alba, M. Contreras, J. Crespo and L. Piedra, J. Chromatogr. A, 1045 (2004) 125. Y. Pic6, G. Font, J.C. Molt6 and J. Maiies, J. Chromatogr.A, 882 (2000) 153. J. Abiin, A.J. Oosterkamp and E. Gelpi, J. Mass Spectrom., 34 (1999) 244. C.G. Herbert and R.A.W. Johnstone, Mass SpectrometryBasics. CRC Press, Boca Raton, FL, 2003, p. 474. R.T. Krause, J. Assoc. Off. Anal. Chem. Int., 68 (1985) 726. M. Hiemstra, J.A. Joosten and A. de Kok, J. Assoc. Off Anal. Chem. Int., 78 (1995) 1267. A. Sannino, J. Assoc. Off. Anal. Chem. Int., 81 (1998) 1048. M. Hiemstra, A. Toonen and A. de Kok, J. Assoc. Off. Anal. Chem. Int., 82 (1999) 1205. J. Sherma, J. Assoc. Off. Anal. Chem. Int., 80 (1997) 283. M. Careri and A. Mangia, Trends Anal. Chem., 15 (1996) 538. H. Obana, M. Okihashi, K. Akutsu, Y. Kitagawa and S. Hori, J. Agric. Food Chem., 50 (2002) 4464. A.R. Ferndndez-Alba, A. Valverde Garcia, A. Aguera and M. Contreras, J. Chromatogr.A, 721 (1996) 97.
LC-MS applications for pesticide food analysis 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
B. K0ppen and N.H. Spliid, J. Chromatogr.A, 803 (1998) 157. D. Baglio, D. Kotzias and B.R. Larsen, J. Chromatogr.A, 854 (1999) 207. M. Takino, S. Daishima and T. Nakahara, Analyst, 126 (2001) 602. E. Dijkman, D. Mooibroek, R. Hoogerbrugge, E. Hogendoorn, J.-V. Sancho, O. Pozo and F. HernAndez, J. Chromatogr. A, 926 (2001) 113. M. Anastassiades, E. Scherbaum and W. Schwack, Dtsch. Lebensm. Rundschau, 97 (2001) 176. A.R. Fernandez-Alba, A. Tejedor, A. Agiera, M. Contreras and J. Garrido, J. Assoc. Off. Anal. Chem. Int., 83 (2000) 748. D. Perret, A. Gentili, S. Marchese, M. Sergi and D. Ascenzo, J. Assoc. Off. Anal. Chem. Int., 85 (2002) 724. M. Fernandez, R. Rodriguez, Y. Pic6 and J. Mafies, J. Chromatogr.A, 912 (2001) 301. M.S. Young, M.F. Early, C.R. Mallet and J. Krol, J. Assoc. Off. Anal. Chem. Int., 84 (2001) 1608. S. Bogialli, R. Curini, A. Di Corcia, M. Nazzari and D. Tamburro, J. Agric. Food Chem., 52 (2004) 665. M. Fernandez, Y. Pic6 and J. Maiies, J. Chromatogr.A, 871 (2000) 43. N. Wang and W.L. Budde, Anal. Chem., 73 (2001) 997. L.J. Marek and W.C. Koskinen, J. Agric. Food Chem., 44 (1996) 3878. E. Ayano, H. Kanazawa, M. Ando and T. Nishimura, Anal. Chim. Acta, 507 (2004) 211. A. Di Corcia, M. Nazarri, R. Rao, R. Samperi and E. Sebastiani, J. Chromatogr. A, 878 (2000) 87. S. Lacorte, C. Molina and D. Barcel6, J. Chromatogr.A, 795 (1998) 13. C. Molina and D. Barcel6, Anal. Chem., 66 (1994) 4444. D.R. Doerge and S. Bajic, Rapid Commun. Mass Spectrom., 6 (1992) 663. B.A. Ingelse, R.C.J. van Dam, R.J. Vreeken, H.G.J. Mol and O.M. Steijger, J. Chromatogr.A, 918 (2001) 67. I. Ferrer, E.M. Thurman and D. Barcel6, Environ. Sci. Technol., 34 (2000) 714. A. Cary, Technical Report No. 620, 7000 Series Finnigan MAT, 1994. B.L.M. van Baar, In: D. Barcelo (Ed.), Applications of LC-MS in Environmental Chemistry, J. Chromatogr. Lib., Vol. 59, Elsevier, Amsterdam, 1996, p. 71. P.G.M. Kienhuis and R.B. Geerdink, Trends Anal. Chem., 19 (2000) 249. A. Di Corcia, C. Crescenzi and A. Lagana, J. Agric. Food Chem., 44 (1996) 1930. E. Lacassie, M.F. Dreyfuss, J.L. Daguet and M. Vignaud, J. Chromatogr.A, 830 (1999) 135. S.J. Sout, A.R. da Cunha, G.L. Picard and M.M. Safarpour, J. Assoc. Off. Anal. Chem. Int., 81 (1998) 685. R.K. Juhler and M. Vahl, J. Assoc. Off. Anal. Chem. Int., 82 (1999) 331. M. Vahl, A. Graven and R.K. Juhler, FreseniusJ. Anal. Chem., 361 (1998) 817. A.I. Valenzuela, D.S. Popa, M.J. Redondo and J. Maies, J. Chromatogr.A, 918 (2001) 59. K.A. Barnes, J.R. Startin, S.A. Thorpe, S.L. Reynolds and R.J. Fussell, J. Chromatogr. A, 712 (1995) 85. G.S. Nufies, M.P. Marco, M.L. Ribeiro and D. Barcelo, J. Chromatogr. A, 823 (1998) 109.
435
I. Ferrer, J. Abian and A.R. Fernandez-Alba 53 54 55 56 57 58 59 60 61 62 63 64 65
66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82
436
K.A. Barnes, R.J. Fussell, J.R. Startin, S.A. Thorpe and S.L. Reynolds, Rapid. Commun. Mass Spectrom., 11 (1997) 159. X. Zang, E.K. Fukuda and J.D. Rosen, J. Agric. Food Chem., 46 (1998) 2206. M. Fernandez, Y. Pico, S. Girotti and J. Maiies, J. Agric. Food Chem., 49 (2001) 3540. A.C. Hogenboom, W.M.A. Niessen, D. Little and U.A.Th. Brinkman, Rapid Commun. Mass Spectrom., 13 (1999) 125. H.F. Schroder, Water Sci. Technol., 23 (1991) 339. H.F. Schroder, J. Chromatogr., 647 (1993) 219. H.-Q. Li, F. Jiku and H.F. Schroder, J. Chromatogr. A, 889 (2000) 155. D.A. Volmer and J.P.M. Hui, Arch. Environ. Contam. Toxicol., 35 (1998) 1. N. Zhang, S.T. Fountain, H. Bi and D.T. Rossi, Anal. Chem., 72 (2000) 800. T. Reemtsma, J. Chromatogr. A, 1000 (2003) 477. M. Rodriguez and D.B. Orescan, Anal. Chem., 70 (1998) 2710. R.B. Geerdink, A. Kooistra-Sijspersma, J. Tiesnitsch, P.G.M. Kienhuis and U.A.Th. Brinkmann, J. Chromatogr.A, 863 (1999) 147. F. Andr6, K.K.G. De Wasch, H.F. De Brabander, S.R. Impens, L.A.M. Stolker, L. van Ginkel, R.W. Stephany, R. Schilt, D. Courtheyn, Y. Bonnaire, P. First, P. Gowik, G. Kennedy, T. Kuhn, J.-P. Moretain and M. Sauer, Trends Anal. Chem., 20 (2001) 435. I. Halasz, Anal. Chem., 36 (1964) 1428. A. Osterkamp, E. Gelpi and J. Abian, J. Mass Spectrom., 33 (1998) 976. W.M.A. Niessen and J. Van der Greef (Eds.), Liquid Chromatography Mass Spectrometry. Principlesand Applications. Marcel Dekker, New York, 1992. W.M.A. Niessen (Ed.), Liquid Chromatography-Mass Spectrometry. Marcel Dekker, New York, 1999. J.F. Banks, J. Chromatogr.A, 743 (1996) 99. Y. Chen, M. Kele, A.A. Tuinman and G. Guiochon, J. Chromatogr. A, 873 (2000) 163. A. Asperger, J. Efer, T. Koal and W. Engewald, J. Chromatogr.A, 987 (2001) 65. N.H. Spliid and B. Koppen, J. Chromatogr. A, 736 (1996) 105. R.B. Geerdink, P.G.M. Kienhuis and U.A.Th. Brinkmann, J. Chromatogr., 647 (1993) 329. C. Crescenzi, S. Di Corcia, A. Marchese and R. Samperi, Anal. Chem., 67 (1995) 1968. R. Willoughby, E. Sheehan and S. Mitrovich, A Global View of LC-MS. Global View Publishing, Pittsburgh, PA, 1998. F. Susantoand and H. Reinauer, Fresenius J. Anal. Chem., 356 (1996) 352. C. Bocchi, M. Careri, F. Groppi, A. Mangia, P. Manini and G. Mori, J. Chromatogr.A, 753 (1996) 157. R. Andreoli, M. Careri, P. Manini, G. Mori and M. Musci, Chromatographia,44 (1997) 605. S.D. McCrossen, D.K. Bryant, B.R. Cook and J.J. Richards, J. Pharm. Biomed. Anal., 17 (1998) 455. W.W. Bullen, C.D. Lathia, R.B. Abel and R.N. Hayes, J. Pharm. Biomed. Anal., 17 (1998) 1399. T. Reemtsma, Trends Anal. Chem., 20 (2000) 533.
LC-MS applications for pesticide food analysis 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
D. Baglio, D. Kotzias and B.R. Larsen, J. Chromatogr. A, 854 (1999) 207. S. Chiron, S. Papilloud, W. Haerdi and D. Barcelo, Anal. Chem., 67 (1995) 1637. M. Hiemstra and A. de Kok, J. Chromatogr.A, 972 (2002) 231. K. Bester, G. Bordin, A. Rodriguez, H. Schimmet, J. Pauwels and G. Van Vyncht, Fresenius J. Anal. Chem., 371 (2001) 550. P. Kebarle and L. Tang, Anal. Chem., 65 (1993) 972A. M.G. Ikonomou, A.T. Blades and P. Kebarle, Anal. Chem., 62 (1990) 957. B.A. Thomson and J.V. Iribarne, J. Chem. Phys., 71 (1979) 4451. M. Stuiber and T. Reemtsma, Anal. Bioanal. Chem., 378 (2004) 910. H.G.J. Mol, R.C.J. van Dam and O.M. Steijger, J. Chromatogr. A, 1015 (2003) 119. B.K. Choi, A.I. Gusev and D.M. Hercules, Anal. Chem., 71 (1999) 4107. B.K. Choi, D.M. Hercules and A.I. Gusev, J. Chromatogr. A, 907 (2001) 337. B.K. Choi, D.M. Hercules and A.I. Gusev, Fresenius J. Anal. Chem., 369 (2001) 370. R. Pascoe, J.P. Foley and A.I. Gusev, Anal. Chem., 73 (2001) 6014. J.V. Sancho, O. Pozo and F. Herndndez, Analyst, 129 (2004) 38. B.K. Matuszevski, M.L. Constanzer and C.M. Chavez-Eng, Anal. Chem., 70 (1998) 882. C. Blasco, M. Fernandez, Y. Pic6 and G. Font, J. Chromatogr.A, 1030 (2004) 77. J. Hajslova and J. Zrostlikova, J. Chromatogr. A, 1000 (2003) 181. E.T. Gangl, M. Annan, N. Spooner and P. Vouros, Anal. Chem., 73 (2001) 5635. D. Temesi and B. Law, LC-GC, 17 (1999) 626. A.M. Kamel, P.R. Brown and B. Munson, Anal. Chem., 71 (1999) 5481. S. Zhou and M. Hamburger, Rapid Commun. Mass Spectrom., 9 (1995) 1516. M. Jemal, Z. Ouyang and D. Teitz, Rapid Commun. Mass Spectrom., 12 (1998) 429. F.E. Kuhlmann, A. Apffel, S.M. Fischer, G. Goldberg and P.C. Goodley, J. Am. Soc. Mass Spectrom., 6 (1995) 1221. S. Ito and K. Tsukada, J. Chromatogr.A, 943 (2001) 39. R.K. Boyd, Rapid Commun. Mass Spectrom., 7 (1993) 257. D.C. Muddiman, A.I. Gusev and D.M. Hercules, Mass Spectrom. Rev., 14 (1996) 383. J. Hau, S. Riediker, N. Varga and R.H. Stadler, J. Chromatogr.A, 878 (2000) 77. J. Zrostlikovd, J. Hajslovd, J. Poustka and P. Begany, J. Chromatogr. A, 973 (2002) 13.
437
Chapter 10
Proficiency tests in pesticide residue analysis Christoph von Holst and Lutz Alder
10.1
INTRODUCTION
Laboratories analysing food samples for the determination of pesticide residues need to ensure that the reported results are within defined uncertainty limits. One important measure is applying a quality system such as ISO 17025 [1] showing that the analytical procedures are carried out following specific rules. In addition, laboratories should repeatedly participate in proficiency tests (PTs) to demonstrate sufficient quality of the analytical results. Occasionally, laboratories analyse samples for a particular purpose for which specific quality criteria have to be met. This, for example, applies to laboratories that contribute to a monitoring programme, coordinated by the European Commission, for the determination of pesticide residues in certain products of plant origin. These studies are conducted annually in order to establish whether the pesticide concentrations determined in food complies with maximum residue limits set by European legislation and to assess current dietary exposure. Amongst other aspects, the reliability of the data submitted by the Member States is of high importance in order to be able to draw unbiased conclusions from the monitoring programme. In a recent Abbreviations: AMC, UK analytical methods committee; ANOVA, Analysis of variance; FFP, Fitness for purpose; FSD, Factor standard deviation; GC/MS, Gas chromatography/mass spectrometry; LC/MS, Liquid chromatography/mass spectrometry; MAD, Median of the absolute deviation of the laboratories' results from the median of the results; MRL, Maximum residue limit; PT, Proficiency test; RLP, Relative laboratory performance; RSD, Relative standard deviation; RSDr; Relative repeatability standard deviation; RSZ, Rescaled sum of scores; SD, Standard deviation; SDMAD; Standard deviation based on the MAD, SD,; Repeatability standard deviation; SDR; Reproducibility standard deviation; SLMB, Swiss Food Manual; SSZ, Sum of squared scores; Qn method; Method for the calculation of the assigned value and SDR using robust statistics. Comprehensive Analytical Chemistry XLll Fernindez-Alba (Ed.) © 2005 Elsevier B.V. All rights reserved
439
C. von Hoist and L. Alder Commission Recommendation [2], special emphasis has therefore been placed on the following quality assurance measures that have to be taken by the Member States' laboratories participating in the co-ordinated monitoring programme. These measures include (a) the accreditation of the laboratory, (b) the implementation of quality control guidelines for pesticide residue analysis as described in the Commission Recommendation [2] and (c) participation in the European Commission's PTs in conjunction with attendance at a workshop to discuss the outcome of these trials. These PTs cover a large number of pesticides at various concentration levels. Given the high number of laboratories participating in the PTs, the results from these trials mirror quite well the current quality of pesticide analyses when evaluating the situation at a European level. The aim of the chapter is to elaborate important aspects of PTs, especially when conducted in the field of pesticide analysis. Due to the sound database available from European PTs, we used the results from a PT carried out in 1999 to illustrate the various aspects. This PT (PT 3) was coordinated by the Swedish National Food Administration (Uppsala) [3]. The pesticides and the matrix covered in PT 3 were selected taking into account current European legislation regarding MRLs of certain pesticides [4,5] and the demands for the coordinated Community monitoring programme [2] that is carried out annually. When organising PTs, the coordinator of the study has to address many questions prior to the ring trial in order to establish a clear plan for the exercise. It is important that the participants of the PT are informed about the procedure to be applied for the data treatment before analysing the test material. This especially applies to the following aspects: *
*
·
·
440
Which is the true concentration of the analyte in the test material? The spiked amount is not necessarily the best estimate and different approaches for its determination can show different results. Which deviation from the true concentration is acceptable and should this value depend on the achievable precision of the currently available analytical methods? This aspect also addresses the problem of establishing a suitable target SD. What are the pros and cons of using conventional statistics compared with robust statistics when applied to the calculation of the SD of the submitted results? How should a false negative result be assessed, taking into account the closeness between the reporting level of the applied analytical method and the assigned value of concentration of the analyte in the test material?
Proficiency tests in pesticide residue analysis For instance, the test sample contained 0.052 mg/kg of the target analyte, but a laboratory did not find the substance since the reporting level of 0.05 mg/kg was only slightly below the assigned value of concentration. * Which criteria should be used to establish a false positive value, requiring the organiser to show that the concerned analyte was not present in the test material? How does this process take into account the corresponding concentration level? For instance, a false positive result at 1 mg/kg can more easily be disproved compared with an analyte that was reported at 0.001 mg/kg. * How should a laboratory be treated if its reporting levels were significantly above the achievable reporting level as shown by the other laboratories participating in the PT? · Which statistical tests are suitable to detect laboratories reporting results that consistently deviate from the assigned value of concentration, especially when analysing a large number of pesticides in a PT? A particular emphasis is placed on elaborating different statistical means for data evaluation. Using examples from a European PT, we show that selecting a specific statistical approach will influence the outcome of the analysis, indicating that the organiser should be aware of these aspects.
10.2
GUIDELINES FOR PROFICIENCY TESTS
PTs are interlaboratory comparisons, in which the participating laboratories determine the content of an analyte in well-characterised test material by employing their own analytical method. After completion of the analysis, the results are sent back to the coordinator of the trial for statistical assessment in order to evaluate the performance of a laboratory for a specific analytical task. In addition, the laboratories are requested to submit details about the analytical methods in order to assist the coordinator of the trial in evaluating the results. This information could also help laboratories to track down possible reasons for poor scores obtained in the PT. ISO Guide 43-1 [6] gives a general overview of proficiency testing, whereas the International Harmonized Protocol for Proficiency Testing of Chemical Laboratories [7] contains more detailed information, including examples of the statistical analysis. Recently, Eurachem issued a document about PTs facilitating interpretation of the results for the participating laboratories [8]. By subjecting the reported results to statistical analysis, the assigned value of concentration, the corresponding variability of the analytes and 441
C. von Holst and L. Alder the proficiency of the laboratories are determined, indicating the distance of each result from the assigned value. ISO Guide 43-1 [6] introduces the z-score as the most important performance score for laboratories, allowing for evaluating the deviation of the laboratories' results from the assigned value of concentration, since these differences are related to the target SD. In contrast, the alternative Q-score [7] relates the obtained difference to the assigned value of concentration, thereby giving no information on whether this difference is acceptable or not. Unfortunately, this guide does not specify the statistical means to be applied for the calculation of the SD of the submitted results. To establish the assigned value of concentration, various options are given. Some statistical aspects regarding evaluation of data from interlaboratory studies are given in ISO Standard 5725 [9], including tests for outlier detection, which mainly focus on assessment of method performance characteristics. Moreover, this ISO standard gives detailed information about the calculation of the assigned value of concentration and the SD of the results when applying conventional statistics for data evaluation. Although the harmonised protocol [7] addresses more practical aspects of conducting ring trials, there are still some open questions under discussion, such as the selection of the appropriate target SD, which could either be the actual variation of the data or a predefined SD applying the fitness for purpose concept [10]. These topics will also be elaborated in the chapter. 10.3
REQUIRED CHARACTERISTICS OF THE TEST MATERIAL
Irrespective of the scheme for PTs that is applied, the coordinator of a study needs to prepare suitable and well-characterised test materials. The test materials should contain the target analytes at different and relevant concentration levels in a typical matrix. Incurred test materials are superior to fortified samples. Special emphasis should be placed on checking for sufficient homogeneity in terms of uniform distribution of the target analytes in the samples. The proof of sufficient homogeneity of the test material is an important prerequisite of the study and the results of this examination should be presented in the final report, thereby avoiding possible criticism of poorly working laboratories as to the suitability of the test material used. According to the harmonised protocol [7], homogeneity can be tested by random selection of 10 samples taken from the prepared test material and analysis of them in duplicates. Subjecting the results to statistical assessment applying ANOVA gives an estimate of (a) the within-bottle standard deviation (SDwithin-bottle)
442
Proficiency tests in pesticide residue analysis reflecting the analytical error and (b) the between-bottle standard deviation (SDbetween-bottle), which also includes an error term for possible heterogeneity. If the quotient (SDbetween-bottle) 2/(SDwithinbottle) 2 is not larger than the F-value,
which is tabulated for the significance level = 0.05, the material is considered to be homogeneous enough for the purpose of the ring trial. Although ANOVA is a very convenient tool to conduct the statistical evaluation of the homogeneity data, the calculation of the various statistics can also be performed using a spreadsheet programme applying the following equation for the within-bottle SD:
SDwithin-bottle -
1
(10.1)
10
where s, s2 and so are the within-bottle variances of the 1st, 2nd and 10th bottle, respectively, assuming that 10 bottles were used for the homogeneity study. The between-bottle SD is defined by the following equation:
SD
SDbetween-bottle
| 2((x_- .2 + (x2 - X)2 +. (10-
1)
+ (xl 0 -
2)
(10.2)
where xl, x2 and x10 are the average values of the 1st, 2nd and 10th bottle, respectively, and X is the average value of all values. When both SDs are significantly different, the SD of the sampling error (SDsampling-error) describing the heterogeneity of the test material can be
calculated according to the following equation: SDsamplingerror
J SDbetween-bottle
- SDwithin-bottle
(10.3)
Sometimes, it may occur that the analytical error is abnormally low, especially when the duplicate analyses of the samples are not analysed in random order and the two sub-samples from the same bottle are measured one after the other. In this case, the quotient of the between-bottle variance and the within-bottle variance may exceed the critical F-value, thereby indicating poor homogeneity of the test material. However, even in these cases, sufficient homogeneity is demonstrated if SDsampling-error (Eq. (10.3)) is smaller than 30%
of the target SD of the PT. This procedure is exemplified using the PT 3 homogeneity test data of two pesticides (vinclozolin and propoxur), which are shown in Table 10.1. For vinclozoin, the obtained F-value of 1.58 was below the critical F-value of 3.0, thereby confirming sufficient homogeneity. The opposite was observed when evaluating the results from propoxur since
443
C. von Holst and L. Alder TABLE 10.1 Report on homogeneity study of two pesticides from PT 3 Sample id
Vinclozolin (mg/kg)
Propoxur (mg/kg)
Replicate
Replicate 2
Replicate 1
Replicate 2
1 2 3 4 5 6 7 8 9 10
0.178 0.189 0.222 0.206 0.198 0.202 0.199 0.176 0.188 0.104
0.186 0.201 0.192 0.195 0.198 0.213 0.198 0.175 0.181 0.189
0.231 0.247 0.244 0.252 0.246 0.255 0.237 0.213 0.228 0.217
0.241 0.249 0.241 0.254 0.233 0.258 0.243 0.243 0.224 0.229
Mean
0.189 0.021 0.033 1.58 3.0 Passed
SDwithin-bottle SDbetween bottle
F-value F-crit ( = 0.05) F-value < F-crit SDsampling-error SDtarget SDsampling-error < 0.3
0.038
0.239 0.0084 0.0305 3.63 3.0 Failed 0.0096 0.048 Passed
SDtarget
Abbreviations explained in the text. Data taken from the report on PT 3 [3].
the F-value of 3.63 exceeding the critical F-value indicated a lack of homogeneity. However, SDsampling.error amounted to only 18% of the target SD, showing that this error contribution is negligible. Therefore, propoxur also passed the test for sufficient homogeneity. Although this procedure is applied in many PT schemes, McClure [11] showed that this specific design (10 samples analysed in duplicates) is not very efficient at rejecting inhomogenous material. The author of this paper concluded that additional statistics tests have to be applied, even when results from ANOVA do not indicate heterogeneity of the test material, in order to avoid the possibility that this result is due to an unacceptable high error of the analytical method applied. On the other hand, the preparation of test material can contribute a considerable portion to the overall budget of the proficiency testing, thereby requiring the homogeneity test to minimise the risk of erroneous rejection
444
Proficiency tests in pesticide residue analysis of homogenous material. Fearn and Thompson [12] presented an alternative test that takes this aspect into account. It can be concluded that it is up to the co-ordinator of a study to decide which approach would fit best the specific purpose of the study. Furthermore, the stability of analytes must be ensured before distribution of test samples. When organising a PT in the field of pesticide analysis, the coordinator has also to show the absence of pesticides other than the target analytes in the test samples, requiring the application of confirmatory methods such as GC/MS or LC/MS. In PT 3, naturally incurred cucumber homogenate fortified with additional pesticides was used as test material. The results of the statistical assessment revealed sufficient homogeneity for all analytes. An overview of the pesticides covered in PT 3, the mode of adding of the respective analyte (spiked or incurred) and the mean value obtained in the homogeneity study are shown in Table 10.2.
TABLE 10.2 Composition of the test material in PT 3 Pesticide
Acephate Aldicarb (sum) Carbendazim Deltamethrin Diazinon Endosulfan (sum) Imazalil Metalaxyl Methamidophos Methomyl Permethrin Pirimiphos-methyl Propoxur Vinclozolin (parent compound only)
Incurred
Spiked
Spiking level (mg/kg)
X X
0.18 0.49
X
0.18
X
0.19
X X
0.27 0.26
X X X X X X X X
Homogeneity test Mean (mg/kg)
RSD (within-bottle)
0.166 0.409 0.553 0.100 0.134 0.084 4.545 0.543 0.941 0.149 0.516 0.038 0.239 0.189
4.2 4.4 4.0 11 5.2 3.6 5.4 3.3 3.7 5.4 10 5.3 3.3 11
Data taken from the report on PT 3 [31.
445
C. von Holst and L. Alder 10.4
THE PROTOCOL FOR THE PROFICIENCY TEST
Prior to sending out the samples to the participating laboratories, the coordinator has to make available a detailed protocol explaining the statistical treatment of the data to all participants. This is because the currently available guidelines still leave some freedom to the organiser of a ring trial. The methods for calculating the assigned value of concentration, the SD and the z-score are of particular interest to participants. It is also important to inform the laboratories about who has access to the participants' code. An example of such a protocol is given by FAPAS [13], which is a proficiency testing scheme in the area of food and feed. There are also some particular needs when conducting a PT in the field of pesticides. Owing to the high number of pesticides that could be present in the sample, the participants have to be informed about a list of compounds from which the organiser of the trial singles out some pesticides for preparing the test material. This information assists the laboratories in selecting the appropriate methodology in order to cover the whole range of possible analytes. Of course, those pesticides that are selected from this list to be added to the test material remain unknown to the participants of the trial. In particular, the laboratories need to know how false positive and false negative results are treated. False negative results occur when a laboratory is not able to detect a pesticide added to the test material, whereas a false positive corresponds to a reported pesticide that is not present in material. The organiser of a PT should give information in the protocol on how to evaluate false negative and false positive results of laboratories in terms of z-scores. This also includes evaluation of laboratories reporting a false negative result due to the use of an unacceptable high limit of detection. The protocol for PT 3 contained a list of 48 pesticides that may be present in the samples and the participants had to fill out a form indicating whether they were seeking the individual pesticide along with the corresponding reporting level. In addition, the laboratories had to declare which pesticide was determined and which was the achieved reporting level. The protocol also contained a sheet to be used by the participants for reporting the analytical results along with information about the use of recovery information and the analytical method applied, including confirmatory methods such as GC/MS or LC/MS. Special emphasis has to be placed on describing the evaluation procedure of the submitted results. In PT 3, the organiser considered a result as false positive if the corresponding pesticide was listed in the above-mentioned list but not applied or added during preparation of the test material. If more 446
Proficiency tests in pesticide residue analysis than 5% of the laboratories had reported the presence of a specific pesticide above 0.01mg/kg that was not supposed to be in the test material, the organiser would have applied confirmatory methods to decide on each individual case. In the case of a confirmed false positive result, the laboratories would gain a z-score of +5, indicating that the laboratory had reported an unacceptable result. However, when the reported concentration of the false positive result was in the range of the lower limit of analytical determination, as indicated by a MRL with an asterisk (MRL*) in the corresponding EU legislation, the attribution of an unacceptable z-score was suspended. For instance, a laboratory with a result that was only twice the MRL* value would have received a z-score of +2 corresponding to the verbalised score "satisfactory" instead of +5 that indicates an unsatisfactory result. The z-score of 2 is obtained when replacing X with the MRL* in Eq. (10.4) (defined in section 10.5) and taking 50% of the MRL* as target SD (a). This procedure was justified by the fact that at such low concentrations the RSD is relatively high (up to 50%). In PT 3, 10 pesticides were considered as false positive, thereby leading to a z-score of 5 for the laboratories concerned whereas in one case an analyte was found at a concentration even below the corresponding MRL* value. Therefore, the laboratory concerned did not receive an unacceptable z-score. In a similar way, laboratories that were not able to detect a pesticide in the test material, thereby indicating a false negative result, gained an unacceptable negative z-score. The value of the z-score was determined by substituting 0 for x in the equation for calculating the z-score, as defined in section 10.5 (Eq. (10.4)), taking the assigned value of concentration and target SD selected for the compound. In order to finalise the PT within the scheduled time frame, the protocol should contain precise information about the shipment of the samples and the deadline for submitting the results.
10.5
THE z-SCORE
PTs are designed to inform laboratories about the difference between the result of their analysis and the assigned value of concentration when analysing test material of unknown analyte concentration. In general, this difference is expressed in terms of the z-score [6], which relates the deviation of the laboratory's results from the assigned value to the target SD. The z-score defined according to Eq. (10.4) is a dimensionless parameter 447
C. von Hoist and L. Alder and therefore allows comparison of results obtained in different PTs:
z=
x-X
(10.4)
In this equation, x is the participant's result, X is the assigned value of concentration and is the target SD of the PT. Calculating the z-score, the results reported by the laboratories are transformed to the "number" of SDs differing from the assigned value. For instance, a z-score of 2 indicates a result that was twice the target SD above the assigned value. z-scores between 0 and + 2 are considered as satisfactory and z-scores above ± 3 are unsatisfactory. Values between + 2 and + 3 or - 2 and - 3 are questionable. By applying these criteria, the probability for a well-performing laboratory gaining a z-score above ± 2 is about 5%. The SD of a well-performing laboratory is equivalent to the target SD of the PT. An important aspect that needs to be clarified before conducting the ring trial is the method used for calculating the assigned value of concentration and the selection of the target SD. Assigned value of concentration. In general, the assigned value is calculated from the submitted results. Occasionally, this value is derived from a part of the participating laboratories that are expected to be expert laboratories, as described by de Boer and Wells [19]. The organiser of the PT can also establish the assigned value without evaluating the submitted results when using test material with a known concentration of the target analyte, such as in the case of reference material. Target SD. This expression describes the acceptable SD of wellperforming laboratories. For example, when using the actual variability of the submitted results of the participating laboratories, the performance of the laboratories is compared with an average analytical performance observed in the PT concerned. Alternative concepts are based on using a target SD adjusted to the required accuracy of the results derived from the later use of the data. For instance, laboratories delivering data to monitoring programmes might need to measure all analytes with sufficiently low variability in order to spot a yearly trend of the concentration of this compound. Applying this concept would, therefore, allow a check on whether the performance of a laboratory is fit for a specific purpose. There is no general rule as to choice of a suitable target SD. Frequently, this value is estimated based on the Horwitz equation [14], which describes an empirically derived relation between the expected SDR and the corresponding concentration of the analyte. Thompson [15] showed that this relation can also be considered as a FFP criterion: laboratories are continuously 448
Proficiency tests in pesticide residue analysis
adjusting the performance of the applied methods to user requirements, thereby leading to reproducibility-precision data that reflect minimal performance criteria that the laboratories have to fulfil. In a recent publication, Alder et al. [16] showed that the Horwitz equation does not apply to results from the analysis of pesticides in vegetable test material revealing a constant RSD of 25% covering a concentration range from 1 g/ kg to 10 mg/kg. Such a RSD of 25% may be interpreted as a FFP criterion in usual pesticide residue analysis. Establishing the target SD is critical since the value of the selected SD has a strong impact on the laboratories' z-scores. For instance, if the target SD is set applying the FFP concept, thereby using a lower target SD than the actual SD of the results, a higher portion of the participating laboratories gain z-scores above 2. In contrast, taking the SD of the submitted results leads to a high portion of satisfactory z-scores if the majority of the participating laboratories are poorly performing as indicated by a large value of the SD. In the former case, laboratories are treated too harshly, whereas in the latter case, laboratories are treated too favourably. In Fig. 10.1, we used the laboratories' results from the analysis of methamidophos in PT 3 to compare the expected frequency distributions derived from the robust SD and the FFP A 114
12 10
a .
8
o
6 ,0 Q
E
4
2 2 0 0
0.2
0.4
0.6
0.8
1
Concentration (mg/kg)
1.2
1.4
More
---
Robust SD FFP SD
Fig. 10.1. Frequency distribution of the laboratories' results from methamidophos (PT 3). Number of laboratories: 92. The relative robust SD was 47% and the relative FFP SD was 30%.
449
C. von Holst and L. Alder SD, respectively, with the frequency distribution of the submitted data. The figure demonstrates that the expected frequency distribution based on the robust SD fits well with the distribution of the submitted data, whereas the expected distribution based on the FFP SD is narrower compared with the submitted concentrations. Therefore, the results from laboratories regarding the analysis of methamidophos are treated more harshly when using the FFP SD instead of the robust SD for the calculation of the z-scores. It is the task of the organiser to decide which target SD fits the needs of the respective PT. In PT 3, the organiser of the PT calculated the robust SD of the laboratories results applying the Qn method [17] along with the algorithm suggested by the SLMB [18] to evaluate the overall performance characteristics of the analytical methods. In addition, conventional statistics as described by the ISO standard 5727 [9] was employed to compare the results obtained with robust statistics. However, the z-scores were calculated using a constant relative target SD of 20%, irrespective of the assigned value of concentration of the pesticides. This value was derived from the achievable recovery rates established in the quality control guideline [2] for pesticides, which is mandatory for all laboratories delivering data to the EC monitoring programme. Three pesticides included in this ring trial were considered as very difficult to determine, and therefore a higher relative target SD of 30% was accepted. 10.6
STATISTICAL TREATMENT OF DATA
Calculating the z-score does not necessarily require statistical treatment of the data when using a reference value as the assigned value of concentration and when selecting a predefined value for the target SD. However, more information can be derived from the PT if the SD of the submitted results is calculated. For instance, if a large number of highly experienced laboratories participated in the PT, the calculated SD is a good indicator for the currently achievable precision, taking into account the available methodology and specific characteristics of the respective analyte. If the assigned value and target SD are derived from the submitted data, the coordinator has to decide on the statistical approach to be applied for data evaluation. When subjecting the analytical results to statistical analysis, the coordinator of a ring trial needs to cope with extreme values. Extreme values can lead to the wrong estimate of the assigned value of concentration and the SD of the data if they do not belong to the results from the well-working laboratories. Rejecting outlier laboratories is easily justified when detecting a spurious error revealed from close scrutiny of the documents delivered along 450
Proficiency tests in pesticide residue analysis with the result of the analysis. However, in most cases, identification of outliers is only based on the fact that a result exceeds an acceptable error range. When analysing blind duplicates, a high variation of results within a laboratory is also an indicator of poor performance, thereby leading to the rejection of this laboratory. In addition, data from ring trials often deviate from normal distribution, thereby making the application of conventional statistics more difficult.
10.6.1 Conventional approach for data analysis The conventional approach to statistical assessment of data from interlaboratory studies is given in the ISO standards 5725 [9]. The main characteristics of conventional statistics in this context are the use of statistical tests in order to detect and reject extreme values if required. By analysing the test material in replicates, the evaluation of the results allows estimation of the SDr reflecting the analytical error when a sample is analysed by the same laboratories. In addition, the SDR is determined, indicating the analytical error when samples are analysed by different laboratories. When the assigned value of concentration is derived from the submitted data, the average value of the data after rejection of outliers can be used. Special emphasis is placed on the use of statistical tests for the detection of extreme values that presumably occurred due to severe errors when analysing the test material. These values should be removed from further statistical assessment in order to avoid a distorted estimation of the statistics. ISO standard 5725 [9] recommends using Cochran's test for the detection of outliers with high spread of the replicates. In addition, Grubbs' single and paired tests are utilised to identify laboratories, which reported an average concentration deviating significantly from the overall mean value of the submitted results. Repeated application of these tests to the remaining data is suggested but this ISO standard does not specify the maximum number of cycles to be applied. Rejecting a high number of extreme values could therefore result in an underestimation of the SD of the data. Following the elimination of extreme values, the data are subjected to ANOVA for the determination of SDr, SDR and the assigned value of concentration. Removing extreme values from the results prior to the subsequent calculations is a critical aspect since eliminating too many values may underestimate the true variability of the data, whereas the estimated SD can become too high if an outlier laboratory is not detected. The sensitivity of 451
C. von Holst and L. Alder conventional statistics to slight differences of extreme values will be elaborated in section 10.6.5 using an example from PT 3. In order to circumvent this problem, the application of robust statistics to the evaluation of results from interlaboratory studies has been examined. Robust statistics has the advantage of not requiring the elimination of extreme values.
10.6.2 Robust statistics for data analysis Robust statistics is a relatively new approach when applied to statistical evaluation of results from interlaboratory trials. Robust statistics calculates the assigned value of concentration and the SD of the submitted results without rejection of outliers. However, applying robust statistics, the assigned value and the SD are mainly determined by the results from the well-working laboratories, whereas poorly working laboratories influence the target values to a smaller extent. Two papers published by the UK Analytical Methods Committee (AMC) [20,21] elaborated the main advantages of robust statistics, such as less sensitivity to the presence of extreme values and the capability to cope with deviation of the frequency distribution of the results from normal distribution. In addition, an algorithm for calculating robust estimates using standard Fortran 77 is given [21]. Similarly, ISO-5725 part 5 [9] examines the impact of rejecting a various number of extreme values on the estimation of the assigned value of concentration and the SD, thereby suggesting that robust statistics can be considered as an alternative methodology for the evaluation of results from interlaboratory trials. A typical example of a robust estimate for the assigned value of a population is the median, which is defined as the number in the middle of a set of numbers. Individual extreme values do not influence this number, irrespective of their magnitude. A corresponding robust estimate for the SD (SDMAD) is the median of the absolute deviations of the individual numbers from the median (MAD) of these numbers, multiplied by an appropriate factor to achieve correspondence with the value of the SD if the data were normally distributed. The robust estimate of the SD is based on one half of the results distributed around the assigned value of concentration, which derives from the best performing laboratories, whereas the influence of poorly performing laboratories is minimal. Although using the median and SDMAD allows for simple data treatment, the organiser of PT 3 applied the Qn method [17] and the algorithm proposed by the SLMB [18] due to the higher precision of the estimates achievable with the latter approaches.
452
Proficiency tests in pesticide residue analysis Conventional statistics is more efficient at estimating the assigned value and the SD compared with robust statistics if the data are really normally distributed [18]. However, in general, results from PTs do not meet this criterion, especially given the presence of extreme values, therefore requiring the use of alternative methods of data treatment. 10.6.3 Robust statistics according to the SLMB The method suggested in the SLMB [18] is not comprised of a single calculation step but involves an iterative procedure for the estimation of the assigned value of concentration and the SD. The iterative procedure consists of a number of cycles containing repeated application of the same algorithm. In principle, an iterative process starts with rough estimates of the target values, which become more precise when running repeated cycles of the procedure. At a certain stage, additional cycles do not change the estimates anymore, thereby indicating when to stop the procedure. In this chapter, we elaborate only important aspects of the SLMB process, whereas a comprehensive description of the algorithm is given in the SLMB [18]. Using robust statistics for the evaluation of data from PTs requires implementation of the algorithm, which is not particularly difficult when using commercially available software packages such as visual basic and EXCEL. The AMC [21] suggested a very similar procedure that is also exemplified in ISO 5725 part 5 [9] but in PT 3, the SLMB algorithm was selected due to the higher fraction of extreme values that can be accommodated in the whole data set without jeopardising the statistical evaluation. By applying the procedure proposed in the SLMB, the target value of concentration and the SD are determined one after the other, both utilising the same algorithm. At first, the target value of concentration is calculated using the median as an estimate for the first cycle of the iteration. In all cycles for the calculation of the assigned value, SDMAD is used as SD. Based on these values, an acceptance range is established, leaving reported results within this range unchanged, whereas data outside this range are substituted by the lower or upper limit of the acceptance range. The following equation is applied to the calculation of the acceptance range of the first cycle: First cycle: acceptance range = median ± c-SDMAD
(10.5)
where c is a factor ranging between 1 and 2. In this study, a factor of 1.5 was used, thereby keeping the results from about 86% of the participating laboratories unchanged. From the acceptance range of the first cycle, a new estimate of the assigned value is calculated, leading to a modified acceptance
453
C. von Hoist and L. Alder range: Consecutive cycles: Acceptance range,,, = assigned value,,,new ±-c-SDMAD
(10.6) (10.6)
This procedure is repeated until the assigned value of the current cycle does not differ from the assigned value calculated in the preceding cycle. Subsequently, the robust SD is determined by applying equations that are slightly modified compared to Eqs. (10.5) and (10.6). However, this time the assigned value calculated in the previous step is held constant, whereas a modified estimate of the SD is calculated from the acceptance range determined in the respective cycle. Again, this procedure stops when the SD does not change anymore. Since substituting extreme values by suitable limits is the most important characteristic of this algorithm compared with conventional statistics, where extreme values are rejected, an example from PT 3 is used to illustrate the principle of the procedure. For simplicity, a sub-sample from the submitted results for the determination of acephate is taken. In Fig. 10.2, the data, along with the median and the lower and upper limits of the acceptance range, are shown. It is obvious that the result of 0.023 mg/kg reported by laboratory 1 is too low, whereas the opposite holds true for laboratories 12 and 13, which
0.3 Median = 0.123; RSDMAD = 18 % Lower limit= 0.09; upper limit= 0.16
0.25
0)
0.2
E
c 0.15 a2 '~
0.1-
0
o
I I.
....
.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. :z
aI .... fil.f*.l. . . . -
--
N il·
1
'
I ...
!
k·r"'
;0-I z
2
0.05-
~Z
0
1
2
3
4
5
6 7 8 Laboratory
9
10
I..[ s
11
12
13
Fig. 10.2. Working example for the estimation of the assigned value of concentration applying robust statistics according to the SLMB. Data were taken from participants' results of PT 3 for the determination of acephate. RSDm"u is the relative standard deviation based on the MAD.
454
Proficiency tests in pesticide residue analysis 0.251-
0.2
0.20 -
Assigned value = 0.129; RSD= 25 % Lower limit = 0.09; upper limit = 0.17
E 0.15
_
a) C
.; ...
.S 4.. A.., _
I: I1
0.10
o
_
1. I.,
,
A..
I.
. _. JI
__
_
I. .
4... .
I:: I
0 n
I "
I
1
2
3
4
5
6
.
1
;_
_
ml.::0:t s
..
.
f.D
...l
l_
_
I''1
7
8
i·
:00
9
10
T
:;
l
11
H
z
e
:::
I
0
NE :] :[ M I....MI
g
_ _~~~~~~~
I:
o 0.05
~sr
..................................
...............................................................
Ix
I I
.gH. I N WI N _ N
12
13
Laboratory Fig. 10.3. Working example for the estimation of the standard deviation applying robust statistics according to the SLMB. Data were taken from participants' results of
PT 3 for the determination of acephate. The figure represents the last cycle of the iterative process. RSD calculated using the data from the last cycle.
reported 0.17 and 0.28 mg/kg, respectively. Therefore, a new data set is established in which the original result from laboratory 1 is substituted by 0.09 mg/kg and the results from laboratories 12 and 13 are replaced by 0.16 mg/kg. From these data, a modified assigned value of concentration is calculated and substituted in Eq. (10.6) to calculate a modified acceptance range. In our example, the median of the example from Fig. 10.2 is 0.123 mg/kg and the assigned value after the second step is 0.129. This value remained constant in the subsequent cycle, thereby indicating the termination of the iteration. The calculation of the robust RSD required five cycles leading to a value of 25%, as shown in Fig. 10.3, which was increased compared with the RSD based on the MAD, which was 18% (Fig. 10.2). It can be summarised that robust statistics according to the SLMB calculates the assigned value and the SD of the submitted results in a consecutive manner. Both calculations are comprised of an iterative procedure. 10.6.4 Robust statistics according to the Qnlmedian method For the robust estimation of SD using the Qn method [17], all differences between individual laboratory data are calculated. Assuming a data set from 455
C. von Holst and L. Alder three laboratories with one analytical result (x), respectively, three differences Xlab2 - Xlabl, Xlab3 - Xlabl
and
Xlab3 - Xlab2
can be calculated. In the case of 100
laboratories, the number of differences is 100 x (100 - 1)/2 = 4450. With n laboratories, n x (n - 1)/2 differences between individual data must be determined. The robust SD is the 25th percentile (1st quartile) of these differences, multiplied by an "alignment factor" of 2.22. Since small differences between analytical results are more likely to occur near the true mean value, wellperforming laboratories contribute more to the robust SD than laboratories with extreme results. However, by chance, two data from poor-performing laboratories may differ little and can thus influence the 25th percentile. Therefore, not only data from the best laboratories are included in the estimation of the robust SD. 10.6.5 Limitations of conventional statistics regarding the detection of outliers By applying conventional statistics to data evaluation, the submitted results are screened for the presence of extreme values that influence the assessment of the SD when not being removed prior to the subsequent statistical assessment. However, this procedure can lead, in some cases, to exceptionally high or low estimates of the SD, depending on whether extreme values are detected as outliers or not. We used the data from the determination of acephate (median: 0.123 mg/kg), as shown in Fig. 10.2, to illustrate the effect of rejecting extreme values on the calculation of the SD. In addition, we examined the influence of the reported result of laboratory 12 on the identification of extreme values when subjected to small changes. The outcome of the examination is shown in Table 10.3. By applying conventional statistics, laboratory 1 (reported result: 0.032 mg/kg) and laboratory 13 (reported result: 0.279 mg/kg) were identified as outliers and removed from the data set. Calculating the RSD from the remaining results led to a RSD of 15%, which was considerably lower compared with a RSD of 42% obtained when analysing all data. The sensitivity of conventional statistics regarding the identification of outliers is demonstrated by assuming that laboratory 12 had reported a somewhat higher value than 0.17 mg/kg. For instance, in the case of reporting 0.18 mg/kg, the result of laboratory 13 was not identified as an extreme value and therefore not removed from the data set, thereby leading to an increased RSD of 31%. If laboratory 12 had reported 0.20 mg/kg, no laboratory would have been considered as an outlier, resulting in a very 456
Proficiency tests in pesticide residue analysis TABLE 10.3 Effect of the variation of the results of a single laboratory on the statistical analysis applying conventional and robust statistics Assumed results (mg/kg) of laboratory 12
0.170
0.180
Outlier (Lab code)
1 and 13
1
Mean without outlier rejection Mean after removal of outliers Median (robust mean)
0.134 0.129 0.123
0.134 0.142 0.123
0.136 0.136 0.123
Relative SD without outlier rejection Relative SD after removal of outliers Relative SDQn estimator
41% 15% 26%
41% 31% 26%
42% 42% 26%
0.200
high RSD of 42%. However, the RSD calculated applying robust statistics was, for all cases, 26%. In contrast with conventional statistics, the robust estimates of the assigned value of concentration and the SD do not depend on slight variation of the reported results of laboratory 12. In conclusion, the identification of outliers can have a large impact on the assessment of the RSD, whereas robust statistics is less susceptible to the presence of extreme values.
10.7
THE RESULTS FROM PT 3
The results of PT 3 are shown in Table 10.4. Quite different pesticides in terms of physico-chemical characteristics and efforts required for their determination have been included in the programme, as indicated by the obtained RSD of the results, which varied from 22% for diazinon to 60% for imazazil. In this study, a constant target SD of 20% for easy to analyse pesticides and 30% for difficult to analyse pesticides was chosen in order to calculate the z-scores. Comparing these two fixed values with those obtained from the statistical analysis revealed that, in all cases, the fixed SD was lower. By using the fixed SD, the z-scores became larger, as discussed in section 10.4, thereby leading to a higher number of laboratories with questionable and unacceptable results. An overview of the outcome of PT 3 is presented in Table 10.5. In no case were 95% of the results from all EU laboratories satisfactory, which should be expected if all laboratories met the target quality criteria. Well over 80% of laboratories obtained satisfactory results for carbendazim, diazinon 457
C. von Hoist and L. Alder TABLE 10.4 Results of statistical analysis: performance characteristics of the analytical methods employed
Acephate Aldicarb Carbendazim Deltamethrin Diazinon Endosulfan Imazalil Metalaxyl Methamidophos Methomyl Permethrin Pirimiphos-methyl Propoxur Vinclozolin
True concentration (mg/kg)
RSD(Qn) (%)
RSDTarget for z-scoring (%)
0.15 0.47 0.49 0.11 0.14 0.076 4.2 0.52 0.63 0.12 0.54 0.050 0.26 0.22
49 22 24 37 22 29 60 32 49 30 30 31 24 28
30 20 20 20 20 20 30 20 30 20 20 20 20 20
Data taken from the report on PT 3 [3]. TABLE 10.5 Results of statistical analysis: performance characteristics of the participating laboratories based on the target standard deviation
Acephate Aldicarb Carbendazim Deltamethrin Diazinon Endosulfan Imazalil Metalaxyl Methamidophos Methomyl Permethrin Pirimiphos-methyl Propoxur Vinclozolin
Satisfactory results (%)
Questionable results (%)
Unsatisfactory results (%)
66 68 82 62 85 69 70 71 70 63 87 77 71 84
10 11 7 15 7.5 11 18 10 15 12 14 5 15 10
24 21 11 23 7.5 20 12 19 15 25 8 18 14 6
Data taken from the report on PT 3 [3].
458
Proficiency tests in pesticide residue analysis and vinclozolin. In contrast, only just over 60% of laboratories obtained satisfactory results for deltamethrin and methomyl. 10.8
USING SUMMED SQUARED SCORES FOR DATA EVALUATION
In the case of a systematic error leading to a consistently lower recovery rate, all z-scores are expected to decrease. A simple measure to identify such a type of error is the RSZ calculated as follows: RSZ = (z-score1
+
z-score 2 +'
+
z-scoren)
(10.7)
n where z-scorel, z-score2 and z-scoren are related to the z-score of the 1st, 2nd and nth analyte reported by the same laboratory and n is the number of combined z-scores. The RSZ is an indicator of bias since the sign of each z-score is taken into account. It can be interpreted in the same way as z-scores, e.g., a RSZ between - 2 and + 2 should be attributed as satisfactory. Moreover, it is not sufficient to establish the general performance of laboratories based on a table of z-scores evaluating the z-scores separately. For instance, exclusively "satisfactory" z-scores for all analytes do not make a laboratory necessarily good, especially when many z-scores are outside the range - 1 to + 1. For a laboratory delivering results with a variability that is equivalent to the target SD, the probability of each z-score outside - 1/+ 1 is 31.5%. The statistical chance to get two z-scores, which are both beyond the - 1/+1 range, is not higher than 0.313 x 0.315 = 0.099 or 9.9% when evaluating the results from two analytes. In the same way, the chance for three z-scores outside the range defined by - 1/+1 is not greater than 3% when participating in a PT with three pesticides! Consequently, the proficiency of a laboratory with all z-scores outside the range - 1/+1 is not sufficient, even if all these z-scores are "satisfactory," i.e., in the range - 2/+ 2. There is also a high risk that poorly performing laboratories gain a "satisfactory" z-score when evaluating only the results from PTs separately. In Fig. 10.4, the probability of obtaining a satisfactory and a questionable z-score is shown depending on the proficiency of a laboratory that is expressed in terms of the relative repeatability SD, RSDr. In this example, the target RSD is set at 20% and the bias of the results of the laboratory is assumed to be zero. The insufficient power of single z-score evaluation is exemplified using the worst case presented in this figure, which is a laboratory with a RSDr of 50%. Although this value is 2.5 times higher than the target SD used for calculating the z-score, this poorly performing laboratory has a chance of 459
C. von Holst and L. Alder 100% =_-=_ _-____
80% = 60% Ma
2 40%-
a_
20% 0O%/
10
15
20
25
30
35
40
RSDr (%)
45
50
z-score: -2 to 2 - -
- z-score: -3 to 3
Fig. 10.4. Probability of gaining a satisfactory or questionable z-score depending on laboratory performance expressed in terms of the RSDr. The calculation was based on a target standard deviation of 20% and the laboratory bias was assumed to be 0. about 60% to achieve a satisfactory z-score. Moreover, the chance of gaining a z-score above + 3 or - 3, and thereby indicating a bad performance by this laboratory, is only 20%. Therefore, the assessment of the general performance of a laboratory needs a thorough consideration of all z-scores obtained. However, by visual inspection of a specific combination of these z-scores, it is not possible to examine whether the performance of the laboratory is satisfactory. In order to evaluate the proficiency of a laboratory it is required to compare the obtained combination of z-scores with a "standard" combination of z-scores of a hypothetical laboratory that delivers results within the target SD, assuming a certain confidence level. The appropriate combined z-score is the SSZ [7] value that is calculated according to the following equation: SSZ = z-score2 + z-score 2 + * + z-score2
(10.8)
where z-scorel, z-score 2 and z-scoren are related to the z-score of the 1st, 2nd and nth analyte reported by the same laboratory and n is the number of combined z-scores, which is calculated for each laboratory. The SSZ value of a hypothetical laboratory that produces results within the target SD is the tabulated limit of the X2 distribution at a specific confidence level. If the SSZ value of a laboratory is above this tabulated limit, the performance of this laboratory is not satisfactory. A simple theoretical exercise may illustrate the basis of these limits. A "standard performing" laboratory determines 10 analytes each with the same 460
Proficiency tests in pesticide residue analysis
0
a, 0U-
2
4
6
8
10
12
14 16 18 SSZ value
20
22
24
26
28
30
Fig. 10.5. Simulated distribution of the SSZ values of a standard performing laboratory when participating in 10,000 proficiency tests and analysing 10 pesticides. The arrows show the SSZ limits indicating good and satisfactory performances. average performance characterised by the target SD in 10,000 PTs. Owing to random analytical errors, this laboratory will get different results (xl, x2... x 0) in each round leading to dissimilar z-scores (z-scorel,z-score 2 ...z-scorelo) and SSZ values. The distribution of the SSZ values of these 10,000 PTs was simulated and the results of this simulation are shown as a histogram of the obtained SSZ values in Fig. 10.5. Evaluating the histogram showed that about 9550 SSZ values are below a limit of 18.6 for the SSZ that corresponds to the X2 value listed for 10 observations and a probability of 95.5%. In analogy to the z-score range of + 2/- 2, all SSZ values smaller (i.e., better) than the 95.5th percentile are called "satisfactory". The other important percentiles are 68.3rd for "good", 99.7th for "questionable" and above 99.7th for "poor". The limits of the SSZ that correspond to these percentiles depend on the numbers of combined z-scores and are listed in Table 10.6. A disadvantage of the limits of SSZ values is their dependence on the number of analytes, therefore, requiring the use of Table 10.6, which contains the limits of the SSZ values for various numbers of analytes. However, the use of this table can be avoided when calculating the square root of the mean SSZ value describing the RLP, which was introduced by Uhlig and Lischer [221. The RLP is calculated according to the following equation:
RLP= RLP - ~-
(10.9)
461
C. von Holst and L. Alder TABLE 10.6 Limits for good, satisfactory and questionable values for the SSZ depending on the number of z-scores being combined Number of SSZ limits depending on percentile combined z-scores Satisfactory Questionable Good (68.27%) (95.45%) (99.73%)
RLP
12.8 14.3 15.8 17.2 18.6 20.0 21.3 22.7 24.0 25.3 26.7 28.0 29.2 30.5 31.8 44.2
1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1
6 7 8
7.0 8.2
9
10.4 11.5 12.6 13.7 14.8 15.9 17.0 18.1 19.2 20.3 21.4 22.4 33.1
9.3
10 11 12 13 14 15 16 17 18 19 20 30
20.1 21.8 23.6 25.3 26.9 28.5 30.1 31.7 33.2 34.7 36.2 37.7 39.2 40.6 42.1 56.0
Good Satisfactory Questionable 1.5 1.4 1.4 1.4 1.4 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.2
1.8 1.8 1.7 1.7 1.6 1.6 1.6 1.6 1.5 1.5 1.5 1.5
1.5 1.5 1.5 1.4
where n is the number of z-scores used for calculating the SSZ. As shown in Table 10.6, the RLP value does not depend very much on the number of combined z-scores. In order to simplify the evaluation of results from PTs, the following constant values may be used to characterise the general laboratory performance assuming a number of about 7-15 z-scores, which are combined in a typical PT for pesticide residues: Good performance: RLP c 1.1 Satisfactory performance: RLP > 1.1 and - 1.35 Questionable performance: RLP > 1.35 and < 1.6 Unsatisfactory performance: RLP > 1.6 The somewhat increased risk of treating too harshly laboratories that analysed less than nine analytes is considered to be acceptable since the laboratories can minimise this risk by participating repeatedly in PTs, thereby increasing the overall number of z-scores included in the RLP.
462
Proficiency tests in pesticide residue analysis
A simple example is used to illustrate the application of this evaluation scheme. Consider a laboratory analysing nine pesticides and three times gaining a z-score of 1.2, 1.5 and 1.8, respectively. All z-scores are below 2, but the RLP value calculated according to Eq. (10.8) is 1.4, therefore, indicating that the overall performance is beyond the satisfactory range. In addition, it should be pointed out that the criteria for good, satisfactory and questionable performances only apply if the SSZ value is calculated using the robust SD of the respective analyte and not the FFP SD. Given the easy calculation of SSZ values and the improved capability of detecting poorly performing laboratories, we strongly recommend using combined z-scores when evaluating results from PTs in the field of pesticide analysis. 10.9
AN ALTERNATIVE APPROACH BASED ON THE FACTOR CONCEPT
Some pesticides covered by PT 3, such as acephate, imazalil and methamidophos, were characterised by a very high robust RSD ranging from 49 to 60%, revealing inconsistencies when calculating the satisfactory and questionable ranges of the pesticide concentrations. This aspect is elaborated using the results from imazalil focusing on the limits describing a z-score of - 3 or - 2 and +2 or +3, respectively. In Fig. 10.6, the frequency distribution of the laboratories' results along with the satisfactory ranges based on the robust SD and the FFP SD are shown. For instance, a satisfactory z-score of - 2 based on the robust SD would correspond to a concentration of - 0.8 mg/kg, which is even below 0. However, the laboratory that reported a value of 9.2 mg/kg would gain an unsatisfactory result. This clearly demonstrated that using a very high target SD treated results below the assigned value too leniently whereas the opposite held true when the results were above the assigned value. Satisfactory ranges based on the FFP SD were narrower compared with those based on the robust SD since results below 1.7 mg/kg instead of - 0.8 mg/kg or above 6.7 mg/kg instead of 9.2 were considered as questionable or even unsatisfactory. However, considering the assigned value of 4.2 mg/kg, high values were still treated more harshly compared with low values. This aspect is more obvious when looking at the questionable ranges since a z-score of -3 corresponded to a concentration of 0.4 mg/kg whereas a z-score of +3 corresponded to a concentration of 8.2 mg/kg. The former limit involved a 10-fold error and the latter limit only a 1.9-fold error but both errors were equally treated. 463
C. von Holst and L. Alder b1
14
n
.W 12E 10 o 0
,
Satisfactory range (mg/kg) 9.2 Robust SD (60 %) -0.8 1.7 6.7 FFP SD (30 %)
-
0
86
C0:dS
E
E
z
of
420
I
0
I
1
2
II .
3
::
.
4
.
5
.
6
.
7
A i
..
8
9
10
.
11
12
13
Concentration (mg/kg)
Fig. 10.6. Frequency distribution of the results from imazalil in PT 3 along with the satisfactory range defined by ± 2 for the z-score. The class width was set at 1 mg/kg showing the corresponding average concentration. For instance, the class indicated by 4.5 mg/kg covers the range from 4 to 5 mg/kg. Two satisfactory ranges based on the robust SD and the FFP SD were calculated. The bar in grey represents the class containing the assigned value of concentration (4.2 mg/kg). Recently, Hill and von Holst [23,24] examined this inconsistency, showing that data from analytical measurements have a skewed distribution rather than a normal distribution owing to the way single errors are combined. In addition, they showed that applying alternative statistical approaches reflecting the skewed distribution treats results below and above the assigned value more equally compared with concepts requiring the data to be normally distributed. A simple example demonstrates why error propagation in analytical chemistry can result in a skewed distribution of the data. We assume a typical analytical procedure in which an analyte is first extracted from a matrix and then subjected to a clean-up process. Both steps are characterised by the same recovery rate of 80% with a SD of 16%. Assuming normal distribution of both error sources, the recovery rate of the individual steps would range from 48 to 112% ( = 0.05). When calculating the outcome of the analysis, it is important to consider that recovery rates from both steps are combined by multiplication. The same applies to errors of the recovery rates of the individual steps that propagate by multiplication and not by addition. For instance, the average recovery rate of the analyte after passing both steps is 64% if the recovery rates of the extraction step and the clean-up step were 80%,
464
Proficiency tests in pesticide residue analysis --250 200 o 150 C:
a)
2 100
LL
50 0
25
35
45
55
65 75
85
95 105 115 125 135
Recovery rate (%)
Fig. 10.7. Simulated frequency distribution of the recovery rate of an analytical procedure comprised of two steps, which are combined by multiplication. The simulation was based on 1000 data and the class width was set at 10% indicated by the corresponding average value for the recovery rate. For instance, the class indicated by 65% contains all results that are between 60 and 70%. The bar in grey represents the class containing the mean value of the combined recovery (64%). respectively. To elucidate the influence of combining errors by multiplication on the distribution of results, we calculated 1000 random values of the recovery rates from both steps respectively based on a normal distribution (mean value = 80%, SD = 16%). Subsequently, the product of two random values of the recovery rate-one value for each step-was calculated 1000 times and the resulting frequency distribution of the 1000 values is depicted in Fig. 10.7 showing a skewed distribution of the results. The obtained frequency distribution was used to determine the range corresponding to the mean value ± 2SD. The recovery rate at the lower limit (2.275th percentile) for the combined recovery rate gave a value of 30% and for the upper limit (97.725th percentile), a value of 123%. Both values have the same probability of occurrence but the difference of the higher value from the mean value is 59% (123-64), which is larger compared with the difference between the mean value and the lower limit, which is only 34% (64-30). This simulation indicates that using satisfactory error ranges based on limits that are equal for values below and above the mean value is not appropriate when errors propagate by multiplication. The alternative concept is based on expressing errors in terms of factors. For instance, a factor of 2 corresponds to an error range from 0.5 to 2 mg/kg when the true value is 1 mg/kg. Appropriate factors describing satisfactory 465
C. von Holst and L. Alder TABLE 10.7 Limits for satisfactory and questionable z-scores calculated with different statistical approaches using results from PT 3 z-score
Imazalil (n = 84)
Diazinon (n =106)
SD
Lower limit (mg/kg)
Median (mg/kg)
Upper limit (mg/kg)
-3
-2
0
2
3 12
Robust SD (%)
60
- 3.36
- 0.8
4.2
9.2
FFP SD (%)
30
0.42
1.7
4.2
6.7
FSD Robust SD (%) FFP SD (%) FSD
1.66 22 20 1.26
1.4 0.05 0.06 0.08
1.8 0.08 0.09 0.10
4.2 0.15 0.15 0.15
7.0 0.22 0.21 0.19
8
12.5 0.25 0.24 0.27
SD, standard deviation; FFP SD, fitness for purpose standard deviation; FSD, factor standard deviation.
error ranges along with z-scores based on the factor concept can easily be derived from the submitted PT data [23]. In Table 10.7, the results obtained with conventional statistics and the factor concept were compared using the PT 3 results from imazalil as difficult to analyse analyte and diazinon as less difficult to analyse analyte. The results from the various statistical methods coincided quite well for diazinon whereas the opposite applied to imazalil. Evaluating the results from the other PT 3 analytes (data not shown) revealed that good correspondence was always obtained when the RSD of the submitted results was below 25%. The influence of the various statistical methods on the satisfactory and questionable ranges was obvious when looking at the results from imazalil, which was characterised by a high variability, as shown in Table 10.7. Based on the robust SD, the satisfactory and questionable concentration ranges for z-scores of - 2 and - 3, respectively, were negative and therefore they were not suitable for setting appropriate limits. Positive concentration ranges for the lower limits were received for the z-scores based on the FFP SD and the FSD. The satisfactoryz-scores calculated with the FFP SD and the FSD were quite similar but major differences were observed when comparing the questionable z-scores since the z-score of - 3 based on the FFP SD was 0.42 mg/kg, which was much lower than the corresponding z-score of 1.4 mg/kg based on the FSD. For instance, a laboratory with a reported result of 1 mg/kg would gain a questionable z-score when the evaluation was conducted using the FFP SD but an unsatisfactoryz-score when the factor concept was applied. In contrast, the factor concept is more favourable to high values compared with the 466
Proficiency tests in pesticide residue analysis conventional approach. These comparisons also illustrate that the error ranges established by the factor concept are asymmetricalsince the difference between the upper limit, which was 12.5 mg/kg for a z-score of +3, and the median value of 4.2 mg/kg was 8.3 mg/kg, whereas the difference between the lower limit, which was 1.4 mg/kg for az-score of - 3 and the median was only 2.8 mg/kg. Hill and von Holst [23] concluded that establishing these asymmetrical ranges would reflect propagation of analytical errors, as exemplified in Table 10.7, better than applying conventional statistics.
10.10 CONCLUSIONS This chapter has dealt with various aspects of PTs when organised in the field of pesticide analysis. It was shown that the currently available guidelines contain only the basics of PTs, indicating that the organiser of the exercise still needs to decide on essential features of the PT such as the way false positive and false negative results are evaluated or which statistical methodology is employed. Calculating the SD of the laboratories' results requires the use of suitable methods to cope with extreme values and the evaluation of the data from PT 3 showed that robust statistics is superior to conventional statistics due to its low sensitivity towards the presence of abnormally high or low values. This chapter also elaborated the advantage of using the sum of squared z-scores (SSZ) in order to facilitate the evaluation of the laboratories' proficiency when a large number of z-scores is available. Calculating SSZ allows examination of whether the variability of the laboratories' results is within the target SD of the PT. This is achieved by comparing the obtained SSZ with a critical value tabulated for various confidence levels. The mean square root of the SSZ is the RLP indicating the ratio between the achieved variability of each laboratory and the target SD. Increasing the number of z-scores to be included in the calculation of the SSZ enhances the capability of the PT to screen for laboratories showing consistently poor performances. However, we propose constant critical limits of the RLP indicating good, satisfactory and questionable performances, irrespective of the number of analytes, to facilitate the evaluation of results from PTs, especially when the laboratories did not analyse the same number of analytes. The laboratories' results of imazalil, methamidophos and acephate are characterised by a very high SD, indicating that the currently available analytical methods need to be significantly improved. Using the robust SD of these compounds for calculating the z-scores results in a very broad acceptable concentration range. In addition, results at the low extremes are treated 467
C. von Holst and L. Alder more leniently than results at the high extremes, demonstrating that z-scores are not an appropriate means to detect poor-performing laboratories when using the robust SD as target SD. A recently proposed alternative concept is based on expressing z-scores in terms of factors, thereby taking into account the skewed frequency distribution of results from PTs. Using factors to describe the uncertainty of the results has the advantage of leaving extremely low values out of the acceptable range. However, some aspects of this approach, such as the application of SSZ, still need to be elaborated.
REFERENCES 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
468
International Organization for Standardization, ISO/IEC 17025: 1999 (E) General requirements for the competence of testing and calibration laboratories ISO, Geneva. Commission Recommendation 99/333/EC, Official J. L 128, (1999) 25. A. Andersson, EuropeanCommission'sproficiencytest on pesticide residues in fruit and vegetables, proficiency test 3. National Food Administration, Uppsala, 1999. Council Directive 90/642/EEC, Official J. L 350, (1990) 71. Commission Directive 98/82/EC, Official J. L 290, (1998) 25. International Organization for Standardization, ISOIIEC 43-1: (E) Proficiency Testing by Interlaboratory Comparisons, part 1: Development and operation of proficiency testing schemes. ISO, Geneva, 1997. M. Thompson and R. Wood, J. Assoc. Off. Anal. Chem. Int., 76 (1993) 926. Eurachem, Selection, Use and Interpretation of Proficiency Testing (PT) Schemes by Laboratories, http://www.eurachem.bam.de/, 2000. International Organization for Standardization, International Standard 5725, part 1-part 6, Accuracy (Trueness and Precision) of Measurement Methods and Results. ISO, Geneve, 1998. M. Thompson and Tom Fearn, Analyst, 121 (1996) 275. F.D. McClure, J. Assoc. Off. Anal. Chem. Int., 84 (2001) 947. T. Fearn and M. Thompson, Analyst, 126 (2001) 1414. FAPAS Sectretary®, Protocol for the Food Analysis Performance Assessment Scheme. Central Science laboratory, York, 1997, http://ptg.csl.gov.uk/. W. Horwitz, Anal. Chem., 54 (1982) 67A. M. Thompson, Analyst, 124 (1999) 991. L. Alder, W. Korth, A.L. Patey, H.A. van der Schee and S. Schoeneweiss, J. AOAC Int., 84 (2001) 1569. C.H. Muller, Uhlig, Biometrika, 88 (2001) 353. Anonymous, Statistik und Rinversuche. In: Schweizerische Lebensmittelbuchkommission (Ed.), Schweizerisches Lebensmittelbuch, Vol. VII. Eidgenoessische Drucksachen- und Materialzentrale, Bern, 1989, p. 60. J. de Boer and D. Wells, Mar. Pollut. Bull, 32 (1996) 642. Analytical Methods Committee, Analyst, 114 (1989) 1693.
Proficiency tests in pesticide residue analysis 21 22 23 24
Analytical Methods Committee, Analyst, 114 (1989) 1699. S. Uhlig and P. Lischer, Analyst, 123 (1998) 167. A. Hill and C. von Holst, Analyst, 126 (2001) 2044. A. Hill and C. von Holst, Analyst, 126 (2001) 2053.
GLOSSARY Analysis of variance: Statistical method to partition the overall variance of a set of data into within- and between-subjects variance. Subjects can be the results from the laboratory participating in an interlaboratory comparison. Assigned value of concentration: The estimate of the true concentration of the analyte used to calculate the z-score. Eurachem: A network of organisations in Europe having the objective of establishing a system for the international traceability of chemical measurements and the promotion of good quality practices. http://www. eurachem.ul.pt/. Fitness-for-purpose criterion: Criterion for setting the target standard deviation by taking into account the tolerable uncertainty derived from the later use of the data. Homogeneity study: Study to establish whether the homogeneity of the test material is fit for the purpose of the proficiency test. Interlaboratory comparison: ISO Guide 43-1: Organisation, performance and evaluation of tests on the same or similar test items by two or more laboratories in accordance with predetermined conditions. Proficiency testing: ISO Guide 43-1: Determination of laboratory testing performance by means of interlaboratory comparisons. Repeatability: ISO 5725-1: Precision under repeatability conditions. Reproducibility: ISO 5725-1: Precision under reproducibility conditions. Robust statistics: Statistical methods used for estimating the mean value and the standard deviation by minimising the influence of extreme values on these estimates. Target standard deviation: Standard deviation used to calculate the z-score.
Q-score: Measure of the laboratories' performance by relating the difference between the laboratories' results and the assigned value to the assigned value. z-score: Measure of the laboratories' performance by relating the difference between the laboratories' results and the assigned value to the target standard deviation.
469
List of Abbreviations A ADI (Acceptable Daily Intake), 50, 51, 66 ADS (Alkyl Diol Silicas), 178 AED (Atomic Emission Detector), 188, 209, 291, 319, 320, 348 AMC (Analytical Methods Committee), 439, 452, 453 AMDIS (Automated Mass Spectral Deconvolution and Identification System), 297, 306-308, 328, 333, 334 ANOVA (Analysis Of Variance), 439, 442-444, 451 APCI (Atmospheric Pressure Chemical Ionization), 122, 256, 258, 370-372, 376, 385, 392-394, 398, 409, 410, 413 API (Atmospheric Pressure Ionization), 21, 30, 370, 371, 374, 392, 396, 409-412, 414, 423, 426 APPI (Atmospheric Photoionization Interface), 393, 409 ARC (Automatic Reaction Control), 351 ARfD (Acute Reference Dose), 50, 51, 66, 70-72 ASE (Accelerated Solvent Extraction), 75, 84, 131, 135, 145, 240, 241, 330
C CA (Collision Activation), 287 CCPR (Codex Committee on Pesticide Residues), 44, 73 CE (Capillary Electrophoresis), 140, 259, 260 CEN (Comit6 Europ6en de Normalisation), 95, 97, 98 CF-FAB (Continuous Flow Fast Atom Bombardment), 376 CID (Collision Induced Dissociation), 287, 288, 290, 373-376, 378, 379, 385, 394, 396, 398,413 CNL (Constant Neutral Loss), 416 CVF (Cone Voltage Fragmentation), 413, 414 D DAD (Diode Array Detection), 159, 187, 249, 256, 257, 261 DI (Direct Immersion), 201, 203, 205-207, 217, 218, 251-253 DLI (Direct Liquid Introduction), 370 DMA (N,N-Dimethylaminoethyl Methacrylate), 195 DMI (Difficult Matrix Introduction), 292 DSI (Direct Sample Introduction), 219, 343 E ECD (Electron-Capture Detector), 24, 83, 85, 126, 128, 132, 140, 471
List of Abbreviations 159, 188, 189, 205, 241-243, 245, 254, 291, 313, 333, 340, 349, 350, 364, 369 ECNCI (Electron Capture Negative Chemical Ionization), 282 EFSA (European Food Safety Authority), 65, 66, 74 EI (Electron Ionization), 15, 30-32, 272-274, 279-286, 288, 295, 296, 299, 313, 320, 331, 333, 335, 342, 343, 346, 349-353, 356, 358, 359, 393 ELISA (Enzyme-Linked ImmunoSorbent Assay), 18, 192 EMRLs (Extraneous Maximum Residue Limits), 43 EPP (Electronic Pressure Programmer), 243 ESE (Enhanced Solvent Extraction), 131 ETU (Ethylene Thiourea), 14 F FAB (Fast Atom Bombardment), 370 FFP (Fitness For Purpose), 439, 442, 466, 469 FIA (Flow-Injection Analysis), 25 FMAE (Focused Microwave-Assisted Extraction), 155 FPD (Flame Photometric Detection), 83, 126, 128, 132, 188, 189, 241, 242, 250, 291, 313, 324, 325, 327, 328, 340, 349 FQPA (Food Quality Protection Act), 406, 407 FT-ICR (Fourier-Transform Ion Cyclotron Resonance), 286, 297 FWHM (Full Peak Width at one-Half Maximum), 381, 382, 384
472
G GAP (Good Agricultural Practice), 3, 7, 39, 43, 44, 47, 50, 56, 61, 62, 64, 74, 75 GCBs (Graphitized Carbon Blacks), 176 GC-MS (Mass Spectrometry Coupled with GC), 15, 29, 69, 83, 85, 92, 126, 132, 152, 189, 205, 209, 210, 240-245, 248, 249, 252, 253, 263, 269, 272, 286-289, 291-295, 297, 298, 302, 306, 310, 311, 313, 316, 319-322, 333-335, 339-342, 344, 349, 351, 352, 358, 363, 364, 370, 373-376, 378, 408, 421, 433 GEMS (Global Environment Monitoring System), 51 GPC (Gel Permeation Chromatography), 75, 84, 85, 90-94, 106, 119, 121, 126, 128, 137, 149, 153, 157, 159, 177, 184, 186, 187, 189, 193, 221, 222, 236, 241, 242, 246, 250, 251, 343, 344 GPE (Gum Phase Extraction), 211 H HPGPC (High-Performance Gel Permeation Chromatography), 91, 93, 106 HPmSEC (High Performance SECMini-Columns), 119 HS (Head-Space), 201, 203, 242, 251-253 I IS (Immuno-Sorbents), 166, 179, 190-194 ITD (Ion Trap Detectors), 188, 189, 312, 340, 343, 350, 351, 364
List of Abbreviations
J JMPR (Joint Meeting on Pesticides Residues), 70-74
LPME/HF (Liquid-Phase Microextraction with Hollow Fibre), 213 LRMS (Low Resolution Mass Spectrometry), 287 LSE (Liquid Solid Extraction) LSP (Liquid Solid-Partitioning), 177 LVI (Large Volume Injection), 122, 235, 243-245, 248, 250, 256, 259, 264, 292
L LCL (Lowest Calibrated Level), 19, 20, 26, 28, 31, 35, 37 LC-MS (Mass Spectrometry with Liquid Chromatography), 18, 21, 22, 29, 30, 126, 132, 152, 159, 193, 255, 256, 258, 259, 261, 263, 264, 269, 270, 272, 279, 287, 288, 290, 369-371, 373-376, 378-381, 385, 395, 403, 408-412, 417, 418, 420, 421, 423, 424, 426, 428, 429, 433, 434, 439 LC-UV (Liquid Chromatography with UV-Absorption Detection), 23, 410, 412, 418, 419, 424, 433 LIMS (Laboratory Information Management System), 10 LLE (Liquid-Liquid Extraction), 157, 167, 212, 214, 260 LLP (Liquid-Liquid-Partitioning), 117, 118, 120, 123-125, 135, 137, 139, 140, 161, 167, 184, 185, 187-189, 199, 200, 202, 209 LODs (Limits Of Detection), 20, 205, 206, 241-243, 246, 251, 257, 258, 260, 262-264, 343, 349, 361, 363, 423, 428
M M+(Molecular Ion), 30, 270-276, 278, 280-283, 295, 302, 303, 308, 335, 349, 351, 363, 373-376, 378, 379, 384, 386-388, 396, 413, 418 [M+H]+ (Protonated Molecula), 22, 272, 280, 281, 351, 371, 372, 374-376, 378, 380 [M - H]- (Deprotonated Molecula), 372, 374-376, 378, 380 MAD (Median of the Absolute Deviations), 439, 452, 454, 455 MAE/FMAE (Microwave-Assisted Extraction/Focused MicrowaveAssisted Extraction), 155-159, 207, 217, 218 MIPs (Molecularly Imprinted Polymers), 162, 166, 193-198 MISPE (Molecular Imprinted Solid Phase Extraction), 193, 195-197 MMLLE (Microporous Membrane Liquid-Liquid Extraction), 212 MRLs (Maximum Residue Limits), 3, 7, 20, 22, 26, 27, 29, 31, 37, 40-43, 46, 47, 50, 51, 56, 58-66, 70, 73-76, 86, 115, 117, 235, 236, 243, 253, 339, 358, 403, 407, 408, 412
ITMS (Ion Trap Mass Spectrometer), 341, 342, 356, 363, 364 ITSPME (In-Tube Solid Phase Microextraction), 211
473
List of Abbreviations MRM (Multiple Reaction Monitoring) 83, 84, 92, 122, 152, 153, 167, 240, 290, 384, 386-388, 390-392, 397, 409, 414 MRMs (Multi Residue Methods), 1, 5, 9, 75, 81, 82, 85, 89, 90, 92, 93, 95, 101, 105, 115, 117-120, 123, 125, 154, 177, 185 MS (Mass Spectrometry), 1, 5, 15, 30, 31, 219, 269, 293, 340, 383,412, 422, 433 MS/MS (Tandem Mass Spectrometry), 5, 22, 30, 126-128, 159, 287-290, 320-322, 342, 343, 352-359, 361, 363, 364, 370, 371, 374, 379, 383-392, 396, 397, 409, 416, 429 MSD (Mass Selective Detector), 126, 132, 314, 316, 323, 333, 335, 343 MSPD (Matrix Solid Phase Dispersion), 129-132, 209, 217, 218 MSs (Member States), 42, 47, 50, 58, 63-66 N NCI (Negative Chemical Ionization), 282, 323-326, 328, 329, 331, 333, 349-352 NEDI (National Estimated Maximum Daily Intake), 51 NICI (Negative Ion Chemical Ionization), 31, 32, 364 NIST (National Institute of Standards), 296, 306 NPD (Nitrogen-Phosphorus Detection), 93, 128, 132, 189, 247, 291, 313, 323-325, 327, 328, 340, 349 NVP (N-Vinylpyrrolidone), 175
474
O OTT (Open Tubular Trapping), 198, 210, 211 P PA (Polyacrilate), 201, 202, 205, 252 PBM (Probability Based Matching), 301 PCI (Positive Chemical Ionization), 282-287, 323, 331, 333, 349-352, 356 PDMS (Polydimethylsiloxane), 161, 198, 199, 201, 202, 205, 206, 208-211, 217, 218, 252, 254 PFE (Pressurized Fluid Extraction), 131 PFK (Perfluorkerosine), 299 PFTBA (Perfluorotributylamine), 299 pKa (Acid Constants), 170-172, 394 PLE (Pressurized Liquid Extraction), 84, 105, 116, 124, 125, 131, 133-136, 138, 139, 154, 157-159, 210, 217, 218, 220 PPP (Plant Protection Products), 40, 41, 43, 64 PS-DVB (Polystyrene Divinyl Benzene), 119, 162, 170, 174-176, 179, 183, 184, 187, 189, 190, 201 PSE (Pressurized Solvent Extraction), 131 PT (Proficiency Test), 40, 439-450, 452-455, 457-459, 461-464, 466-468 PTV (Programmable Temperature Vaporizer), 122, 208-210, 214, 236-239, 241, 242, 245-248, 250, 264
List of Abbreviations
Q QMS (Quadrupole Mass Spectrometer), 341, 364 R RAMs (Restricted Access Materials), 178 RASFF (Rapid Alert System for Food and Feed), 40, 65, 66 RFs (Response Factors), 20, 423, 428 RIC (Reconstructed Ion Chromatograms), 29, 30, 299, 302-306, 312, 315, 316, 320, 323, 325, 328, 331, 332 RLP (Relative Laboratory Performance), 439, 461-463, 467 RP (Reversed-Phase), 126, 128-130, 132, 163, 165-169, 174, 177, 184, 185, 190, 217, 218 RPLC (Reverse Phase Liquid Chromatography), 250 RSD (Relative Standard Deviation), 27, 31, 36, 86, 116, 149, 151, 205, 206, 256, 439, 445, 447, 449, 454-458, 463, 466 RSDr (Relative Repeatability Standard Deviation), 439, 459, 460 RSZ (Rescaled Sum of Scores), 439, 459, 460 RTL (Retention Time Locking), 319, 320, 323, 348, 363 S S/N (Signal-To-Noise Ratio), 24, 30, 31, 344, 352, 361, 363, 394 SBSE (Stir Bar Sorptive Extraction), 119, 139, 140, 185, 198, 208-212, 216-222, 363, 433
SD (Standard Deviation), 439, 440, 442-444, 446-457, 459-461, 463-468 SDE (Single Drop Extraction), 212 SDr (Standard Deviation Repeatability), 439, 451 SDR (Standard Deviation Reproducibility), 439, 451, 458 SEC (Size Exclusion Chromatography), 90, 118, 119, 250 SF (Supercritical Fluids), 141, 145, 146, 155 SFE (Supercritical Fluid Extraction), 12, 75, 105, 116, 124, 125, 127, 128, 131, 134, 135, 138, 140146, 148-150, 152-155, 157, 159, 216-218, 220, 221, 240, 241, 343 SIM (Selected Ion Monitoring), 30, 31, 209, 243, 244, 287, 288, 292, 293, 295, 298, 299, 308-313, 340, 341, 343-346, 348, 380, 386, 416 SLME (Supported Liquid Membrane Extractions), 213, 214 SMB (Supersonic Molecular Beam), 335, 363 SPE (Solid Phase Extraction), 75, 83, 84, 87, 119, 121-123, 137, 139, 149, 151, 152, 157, 159, 162-169, 174, 176-190, 192-195, 197-199, 201, 207, 211, 216, 217, 220-222, 236, 240, 241, 249, 251, 257, 258, 260, 262-264, 343 SPME (Solid Phase Microextraction), 25, 119, 139, 140, 157, 161, 185, 198-212, 216-222, 235-238, 241, 242, 251-253, 255, 258, 261, 264, 363 SRM (Selected Reaction Monitoring), 259, 288, 289, 387
475
List of Abbreviations SSZ (Sum Of Squared Z-Score), 439, 460-463, 467, 468 STMR (Supervised Trials Medium Residue), 50, 59-61 SVE (Solvent Vapor Exit), 242, 250, 251 SWE (Subcritical Water Extraction), 139
TOF (Time Of Flight), 242, 248, 293, 294, 296-298, 313, 322, 328, 331, 334, 370, 371, 374, 381-385, 396, 399, 415, 417, 418, 421, 422, 425, 433 TSP (Thermospray), 370, 396, 410, 412
T TEOS (Tetraethoxysilan), 174 TIC (Total Ion Current), 298, 301-306, 314-317, 321, 324-332, 358, 359 TMDI (Theoretical Maximum Daily Intake), 51, 58-61
U USE (Ultrasonic Extraction),
476
160
W WTO (World Trade Organisation), 47, 73, 75
Subject Index a-cleavage 275 absorption 198-9 accelerated solvent extraction see pressurized liquid extraction acceptable daily intake (ADI) 50-1, 66 acetamiprid 426 acetone 83-4, 95-105 acetonitrile (MeCN) 82-3, 86, 394 active substances, EU definition 41-2 acute reference dose (ARfD) 50, 72 ADI see acceptable daily intake ADS see alkyl diol silicas adsorbent types 161-2 adsorptive extraction techniques 161-99 AED see atomic emission detector Alert communication example 67-70 alkyl diol silicas (ADS) 178 alpha-cleavage, mass spectrum 275 AMDIS see automated mass spectral deconvolution and identification system analysis of variance (ANOVA) 439, 442-4, 451 Analytical Methods Committee (AMC) 439, 452-3 analytical protocols 97, 446-7 analytical steps 216, 219 ANOVA see analysis of variance APC-EC Partnership Agreement 73-4 APCI see atmospheric pressure chemical ionization aperture plugging 410-11 API (atmospheric pressure ionization) see liquid chromatography/ atmospheric pressure ionization/ mass spectrometry
APPI see atmospheric photoionization interface applications liquid chromatography/mass spectrometry 403-34 microwave-assisted extraction 158-9 molecular-imprinted polymers 196-7 pressurized liquid extraction 135-7 solid-phase extraction 182, 185, 192-3 solid-phase micro-extraction 204-7 split/splitless injection 240 supercritical fluid extraction 148-54 ARfD see acute reference dose assigned value of concentration 448 atmospheric photoionization interface (APPI) sources 393, 409 atmospheric pressure chemical ionization (APCI) HPLC-APCI-MS 122 LC-APCI-MS 258 LC-API-MS 392-4, 409-11, 413 LC-MS 370-2, 376, 378-9, 398 LC-MS-MS 385 loop injection 256 atmospheric pressure ionization (API) see liquid chromatography/ atmospheric pressure ionization/ mass spectrometry atomic emission detector (AED) 188, 209, 291, 319-20, 348 AuPest data evaluation 314-19 automated mass spectral deconvolution and identification system (AMDIS) 297, 306-8, 328, 333-4
477
Subject Index automation background subtraction 300-1 GC-MS screening with full scan acquisition 313-20 sample handling 114 search 324, 327 simultaneous identification 361-2 solid-phase extraction 181-2 trend towards 220-2 azoxystrobin 405-6 background ions 300 background subtraction 300-4 batch mode 166-7 baths 160 Bio Beads SX-3 92-4 bound residues 106 bromophos 285 buffers 395 buprofezin 408 calibration 299, 358, 360, 424-5 California 404 capillary electrophoresis (CE) 259-60 carbendazim 419 Carbofrit 356-7 carbon-based adsorbents 176-7 carbon dioxide 141, 143-5, 148, 153-5 cartridge format 179 cavitation 160 chemical ionization (CI) 279-82, 322-8, 349-52 children 42-3, 52-6, 58-61 chlorpyrifos-methyl 310-11 chlorpyrifos 346-7, 353-4 cholinesterase-inhibiting pesticides 405 CI see chemical ionization CID see collision-induced dissociation clean-up procedures 90-3 see also extraction introduction 344
478
liquid-liquid partitioning 89-90, 117-18, 198 membrane-assisted micro-extractions 212-14 new developments 113-222 cleavage 275 CNL see constant neutral loss Codex Alimentarius Commission 40, 73, 76 co-eluting peaks identification 354-5 collision-induced dissociation (CID) 287-8, 290 LC-API-MS 413 LC-MS mass spectrum 374-5 LC-MS target screening 396-7 column mode for SPE 166-7 commercial SPME fibres 202 comminution see homogenization compensation for matrix effects 431-3 complementary information 282-6, 379 compound identification European Union points system 421-3 GC-MS 299-312, 342-4 LC-API-MS 412 LC-MS 396-9 simultaneous quantification 361-2 comprehensive two-dimensional gas chromatography with TOF-MS 334 cone voltage fragmentation (CVF) 413-14 confidence levels 339-40 constant neutral loss (CNL) 416 cryogenic processing 116-17 CVF see cone voltage fragmentation cycles of pressurized liquid extraction 134 data processing 298-9, 313-20 see also statistical evaluation daughter ions 288-90, 386-7 DDT 43, 46
Subject Index deconvolution of mass spectra 328, 330, 332 deprotonated molecules 374-8 desorption 192, 252 deterministic approach 50-1 DG SANCO see Health and Consumer Protection General Direction diagnostic ions 345-8 diazinon 376-8 diazoxon 376-8, 391 dichlorprop methyl ester 278 dichlorvos 355 dicrotophos 283-4 difficult matrix introduction (DMI) 292 difluorobenzamide 413 direct immersion solid-phase microextraction (DI-SPME) 201, 205-7, 217-18, 251-2 Directives 40-2, 406-7 dirty sample introduction 322 disk format 179-80 dispersion of samples 124-6, 128 DI-SPME see direct immersion solid-phase micro-extraction disposable pipette tips 180-1 2,4-D mass spectra 276-7 DMI see difficult matrix introduction doublet method 382-3 drying solvent extracts 89 ECNCI see electron capture negative chemical ionization efficiency 95-105, 219-22 electron capture negative chemical ionization (ECNCI) 283-6, 323-4 electrospray ionization (ESI) 371-3, 392-4, 409-10 electrostatic interactions 195 eluent composition 394-5 EMDI see estimated maximum daily intake endosulfan 408
enhanced solvent extraction see pressurized liquid extraction environmental analysis 139-40 equilibration 202-3 error propagation 464-5 ESI see electrospray ionization estimated maximum daily intake (EMDI) 51, 59-61 ethyl acetate (EtAc) 85-6, 95-105 European Union Directives, 'reduced risk' pesticides 40-2, 406-7 European Commission, proficiency tests 439-68 identification points system 421-3 legislation 39-74 monitoring programs 64-5 extraction acetone/ethyl acetate comparison 95-105 adsorptive techniques 161-98 hot water technique 138-40 liquid-liquid partitioning 89-90, 117-18, 198 membrane-assisted micro-extraction 212-14 microwave-assisted 155-9, 217-18 organic solvents 79-82 pH effects 86-8, 394 pressurized liquid 131-8, 217-18 solid-phase micro-extraction/gas chromatography 252-3, 255 solid support materials 124-31 sonication-assisted 160-1, 217-18 supercritical fluid 141-55, 217-18 time 147, 158 extreme values 450-7 EZ Flash assembly 333 factor concept approach 463-7 fast supersonic GC-MS 334-5 FFP see fitness-for-purpose fibre coatings 202, 252
479
Subject Index fibre rods 199-208 fitness-for-purpose (FFP) 449-50, 466 flusilazole 332 focused microwave-assisted extraction systems (FMAE) 157 folpet 278-9 food consumption 52-61 food processing 76-7 Food Quality Protection Act (FQPA) 406-7 formats, SPE 178-81 Fourier-transform ion cyclotron resonance (FT-ICR) instruments 297 FQPA see Food Quality Protection Act fragmentation see also collision-induced dissociation LC-API-MS 412-18 LC-MS 376 mass spectrum 273-6 pathway 270 patterns 277-9 FT-ICR see Fourier-transform ion cyclotron resonance full peak width at one half maximum (FWHM) 381-2 full-scan acquisition 313-20 full-scan GC-MS screening 304-6 functionalized polymeric resins 175 fungicides 87 fused-silica fibre rods 199-208 FWHM see full peak width at one half maximum GAP see Good Agricultural Practice gas chromatography (GC) 237-55 gas chromatography/mass spectrometry (GC-MS) 269-335 applications 339-64 automated screening with full scan acquisition 313-20 chemical ionization 279-82, 322-8 compound identification 299-312 fast supersonic 334-5
480
high resolution MS 286-7 multi-residue screening 291-9 resistively heated 333-4 sample injection 248 gas chromatography/tandem mass spectrometry (GC-MS-MS) 287-91, 320-2, 352-64 gas chromatography/time-of-flight mass spectrometry (GC-TOF-MS) 328-34 GC-MS see gas chromatography-mass spectrometry GC-MS-MS see gas chromatography/ tandem mass spectrometry GC-TOF-MS see gas chromatography/ time-of-flight mass spectrometry gel-permeation chromatography (GPC) 90-3, 119, 249-51 German consumption data 52-6, 58-61 Good Agricultural Practice (GAP) 39, 44-5, 50 GPC see gel-permeation chromatography gum-phase extraction (GPE) 211 haptens 191 headspace solid-phase micro-extraction/ gas chromatography/mass spectrometry (HS-SPME-GC-MS) 253 headspace solid-phase micro-extraction (HS-SPME) 200, 251-3 Health and Consumer Protection General Direction (DG SANCO) 40 heat-assisted extraction methods 155-9 high-performance gel-permeation chromatography (HPGPC) 93 high-performance liquid chromatography (HPLC) 255-63 high-performance liquid chromatography/mass spectrometry (HPLC-MS) 120 high-resolution mass spectrometry (HRMS) 286-7, 380-4
Subject Index homogeneity 77-8, 96-7, 115-17 Horwitz equation 448-9 hot splitless injection 240 hot water extraction 138-40 HPGPC see high-performance gel permeation chromatography HPLC-MS see high-performance liquid chromatography/mass spectrometry HRMS see high-resolution mass spectrometry HS-SPME see headspace SPME HS-SPME-GC-MS see headspace solidphase micro-extraction/gas chromatography/mass spectrometry Hydromatrix 127-8 hydrophobic interactions 165 hyphenation 181-2, 220-2 see also individual techniques identification see compound identification imazalil 463-4 imidacloprid 419-22 immunosorbents (ISs) 190-3 import tolerances 62-3 imprinted polymers 194-5 inductive cleavage 275 injection techniques 235-64 injector volume 237 inorganic normal-phase adsorbents 177-8 instrumentation APCI source 371 GC-MS advances 363-4 GC-MS multi-residue screening 292-3 ion-trap detectors 295-6 LC-MS 391-2 magnetic sector instruments 294 microwave-assisted extraction 156-7 pressurized liquid extraction 133 quadrupole instruments 294-5
quadrupole ion-trap MS-MS 390 solid-phase micro-extraction 200 stir bar sorptive extraction 208-9 supercritical fluid extraction 141-3 time-of-flight instruments 296-7 triple quadrupole LC-MS-MS 387 interaction mechanisms 165-6 inter-comparison studies 95-105 interdependence of analytical steps 216, 219 interfaces 409-11 interlaboratory trials see proficiency tests international fora activities 73-4 international harmonization 47, 441 International Harmonized protocol for Proficiency Testing of Chemical Laboratories 441 in-tube solid-phase micro-extraction (ITSPME) 210-11 ion detection 297 ion formation 270-1, 371-2 ion-trap detectors (ITDs) 295-6, 340-2 see also quadrupole ion-trap MS-MS IS see immunosorbents ISO 17025 439 ISO Guide 43-1, 441-2 ISO Standard 5725 442 isotopic peaks 272-4, 375-6 ITDs see ion trap detectors ITSPME see in-tube solid-phase micro-extraction laboratory proficiency tests 439-68 large volume injection/liquid chromatography/tandem mass spectrometry (LVI-LC-MS-MS) 256, 259 LC-MS see liquid chromatography/mass spectrometry LC-TOF-MS see liquid chromatography/ mass spectrometry/time-of-flight legislation, EU 39-74
481
Subject Index level of determination (LOD) 62-3 level of quantification (LOQ) 43, 46 libraries 299, 301-2, 398-9 linear calibration curves 424 lipid content 147 liquid chromatography/atmospheric pressure ionization/mass spectrometry (LC-API-MS) 392-5, 409-18 liquid chromatography (LC) coupled capillary electrophoresis 259-60 HPLC-SPME 261 injection techniques 257-8 on-line SPME-coupled 261-3 liquid chromatography/mass spectrometry (LC-MS) 369-99 applications 403-34 compound identification 396-9 eluent composition 394-5 future trends 433-4 high resolution 380-4 instruments comparison 391-2 ionization 371-2 isotopic peaks 375-6 mass spectrum 372-4 matrix interference 418-23, 425-33 operational factors 392-5 positive/negative ion modes 378-9 quadrupole instruments 380 quadrupole ion-trap MS-MS 389-91 quantitation 423-33 structural information 374-8 liquid chromatography/mass spectrometry/time-of-flight (LC-TOF-MS) 380-3 liquid chromatography/tandem mass spectrometry (LC-MS-MS) 370-1, 384-9 liquid-liquid matrix-extracted samples 429-30 liquid-liquid partitioning (LLP) 89-90, 117-18, 198
482
liquid phase micro-extraction (LPME) 213-14 liquid-solid-extraction see solid-phase extraction LLP see liquid-liquid partitioning LOD see level of determination long-term stability 247 loop injection 255-6, 259 LOQ see level of quantification low-pressure GC-MS 248 LPME see liquid phase micro-extraction LVI-LC-MS-MS see large volume injection/liquid chromatography/ tandem mass spectrometry macroporous normal-phase adsorbents 124-6, 128 MAE see microwave-assisted extraction magnetic sector instruments 294 manual verification 302-4 mass accuracy 433 mass spectra 269-71 2,4-D 276-7 complementary information 282-6 dichloprop methyl ester 278 fragmentation reactions 273-6 interpretation 276-9 isotopic peak 272-3 LC-MS 372-4 molecular ion peak 272 negative ions 281-2 positive ions 280-1 structural information 271-9 mass spectral libraries 299, 301-2 mass spectrometers 293-9 matrix characteristics 158 matrix-induced enhancement effect 358, 360 matrix interference compensation 431-3 GC injection technique 237, 240, 243 GC-MS 342, 344 LC-MS 418-23, 425-33
Subject Index programmable temperature vaporizers 246 quantification of analytes 358-60 reduction 428-31 matrix solid-phase dispersion (MSPD) 129-31, 217-18 maximum residue limits/levels (MRLs) buprofezin 408 endosulfan 408 meaning 43-6 overview 42 review 407 sample introduction techniques 235-6 setting 42, 47-63 systems 40 MeCN see acetonitrile membrane-assisted micro-extractions 212-14 membranes see disk format methamidophos diagnostic ions 346-7 dichlorvos GC-MS-MS co-elution 355 structure 405-6 supercritical fluid extraction 148-50, 152 methanol 394 methiocarb 405-6 microporous membrane liquid-liquid extraction (MMLLE) 212 microwave-assisted extraction (MAE) 155-9, 217-18 miniaturization 120, 180, 220 MIPs see molecular imprinted polymers MISPE see molecular-imprinted polymer solid-phase extraction mixed-mode polymeric sorbents 175-6 MMLLE see microporous membrane liquid-liquid extraction modifiers 144-5 molecular imprinted polymers (MIPs) 193-8 molecular imprinted polymer solid-phase extraction (MISPE) 193-8
molecular ions 272, 374-8 monitoring programs 63-5 MRM see multiple reaction monitoring MRMs see multi-residue methods MS-MS see tandem mass spectrometry MSPD see matrix solid-phase dispersion multiple reaction monitoring (MRM) 387-8 multi-residue methods (MRMs) extraction efficiency comparison 93-105 GC-MS screening 291-9 historic development of methods 82-6 new developments 117-24 solid-phase extraction cleanup 188-9 solvent extraction 81-2 stir bar sorptive extraction comparison 210 traditional method limitations 118 myclobutanil 332 National Estimated maximum Daily Intake (NEDI) 51, 56 National Institute of Standards (NIST) 306-8 negative chemical ionization (NCI) 323-8, 349-52 negative ion mode 378-9 negative ions 281-2 neutral loss mode 387-8 new analytical approaches 214-22 noise analysis 307 non-covalent imprinting 194-5 non-polar substances 141, 144-5, 148, 153-5 non-target analysis 398-9, 412, 415-18 normal-phase (NP) adsorbents 177-8 OECD see Organization for Overseas Economic Development off-line hyphenation 221 omethoate 148-50, 152 on-column injection 244-5
483
Subject Index on-line activities hyphenation 220-2 LC-MS coupling 370 'on-line' principle 120-1 solid-phase extraction 261-3 gel-permeation chromatography 249-51 OP see organophosphate open tubular trapping (OTT) 210-11 optimization 252 organic solvents 79-82 Organization for Overseas Cooperation and Development (OECD) 73-4 organophosphate (OP) pesticides 283 OSP-2 device 249 OTT see open tubular trapping outlier laboratories detection 450-7 parent ions 288-90 partitioning 89-90, 117-18, 124-31, 161-98 PBM see probability based matching PCDDs see polychlorinated dioxins PCI see positive chemical ionization PDMS see polydimethylsiloxanes pesticide use 403-6 pH 86-8, 394 pi-pi interactions 165 plant protection products (PPPs) 40-1, 43 PLE see pressurized liquid extraction polar pesticides 209 polychlorinated dioxins (PCDDs) 286-7 polyclonal antibodies 191-2 polydimethylsiloxanes (PDMS) 161 polymeric sorbents 174-6 polystyrene divinyl benzene (PS-DVB) 174-5 positive chemical ionization (PCI) 349-52 positive ion mode 378 positive ions 280-1 PPPs see plant protection products
484
precursor-ions 354 precursor-ion scan mode 387, 389 preparation of laboratory samples 75-7 pressure 134, 140, 145-6 pressurized liquid extraction (PLE) 131-8, 217-18 probability based matching (PBM) 301 probe units 160 processing see food processing; sample processing product-ions see daughter ions proficiency tests (PTs) 439-68 error propagation 464-5 factor concept approach 463-7 guidelines 441-2 organizational rules 440-1 protocol 446-7 PT 3 results 457-9 Qn/median method 455-6 SSZ values 460-3 statistical evaluation 443-4, 450-7 summed squared scores 459-63 z-score 442, 447-50, 459-63, 466 programmable split-splitless (PSS) 245-6 programmable temperature vaporizers (PTVs) 245-8 Prospekt device 249 proteomics 370-1 protocols 97, 446-7 protonated molecules 374-8 PS-DVB see polystyrene divinyl benzene PSS see programmable split-splitless PTs see proficiency tests PTVs see programmable temperature vaporizers purification see clean-up procedures; extraction Qn/median method 455-6 Q-score 442 Q-TOF-MS see quadrupole/time-offlight/mass spectrometry
Subject Index quadrupole GC-MS instruments 294-5 quadrupole ion-trap MS-MS 389-91 quadrupole LC-MS instruments 380 quadrupole mass spectrometer (QMS) 341-2 quadrupole/time-of-flight/mass spectrometry (Q-TOF-MS) 383-4 quality standards 306-8, 439, 441-2 quantification ions 361 quantitation 204, 308-9, 358-63, 423-33 QuEChERS-method 122-3, 217-18 RAMs see restricted access materials rapid alert system in food and feed (RASFF) 65-72 rearrangement reactions 275-7 'reduced risk' groups of pesticides 404-7 regulatory measures 63-5, 403-9 relative standard deviation (RSD) 457 reliability of results 339-40 reporting programs 63-5 reproducibility 423-4 resistively heated GC-MS 333-4 restricted access materials (RAMs) 178 retention time locking (RTL) 319-20, 348 reversed-phase (RP) materials 132, 163, 168, 174, 183-4, 186-7 reverse phase liquid chromatography (RPLC) 250 Review Programme in Europe 407 RIC chromatograms 302-6, 325 robustness of systems 410-11, 424-5 robust statistics 452-6 RP see reversed-phase materials RPLC see reverse phase liquid chromatography RSD see relative standard deviation RTL see retention time locking
safety, solvents 81 salting out 144 salts 89-90 sample composition 134-5, 203-4 sample handling 75-106 automation 114 extraction/clean-up terminology 114-15 homogenization 115 hot water extraction 138-40 HPLC/MS 120 matrix solid-phase dispersion 129-31 microwave-assisted extraction 155-9 new developments 113-222 on-line principle 120-1 organic solvent extraction 79-82 QuEChERS-method 122-3 solid support materials extraction methods 124-31 sample interferences 240, 243 sample introduction 235-64, 322 sample preparation 142-3 sample processing homogeneity 77-8, 96-7, 115-17 pesticide loss 78-9 supercritical fluid extraction 142 sample size 130 SBSE see stir bar sorptive extraction
SC-CO2 see supercritical carbon dioxide Schweizerische Lebensmittelbuchkommission (SLMB) algorithm 452-5 SDE see single-drop extraction selected ion monitoring (SIM) 287-8, 308-12, 344-9 selected reaction monitoring (SRM) 387 semivolatile compounds 238 sensitivity 243-4, 423-4 Sephadex LH-20 92 SFE see supercritical fluid extraction signal suppression 427-8 SIM see selected ion monitoring
485
Subject Index simplification trends 219-20 simultaneous identification/ quantification 361-2 single-drop extraction (SDE) 212 single-reaction monitoring (SRM) 288-90 size exclusion chromatography see gel permeation chromatography skimmers 410-11 SLMB see Schweizerische Lebensmittelbuch-kommission SLME see supported liquid membrane extractions SMB see supersonic molecular beam solid-phase dynamic extraction (SPDE) 211 solid-phase extraction (SPE) 162-90 automation/hyphenation 181-2 formats 178-81 immunosorbents 190-3 interaction mechanisms 165-6 method development 119, 164 molecular-imprinted polymers 193-8 procedure 167-8 sorbents 168-78 solid-phase micro-extraction/gas chromatography/mass spectrometry (SPME-GC-MS) 253, 255 solid-phase micro-extraction (SPME) 199-208 gas chromatography 251-5 GC-MS coupled 363 HPLC coupled 261 in-tube SPME 210-11 SPME-GC-ECD 254 wire-in-tube SPME 211 solid-phase trapping systems 142 solid support materials 124-31 solvent-free extraction 199-208 solvents 79-82, 133, 157-8, 198 sonication 160-1, 217-18 sorbent-extraction see solid-phase extraction
486
sorbents 168-78, 262 see also immunosorbents source selection, LC-MS 392-4 SPDE see solid-phase dynamic extraction SPE see solid-phase extraction spectra identification 398-9 spiking solutions 96-7, 99-103 spinosad 405-6 split/splitless injection 237-44, 357 SPME see solid-phase micro-extraction SRM see single-reaction monitoring SSZ see sum of squared z-score stability 78-9, 247 standard deviation 443-4, 450-7 see also z-score standards see Directives; ISO; maximum residue limits/levels; quality standards statistical evaluation 443-4, 450-7 stir bar sorptive extraction (SBSE)
208-10, 217-18, 363 strategy development 214-22 structural information 271-9, 374-8 subcritical water extraction see hot water extraction sum of squared z-score (SSZ) 459-63, 467-8 supercritical carbon dioxide (SC-CO 2) 141, 144-5, 148, 153-5 supercritical fluid extraction (SFE)
141-55, 217-18 supersonic GC-MS 363 supersonic molecular beam (SMB) 363 supported liquid membrane extractions (SLME) 213 support materials 125-8 tandem mass spectrometry (MS-MS)
287-91, 352-8, 384-9 target compound analysis 309-12,
396-7, 412-15 target standard deviation 448-50
Subject Index 2,3,7,8-TCDD see 2,3,7,8-tetrachlorodibenzo-p-dioxin tea 323 temperature cryogenic processing 116-17 microwave-assisted extraction 158 pressurized liquid extraction 133-4 solid-phase micro-extraction 203 supercritical fluid extraction 145-6 temperature vaporizers 245-8 TEMPUS instrument 331 terminology 114-15, 139 test materials 442-5 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD) 286-7 theoretical maximum daily intake (TMDI) 51, 58-61 thermal desorption 199-200 thiacloprid 418 TIC chromatograms 301-6, 324-7, 329, 331 time see extraction time time-of-flight (TOF) 334, 380-3, 417 see also individual techniques TMDI see theoretical maximum daily intake
TOF see time-of-flight traditional approaches 118 trapping 146-7 see also ion-trap detectors; open tubular trapping; quadrupole ion-trap MS-MS; solid phase trapping systems triflumizole 426 triple quadrupole LC-MS-MS 385-9 ultrasonic extraction see sonicationassisted extraction uses see applications vacuum maintenance 370 variability factors 71-2 voltage selection 413-15 water control 144 water samples 244 wire-in-tube solid-phase microextraction 211 World Health Organization (WHO) 57-8 World Trade Organization (WTO) 73 z-score 442, 447-50, 459-63, 466
487
