Colour Measurement: Principles, Advances and Industrial Applications

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Colour measurement

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The Textile Institute and Woodhead Publishing The Textile Institute is a unique organisation in textiles, clothing and footwear. Incorporated in England by a Royal Charter granted in 1925, the Institute has individual and corporate members in over 90 countries. The aim of the Institute is to facilitate learning, recognise achievement, reward excellence and disseminate information within the global textiles, clothing and footwear industries. Historically, The Textile Institute has published books of interest to its members and the textile industry. To maintain this policy, the Institute has entered into partnership with Woodhead Publishing Limited to ensure that Institute members and the textile industry continue to have access to high calibre titles on textile science and technology. Most Woodhead titles on textiles are now published in collaboration with The Textile Institute. Through this arrangement, the Institute provides an Editorial Board which advises Woodhead on appropriate titles for future publication and suggests possible editors and authors for these books. Each book published under this arrangement carries the Institute’s logo. Woodhead books published in collaboration with The Textile Institute are offered to Textile Institute members at a substantial discount. These books, together with those published by The Textile Institute that are still in print, are offered on the Woodhead website at: www.woodheadpublishing.com. Textile Institute books still in print are also available directly from the Institute’s website at: www.textileinstitutebooks.com A list of Woodhead books on textile science and technology, most of which have been published in collaboration with The Textile Institute, can be found on pages xv–xxi.

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Woodhead Publishing Series in Textiles: Number 103

Colour measurement Principles, advances and industrial applications Edited by M. L. Gulrajani

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Published by Woodhead Publishing Limited in association with The Textile Institute Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington Cambridge CB21 6AH, UK www.woodheadpublishing.com Woodhead Publishing, 525 South 4th Street #241, Philadelphia, PA 19147, USA Woodhead Publishing India Private Limited, G-2, Vardaan House, 7/28 Ansari Road, Daryaganj, New Delhi-110002, India www.woodheadpublishingindia.com First published 2010, Woodhead Publishing Limited © Woodhead Publishing Limited, 2010 The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publisher cannot assume responsibility for the validity of all materials. Neither the authors nor the publisher, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing Limited. The consent of Woodhead Publishing Limited does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing Limited for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library.

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Contents

Contributor contact details Woodhead Publishing Series in Textiles

xi xv

Part I Theories, principles and methods of measuring colour

1

1

3

Colour vision: theories and principles V. V. PÉREZ, D. DE FEZ SAIZ and F. MARTINEZ VERDÚ, University of Alicante, Spain

1.1 1.2 1.3 1.4 1.5 1.6 2

Introduction Human colour vision Chromatic perception Defective colour vision Colour constancy Bibliography

3 6 10 12 15 17

Scales for communicating colours

19

A. K. ROY CHOUDHURY, Government College of Engineering and Textile Technology, India

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10

Introduction Systematic arrangements of colours Colour order systems Various colour order systems Comparison and interrelation of various systems Accuracy of colour order systems Computer-based systems Universal colour language (UCL) Future trends References

19 22 23 31 51 54 54 61 63 65

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Contents

Expressing colours numerically

70

V. C. GUPTE, Advanced Graphic Systems, India

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13

Introduction Colour specifications The Commission Internationale de l’Eclairage (CIE) system The CIE standard light sources/illuminants The CIE Standard Observer and unreal primaries Computation of tristimulus values Reflectance measurement Chromaticity coordinates and chromaticity diagram Usefulness of the CIE XYZ system Limitations of the CIE system Transformation and improvement of the CIE system Future trends References

70 70 72 72 74 77 79 80 81 82 82 86 86

4

Visual and instrumental evaluation of whiteness and yellowness

88

R. HIRSCHLER, SENAI/CETIQT Colour Institute, Brazil

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4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8

Introduction: whiteness and yellowness Visual assessment of whiteness Measuring techniques and instruments Indices for whiteness and yellowness Applications in industry, cosmetics and dentistry Future trends Sources of further information and advice References

88 90 95 100 111 115 117 119

5

Use of artificial neural networks (ANNs) in colour measurement

125

M. SENTHILKUMAR, PSG College of Technology, India

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10

Introduction Artificial neural networks (ANNs): basic principles Architecture of an artificial neural network Learning process Feed-forward neural network Training of an artificial neural network using back propagation algorithm Application of artificial neural networks to colour measurement Recipe prediction Evaluation of the ANN method Case studies

© Woodhead Publishing Limited, 2010

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Contents

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5.11 5.12 5.13

Future trends Sources of further information and advice References

141 144 144

6

Camera-based colour measurement

147

F. MARTÍNEZ-VERDÚ, E. CHORRO and E. PERALES, University of Alicante, Spain, M. VILASECA and J. PUJOL, Technical University of Catalonia, Spain

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7

Introduction Principles of camera-based colour measurement Procedures of camera-based colour measurement Strengths and weaknesses Case studies Future trends Conclusions Sources of further information and advice References

147 149 151 154 158 162 163 163 163

Colour shade sorting

167

M. L. GULRAJANI, India Institute of Technology, India

7.1 7.2 7.3 7.4 7.5 7.6 7.7

Introduction (555) Fixed-grid shade sorting system Clemson Colour Clustering K-means clustering Modified CCC shade sorting method Shade sequencing and clustering References

167 168 174 178 180 180 182

8

Determining uncertainty and improving the accuracy of color measurement

184

J. A. LADSON, Color Science Consultancy, USA

8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11

Introduction to determining uncertainty Uncertainty Definitions Tables of results Conclusions: determining uncertainty Improving accuracy: the absolute correction of instrumentally generated spectrometer values Introduction to improving accuracy Experimental modeling Applications Conclusions: improving accuracy References

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Contents

Colour measurement and fastness assessment

196

M. BIDE, Department of Textiles, Fashion Merchandising and Design, University of Rhode Island, USA

9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9

Introduction: colour and colourfastness The use and usefulness of colourfastness testing Colourfastness test method development Colourfastness test standard setting organizations Standard colourfastness test format Testing for colourfastness: specific tests Colourfastness testing: assessment of results (colour measurement) Conclusions References

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Part II Colour measurement and its applications

219

10

221

Colour measurement methods for textiles N. S. GANGAKHEDKAR, Compute Spectra Color Pvt. Ltd., India

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10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10 10.11 10.12 10.13 10.14

Introduction Colour as numbers Colour specification Metamerism Reasons why colours do not match Visual versus numerical pass/fail Colour measurement techniques for textiles On-line colour measurement Colour of dry and wet fabrics Inspection of colour of finished fabrics: a case study Future trends Conclusions Sources of further information and advice References

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11

Grading of cotton by color measurement

253

B. XU, The University of Texas, USA

11.1 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9

History of cotton color grading USDA cotton color grades HVI colorimeter Factors affecting cotton color grade Color measurement using color image analysis Using neural networks Using fuzzy logic Conclusions References

© Woodhead Publishing Limited, 2010

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Contents

12

Colour measurement of paint films and coatings

ix

279

N. S. GANGAKHEDKAR, Compute Spectra Color Pvt. Ltd., India

12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 12.11 12.12 12.13 12.14

Introduction Quality control of paints Sample preparation for colour measurement Pigment quality control Problems in match prediction: paint applications Computer colour matching for paints Colour control system Measuring colour properties of wet paints Instant colour matching at the paint shop Colour matching of automotive paints Future trends Conclusions Sources of further information and advice References

279 280 291 292 295 295 297 299 300 307 309 309 310 310

13

Colour measurement of food: principles and practice

312

D. B. MACDOUGALL, Formerly of the University of Reading, UK

13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 13.10 13.11 13.12

Introduction Colour vision: trichromatic detection The influence of ambient light and food structure Appearance Absorption and scatter Colour description: the CIE system Colour description: uniform colour space Instrumentation Food colour appearance measurement in practice Illuminant spectra and uniform colour Conclusions and future trends References

312 313 316 317 318 319 320 325 327 336 337 339

14

Colorimetric evaluation of tooth colour

343

A. JOINER, Unilever Oral Care, UK

14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8 14.9 14.10

Introduction The human dentition and its environment Optical properties of teeth The colour of teeth Factors that impact tooth colour and its perception Tooth whiteness Measurement of tooth colour Measurement of extrinsic stain Methods to improve tooth colour Future trends

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14.11 Sources of further information and advice 14.12 References

362 363

15

371

Hair color measurement D. J. TOBIN, University of Bradford, UK

15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9 15.10

Introduction Background Natural hair color Gray hair and age Effect of environment Artificial hair coloring shades Color measurement methods and instruments Future trends Sources of further information and advice References

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Index

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Contributor contact details

(* = main contact)

Chapter 1

Chapter 3

Valentín Viqueira Pérez,* Dolores de Fez Saiz and Dr Francisco Martínez-Verdú Department of Optics, Pharmacology and Anatomy Faculty of Sciences University of Alicante Alicante Spain

V. C. Gupte Advanced Graphic Systems 601/602, Trade World B Wing, Kamla City Senapati Bapat Marg, Lower Parel Mumbai-400013 India

E-mail: [email protected]

Chapter 4

Chapter 2 Professor (Dr) A. K. Roy Choudhury Govt College of Engineering and Textile Technology Serampore-712201 Hooghly (W.B.) India

E-mail: [email protected]

Dr Robert Hirschler SENAI/CETIQT Colour Institute Rua Dr. Manuel Cotrim 195 Rio de Janeiro Brazil, 20961-040 E-mail: [email protected]

E-mail: [email protected]

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Contributor contact details

Chapter 5

Chapter 8

M. Senthilkumar Department of Textile Technology PSG College of Technology Peelamedu Coimbatore-641004 Tamil Nadu, India

Jack A. Ladson Color Science Consultancy 1000 Plowshare Road Yardley, PA 19067 USA E-mail: [email protected]

E-mail: [email protected]

Chapter 9 Chapter 6 Francisco Martínez-Verdú*, Elisabet Chorro, Esther Perales Department of Optics, Pharmacology and Anatomy University of Alicante Carretera de San Vicente del Raspeig s/n, 03690 – Alicante (Spain) E-mail: [email protected]; elisabet.chorro@ ua.es; [email protected]

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Meritxell Vilaseca, Jaume Pujol Center for Sensors, Instruments and Systems Development (CD6) Technical University of Catalonia Rambla de Sant Nebridi 10, 08222 – Terrassa (Spain) E-mail: [email protected]; pujol@ oo.upc.edu

Dr Martin Bide Department of Textiles, Fashion Merchandising and Design University of Rhode Island RI 02881 USA E-mail: [email protected]

Chapter 10 Dr N. S. Gangakhedkar Compute Spectra Color Pvt. Ltd., India 306, So Lucky Corner Chakala, Andheri East Mumbai-400 099 India E-mail: narendra.gangakhedkar@ gmail.com

Chapter 11

Dr M. L. Gulrajani Department of Textile Technology India Institute of Technology New Delhi-110016 India

Dr B. Xu, Professor The University of Texas School of Human Ecology University of Texas at Austin Gearing Hall 225 Austin, TX 78712 USA

E-mail: [email protected]

E-mail: [email protected]

Chapter 7

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Contributor contact details

Chapter 12

Chapter 14

Dr N. S. Gangakhedkar Compute Spectra Color Pvt. Ltd., India 306, So Lucky Corner Chakala, Andheri East Mumbai-400 099 India

Dr Andrew Joiner Unilever Oral Care Quarry Road East, Bebington Wirral CH63 3JW UK

E-mail: narendra.gangakhedkar@ gmail.com

Chapter 15

Chapter 13 Dr D. B. MacDougall Formerly of the University of Reading Whiteknights PO Box 217 Reading RG6 6AH UK

xiii

E-mail: [email protected]

Dr D. J. Tobin Centre for Skin Sciences School of Life Sciences, University of Bradford Richmond Road, Bradford West Yorkshire BD7 1DP UK E-mail: [email protected]

E-mail: douglas.macdougall1@ btinternet.com

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Part I Theories, principles and methods of measuring colour

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Colour measurement

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© Woodhead Publishing Limited, 2010

1 Colour vision: theories and principles V. VIQUEIRA PÉREZ, D. DE FEZ SAIZ and F. MARTINEZ VERDÚ, University of Alicante, Spain

Abstract: When viewing any scene, the human visual system is able to extract information regarding light wavelength, which is why we see in colour. This chapter discusses the mechanisms of human colour vision. The chapter first reviews the anatomy and the physiology of the visual system, and then describes the generic ATD models of colour vision. From these models, the chapter discusses the topics of colour appearance, colour constancy, and defective colour vision. Key words: ATD models, colour appearance, defective colour vision, colour constancy, mechanisms of chromatic adaptation.

1.1

Introduction

1.1.1 Advantages of colour vision When viewing any scene, the human visual system is able to extract information regarding light wavelength, which is why we see in colour. But what advantages does this ability give to human vision? In evolutionary terms, the advantages for our ancestors are clear: seeing in colour makes it easier to detect food, such as the colour of fruit against the green of a leafy background, and the ability to detect animals hidden from view, be they predators or possible prey. Today, considering the fact that people with chromatic anomalies or deficiencies are able to lead a normal life, we would tend to think that colour vision is not a relevant factor. But consider the sheer amount of information that surrounds us, and how much of it is based on colour – is it not surprising to realise that most information is actually colour-coded? Traffic signs, advertising, graphic design, the internet. … Not only will people who have difficulties seeing in colour not be qualified for certain jobs, it could also mean that they do not properly recognise information surrounding them in their normal life.

1.1.2 Anatomy and physiology of the human visual system (rods; L, M and S cones LGN and cortical areas) The visual system is the part of the brain that makes us see. It does this by interpreting information from available light to build a representation of the outside world. The process begins when light passes through the eye’s transparent elements and hits the retina. All the elements involved before the retina form the ocular optic system (optically, the eye consists of a series of refractive surfaces 3 © Woodhead Publishing Limited, 2010

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Colour measurement

defined by transitions between air, fluid and solid tissues, the study of which is not the purpose of this book). The human eye is roughly spherical in shape (Fig. 1.1), and is made up of three distinct layers of tissue: sclerotic coat, choroid coat and retina. The sclerotic coat is the outer layer. It is white and extremely tough, except in the front where it forms the transparent cornea which contributes to the image-forming process by refracting light entering the eye. The surface of the cornea is kept moist and dustfree by the secretion from the tear glands. The intermediate layer is the choroid coat. This layer is deeply pigmented with melanin that reduces reflection of stray light within the eye. The choroid coat forms the iris, a diaphragm of variable size whose function is to adjust the size of the pupil to regulate the amount of light admitted into the eye. The pupil contraction is under the control of the autonomic nervous system: in dim light, the pupil opens wider letting more light into the eye; in bright light the pupil closes down. The retina is the inner layer of the eye. It contains the light receptors, the rods and cones. Inside the eye, the cavity between the lens and cornea – called the anterior chamber – is filled with a gel-like fluid called aqueous. The lens is located just behind the iris. The lens is a flexible unit that consists of layers of tissue enclosed in a tough capsule. It is suspended from the ciliary muscles by the zonule fibres. Behind the lens is the vitreous: a thick, transparent substance that fills the back of the eye. It is composed mainly of water and comprises about two-thirds of the eye’s volume, giving it form and shape. The next element is the retina, a neurosensory layer that initiates the neural processes of vision. An inverted image of the outside world is projected onto the retina, and the visual system then has the complex task of rebuilding a three-dimensional image

Sclera

Choroid Retina

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Cornea

Fovea Pupil

Lens Optic nerve

Iris Ciliary body 1.1 Human eye structure.

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Colour vision: theories and principles

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from this two-dimensional projection. The end result of this process is visual perception. The retina is in fact a prolongation of the brain. Anatomically, it is structured in ten layers for different types of neurones: photoreceptors (cones and rods), which are in fact modified neurones, and horizontal, bipolar, amacrine and ganglion cells. However, the photoreceptor’s mosaic is not evenly distributed along the retina. In physiological terms, it has a central zone and an outer area. The central zone includes the fovea, a small pit that ensures best visual acuity. The fovea is located in an area known as the macula lutea (Latin for yellow spot), which takes its name from the yellow pigment that covers it. The central retina includes another region, the optic disc, at the exit of the optic nerve, formed by the ganglion cell axons leaving the eyeball to form the optic nerve. This elongated pink disc is located in the nasal area and usually measures around 1.5 mm2. It has no photoreceptors, and as a result forms a blind spot in our vision, where we cannot see (since there are no cells to detect light, this part of the field of vision is not perceived). The rest, named the peripheral retina, has a low concentration of photoreceptors. The retina is inverted, in such a way that light passes through its entire structure before hitting the photosensitive layer. A photoreceptor is a specialist neurone capable of performing phototransduction, by which light is converted into electrical signals, which are then transmitted from neurone to neurone. Cones and rods are anatomically and physiologically different. Cones are much more abundant in the central retina, so the fovea contains only cones and, more importantly, the cones connect with the bipolar and ganglion cells at a proportion of 1:1:1, whereas in the peripheral retina area, several hundred or thousands of cones converge to a single bipolar cell. This explains why visual acuity is so much sharper in the fovea than at any other point on the retina. In terms of light sensitivity, rods can function in low light, whereas cones need much higher levels of light. So, rods are responsible for night vision, and cones are used for daytime vision, seeing colours and visual acuity. When light hits a photoreceptor, it sends a proportional synaptic response to a bipolar cell, which in turn sends the signal to a ganglion cell. Furthermore, there are two substrata at a synaptic level, with horizontal cells and amacrine cells (see Plate I in colour section between pages 42 and 43). These modify the initial signal to an extent that, after two or three synapses, the signal contains much more complex information than a simple point-to-point visual representation. From the ganglion cells, the signal reaches the appropriate left and right lateral geniculate nuclei (LGNs). The function of the LGNs is complex. We know that there is a three-system separation of fibres (PC cells, MC cells and KC cells), relating to Magno, Parvo and Konio systems. The LGNs have six layers, numbered from 1 (innermost layer) to 6 (outermost layer). Layers 1 and 2 contain the largest cell bodies and constitute the magnocellular system, whereas layers 3, 4, 5 and 6, which have smaller neurones, are part of the parvocellular system. Layers 2, 3

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and 5 receive fibres from the homolateral eye, and layers 1, 4 and 6 from the contralateral eye. Between each layer are the KC cells, which belong to the koniocellular system. Eighty per cent of the cells form part of the parvo system, ten per cent belong to the magno system, and ten per cent to the konio system. The LGN signals are sent to the primary visual cortex (V1), located at the back of the brain. The V1 fibres are then projected to area V2, and from V2 to V3, V4 and V5. Another important fact is that more than half of the LGN and V1 neurones process information from the fovea. A process therefore exists that gives priority to the part of the scene that is projected onto the fovea (the fixation point).

1.2

Human colour vision

1.2.1 Chromatic stimulus and perceived colour When we observe a scene, our visual system creates a perception of the outside world from the radiating energy that reaches our eyes from the objects within that scene. One of the elements of that perception is colour. But where is the colour? Is it external, or is it inside our visual system? The answer is clear: there are different chromatic stimuli in any scene that we view, and our visual system is able to capture that information, relating to the wavelength of light, which is how colour ‘emerges’. Colour, therefore, is something internal. Colour is perception. The chromatic stimulus is electromagnetic radiation from sources and objects that hits the optic system and triggers the visual process. Perceived colour, therefore, is a sensation produced by the chromatic stimulus that makes it possible to differentiate that stimulus from others with the same area, duration, shape and texture. As we shall see, chromatic stimuli have three variables, which are directly related to three perceptual variables.

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The trichromatic theory (Young–Helmholtz–Maxwell) In the early nineteenth century, Thomas Young (1801) suggested that the retina contained three types of nerve fibres that can be stimulated to a greater or lesser extent by the different wavelengths that correspond to red, green and violet colours. Several years later, James C. Maxwell would demonstrate that any colour in the spectrum could be matched with three monochromatic primary colours: red, green and blue. These two ideas were concurrent, and gave a clear, simple explanation of colour vision. Thus, a colour that stimulates the red and green particles in equal measure will be perceived as yellow; if it stimulates green and violet, we see blue. Chromatic vision is simply a matter of additive mixing. At around the same time, the German physicist Hermann V. Helmholtz also suggested that subjects with deficient colour vision (dichromats) had reduced

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Colour vision: theories and principles

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forms of normal vision, which lacked one of the three receptor types. This explains the fact that dichromats accept as equal the same kind of matchings as do subjects with normal vision. However, there were weak points in the theory in terms of colour appearance: if everything can be explained through mixes of colours, why are there no reddish greens or bluish yellow colours? Theory of chromatic opponency Ewald Hering (1872–4) was more concerned with the appearance of colours. In his reports to the Imperial Academy of Sciences in Vienna, Hering proposed the existence of three opponent processes generated at some point in the visual process: red-green, blue-yellow and black-white mechanisms. The idea of opponency is that the red-green categories (and, similarly, the blueyellow and light-shadow categories) are opposed and represent two extremes of variations in a continuum. More red necessarily implies less green. Despite having no experimental proof, Hering challenged both the established trichromatic theory and the prevailing physiological theory (Müller’s doctrine of specific nerve energies) head-on: ‘a nerve fibre is capable of conducting only one type of qualitative information’. A nerve fibre, therefore, could not transmit both red and green information, as they are qualitatively different. These two theories, which in principle seemed to take opposing views, are in fact compatible; the three retina cone types would in fact correspond to trichromacy, and the sensor responses by these cones would then be combined in the three red-green, blue-yellow and light-shadow mechanisms (although, as we shall see, dark-light is an additive rather than an opposing mechanism). It was not until 1957, a hundred years later, that D. Jameson and L. M. Hurvich would prove the existence of the opposing mechanisms. Paradoxically, Hering himself had suggested that these two theories did not have to be necessarily incompatible. Today we know that the parvo cells are responsible for the red-green mechanisms (L – M cells and M – L cells), and the konio cells are responsible for the blueyellow mechanisms S – (L + M) cells. Magno cells, on the other hand, are not opposing cells, but rather add the inputs from the L, M and S cells. Therefore it is an additive mechanism. ATD models (two-stage models) Each of these previous theories can explain a series of phenomena: the trichromatic theory reproduces many psychophysical experiments, but it does not predict appearance, which the colour opponency theory does. Combining the two theories leads to models that combine an initial trichromatic stage at the receptor level (first stage), with a stage of neural processing ruled by colour opponency mechanisms (second stage), comprising a non-opposing luminance

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mechanism (A) and the two opposing chromatic mechanisms: green-red (T) and blue-yellow (D). To a certain extent, the CIELAB values, which are very useful for industrial colorimetry, represent these concepts as Lightness (L) and can be linked to the achromatic channel (A); the chromatic coordinate a* to the greenness-redness channel (T) and the chromatic coordinate b* to the yellownessblueness channel (D). The model as a whole is summed up in an ATD matrix resulting from the combination of the L, M and S cone signals, as shown in equation 1.1. The three independent channels are listed as A (achromatic), T (tritan) and D (deutan): A T

=

D

a11

a12

a13

L

a21

a22

a23 *

M

a31

a32

a33

S

[1.1]

The Boynton model (1986) is a good representation of these two-stage models. It proposes three cone types, the spectral response curves of which are the Smith and Pokorny fundamentals. The coefficients for the resulting matrix are shown in equation 1.2, and the general functioning for this model is summarised in colour Plate II. The illustration also shows how the S cones contribute to the achromatic channel, though this value is very low and in the matrix is worth zero. A T D

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=

1

1

0

L

1

−2 0 *

M

1

1

−1

S

[1.2]

Why does the matrix have these coefficients? If we look at the single 575 nm yellow in the Smith and Pokorny fundamentals (Fig. 1.2), (L – M) would be a positive value, i.e. red would predominate over green, and we would see a reddish orange colour. However, the M cones’ response becomes balanced by applying a factor of two, so that in 575 nm they are cancelled out (T = 0) and a pure yellow colour is perceived. The red-green channel gives no signal (T = 0), and the blueyellow channel gives a response tending towards yellow. Another justification for this factor of two is in the proportion of cones in the retina: L:M = 10:5. According to this model, there is also a small intrusion of the S cones in the RG channel, though generally it is not taken into consideration. The oponnent process yellow-blue receives an amplified signal with a negative sign from relatively small values of the S cone, and a positive signal from the L and M cones: (L + M) – S. If S > L + M, the channel’s response is negative and is interpreted in upper states of the visual process as blue. But if S < L + M, the channel’s response is positive and the colour is interpreted as yellow. For lengths above 520 nm, the S cones give no signal; the channel signal will always be positive and interpreted as yellow. Finally, we can state that the L + M signal is transmitted by different nerve fibres to those that transmit the L – 2M signal.

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Colour vision: theories and principles

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40 S cones M cones L cones

30

Log sensitivity

20 10 0 –10 –20 –30 –40 350

400

450

500

550

600

650

700

750

Wavelength (nm)

1.2 Smith and Pokorny fundamentals.

Almost all chromatic vision models largely coincide in this general structure that we have just seen for the Boynton model: there are two opposing channels (channels T and D), and one additive channel (no opponent) for luminance (channel A). The differences are in the matrix coefficients. However, there are certain points that have not been fully clarified: Channel T: There is L – M opponency, but there are doubts over contribution from the S cones. If there is a contribution, it would be of the (L + S) – M type. Channel D: There is L – S opponency, but there are doubts over contribution from the M cones to this channel. Channel D: The achromatic channel is L + M, but there are doubts over contribution from the S cones to this channel. If there is a contribution, it would be of the L + M + S additive type. Nevertheless, most of the experimental results point to such a contribution not existing. Other more complex models, also exist, such as the De Valois-De Valois (1993) model. This considers a second opposing stage of cones (ATDinterm) that generates three colour opponent signals (rather than two) at the LGN cell level, corresponding approximately to L – M, M – L and S – (L + M). A third perceptual (linear) opposing stage would then occur, generating one achromatic and two chromatic channels. Finally, there are other non-linear models that succeed in explaining effects resulting from adaptation to a light source, and the influence of the background and the environment surrounding the stimulus, etc. In this case, non-linearities need to

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be introduced into the ATD response calculation. The 1990 Guth model, and subsequent modifications up to 1995, are a good example of these models. The general outline for these models is as follows (equation 1.3): from LMS an initial non-linearity is introduced, obtaining LMSs that are adapted to those conditions. Following this, the ATD exchange matrix and a second non-linearity are applied to obtain the adapted ATDs. L M S

L NL

M S

A M adap

T D

A NL

T

[1.3]

D adap

xn— . The non-linearities are normally of the following type: a— + xn As always, the objective with this model is to explain chromatic vision by reproducing the behaviour of the physiological A, T and D mechanisms.

1.3

Chromatic perception

1.3.1 Chromatic discrimination

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Colour differences are of enormous importance in industry. Understanding the human visual system’s degree of tolerance to colour differences is fundamental in knowing the maximum tolerable error in formulating a paint or printing a fabric. The most important aspect is not in fact the differences of the various components of colour (hue, colourfulness and lightness), but rather the perceptive differences of colour as a whole. In 1934, Wright evaluated colour differences with constant illumination by means of a 2° bipartite field, with 100 td retinal illumination throughout the whole spectrum and for five directions of the chromaticity diagram. In this way, he determined intervals or segments with edges that represent a constant difference in chromaticity. As a result of this experiment, it was deduced that the CIE1931xy space had very little uniformity, as two very distant points in the area of greens differ in colour just as two very close points in the area of blues and purples do. In 1942, MacAdam took these studies further. Using a bipartite field, a fixed reference stimulus was placed in one of the two fields, and colour matching was carried out in the other; the test was for 2° and was surrounded by an adaptation field of white light of the same luminance (200 td). After 50 matchings, two symmetrical points were defined in the same direction. By repeating this same operation for various directions and joining the points (within the standard deviations), an ellipse was generated. MacAdam showed the results in the CIE1931xy space for 25 reference colours. The result of joining the experimental colour discrimination points is an ellipse. These ellipses are of different sizes, orientation and shape throughout the chromaticity diagram. The ellipses are smaller in the area of blues, of medium size

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Table 1.1 Physical and perceptual colour descriptors for isolated and related colours Chromatic stimuli

Isolated colour

Related colour

Wavelength (nm) Luminance (cd/m2) Colorimetric purity

Hue Brightness Colourfulness

Hue Lightness Chroma

in the reds and larger in the greens. By considering the distance from the centre of the ellipse to the edge as the unit of colour difference, it can be confirmed that this distance is not the same from one ellipse to another; it can thus be deduced that the CIE1931xy space is not uniform. In a space of uniform representation, representing colour differences would obtain circumferences of equal radius, regardless of the centre colour chosen, rather than ellipses of different sizes and orientation. As a result of these works, attempts have been made to find a more uniform colour representation space. The most commonly used are CIELAB, CIELUV, Guth’s ATD and SVF, among others.

1.3.2 Appearance of colour: chromatic effects Colour is trivariant, which means that in an isolated colour stimulus, we can distinguish three separate qualities: hue, brightness and saturation. When one studies a related colour, i.e. one that forms part of a scene, the descriptors refer to the environment, and one thus speaks of hue, lightness and colourfulness. Similarly, any colour is defined by means of three physical variables that correspond to these three perceptive variables: wavelength to hue, luminance to brightness, and colorimetric purity to saturation (see Table 1.1). However, these relationships do not display absolute independence, but rather there is interference between parameters. A variation in a single parameter (L, Pc, λ) can affect not only the perceptual attribute to which it is associated, but the other two as well. On the other hand, environmental modifications also affect colour. These effects cannot be explained by a linear model, as occurs with characterisation by three-way stimulus values or by means of opponency. We thus know that there must be some subsequent process, and that it cannot be linear. Studies of these chromatic effects are many and varied, and are beyond the scope of this work; we can merely provide a brief explanation of some of the better known and more widely considered of these chromatic effects. •

Bezold-Brücke effect: ‘A variation in luminance can alter the tone, thus changing its colour appearance.’ Bezold and Brücke (1873 and 1878) discovered this effect independently, and both indicated that at high luminance levels, reds and yellowish greens tended towards yellow, whereas greenish blues and violets became blue. However, they also confirmed that there are

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•

•

•

•

•

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Colour measurement three hues that do not vary: yellow (571 nm), green (506 nm) and blue (474 nm). This effect shows that tone and luminosity attributes are not completely independent. Aubert-Abney effect: ‘By adding white to a purple or a monochromatic colour, not only is saturation reduced, but hue changes also occur.’ This effect can be seen in Munsell’s Atlas colour samples, with equal tone and different chroma in the xy diagram. Theoretically, they should be straight lines of constant λd, but instead a curve is obtained that becomes greater the nearer the samples are to the locus. Again, there are some exceptions to this behaviour: for 572 nm yellow and for a purple (0.240,0.035) (λc = 559 nm). Helmholtz-Kohlrausch effect: ‘In heterochromatic Matching and with identical luminance, Brightness varies with the chromaticity of the stimulus.’ With identical luminance levels, achromatic colours display lower luminosity than chromatic colours. And equally, the other way round, with identical luminosity levels, achromatic colours display greater luminance than chromatic colours. In any matching of a white with any colour, then (Lwhite / Lcolour) > 1 applies. This ratio nears 1 when the colour is more desaturated, and reaches values higher than 1.6 for monochromatic colours. Simultaneous contrast: An increase in the brightness of a stimulus with the simultaneous decrease of background luminance. A dark environment makes one grey seem lighter than another with a white environment. Crispening effect: An increase in the difference between two stimuli occurs if the background is similar to the stimuli; in this case the brightness differential threshold is lower. Apparent mix: When the spatial frequency of an object is high, a chromatic mix of background and stimulus is produced. This colour quality is applied in artistic painting, with its maximum expression in the pointillist movement. George Seurat used this technique; his brushstrokes are tiny points, with no merging between them on the canvas, yet when viewed from a certain distance they create the desired combinations on the retina. The same occurs with the pixels of a television screen.

1.4

Defective colour vision

1.4.1 Anomalies and deficiencies in colour vision Most people see colour in the same way, which we can call normal chromatic vision. However, other people’s sight behaves abnormally. In most of these cases, they are able to differentiate colours, but their chromatic vision is much poorer than that of someone with normal sight. There are many colours which for a normal observer are clearly different and which the person with chromatic deficiency views as the same. The reason can be explained with any ATD model, by considering the hypothesis that chromatic deficiency is due to the lack of one of the three cone

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Table 1.2 Types of colour vision depending on the cones Type of cones

Type of chromatic vision

Normal vision

LMS

Normal

Defective vision Protanopia Deuteranopia Tritanopia Monochromatism Achromatism

–MS L–S LM– ––S –––

Defective (R/G confusion) Defective (R/G confusion) Defective (B/Y confusion) No chromatic vision No chromatic vision

Anomalous vision Protanomaly Deuteranomaly Tritanomaly

L’ M S L M’ S L M S’

Irregular (R/G confusion) Irregular (R/G confusion) Irregular (B/Y confusion)

types. This lack of a cone type means a failure in either the T or the D chromatic mechanisms. Consider the Boynton model. The chromatic channels are T = L – 2M and D = (L + M) – S. If one of the two terminals is cancelled out, the channel response will always be of the same sign. What happens if the L cone is missing? Channel D would function, but in an anomalous way (reduced to D = M – S), and its response would differ to that of the normal channel, but a positive or negative response would still be possible; however, channel T (T = – 2M) would always respond in the same direction. Clearly, chromatic vision would thus be greatly reduced. In the hypothetical case of two cone types being missing (L and M), neither chromatic channel could function, and the subject would have no chromatic vision of any kind. A person lacking one cone type is known as a dichromat. Given that there are three types (L, M and S), there can be three separate categories, known as protanopia (lacking L cones), deuteranopia (lacking M cones) and tritanopia (lacking S cones). Individuals with these deficiencies have sight that differs between the three types. Table 1.2 includes all types of reduced colour vision currently reported. As well as chromatic deficiencies, another anomaly type also exists: people who have all three cone types, one of which has a slightly displaced curve response for that pigment. In this case, the person will also suffer from poor colour discrimination. These cases are known as protanomalous, deuteranomalous and tritanomalous. In people with protanomaly, maximum pigment L absorption is not at 560 nm, but instead is displaced slightly to shorter wavelengths and is thus closer to the M pigment. If we analyse the response to a red 650 nm wavelength, for example, sensitivity for this colour is reduced when compared with someone with normal sight, and as a result, in a yellow match with a sum of red and green, people with protanomaly have to add more red than a normal observer.

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People with deuteranomaly suffer a similar problem, but in their case it is the M pigment that is abnormally displaced towards red. Maximum absorption is no longer 540 nm, but rather is displaced towards a high wavelength and is thus closer to the L pigment. This hypothesis of abnormal displacement in maximum pigment absorption has been confirmed in works by Piantanida, Sperlig and Rushton, who identified the existence of abnormal pigments in people with protanomaly and deuteranomaly. Tritanomaly has been observed in a very few cases, though the cause is always pathological due to lesions in the optic nerve or retina, rather than from an abnormal pigment. Despite the fact that the vast majority of chromatic vision abnormalities are due to the absence or alteration of a visual pigment, there are other (very few) cases that are the result of pathological disorders.

1.4.2 Confusion lines and colour discrimination tests Imagine two colours, blue and yellow, with the same L and M inputs (Fig. 1.3). If we place these samples, C1 and C2, in the same scene, a subject without S cones will not notice any chromatic difference between the two. As there is no contribution from the S cones, these two colours will show the same response for the T channel and the D channel, and as such the subject will not see any chromatic difference between them. In the CIE31xy chromaticity diagram, the colours that meet this condition are spread along a straight line (there is only one degree of variation). The colours that are indistinguishable to dichromats are located along lines that converge at a single point. This point of convergence is known as the confusion point, and is characteristic for each deficiency type (identifying the chromatic co-ordinates of the missing response mechanism, although in practice there would be an influence from absorption of the pre-receptor media). Figure 1.4 shows the

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L1 L2 3 3 C1 = M1 = 6 C2 = M2 = 6 S1 S2 7 2 A1 1 1 0 3 T1 = 1 –2 0 * 6 D1 1 1 –1 0 A2 1 1 0 3 T2 = 1 –2 0 * 6 D2 1 1 –1 0 TRITANOPIC

=

A1 ≡ A2 T1 ≡ T2 D1 ≡ D2

1.3 Two different colours C1 and C2d will be similar for a tritanopic subject. T and D responses are equal.

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Colour vision: theories and principles 520

0.8

15

530 540

510 0.7

550 560

0.6 570

500 0.5

580

y

590 0.4

0.3

600 610 620

490

0.2 480 0.1 470 460 0.0 0.0

0.1

0.2

0.3

0.4 x

0.5

0.6

0.7

1.4 Protanopic confusion lines.

confusion lines for deuteranopia. As can be seen, this anomaly confuses all monochromatic colours from 540 nm to 700 nm. In protanopia, the confusion point is at the co-ordinates (0.747, 0.253), in deuteranopia it is at (1.080, – 0.080), and in tritanopia it is at (0.171, 0.000). Of all these lines, there is always one that passes through the equienergetic white, and all these colours will be confused with white, including the corresponding cut-off point in the locus, which can be called the neutral point (494 nm for protanopia, 499 nm for deuteranopia and 570 nm for tritanopia). The neutral point is important because it characterises the type of deficiency, and this fact is used to design detection tests and to categorise chromatic anomalies and deficiencies (see colour Plate III).

1.5

Colour constancy

1.5.1 Mechanisms of adaption Adaptation is the process by which the visual system changes its sensitivity, depending on luminance level in the visual field, so as to adapt to existing

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Low luminance response High luminance response 1,2 1,0 0,8 Sensitivity

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Colour measurement

0,6 0,4 0,2 0,0

400

450

500

550 600 Wavelength

650

700

1.5 Von Kries law: each photoreceptor can change its own sensitivity.

light levels. The process can be explained (at least in part) because each photoreceptor on the retina can change its own sensitivity curve depending on the amount of light it receives. It is a slow process that can take several minutes (Fig. 1.5). The visual system uses various mechanisms to adapt to high and low light levels. The iris contracts very quickly, followed by a much slower reaction from the retina’s photoreceptors as they adapt their sensitivity. The change in response from the photoreceptors is a very effective method of adaptation, whereas the contraction of the pupil is thought to be more of an initial defence mechanism to protect the retina against sudden changes in light.

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1.5.2 Chromatic adaptation and gain control mechanisms The human visual system is able to adapt in such a way that the colour of an object remains unchanged, despite any changes in the light. Thus, chromatic adaptation is defined as the ability of the visual system to deduct the light spectrum so as to preserve the chromatic appearance of that object. A sheet of paper seen with daylight or under a light bulb will always seem white, even though sunlight is much more blue than the light from a tungsten bulb, and if we measure the colour of that piece of paper with a photometer in both situations, the results are very different. All colour adaptation models are based on the Von Kries coefficient law: ‘The individual components present in the eye are completely independent of one another and each is adapted exclusively according to its own function.’ According to modern interpretation, the Von Kries law means that each

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Colour vision: theories and principles

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photoreceptor has its own gain control mechanism, so that sensibility is adapted according to equation 1.3. La = KL * L Ma = KM * M

[1.3]

Sa = KS * S La, Ma and Sa are the post-adaptation cone signals, L, M and S are the initial cone signals, and Kn is the gain control mechanisms. Gain control mechanisms can be explained by changes in photoreceptor responses. A high luminance level will split a large number of the cones’ pigment molecules, and as such the number of molecules available to produce a visual response is much lower. This explanation of the gain control mechanisms only refers to cones and rods, and an explanation of chromatic adaptation in its entirety involves understanding other subsequent mechanisms of adaptation – probably neural gain control mechanisms at a horizontal, bipolar and ganglion cell level.

1.5.3 Illuminant discount As we have seen in the previous section, chromatic adaptation is a change in the sensitivity of the chromatic mechanisms, which makes it possible for the colour appearance to remain unchanged. Colour constancy can be seen as an extreme case of chromatic adaptation that associates a colour to an object regardless of the light in which the object is seen. Thus, many well-known objects (our car or our coat, for example) seem to be always the same colour, whether we see them in daylight or under fluorescent lighting. In colorimetric terms, the colours will be different, but the visual system interprets them as being the same. However, failures in this colour constancy also occur. These failures are explained by a marked colour variation in lighting that the visual system is unable to adapt to, or because the system produces an inappropriate adaptation response.

1.6

Bibliography

Chalupa, L.M. & Werner, J., The Visual Neurosciences, Cambridge: The MIT Press, 2003. Fairchild, M.D., Colour Appearance Models, Chichester, West Sussex (UK): John Wiley & Sons, 2000. Foster, D.H., ‘Inherited and acquired colour vision deficiencies: fundamental aspects and clinical studies’. In Vision and Visual Dysfunctions, Vol. VII, Ed. J.R. Cronly-Dillon London: Macmillan Press Ltd., 1991. Gegenfurtner, K.R. & Sharpe, L.T., Colour Vision. From Genes to Perception, Cambridge: Cambridge University Press, 1999. Kaiser, P.K. and Boynton, R.M., Human Color Vision, 2nd edition, Washington, DC: Optical Society of America, 1996.

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Marr, D., Vision, New York: W.H. Freeman and Company, 1982. Schwartz, S.H., Visual Perception: A Clinical Orientation, 3rd edition, New York: McGraw-Hill, 2004. Spillmann, L. & Werner, J.S., Visual Perception: The Neurophysiological Foundations, New York: Academic Press, 1990. Wandell, B.A., Foundations of Vision, Sunderland: Sinauer Associates, 1995.

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Plate I Photograph of a section of vertebrate retina (cones, bipolar, ganglion and amacrine cells). Courtesy of Dr Nicolas Cuenca, Department of Physiology, Genetics and Microbiology, University of Alicante, Spain.

+

+

L

+

M

–

S

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L

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Plate II Boynton’s model representation.

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Plate III Colour vision simulation for protanopic, deuteranopic and tritanopic subjects.

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2 Scales for communicating colours A. K. ROY CHOUDHURY, Government College of Engineering and Textile Technology, Serampore, India

Abstract: In colorant production and application industries, colour order systems or colour notations are required for effective communication, comparison, recording and formulation of colours. This chapter discusses merits-demerits and accuracy of various important colour order systems, namely Munsell, Natural colour system, Ostwald, DIN, OSA-UCS, Coloroid, etc. in the material form as well as digitised form. The comparison and interrelations between various systems are also discussed. Key words: colour order systems, colour notation, colour atlas, colour naming, Munsell system.

2.1

Introduction

While communicating or talking about colour, a language which is understandable by all parties must be followed. A logical scheme for ordering and specifying colours on the basis of some clearly defined attributes is known as the ‘colour notation system’. The attributes are generally three in number, as our vision is trichromatic, and they constitute the coordinates of the resultant ‘colour space’. Colour notation systems also encompass ‘colour order systems’ which typically comprise material standards in the form of a colour atlas. Due to constraints of the colorant gamut, the atlases may depict only a physically realisable subset of a colour order system. Colour notations can be classified into three categories (Rhodes, 2002): 1 Device dependent systems – the most common imaging devices used for reproducing colour are the computer-controlled CRT displays and the colour printers. The associated colour order system and colour spaces are hardwareoriented and they lack perceptually based attributes. 2 Mathematical systems – uniform colour spaces based on mathematical transformation of CIE tristimulus values such as CIELUV and CIELAB belong to this category. 3 Systems based on database of aim points – colour order systems existing principally in physical form, the colour samples of which can be measured to establish a database of aim points. Using interpolation techniques among limited available samples, many more colours can be defined. Humans with normal colour vision can distinguish among some two million colours when viewed against a mid-grey background and perhaps double this 19 © Woodhead Publishing Limited, 2010

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when the background is widely varied (Kuehni, 2005). The orderly and meaningful arrangement has been a matter of concern for the last 2000 years as a colour system which will manage to meet all requirements needs to be based on many years of physical and psychological research and experience. Until the seventeenth century, the prevailing opinion of the great Greek philosopher, Aristotle was that colour is generated from the interaction of darkness and light and that there are seven simple colours out of which all others are obtained by mixture. These seven are white (pure white), yellow, red, purple, green, blue and black (pure darkness). The history of colour order shows that the relationships between colours are rather complex and took two millennia to unravel. Originally, colour order systems consisted of lists of colours, such as those by Aristotle or Alberti. It was only at the beginning of the seventeenth century that the first graphical representation of colour order appeared in the work of Forsius. A different style of graphical representation of colour order was developed by the Belgian Jesuit and scholar François d’Aguilon (1567–1617). In his graphic representation, d’Aguilon showed tonal mixtures of the three chromatic simple colours with white and black as well as intermediates between white and black (a grey scale), with arcs above the line of the simple colours. Below the line, he represented with other arcs the hue mixtures of the three chromatic simple colours (Kuehni, 2003). The modern concept of colour was founded in 1704 by Isaac Newton. Before this, all colour order systems were one dimensional or linear but Newton recognised three colour attributes and drew an incomplete (spectral colours only) chromatic diagram in the form of spectral colours on the circumference and white in the centre. The saturation lines were drawn as radial lines from the white centre to the spectral periphery. Newton was also an alchemist believing in universal harmony. In analogy to musical tones, he chose seven hues in the spectrum: red, orange, yellow, green, blue, indigo and violet (ROYGBIV). However, the choice of seven is always controversial – repeated tests have shown about 120 discernible colours in the spectrum (Kuehni, 2005). LeBlon (1756) was first to make a clear distinction between mixing pigment colours and mixing colours of light. He stated that all visible objects can be represented by three colours, yellow, red and blue, and mixing these three colours makes black or all other colours. He named them as material colours or those used by painters. He further added that for a mixture of spectral colours those proposed by Sir Isaac Newton could not produce black, but the very contrary, white. Moreover, purple is perceivable in object colours only. The first proposal for a three-dimensional double tetrahedron system was made by the German mapmaker and astronomer Tobias Mayer in 1758. A French silk merchant, Gaspard Grégoire, first proposed a three-dimensional object colour order system based on the perceptual attributes hue, (relative) chroma and lightness and an atlas with 1350 samples was introduced before 1813 (Kuehni, 2008a). Matthias Klotz (1748–1821), a German painter, also proposed a three-dimensional

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colour order system based on independent perceptual colour attributes. He proposed the cylindrical colour order system that consisted of a well-defined lightness scale (Kuehni, 2008b). About 100 years later Albert Munsell introduced a system based on intensive scientific studies very similar to the above systems. Four-dimensional Riemannian colour space was first proposed by Helmholtz with the help of a linear element which is difficult to define precisely and hence, the conceptualisation remained unclear. Recent studies (Leonev and Sokolov, 2008) showed that perceived colours can be represented on a spherical colour space of unit radius (hyper-sphere) in 4D Riemannian space. The model devotes a dimension to the stimulus parameter ‘darkness’ recognising the separate signals conveyed by light and dark neuronal channels. The advantage claimed is that the model defines mathematically the relation between the perception of large colour differences and the physical characteristics of luminous stimuli more consistently. However, a four-dimensional space is difficult to visualise. It is not very clear how colour names developed historically. One of the two prevailing opinions is that people of all societies became aware of different colours or colour categories and then named them in the same sequence: white and black, red, green, yellow, blue, brown, purple, pink, orange, grey (Berlin and Kay, 1969). Others think that all colour names are group cultural achievements and there is little common thread. Many colour words are related to materials, such as orange, ultramarine, olive, malachite green, bottle-green, peanut-green, sea-green, etc. These common names refer to the colour of various common objects which can be quickly recognised and memorised by most people. Some names reflect poetic invention, such as Cuban Sand, Ashes of Rose, Blue Fox and so on. But such colour names are very approximate, unreliable and temporary. Their meaning also changes with observer, time, place, style, technology, language, culture, etc. It is common practice to describe colour in terms of hues like red or yellow along with tone or secondary hue such as greenish or bluish and the amount of light reflected such as dark or pale. However when we describe a colour as ‘dark greenish blue’, the description is very inadequate as there may be many thousands of such colours. This problem was realised long ago. The accurate description of colour is essential for communication and for accurate reproduction of colour across a wide range of products. The colour of any object is commonly registered or recorded in two ways, namely: • •

preserving coloured physical samples recording in terms of common colour names.

Physical samples of dyed/printed fabrics, yarns or fibre paint panels, patches of printing inks, coloured papers, etc. are frequently used to express colours in the trade. Collections of such colour samples are very useful as examples of coloured product if the number of colours required is fairly limited. A good example of such collections is the dye-manufacturers’ ‘shade cards’. The shade cards carry

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numerous coloured objects on specific substrates (e.g. pieces of paper or various textile materials) along with procedures and names of the colorants to be used. The paint and printing ink manufacturers also publish shade cards for their products (colours) with names very specific to the concerned industry. However the exemplifications are very limited. They are restricted to the specific type of colorant or substrate and cannot be used for general reference. In the modern age, the celebrated German scientist A. G. Werner (1750–1817) was probably first to standardise colours by developing a method of describing minerals by their external characteristics like colours. In 1814 Werner’s system was bought in book form (entitled Werner’s Nomenclature of Colours, containing 110 samples of colours) by a flower painter of Edinburgh, Patrick Syme. In 1905 a French work, Répertoire de Couleurs, was published containing 365 plates consisting of 1 356 colours as in horticulture, traditional and textile use, described by colour names in various languages.

2.2

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Colour measurement

Systematic arrangements of colours

When we deal with a reasonable number of specimens, say a few thousand, to cover the whole range of possible colours (one million or more), the specimen must be selected according to a system or plan. The colour naming systems were popular for a long time, but they were not very systematic – hence the accuracy of specification was limited. It is absolutely necessary to arrange the colours in a systematic manner to tackle the enormous number of colours we can perceive. It is well known that the colours are three-dimensional. However, the dimensions of colour are expressed in various ways in different fields. For systematic arrangements, the dimensions should be independent of each other. The question is, therefore, which dimensions are to be chosen to arrange colours in a threedimensional space? The most natural and logical approach can be illustrated by Judd’s ‘desert island’ experiment (Billmeyer, 1981). A person sitting idly on a desert island may decide to arrange systematically the large number of pebbles surrounding him according to colour. First he sorts out coloured (i.e. chromatic) and colourless (i.e. achromatic) pebbles. Then he arranges colourless pebbles – black, dark grey, medium grey, light grey and white (Plate IV, step 1, in the colour section between pages 42 and 43). This classification is based on a property called ‘lightness’ or ‘value’. Then chromatic pebbles are classified according to their common colour names. All surface or object colours can be classified broadly into five principal colours namely red, yellow, green, blue and purple (Plate IV, step 2). While the former four can be seen as spectral colours, purple is perceivable in object colours only. If the variation of colour in the pebbles is great we may have reds admixed with yellow, yellow admixed with green and so on. The hues intermediate of the five principal hues can be named as red-yellow, yellow-green, green-blue, blue-purple

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and purple-red (Plate IV, step 3). Now there can be red colours having varying degrees of yellowness, and each may be considered as a separate hue. Hence the above ten hues can be further classified into any number of intermediate hues. For assembling into a decimal system, each of the above ten hues may be classified into a further ten intermediate hues. Hence we can have 100 hues; the number is for convenience and can be changed into any other number. After classifying the coloured pebbles into separate hues, further classification may be done according to lightness or value. For example, pink, light bluish-red, medium bluish-red, dark bluish-red – all may be of the same hue, only varying in lightness (colour Plate IV, step 4). Lightness is actually a measure of the total amount of light reflected in the visible region of wavelength. After classifying the pebbles according to hue and lightness, all pebbles in a group may not be equally colourful – some are very vivid and colourful and the others are dirty and less colourful or pale. This is due to the varying degree of hue or colour content. Two objects of equal lightness means both reflect equal amounts of light. But a colour may be admixed with grey or black and a portion of the reflected light may be due to this grey component. This grey content, or conversely the colour content, is the third dimension of colour called ‘chroma’ or ‘saturation’. So the pebbles of equal hue and lightness can be further classified according to chroma or saturation (colour Plate IV, step 5). Clearly, chroma and saturation have different meanings; the former is the hue content in relation to the brightness of a reference white, while the later is the hue content in relation to its own brightness. Every colour sensation unites three distinct qualities and one quality can be varied without disturbing the other. A colour may be weakened or strengthened in chroma without changing its value or hue. Wright (1984) identified two sets of visual attributes, namely: Group A attributes are lightness, hue and chroma. Group B attributes are whiteness, blackness and chromaticness. According to Wright (1984) and Nayatani (1984), group B attributes are more useful because it is most easily understood and is more fundamental for observers to represent colour appearance. However they are less studied in psychometric (equal perception) terms. The colours of the outermost Munsell (group A) hue circle are close to full colours which is a term for group B attributes.

2.3

Colour order systems

A colour order system is a systematic and rational method of arranging all possible colours or subsets by means of material samples. Once the colours are arranged systematically they are named in some descriptive terms and/or are numbered (Graham, 1985). It is also desirable that the samples included in any colour order system are to be properly specified in terms of any standard colorimetric specification, the most common being the CIE colorimetric system.

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The colour order systems are constrained by the following facts (Fairchild, 2006): • • • •

Orderly (and continuous) arrangement of colours. A logical system of denotation. Perceptually meaningful dimensions. Embodied with stable, accurate and precise samples.

Colour specifiers or atlases are convenient physical forms of any colour order system. Colour order systems are three-dimensional, but atlases are twodimensional so that they can be presented in the form of a book or flat form (Lewis and Park, 1989). They have multiple functions such as: • • • • •

Stand-alone design tool for colour ideas. Quick communication of colour ideas over distance. The larger swatches provide master standards. Basis for specifying colours during colour formulations and colour ideas. Supporting role for instrumental response or visual perception of instrumentally measured colours.

An atlas should fulfil certain criteria such as: 1 2

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4 5

The ideal design should be based on colours uniformly distributed throughout the colour solid. Selection of the substrate for an atlas is very important. Colours illustrated on cotton are readily matched on other substrates using the appropriate class of dyes (Park, 2008). To facilitate accurate assessments, however, some atlases have been prepared on multiple substrates. Moreover, different applications require different colour ranges. The full range of requirements for textiles, paint, plastics and ceramics are quite different. The ideal atlas should be highly stable and should have good fastness properties, particularly to light. It should be simple and easy to understand. The samples are to be reproducible and replacement pieces should be available. It should be cheap, portable and globally used.

However, no atlas is expected to represent visually millions of colours that can be detected by our eyes. There is no ideal colour order system and hence no ideal atlas.

2.3.1 The necessity of a colour order system It is a difficult task to deal with the millions of colours which our eyes can distinguish. We can feel the problem instantly if we try to describe a colour variation from memory or when we try to describe a colour to a man at a distance via communication channels (Roy Choudhury, 1996). The problem was known in

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ancient times and several people have tried to solve it in their own way: Nobel laureate W. Ostwald, American artist A. H. Munsell and many others studied the problem in great detail. In colorant production and application industries, colours are to be communicated, compared, recorded and formulated on a regular basis. This necessitates the systematic classification of colours, which can be done in various ways. The classification may be based on visually or instrumentally assessed colour parameters as various colour order systems were developed originally on the basis of visual attributes, but later supported and modified by instrumental assessment. The main reasons for the widespread interest of colour order systems are for communication about colour over distance and time as well as for analysis and definition of the aesthetic relations among colours (Härd and Sivik, 1983–4).

2.3.2 Classification The colour order systems are of three types (Wyszecki, 1986): 1 2 3

Colorant-mixture system based on subtractive mixture of colorants. Colour-mixture system based on additive mixture of colour stimuli, for example the Ostwald system. Colour appearance system based on the principles of colour perception or colour appearance.

However, Derefeldt (1991) suggests that colour appearance systems are the only systems which are appropriate for general use because these are defined by perceptual colour coordinates with uniform or equal visual spacing of colours. Colorant-mixture systems These systems display the range of colours which can be achieved with declared quantities of colorants. The desired colours are developed by compounding a limited number of pigments or dyes in systematically varied proportions. The main purpose of these systems is to illustrate the range and other properties of a set of colorants. The principle of colorant mixing is subtractive and the colour gamut is restricted by the choice of primary colorants. This is helpful in the reproduction of shades for a particular coloration industry. However the method of application should be identical to that of the atlas. The dyes obtained from different manufacturers vary in colour considerably, but for the paint and printing ink industry the problem is less severe and the manufacturers’ recommended mixed shades are fairly reproducible. Examples of colorant-mixture systems are the colour atlases developed by different dye manufacturers. The ICI Colour Atlas (1969) was a collection of 1 379 original colours and 27,580 variations printed on papers. Similarly other

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atlases were also developed by different dye-makers such as the ‘Tootal Atlas’ (2 200 fabrics, 1970), ‘Hoechst Atlas’, ‘Ciba-Geigy Colour Atlas’ (625 fabrics, 1982) and the ‘BASF Colourthek III’ (2 580 fabrics, 1970). Examples also include ‘Piochere Colour System’ and ‘Martin-Senour Nu-Hue System’ (Wyszecki and Stiles, 1982). The Pantone colour system is basically a colour mixture system. The Pantone system (www.pantone.co.uk) began life in 1963 in the USA for defining colours for printers, but expanded into other fields later, e.g. textiles in 1984, plastics in 1993, and architecture and interiors (1 925 colours) in 2002, each of which has a six digit numerical notation (e.g. # 19-1764) and an ‘inspirational’ colour name. This is widely used in graphic art and also in the textile industry mainly because of its low cost, though the colours are not equally spaced and the shades are prepared on paper using printing inks. It is not a colour order system since it does not include a continuous scale. It is more appropriately considered a colour naming system. The Pantone system is loosely based on a three-dimensional scale using a six digit reference number, two each of which indicate colour strength, hue and tone successively, but CIE specifications are not available. Recently, a textile version of the Pantone atlas (having 1 001 reactive dyed cotton fabrics) has been introduced in the market. Colour-mixture systems

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The basic idea of a colour-mixture system is to show, in the form of material standards, the sequence of colours related either to manipulation of the controls of a tristimulus colorimeter or to variation in simple ways of the proportions of sector areas on a Maxwell disk. So the basic principle of generating colour is additive colour mixing. The tristimulus colorimeter aims to tie in colours with the CIE system of colorimetry, or more specifically with the chromaticity diagram. However, long before the development of the CIE system of colorimetry, the Maxwell disk was used to develop colours of constant dominant wavelength by varying the proportion of the chromatic sector and the achromatic sector (white, grey or black) on the disk. Judd and Wyszecki (1963) preferred an additive colourmixture based colour order system due to its resemblance to our everyday experience of colour perception. The most popular in this category is the Ostwald colour system. A few other examples of colour-mixture systems are as follows. The Colour Standards and Colour Nomenclature atlas of Robert Ridways, a bird curator in the United States National Museum, was published in 1912 containing 1 113 named coloured samples. Each sample is 1" by ½" rectangular matte printed paper. In a page, the lightest sample is at the top followed by seven steps of increasing grey content. Each column represents constant dominant wavelength obtained by rotary mixing of white, black and a chromatic pigment. The system represents 35 dominant wavelengths maintaining approximately uniform hue-spacing. The representation of near-whites is poor, but of near-greys

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is excellent. The system was popular among naturalists for colour specification of plants, flowers, birds, insects, rocks etc. The Colour Harmony Manual is one of the most important colour-mixture systems, published by the Container Corporation of America during 1942–72 (Jacobson, 1972). It consists of a set of 12 hand-books, each showing a pair of complementary hues. Each colour chip is specified by the Ostwald method on a 24-step hue scale, i.e. 12 pairs of complementary hues of constant dominant wavelength. The number 24 was chosen because it is divisible into equal intervals of 2, 3, 4, 6, 8 and 12 for selecting multi-hue harmonies. Each hue-chart shows samples having varying black, white and full-colour content represented by double-letter names such as na, ga, ca, etc. The vertical series in the triangle were called ‘shadow series’ because they have the same dominant wavelength and chromaticity and differ only in reflectance. The first and fourth (last) editions of the manual contain 680 and 949 chips respectively. Light colours and near-whites were not included in the manual and the system cannot readily translate the attributes into useful textile terms. The publication of the manual was discontinued after 1972 mainly due to poor standard of reproduction (Greenville, 1994). The Dictionary of Colour, published in book form by Mearz and Paul (1950), shows a collection of over seven thousand colours (precisely 7 056 numbers) classified into seven hue groups. Considerable effort has been made to describe the colours by commonly used colour names. The names are displayed on the left-hand pages, while the corresponding colours are shown opposite. The names have been gathered from the sources mentioned above and other reliable and established sources and no name has been originated by the authors of the book. The various adjectives (e.g., light, pale, etc.), trade or commercial names have been excluded and emphasis has been solely based on the names’ colour perception. The above system is an intermediate between subtractive colorant-mixture and additive colour-mixture systems. The colours are created by variable-density overprints of inks of different colours. Wherever there is overprint, there is subtraction, while in remaining areas the colours are additively mixed. The system shows a collection of over seven thousand colours printed in the form of a book, but colour variations from copy to copy are reported as these are printed on paper. Near-blacks and light-saturated colours are not included, while the colours used are remarkably permanent. The order of seven hues in The Dictionary of Colour follows the spectrum – red to orange, orange to yellow, yellow to green, green to blue-green, blue-green to blue, blue to red, and purple to red. The plates are divided into twelve rows (A to L) and twelve columns (1 to 12). The rows start with ‘no hue’ at one end and ‘full hue’ at the other end. The columns represent hues as a mixture of two hues in varying quantities. Each of the seven hues is presented in eight successive plates with an increasing grey content. The perfect scale of reduction should be the geometric series, based on the Weber-Fechner law, having a percentage of reflection in the order of 0, 12, 22, 32, 42, 52, 62, 72, 82, 92, 102. However, some of

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the steps like 0 or 100% are impractical and, moreover, smaller intervals were given for lighter colours so that they are in sufficient numbers. In this system, effort has been made to arrange colours in some definite order, however the spacing of the samples is somewhat arbitrary and not equally spaced. The system describes a very small portion of the colours visualised by us. Intermediate colours can be interpolated, but such interpolation cannot be communicated to others because the samples are not spaced equally as per the visual colour perception. Colour appearance systems These systems are based on the perception of colours by an observer with normal colour vision and the scales are chosen to represent attributes of perceived colours. However, attributes represented in various systems are different. The spacing of colours along the scales also varies from one system to another, even when the same attribute is used in both systems. Most of the earlier atlases were in favour of inclusion of colours of long traditional usage, thereby emphasising tighter spacing of colours in some hue regions. Such systems sample the colour solid non-uniformly. In other words, there is no uniform placement of the colour samples throughout the total colour space. Some areas of colour space are over-emphasised, while some areas are poorly presented or not presented at all. A universal urge to arrange the colour chips on the basis of constant hue is strongly felt by the designer of colour order systems. However, the mixture of chromatic colour, black and white is only an approximation to constant hue. The main emphasis of appearance-based systems is the uniform visual spacing. The systems thus allow easy interpolation between the samples represented and extrapolation of colours not illustrated in a given collection. The collections of samples are generally represented in pages of constant hue. The most popular appearance-based colour order system is the Munsell system. Psychometric scales provide a way of assigning numbers to physical stimuli according to the psychological attributes that the stimuli evoke. The relationships between perceptual magnitudes and physical measures of stimulus intensity are assessed by scaling experiments. The types of scales may be as follows (Wyszecki and Stiles, 1982; Kuehni, 2003). ‘Nominal scales’ merely determine whether or not things are equal. The same name or symbol is assigned if they have the same value for the attributes. The colours, for example, can be grouped into yellows, reds, greens, blues, etc. ‘Ordinal scales’ assign numbers in such a way that the order of the numbers corresponds to the order of the magnitudes of the attribute being scaled. The stimulus with higher scale value will be perceived as having more of the attribute. An ordinal scale is subject to logical operations: equal to, greater/less than, etc. ‘Interval scales’ have all the properties of ordinal scales and, in addition, the differences (intervals) between the numbers characterise the sizes of the

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corresponding perceived difference of the attribute (e.g. Celsius and Fahrenheit scales). Colour scales are usually interval scales. ‘Ratio scales’ are interval scales with a natural origin. Examples are length in metres, duration in seconds and temperature in degrees Kelvin. The zero point of the scale corresponds to a stimulus for which the attribute has zero magnitude. As a result, the numbers on the scale are proportional to the perceived magnitudes of the attribute being scaled. Many colour order systems consist of ratio scales. A variety of techniques have been devised for psychometric scaling (Wyszecki and Stiles, 1982; Fairchild, 2006) such as: • • • • • • •

Rank order Graphical rating Category scaling Paired comparisons Partition scaling Magnitude estimation Ratio estimation.

2.3.3 Merits–demerits The advantages of material-based colour order systems (Hunt, 1987) are listed below. 1

2 3 4

5

6

As represented by physical samples, the systems are realistic and easy to understand. It is easy for the eye to specify object colours by comparison with reference to physical samples, rather than by matching with colours in memory (Hunter and Harold, 1978: 300). The atlases are easy to use. In most cases, side-by-side comparisons are made under standard viewing conditions and, as such, no instrument is required. The systems based on perceptual scaling like Munsell and NCS can be used to evaluate mathematical colour appearance models. At present, most of the colour order systems are calibrated in terms of tristimulus values; hence reference can be made to the colour order systems for colour control or for colorant formulations by computer, even in the absence of reference samples. Visually uniform colour spaces, such as Munsell and OSA, can prove a useful way of organising the colours of a digitally controlled colour television monitor. Future uniform colour spaces will probably be defined with the aid of these monitors but with a higher flexibility and wider colour gamut than the complex pigment technology presently in use (Durrett, 1987). Colour order systems can be used as sources for test targets for imaging systems or other measurement devices. The Macbeth Colour Checker chart, commonly used as a test target for imaging systems, is partially based on the Munsell system.

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Some of their limitations are: • •

•

•

•

•

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• • •

A number of colour order systems are used globally and they are not mutually convertible. The actual colour of the physical samples in the atlas may be quite different from the intended aim colour. The error may be of several CIELAB colour difference units and may differ from batch to batch, i.e. poor reproducibility. It is not possible to include all perceivable colours in any colour order system. The Chroma cosmos 5000 is the largest, with 5 000 uniquely dyed samples, whereas most of the atlases consist of around two thousand samples. Any colour atlas is a serious abridgement of the colour world as there are gaps between the available physical samples. Interpolation or extrapolation is, therefore, frequently necessary for colour specifications, the accuracy of which largely depends on the colour discrimination efficiency and experience of the observer. As colour order atlases are composed of a limited number of physical samples, future inclusion of newer samples may be a problem. Though most of the systems keep provision for addition of newer samples, it may occasionally be necessary to alter the spacing. The perceptual scales of colour appearance in a colour order system have been established for a specific viewing condition. No data have been provided with respect to change in viewing conditions and the visual spacing of the samples is valid only if standard illuminating and viewing conditions are maintained. The errors are not likely to be very high if typical indoor daylight is used, but viewing under other artificial lights may result in serious errors. The visual interpolation between atlas samples to determine the notation of colours not represented in the atlas is subjective and may differ between individuals. The phenomenon is known as ‘observer metamerism’ (Roy Choudhury and Chatterjee, 1992). As the system uses physical samples, there are chances of deterioration of the standards due to limited stability of the colorants, extensive use or long exposure to light. High chroma colours may require fluorescent dye or pigment, the use of which is restricted due to limited stability. The manufacturer takes proper care for good performance, but still after a certain interval of time, the genuineness of the sample may be questioned. Moreover the user will be completely unaware of such changes. Most of the colour order systems cannot be used for self-luminous colours such as light sources unless ancillary apparatus is used. Though colour order systems are used for a variety of applications in colour appearance, they are not a substitute for a colour appearance model. The relation between perceptual coordinates of the colour order systems and colorimetric coordinates are complex and cannot be expressed by accurate equations. Approximate transformation equations have been derived by statistic fitting and neural network modelling and look-up table interpolation techniques are used for transformation from the CIE colorimetry to colour order coordinates.

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Various colour order systems

The idea of using a three-dimensional colour solid to represent all colours was developed during the eighteenth and nineteenth centuries. Several different shapes for such a solid were proposed, including a double triangular pyramid by Tobias Mayer in 1758, a single triangular pyramid by Johann Heinrich Lambert in 1772, a sphere by Philipp Otto Runge in 1810, a hemisphere by Michel Eugène Chevreul in 1839, a cone by Hermann von Helmholtz in 1860, a tilted cube by William Benson in 1868 and a slanted double cone by August Kirschmann in 1895 (Kuehni, 2002). These systems became progressively more sophisticated, with Kirschmann’s even recognising the difference between coloured lights and object colours. But all of them remained either purely theoretical or encountered practical problems in accommodating all colours. Furthermore, none was based on any rigorous scientific measurement of human vision; before Munsell, the relationship between hue, value and chroma was not understood. Munsell replaced all historical approaches with the proposal of a balanced colour sphere, later replaced by an irregular solid. Subsequently a large number of colour order systems have been developed in different parts of the globe at different times. However, there is no internationally agreed colour order system to date. Some systems are very popular, some are used occasionally while some are obsolete. Six popular colour order systems and their respective colour attributes are as follows: 1 2 3 4 5 6

Munsell – hue, value and chroma Natural colour system – hue, blackness and chromaticness Ostwald system – hue, lightness and saturation DIN system – hue, saturation degree and darkness degree OSA-UCS – no separate scaling of three attributes Coloroid system – hue, saturation and lightness.

In addition, there are a few less known and newly developed colour order systems such as: Swiss Colour Atlas 2541, Chevreul, Colourcurve, Eurocolour system, Acoat system, Pope colour system (Heila, 1988). A one-dimensional colour order system for dental shade guides has been proposed by O’Brien, Groh and Boenke (1989) by visual ranking of translucent porcelain bioform shade guide teeth of the American Dental Association.

2.4.1 Munsell colour order system Professor Munsell (1905), an artist, wanted to create a ‘rational way to describe colour’ that would use decimal notation instead of colour names (which he felt were ‘foolish’ and ‘misleading’) and so developed the oldest and by far the most popular colour order system to fill the gap between art and science. The Munsell atlas was released in 1915, commercialised in 1929 and the system has been extensively studied by Billmeyer (1987). The Association Internationale de la © Woodhead Publishing Limited, 2010

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Couleur (AIC) study group on colour order systems prepared a computer-based annotated bibliography containing about 400 entries out of which 115 references are on the Munsell system (Billmeyer, 1985). Prior to 1943, the Munsell system was defined by the physical samples composing the 1929 MBC colour chips and thus the basic specification of the Munsell system was the spectral reflectance function of each colour chip. The spacing of the chips was intensively studied by the Colorimetry Committee of the Optical Society of America and in 1943 the CIE tristimulus values of ideally spaced chips were published as the Munsell renotation system (Newhall et al., 1943). Obviously there are many important physical and psycho-physical differences between earlier reflectance based systems and the present tristimulus based systems (Berns and Billmeyer, 1985). The system (colour Plate V) consists of the following three independent dimensions which can be represented cylindrically in three dimensions as an irregular colour solid. 1 Hue (H), measured along circumference of the horizontal circles. 2 Chroma (C) or purity of colour, measured radially outward from the neutral (grey) vertical axis. 3 Value (V), measured vertically from 0 (black) to 10 (white).

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Munsell determined the spacing of colours along these dimensions by taking measurements of human visual responses. In each dimension, Munsell colours are as close to perceptually uniform as he could make them, which makes the resulting shape quite irregular. The perceptual uniformity of the system is only valid under illuminant C, a uniform middle grey (N5) background with a sufficiently high illumination level (greater than 500 lux). The Munsell system divides each horizontal hue circle into five unique or principal hues: Red (5R), Yellow (5Y), Green (5G), Blue (5B), and Purple (5P), along with five intermediate hues (5YR, 5GY, 5BG, 5PB, 5RP) halfway between adjacent principal hues. In the original Munsell book, each hue sector (H) is divided further into four finer categories, namely, 2.5H, 5H, 7.5H and 10H (0 for the next H). However, each of these ten steps may also be broken into further ten sub-steps, so that 100 hues are obtained with integer values (though Munsell originally sampled only 20 hues, later 40). The naming of these hues starts from the mid-point between major hues and numbered from 0 to 10, e.g. 5R, 6R-9R, 10R or 0YR, then 1YR, 2YR and so on. For the modern Munsell hue scale to be visually uniform, Kuehni (2005) observed that different numbers of Munsell hue steps are required between average unique hues namely: Unique hue sector → red to yellow yellow to green green to blue blue to red 20 23 26 31 Munsell hue steps → The open-ended chroma increases from 0 for a neutral colour to colours with stronger hue content. There is no maximum for the chroma. The highest chroma

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depends on the hue and value of the samples and the colorant used to produce them. With the available colorants, chroma is generally restricted to a maximum of 16 or 18. A colour is fully specified by three numbers for hue, value and chroma, e.g. H V/C. The Munsell atlas is usually available on painted paper in glossy (1 488 chips) and matt forms (1 277 chips). As the Munsell system is based on polar coordinates, smaller perceptual differences occur in the near-neutral grey region of colour space than in the outermost saturated regions. Physical distance between neighbouring hue increases with increase in chroma, i.e. increase in distance from the neutral axis. In the Munsell system, therefore, the evaluation of near-neutral samples is problematic – hence the ‘Nearly Neutral Collection’ came onto the market in 1990. The atlas contains a range of light and near grey samples, an important colour region for various fields of design and architecture such as wall and house colour, colour of furniture and office equipment, building materials, cosmetics, etc. Each page contains a grey scale running in half steps of lightness from Munsell value 6/ to 9/ and Munsell chroma /0.5 to /4 for 20 hues, two for each of Munsell’s ten principal hues. However it is difficult to combine the two atlases during evaluation. Indow and Watanabe (1980) demonstrated that human observers can memorise the scheme of Munsell notation and, without comparing with the Munsell standard chips, they can specify colour samples in terms of (H V/C). It is not guaranteed that one step in ‘V’, one step in ‘C’ and one step in ‘H’ represent the same size of perceptual difference. The scaling perceptual differences δjk between two Munsell standard chips j and k have shown that chips are embeddable as a configuration of points (Pj) in a 3D space locally Euclidean metric. However, in order to accumulate through this approach a tremendous amount of experimentation is necessary to collect sufficient information necessary to provide the Munsell colour solid with a unified distance scale (Indow and Romney, 2008). The relations between Munsell and CIE variables are very complex. In the CIE chromaticity diagram, lines of constant Munsell hue are curved and the location changes with change of Munsell value. The Munsell value (V) scale is related to CIE luminance factor (Y) by a complex fifth degree polynomial equation called Judd’s polynomial as follows (Newhall, 1940): Y = 1.2219V – 0.23111V2 + 0.23951V3 – 0.021009V4 + 0.0008404V5

[2.1]

The equation was devised by Judd with measurements based on the use of magnesium oxide, and assigned a value of absolute reflectance of 1.026 for 45°/0° illumination and viewing. This can be inverted through iterative methods to obtain the approximation (Rhodes, 2002) as follows: V = 0.01612Y + 2.5649Y1/6 + 1.3455Y1/3 + 0.08797Y–1 – (2.685 × 10–7)Y3 – 3.116

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[2.2]

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Land and Pinney (1955) proposed a simpler equation: V = 2.468Y1/3 – 1.636

[2.3]

However, no simple relation has been reported so far for Munsell hue or chroma with respective CIE parameters. The NBS computer program (Rheinboldt and Menard, 1960) utilises a look-up table followed by interpolation but a simple and faster program has been proposed by Simon and Frost (1987). Artificial intelligence computer programs such as ‘artificial neural network’ (ANN) has been utilised to convert Munsell coordinates into CIE coordinates. Neural network models to imitate some functions of the human brain are described by Tominaga (1993). If we set aside the small problem that a uniform colour space in three dimensions is impossible, then CIELAB does remarkably well. A standard basis for comparison is the distribution of Munsell target colours at Munsell values 4, 6 and 8, out to the limits of surface colour chroma. Ideally the diagram would look like radial spokes within concentric circles. However, Munsell hue loci showed (Hunt, 1978) to depart from straight radial lines having equal angular spacing and departures of the Munsell chroma loci from equally spaced concentric circles. The exaggerated spacing of Munsell chroma into the yellow and yellow-green hues, the displacement in the lines of constant hue as lightness increases (especially in the blue greens), the curving lines of constant violet and green hues and the wide gap in the hue spacing of green colours, are all primarily or partly due to irregularities in the CIELAB system although some are actually problems in the Munsell system. As a rule of thumb, ten units of CIELAB lightness exactly match one unit of Munsell value, and ten units of CIELAB chroma approximately match two units of Munsell chroma. It may be noted that all CIE systems reverse the ordering of Munsell hues. The clockwise movement along the Munsell hue circle results in the change of hue from red to yellow, while in CIELAB the same change requires anticlockwise movement. The chromaticities of the Munsell renotation data set were applied to eight colour-appearance models namely CIELAB, Hunt, Nayatani, RLAB, LLAB, CIECAM97s, ZLAB and IPT by Wyble and Fairchild (2000). In general, the models derived from the Munsell system performed well except for some deviations in hue spacing and linearity. Limitations of the Munsell system Munsell and his successors worked hard to produce colour samples that were ‘perceptually equidistant’ from neighbouring colours on the individual dimensions of value, chroma and hue. The relationship between Munsell step size and perceived colour is not constant across the three dimensions. The equality of visual spacing is such that one value step (on a scale of 10 between white and

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black) is equal to two steps in chroma and 0.3 major hue step (3 step on a 100 step hue scale) at chroma 5 (Nickerson, 1936). In a series of articles, Indow and Aoki (1983) applied multidimensional scaling to the Munsell chips and pointed out a number of minor deviations from perfect equal spacing, both locally and globally. Despite these known minor deviations from the goal of local equality of visual spacing, the Munsell is often considered, because of extensive documentation (some 3,000,000 colour judgements by 40 observers), as the standard against which other colour order systems can be compared. Munsell realised that the natural colour space is highly irregular when it is represented without geometrical preconceptions. Thus, Munsell always conceived of his colour model as a sphere, but allowed for unequal dimensions of chroma at different levels of lightness and across different hues. The range of colours represented in a Munsell atlas is limited by the gamut of paints or inks used to create the colour samples. As a result, no simple geometrical form accurately represents perceptual colour space. All other colour models based on triangles, circles, squares, pyramids, cones, spheres, cubes or cylinders must (and do) grossly distort perceived colour relationships. The Munsell system has a few inherent problems. • • •

A variety of discrepancies were found in the perceptual spacing of colours, depending on their location in the colour space. The quantitative difference between colours could only be defined on a single colour attribute (lightness, chroma or hue) at a time. Complementary colours are not on opposite sides, so that one cannot predict the results of colour mixing very well.

As the intervals of hue, value and chroma are not perceptually equal, it is desirable to have diagonal dimensions added to the Cartesian space for assessment of perceived colour differences in all directions of the colour space using identical psychometric tasks. In plain language, it is impossible to represent uniform colour differences in a three-dimensional colour model. The human colour space is non-Euclidean.

SCOTDIC colour atlas SCOTDIC, a textile version of Munsell created by fusion of two quite different systems – Standard Colour of Textile (Japan) and Dictionnaire Internationale de la Couleur (France), is adopted by over 8 000 companies worldwide. Textile standard colours of the SCOTDIC colour system are widely used as colour tools by fashion colour professionals. The system has three versions – glossy (2 468 colours on polyester crepe fabric), matt (2 038 colours on cotton poplin fabric) and yarn (1 100 colours on wool yarns). It has incorporated many bright colours and the number on the constant hue chart has been increased to 54 (20 for wool).

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The SCOTDIC system uses a six digit code for each standard colour – the first two digits for hue, the second two digits for value and the third two digits for chroma. The prefix corresponds to the material of textile bases – P for polyester, C for cotton, W for wool. Therefore, the notation C-155010 means cotton standard sample having hue = 15, value = 50 and chroma = 10. In a recent study (Roy Choudhury, 2008) of locations of SCOTDIC cotton samples in CIELAB colour space, the following observations were made. •

•

•

For most of the Munsell hues, hue angles vary between 10° and 20°, but for a good number of Munsell hues, the hue angles vary within much broader ranges. For a few borderline Munsell hues, the hue angles vary from 0° to 360°. But if we consider that hues belonging to hue angles 0° and 360° are identical, the range of variation is quite narrow. Figure 2.1 shows constant SCOTDIC loci in CIELAB space at a constant value level of 60. As expected, constant SCOTDIC hue loci form radial lines emerging from origin and for many hues the lines are curved, especially at high chroma. The hues show unequal angular spacing and in many cases the spacing has changed at high chroma. The loci of constant chroma represent near-circles with diameter increasing with increase of chroma. Theoretically all constant chroma points should have been located at constant radial distance from the centre of an a*−b* 80 70 60 50 40

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20

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40

–20 –30 –40

2.1 Constant SCOTDIC hue loci in CIELAB space.

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diagram. In reality, the points are somewhat scattered and the radial distance from the centre is higher for yellow hues, i.e. the near-circles are tilted towards a+b* direction. In other words, for all levels of SCOTDIC chroma, the yellow hues have higher b* values as compared to other hues of identical chroma. The a*−b* values of identical SCOTDIC chroma but of different SCOTDIC values have not overlapped. Close observation shows at higher value levels, the near-circles tilted more towards a+b* direction as compared to near-circles of lower value. The study further showed that the actual SCOTDIC notations are quite different from Munsell notations predicted from reflectance data.

2.4.2 Practical colour coordinate system (PCCS) The Japanese Colour Research Institute (JCRI) developed two colour order systems – one of the largest collections globally, the ‘Chroma Cosmos 5000 atlas’ (1978) and a smaller collection, ‘Chromatogen 707’ (Birren, 1983). The numbers denote the number of chips in the respective atlas. The chips in Chroma Cosmos 5000 are represented in usual Munsell notation of Munsell hue, chroma and value. The chips are arranged in the planes of constant chroma, on which value (0.5 to 9.5 in steps of 0.5) is represented against hue (40 equally spaced hues for all levels of chroma except for chroma = 1, where 20 hues are considered). Later eight hues were supplemented and a few high chroma blue and green hues were dropped. Chromatogen 707 is based on the less known Japanese colour order system, PCCS. The system is a modification of the Munsell system in which chroma is replaced by PCCS saturation which is constant for the purest colour available for each hue. The chips are arranged in seven planes of constant PCCS saturation, on which Munsell value and hue are variables. Of the 5 000 chips in Chroma Cosmos 5000, only 1 325 have similar chips in the Munsell book of colour. Billmeyer and Loppnow (1988) reported that the chips are denoted by Munsell notations, but accuracy of many chips are poor, 31% of the chips had CIELAB colour difference greater than 3 units, 62% between 1 and 3 units and only 7% less than 1 unit against the corresponding Munsell chip. The maximum colour difference observed increases with increasing chroma. Of the 451 pairs having colour difference greater than 3 units, a large number of samples (60%) showed three or more cross-over of reflectance curves against respective Munsell samples. So different sets of colorants were used for JCRI and Munsell chips and some degree of metamerism is inevitable. Of the 83 chips in Chromatogen 707 having Munsell equivalence, only one had colour difference of 3 CIELAB units, 46 chips had the difference between 1 and 3 units and 36 less than 1 unit against the corresponding Munsell chip. So the latter system is more reliable.

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2.4.3 Natural colour system The natural colour system (NCS) was developed in Sweden (Härd and Sivik, 1981). It is accepted as the national standard of Sweden and many other European countries. By 1611 the scientist A. S. Forsius published the embryo of the NCS in his book Physica. The system is based on six primary colours suggested by Leonardo da Vinci (Hesselgren, 1984) and an opponent colour scale proposed by Hering (1872–4). Johansson (1937) first defined the concept of a ‘natural colour system’ and the Hesselgren (1952) colour atlas having 507 samples came onto the market in 1952. After thorough research at the Swedish Colour Centre Foundation, the first commercial atlas containing 1 412 samples was brought into the market by the Swedish National Building Research Foundation in 1979. A second revision was made to improve the accuracy of the samples and to exclude pigment containing harmful lead and cadmium in 1995 and the number of samples was raised to 1 741 with corrected notation of boundary samples and inclusion of low saturated samples. In this system six elementary colours, namely white (W), black (S), yellow (Y), red (R), blue (B) and green (G), are perceived as pure colours and cannot be described by other than themselves. All other colours can be described on the basis of their resemblance to these six elementary colours. The colour names in capital letters indicate pure or full colour and the colour names in small letters indicate the colour content. The three fundamental variables used by NCS are hue, blackness and chromaticness, i.e. the intensity of the colour sensation. Colour Plate VI (a) shows the NCS constant hue triangle with three corners, namely white (W), black (S) and pure chromatic colour (C). The distance of the location of the test colour from the corners indicate the whiteness, blackness and chromatic content respectively. The NCS colour triangle is a radial plane, normal to the hue circle, which shows samples with the same hue. NCS hue, ϕ, is defined as degree of resemblance of the test colour to the nearest two chromatic elementary colours. Y80R indicates 80% resemblance to red and 20% to yellow. The NCS hue circle, shown in colour Plate VI (b), is a horizontal plane showing samples with the same whiteness (or blackness). In the atlas, the samples are at every 20th hue step, starting at Y10R – namely Y10R, Y30R, Y50R, Y70R, Y90R, R10B, etc., ending with G90Y. The colours are placed in a polar coordinate system as in the Munsell system. While the 20 hue steps are acceptable for less chromatic colours, the perceived total difference or interval between the hues will be too large for strongly chromatic colours. Hence for chromaticness, c ≥ 40, the number of hues has doubled by adding extra hues as Y20R, Y40R, etc. In the second edition of the atlas, about a hundred more samples have been included having high whiteness and chromaticness, c ≥ 40. The acceptable tolerance is ±2 units for all NCS attributes. The sequence of NCS hues is similar to the CIELAB arrangement and opposite to the Munsell system. The hue circle sequence is Y → R → B → G → Y. It follows that Y50R is an NCS hue code, but R50Y is not.

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The opponent colour theory suggests that we can perceive the following two chromatic attributes simultaneously: • • • •

Yellowness and redness (e.g. Y80R) Redness and blueness (e.g. R20B) Blueness and greenness (e.g. B70G) or Greenness and yellowness (e.g. G50Y).

In a single colour element, it is not possible to perceive the two chromatic attributes, yellowness and blueness or redness and greenness in combination as described by opponent colour theory. Saturation (m) is expressed with a number between 0 for the achromatic colour and 1 for colour devoid of whiteness and is symbolised by the line S-C. The natural colour system is based on unique psychological perception. The huedifference between the neighbouring colours is not the same. For example, if at constant lightness and chroma there are 10 equal hue steps between unique yellow and unique red, there may be 20 to 50 hue steps of the same size between unique red and unique blue. The colours of different hues, but having equal NCS blackness and chromaticness are described by the colour designers as having a certain equivalence called equality of nuance or weight. From the NCS notations of nuance and hue, the relationship between colours can be illustrated graphically in a three-dimensional model called the NCS colour solid, having biconical shape limited by two points, white (W) and black (S). Every imaginable colour percept of the surface mode can be defined by a point and each point in NCS space denotes only one colour. When the NCS space is seen from above, the NCS hue circle can be seen. The side projection of a half of the NCS space is the colour triangle. The NCS system is diagrammed as equilateral triangles, but the maximum purity colours (called maximal colours) are not at the end point of a triangle. In over half the hues, the full colour is sampled by two or three colours in a vertical row inside the triangle. This is known as the oversaturated area. No simple correlate of CIE lightness or Munsell value is proposed in this system, as NCS blackness is claimed to be more readily perceived. NCS chromaticness, c, is the resemblance of the test colour to the colour of the same hue having maximum possible chromatic content. It can also be defined as the sum of the chromatic elementary attributes (redness, yellowness, greenness and blueness) of a colour. c=r+y+g+b

[2.4]

The most visually intense shade of a surface colour is defined to have a chromaticness of 100 and a blackness of 0 (and a whiteness of 0). A slightly less intense shade of the same hue may have a chromaticness of 80, for example. The lightest possible shade with same intensity of colour has a whiteness value of 20 and a blackness value of 0. The darkest possible shade with the same intensity

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of colour has a whiteness value of 0 and a blackness value of 20. NCS blackness (s) or NCS whiteness (w) is the resemblance of the colour to the perfect black or white respectively. The triangle shape arises as a consequence of the rule that the combined attributes of chromaticness, whiteness and blackness add up to exactly 100. s + w + r + y + g + b = w + s + c = 100

[2.5]

Since the sum of the attributes adds to 100, it is only necessary to quote two of the attributes. The two attributes chosen were blackness and chromaticness. The third attribute, whiteness, is easily obtained by the difference of the sum of the other two from 100, w = 100 – (s + c)

[2.6]

The NCS notation starts with an S, indicating that it is the second edition. Next the nuance is described by four figures (the nuance is the combination of blackness and chromaticness). Lastly, the hue is described by two figures between the two characters for the two sounding elementary colours. The colour S 2060-Y80R (Swedish standard, sc-ϕ), for example, has blackness (s) = 20, chromaticness (c) = 60 and the hue = Y80R (red with 20% yellow). These are shown in colour Plate VI (a) and (b) respectively. The following deductions can be derived from the above notation: The whiteness, w = 100 – (20 + 60) = 20. Redness, r = c × %R = (60 × 80)/100 = 48 Yellowness, y = c × %Y = (60 × 20)/100 = 12 or as c = r + y, y = 60 – 48 = 12.

The saturation, m is the relationship between the chromaticness and whiteness. m = c/(c + w)

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[2.7]

Pure grey colours have no hue and are given nuance notations followed by -N to describe neutral. The pure grey scale is a scale from white to black and the samples are provided from 0300-N which is white, to 9000-N which is black. Among strong chromatic colours of different hues, some appear lighter than others even if they are of the same nuance. Hering described that strongly chromatic yellow has an inherent lightness and strong chromatic blue has an inherent darkness. Lightness has been considered in the NCS system as a quantity of intensity determining how distinctly colours contrast to one another. Accordingly, NCS lightness value (v) of a chromatic colour is determined by comparing the test colour sample with the reference scale samples, with the colours juxtaposed and on the same plane (Härd et al., 1996). The chromatic sample is defined as having the same lightness as that grey reference sample against which its border appears to be minimally distinct (MGT) and the lightness of the grey sample is calculated from its blackness value, s, as follows:

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41 [2.8]

Like the Munsell system, the NCS is embedded in an atlas containing samples of 40 different hues and samples in steps of ten along the blackness and chromaticness scales. The NCS can be interpreted to have a fundamental greyness-chromaticness plane, no whiteness or blackness can be perceived in them. Some non-uniformity observed in chromaticness and hue spacing while analysing the NCS system with a nonlinear colour-appearance model, probably resulted from inaccuracy in the assessing and scaling method. Based on the analysis, a method is developed to predict NCS colour notations from colorimetric values x, y, Y using a nonlinear colour-appearance model (Nayatani et al., 1989). The spacing of NCS aim points in the CIELAB system has been studied by Derefeldt and Sahlin (1986). The aim points had been interpolated to derive CIE values of 16,000 notations in Swedish Standard, SS 01 91 01 (1983), but no accurate analytical relations between the NCS and CIE systems could be derived as in the Munsell system. When the samples are specified by CIE tristimulus values based on extensive visual observations, there may be abstract or conceptual systems, which do not require use of an atlas. The NCS system claimed to reflect universal perceptual processes (Härd and Sivik 1981); hence the conceptual version may be used independent of the atlas version. It is also stated that people without the knowledge of colour assessment can understand and describe the NCS notations of object colours in the absence of an atlas, after short training of about 15 minutes. But another study (Whitfield et al., 1988) showed that the accuracy of colour identification is over-estimated and the performance of the Munsell colour order system in similar situation is not much different.

2.4.4 Ostwald system The German chemist and Nobel laureate, Ostwald, who met Albert H. Munsell in 1905 on a journey to America, attempted to devise a colour order system, similar to that of the American painter, based on perception and equalising the respective differences between individual colours. Expressed in our modern technical language, we can say that Ostwald attempted to construct a perceptual coloursystem using non-empirical methods. In place of Munsell’s three parameters, he selected an alternative group of variables: namely, colour-content, white-content and black-content. He also introduced the special term ‘full colour’, by which he meant a colour which permitted the sensation of one single colour-tone (Munsell ‘hue’) and was not tempered by white or black. To be more accurate, we could say that a full colour is an optimally pure colour – in other words, of maximum saturation and at the same time bright. Full colours are, of course, ideal colours which cannot be reproduced by actual pigments. (When Ostwald published his

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Colour Primer, his full colours contained about 5% white and slightly less black, as he himself admitted.) Ostwald described a double-cone colour solid of related colours based on additive disk mixture. Ostwald (1915, 1916) devised this colour order system and the various physical exemplifications, including the ‘Colour Harmony Manual’ (1942–72), once developed but no longer available. The system was favoured by artists and designers because of the similarity of the construction and artist’s method of preparation of colour mix and has immense historical interest (Derefeldt, 1991). Ostwald’s original system and ‘Colour Harmony’ contained 24 hues, later the number of hues was increased to 60 in the ‘Swiss Colour Atlas 2541’ (SCA-2541) and related atlases (Müller, 1962–5). Like the NCS, Ostwald’s system is also based on Hering’s opponent colour theory. The hue circle was set up with Hering’s four unique hues – red, yellow, green and blue. However, instead of perceptually opponent hues, colorimetric complementary hues (i.e. hues lying on opposite sides of a white point in the chromaticity diagram) are placed opposite to each other. The hues, particularly the last two, are different from the NCS unique hues. The opposite hues, combined in proper proportions in a rotating spinning disk, must appear neutral grey. Other hues are selected by equal visual spacing in each quadrant and are represented by constant dominant wavelengths. The Ostwald hue circle (colour Plate VII (b)) begins with yellow at the 12 o’clock position and proceeds clockwise to red, blue and green, unlike the Munsell system where it starts from red and proceeds to yellow, green, blue, etc. The opposite hues are complementary and give achromatic colour when mixed optically. Ostwald’s colour circle consists of a sequence of 24 hues divided into eight groups of three, named yellow, orange, red, purple, blue, turquoise, sea-green and leaf-green. Like the NCS, the Ostwald system defines all colours as a mixture of full colour (r), black (s) and white (w) and the constant hue is represented in tri-linear space of full-colour content, white content and black content (colour Plate VII (a)). The important difference between the two systems is that in the NCS such planes are defined according to perceptual colour scale, while in the Ostwald system the planes are defined by additive colour mixtures of the three maxima located at the corners of the triangles. The system was set-up with colour appearance in mind (using Hering’s theory), but the samples were selected from additive colour mixing. In other words, the system represents a combination of a colour appearance system and a colour mixture system (Fairchild, 2006). This is based on Hering’s colour equation: r + s + w = 1. The equation was interpreted in terms of reflectance data. However, the real colours used in the disk mixture did not have idealised reflectances. The ‘Ostwald solid’, constructed of equilateral triangles, is much simpler in structure as compared to the Munsell solid. Colour Plate VII (a) shows a vertical cross-section through the Ostwald double-cone solid with two complementary hues (hues 1 and 13) at two ends shown as full colours. The central vertical axis is a grey scale. The full colours locate on the periphery of the central plane. All

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colours of a particular hue are placed on an equilateral triangle, i.e. half of the vertical section through the centre of the double cone shown in the figure is the constant-hue page of the Ostwald system. The runs of colour in this system are straight, symmetrically arranged and the end point colours are easily recognisable. As in Hering’s constant hue triangles, lines parallel to the line joining full colour and white represent colours of equal blackness and those parallel to the line joining full colour and black represent colours of equal whiteness. The colours of equal purity lie on lines parallel to the line joining black and white. For agreement between psychological scaling and psychophysical colour solid, Ostwald applied the Weber and Fechner law, which states that the perceived magnitude of a stimulus is proportional to the logarithmic of the physical stimulus intensity. Sixteen grades (‘a’ to ‘p’) in each direction, i.e. from black to white, black to full colour and white to full colour, resulted in 15 visual equidistant steps There are 120 chromatic samples in a triangle and a double letter system (black and white content) in addition to the hue number is used for their identification. The colour solid can be sliced in four different directions, namely equal hue, equal whiteness, equal blackness and equal purity. The three variables in the Ostwald system are hue, lightness and saturation, with saturation scaled in relation to full colours or optimal colours. The theory of optimal colour stimuli was developed by Austrian physicist, Schrödinger in 1920 and further developed by Rösch in 1928. MacAdam (1935) calculated chromaticity loci of optical colour (called MacAdam limits) as a function of luminous reflectance Y and these are different from the NCS colours of 100% chromaticness. Most saturated colours available at the time were used as ‘full colours’, whereas 100% chromatic colours are imaginary end points. No analytical relation has been proposed between the Ostwald and CIE systems. The Ostwald colour system remained popular for several decades following its introduction, but has now been very largely superseded by the American Munsell and Swedish natural colour systems. This is primarily because the original colours chosen for the system were laid out in such a way that (unlike the Munsell system) their arrangement could not be modified or extended as pigments and dyes of greater saturation were brought onto the market.

2.4.5 DIN system Work by Dr Manfred Richter on the DIN (Deusches Institut für Normund) system started in 1930 with the intention to substitute the older Ostwald system. The first physical embodiment with 600 matt samples was produced in 1960–2, a glossy edition with 1 000 samples was released in 1978–83 and then colorimetrically specified as German Standard DIN 6164 (Richter and Witt, 1986). DIN colour solid forms a modified double cone with distance from the full hue plane to white much shorter than to black. The system defines three scales – darkness degree (D), DIN hue (T) and saturation degree (S).

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Darkness degree (D) is the relative lightness scale with respect to that of an optimal colour having the same chromaticity. This is calculated as follows:

(

)

D = 10 – 6.1723 log 40.7 –Y– – 1 Y0

where Y0 is the maximum possible luminous reflectance of the optimal colour of the same hue defined by MacAdam (1935). For ideal black D = 0 and for ideal white D = 10. The scale is similar to NCS blackness rather than the Munsell value. DIN hue (T) has the usual meaning utilising 24 equally spaced hues of Ostwald hue circle with some simplification by defining lines of constant hue to be straight line radiating from white point in the chromaticity diagram. The hue circle starts with greenish yellow at 12 o’clock, followed by (clockwise) unique yellow (DIN colour 2), unique red (DIN colour 9), unique blue (DIN colour 17) and unique green (DIN colour 21); the missing colour numbers are intermediate hues. Saturation degree (S) is the chromatic amount measured by the perceptual distance from an achromatic sample of the same luminance factor. It is calculated as follows: S = [(u' – 0.2105)2 + (v' – 0.4737)2]1/2/r1

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[2.9]

[2.10]

where (u', v' ) are the CIE 1976 chromaticity coordinates of the colour; (0.2105, 0.4737) are the coordinates of illuminant D65; r1 represents saturation distance and is computed from r = r6/6; r6 is obtained by interpolation from a table according to the values of T and S. Six saturation steps were visually scaled at one lightness level only and extrapolated to other levels. Colours of equal saturation degree are located on roughly elliptical contours in the chromaticity diagram. The DIN colour chart based on the DIN system includes constant hue pages with rectangular sampling of darkness and saturation. Columns on a DIN page represent constant DIN saturation (constant chromaticity) and appear as shadow series (same object seen at different levels of illumination of same illuminant). The system is useful to illustrate the difference between chroma and saturation as it shows how the saturation is related to a shadow series, i.e. an object illuminated by decreasing illuminance levels of the same spectral power distribution. The methods for conversion of CIE and DIN coordinates have been discussed by Richter and Witt (1986). A number of compromises were made to keep a simple relation between DIN and CIE coordinates. The guiding principle of the DIN system is equality of visual spacing, but the equality of visual spacing was maintained locally and not globally in all three dimensions.

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2.4.6 OSA-UCS system If all the corners of a cube are sliced off down to the midpoint of each edge, a special form will result which mathematicians call a cubo-octahedron. Such a structure, with a centre and 12 corner points, was used in 1960 by the Optical Society of America in the design of their colour system. Intensive studies during and after the Second World War showed that it is difficult to represent hue, chroma and lightness in a Euclidean system. The committee on Uniform Colour Scales set-up by the Optical Society of America in 1947 proposed the OSA Uniform Colour Scale or OSA-UCS system which was described by MacAdam (1974, 1978). In spite of being a colour appearance system, it is quite different from the Munsell or NCS systems. It was claimed that best uniform visual spacing can be achieved on a regular rhombohedral (equalsided polygon) lattice, allowing closest uniform spacing in three dimensions. In 1953, the committee aimed to produce 500 chips. However, in 1967 the committee concluded that such an ideal space does not exist and modified its objective to the production of the best approximation to such a lattice for a neutral (/6) background. The committee also noticed the paucity of near-neutral samples and decided to add a series of such samples at half steps centred on L = 0. The near-neutral sample set consists of 134 samples range from L = –1.5 to L = 1.5. The revised atlas consisting of 558 chips (424 in regular set and 134 in pastel set) was produced in 1976. The system is not based on the separate scaling of three attributes like Munsell or NCS. In order to make each sample equally spaced from each of its neighbours, a regular rhombohedral 3-D space is required (Billmeyer, 1987) in which each colour (not lying on the boundary of the object colour solid) is surrounded by 12 neighbouring colours, all at perceptually equal distance from the given colour. If the 12 points of the nearest neighbours are connected, they form a polyhedron known as a cubo-octahedron (Fig. 2.2). The objective of equal colour differences in all directions results in a very different type of colour order system. The OSA space is designated in a three-dimensional Euclidean geometry, similar to opponent colour scale, named lightness (L), yellowness/blueness ( j, from the French term jaune) and greenness/redness (g). Figure 2.2 shows the cubooctahedron along with locations of L, j and g axes, central colour (M) and 12 neighbouring lattices. The signs of the attributes have meaning similar to the opponent colour scale: –L (light), +L (dark), +j (yellow), –j (blue), +g (green), –g (red). For the samples in the atlas j ranges from –6 (blue) to +12 (yellow), g from –10 (red) to +6 (green), L from –7 (dark) to +5 (light). The colour having L = j = g = 0 is neutral grey with 30% reflectance, similar to Munsell N/6. Hue and chromatic amount has no meaning in the OSA system. However, the ASTM subcommittee E12.07 has recently proposed the concept of OSA hue and OSA chroma. OSA hue = arctan (g/j), OSA chroma = ( j2 + g2)1/2

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+g

M –L

+L

–g

+j

2.2 OSA-UCS cubo-octahedron.

The OSA-UCS system uses a rectangular coordinate system in which unit spacing on the vertical (lightness) axis is √2 times on the horizontal chroma axis. Actual physical distance between two colours = [2(ΔL)2 + (Δj)2 + (Δg)2]1/2

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[2.12]

Δ indicates the difference in respective attributes of the two colours. Each colour can be displayed as a part of a two-dimensional array by cutting at various planes – horizontal, vertical and oblique. For the horizontal plane, L = constant. OSA-UCS colour solid is proved to be visually uniform for fairly large colour dissimilarities (14–15 just-noticeable difference units, the approximate size of UCS full step), not for small colour differences (Taylor and Billmeyer, 1988). CIE tristimulus values can be converted into L, j, g coordinates by a series of mathematical equations (MacAdam, 1974; Taylor, 1984), unfortunately the equations are not invertible. The equations and sample point specifications can be found in Wyszecki and Stiles (1982). The CIE tristimulus values are converted to cone sensitivity functions (R, G, B) that are much different from those of Smith and Pokorny. R10 = 0.799X10 + 0.4194Y10 – 0.1648Z10 G10 = –0.4493X10 + 1.3265Y10 + 0.0927Z10 B10 = –0.1149X10 + 0.3394Y10 + 0.717Z10 Lightness and two chromaticness coordinates are calculated as follows:

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[2.13]

Scales for communicating colours

Lightness, L = 5.9

[

1/3

Y0–2 3 + 0.042(Y0 – 30)1/3

]

47

[2.14]

where: Y0 = Y(4.4934x2 + 4.3034y2 – 4.276xy – 1.3744x – 2.5643y + 1.8103) yellowness/blueness, j = C(1.7R1/3 + 8G1/3 – 9.7B1/3) greenness/redness, g = C(–13.7R1/3 + 17.7G1/3 – 4B1/3)

[2.15]

where: C=

1 + 0.042(Y0 – 30)1/3 Y01/3 – 2/3

The variable C adjusts chromaticness for the lightness crispening effect and the variable Y0 adjusts lightness and chromaticness for the Helmholtz – Kohlrausch effect.

2.4.7 Coloroid system This Hungarian colour order system has been designed particularly for the use of architects and designers by Nemcsics (1987, 1993, 1994) and co-workers at the Technical University of Budapest. The system aimed at spacing colours evenly in terms of their aesthetic effects rather than in terms of colour differences as in the Munsell system or perceptual content as in the NCS system. The Coloroid system introduces the phrase ‘aesthetically uniform colour space’ for the first time. A scale is regarded as being aesthetically uniform when it appears to an observer as both complete and exhibiting gradual change. The idea behind this construction will become clear with the realisation that, when planning a coloured environment, a harmony must be created for colours with regard to hue, saturation and brightness. For the designer, aesthetic uniformity is more important than the ability to accurately register small differences in colour and then repeatedly reproduce them at the same value. For him harmonious interplay of the colours was more important than the actual differences between them. A series of experiments of great dimensions have been processed between 1962 and 1996 at the Technical University of Budapest, Hungary, and also in other countries, in order to formulate rules of colour harmony and describe aesthetic relationships. Nearly 80 000 observers performed 26 million elementary observations. The equality of spacing is considered to be ‘evenness’ of appearance in all scales of colours in the system and was done by extensive visual scaling based on harmony threshold instead of perception threshold. In harmony scaling, neighbouring colours are compared with a given group of colours and not with all hues. The colours are divided into five groups – yellow, red, purple, blue and green. The largest deviation between these two types of scales occurs in the green and purple colour ranges. These are areas where colour scales of the Munsell and DIN systems (perceptual scaling) differ most from the Coloroid system (harmony © Woodhead Publishing Limited, 2010

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scaling). The Coloroid system contains proportionally fewer purple and more green samples than the above two systems. Like the Munsell system, the colours are arranged inside a normal circular cylinder with achromatic colours along its axis closed by absolute white and absolute black at two ends and hues varying with the angular coordinate. The system represents colours by three numbers – hue (A), saturation (T) and lightness (V). The system is composed of 48 basic hues having constant dominant or complementary dominant wavelength, numbering 10–76 with some missing numbers. Intermediate hues are represented by decimal numbers (e.g. 12.673). The extreme reds and violets, beyond dominant wavelengths 625 and 450 nm respectively are omitted from the system. In the Munsell system as many as four hues may have the same dominant wavelength depending on saturation. Again the dominant wavelength of colours having the same Munsell hue, say 5YR, but different lightness and chroma, may vary. The Coloroid hue value, beyond the notation ‘A’, can be expressed also by the angular value φ around the D65 white point in the CIE xy system. Coloroid saturation ‘T’ is defined as the percent spectral colour (or the nonspectral purple) in an additive mixture with perfect black and perfect white to match the colour. A linear relation exists between excitation purity and Coloroid saturation. The relation between Coloroid saturation and Munsell chroma is as under: T = kAVC2/3

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[2.16]

where kAV is a variable depending on hue and lightness and ‘C’ is the Munsell chroma. Coloroid lightness ‘V’ is defined as a square-root function of luminance factor claimed to produce optimum aesthetically even spacing. The lightness scale is developed from the equal harmonic interval between absolute white and absolute black. Grey scales of the Coloroid system are reported to vary visually uniformly, whereas those of the Munsell and DIN systems vary more gradually in the darker ranges. Both the Coloroid saturation and lightness are represented on a scale of 1–100. The relation between CIE Y and Coloroid lightness V is same as that of Hunter (1942): V = 10 Y1/2

[2.17]

No analytical relation has been proposed for Coloroid hue and saturation with CIE tristimulus values; however they are directly related to dominant wavelength and excitation purity, which can be linked with the CIE chromaticity diagram. Hirschler (2008) criticised the Coloroid system as follows: •

The system is full of contradictions. It was originally launched as a perceptually uniform system, but after a lot of debate it was decided to call it aesthetically uniform.

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• •

49

Nemcsics investigated unevenness in Munsell colour spacing. At several regions of colour space, the spacing in the Munsell and Coloroid systems are different. This is probably because uniform variations of colour stimuli at several regions elicit uneven variations of colour perception, or human colour perception is uncertain in these regions of colour space. The coordinates for basic hues in the original Coloroid system are specified to three decimals (e.g. 0.001 nm), which is absurd. The Coloroid system treats spectral colours as if they were surface colours and make them the ‘basic colours’ of the system. Moreover pure red and pure blue are considered as primary colours for both additive and subtractive colour mixing. Both are contradictory to generally accepted views.

2.4.8 Other less known systems There are a few less known and newly developed colour order systems such as the RAL system, Chevreul, Pope colour system, Colorcurve, Eurocolour system, Acoat system, etc. Some of these systems are defined by a set of aim points specified in the CIE system. RAL system RAL atlas (BS: 5252) is a successor to the German DIN atlas and is based on CIELAB colour space. It comprises 1 688 colours, each with a seven-digit notation describing hue, lightness and chroma, e.g. RAL 210 60 30. Hue, the horizontal angle in the colour space, runs from 010 to 360 – in 10° increments, so there are 36 hues. Lightness, the vertical axis of the colour space, runs from 0 (black) to 100 (white). Chroma corresponds to the distance from the vertical axis, with achromatic colours at zero chroma. Saturated (maximum chroma) colours vary from hue to hue and with lightness so, as in the CIELAB and Munsell colour spaces, the envelope is an irregular shape. Chevreul colour order system M. E. Chevreul (1786–1889), a French chemist and director of a famous French manufacturing company, described a three-dimensional colour space (Heila, 1991) in the form of a hemisphere in which 12 pure colours and gradations, with the adjacent colour running clockwise, formed a 72-hue chromatic circle from the base. Twenty grades of lightness of the corresponding hue are located on radial lines from the white centre, ending in black on the periphery of the circle. In this ladder the full colour of each hue is located at its appropriate level of lightness. However, Chevreul later placed all full colours on the same grade. The modification of pure colour by grey forms the upper part of the hemisphere in the form of a quadrant, the radius of which is equal to that of the circle. The Chevreul system

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was never fully illustrated. Ten 72-hue circles form the full colours with increasing amounts of black and twelve constant hue ladders represent the key colours of the base plane and were prepared by Chevreul using paper printing techniques. The Chevreul colour order system was useful for the artists desiring to paint in a naturalistic manner as well as for those who were interested to know the change of colour due to light effects. Pope colour system The rounded double cone with tilted central plane colour solid proposed by American art educator Arthur Pope (1880–1974) is based on the subtractive mixing of pigment colorants, primarily to assist the understanding of the relationships among colours produced by the artists putting less importance on colour notations and more an visual perception (Heila, 1988). The system has three attributes, namely: value, hue and intensity. Twelve pure colours (of 100% intensity) are placed at equal distances from the neutral axis. Scales of purity (i.e. the degree of blackness) and brilliance (i.e. the degree of whiteness) lie on the two clearly placed and visually evident planes. This is similar to the black or white content of the Ostwald system. Pope, however, acknowledged that his system is based on approximations rather than scientific measurement. Colorcurve colour order system

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This colour communication system (Stanziola, 1992) represents a combination of colour appearance and colour mixture systems. Eighteen constant lightness planes were constructed using CIELAB space and L* levels ranging from 30 to 95 in steps of 5. A few extra levels at higher L* were included for light colours which are popular for wall paint. At each lightness level, nine hue points with specified (a*, b*) values, i.e. of specific hues, were defined using principles of colour appearance space. Each quadrant of the a*b* plane was filled with a rectangular sampling of additive mixtures of grey and three chromatic (hue) starting points in that quadrant. Equal steps in the Colorcurve designations represent equal additive mixtures between the four starting points. After defining the aim points by additive mixing, the standards were formulated with real pigments. The system is represented by two atlases on nitrocellulose coated paper – the master atlas with 1 200 standards at 18 lightness levels and 956 additional standards in a grey and pastel atlas. As standards are specified by spectral reflectance characteristics, it is possible to prepare a spectral match. The samples thus prepared are universal matches and the viewing illumination is not important in these cases. This is not possible with other colour order systems. The standards in the atlas are circular and not square as in other systems. This avoids contrast illusion of dark spots at the corners between the square samples (Hermann grid illusion) (Fairchild, 2006).

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Eurocolour system The Eurocolour system exhibits planes of constant CIELAB hue angle on which CIELAB chroma and lightness are variable. An atlas was published by Schwabenmuster in Germany (Gall, 1984), but it is no longer available. Acoat system An atlas based on the Acoat system was published by Sikkens GmbH (1978) in the Netherlands for paint industries. Using the techniques of colorimetry, the Acoat colour coding (ACC) system was intended to facilitate the uniform supply of colour-batches and colour-charts while attaining clarity through even spacing in the system and economy through the avoidance of complex conversion procedures between perceived and actual values; to offer, in other words, economically priced colour samples (Döring, 1981). The ACC system comprises a cylinder with a base circle divided into 24 colour segments arranged alphabetically. The two other parameters, brightness and saturation, can register 100 graduations numbered 00 to 99. The chromatic content does not distinguish saturation or chromaticness (Krewinkel, 1979).

2.5

Comparison and interrelation of various systems

The principal goal of colour order systems is to facilitate the specification and communication of colour information. The existing colour order systems will not be superseded by a single universal system because (Rhodes, 2002): • • •

different systems have already been adopted as either national or industry standards, many users are highly experienced with a particular system and the change in the system is time consuming, expensive and less attractive, the historical data, such as colour differences in a system, are difficult to transform into other forms.

In the absence of a universal system, the colour communication may need interrelation and conversion between existing systems. The computer software has been developed for inter-conversion, but the source code has not been published. Smith et al. (1990c) compared different colour scales, using the OSA-UCS as a benchmark for comparison. OSA-UCS atlas samples were mapped on to other colour spaces to check the perceptual spacing of the respective colours atlases. They observed that • •

the NCS system is most radically different in hue spacing from that of the OSA-UCS system, the OSA chroma, Munsell chroma and NCS chromaticness have similar but non-identical axis,

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• • •

Colour measurement OSA chroma, DIN saturation and Coloroid saturation are distinctly different from each other, NCS, DIN and Coloroid achromatic scales are distinctly different from the OSA-UCS lightness scale, and the Munsell and OSA-UCS spaces are closer.

Judd and Nickerson (1975) derived idealised relations between the NCS and Munsell systems. Billmeyer and Bencuya (1987) found that no simple relation could be written between NCS hue, NCS chromaticness and NCS blackness against Munsell hue, Munsell chroma and Munsell value respectively. However, they were convinced that the two systems sample the same underlying colour space. No analytical relation could be formulated, possibly due to incompatibility of their respective aim points. It is suggested that some smoothening of NCS aim points may be necessary, as for Munsell renotation in the Munsell system. One reason for the difference between the two systems is reported to be the lower precision of the magnitude estimation technique used to scale the NCS system as compared to the ratio scaling technique used to scale the Munsell system. A colour notation conversion program was developed (Smith et al., 1990d) for mutual conversion between the Munsell, OSA-UCS, NCS, DIN, Coloroid and CIE systems. The conversion was based on the principle that the colour order systems are defined by their aim points defined by CIE coordinates. Conversion from one system to another, therefore, can be achieved by converting the given point in the source system onto CIE colour space and then by converting the coordinates onto the target colour space. Two problems are associated with this conversion. First, illuminant, illuminating and viewing conditions should be same in both cases. Correction is not possible for variations in the above conditions. Rhodes (1995) tried to compensate the differences in illumination conditions and media through the application of a colour appearance conversion model. Secondly, the aim points and actual samples are not necessarily the same. Smith and Billmeyer (1994) compared the attributes of different colour order systems which can be summarised as follows.

2.5.1 Representation of hue The OSA-UCS and Colorcurve systems use a grid arrangement having only four constant hue planes. Most of the other colour order systems represent constant hue along radial lines. All but the NCS colour order system have colours spaced at visually equal steps around an achromatic axis. NCS is based on four elementary colours located 90° apart on opponent axes as is the CIELAB colour scale. In the Munsell system, the hues are so arranged that equal small hue differences occupy equal angles around the entire hue circle, and hence the unique hues are located at irregular angular spacings – red to yellow 67°, yellow to green 75°, green to

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blue 90° and blue to red 128°. This difference in hue spacing is because the NCS system is based on colour-appearance magnitude, while the Munsell system is based on colour-appearance differences. Since the Munsell system is based on polar coordinates, the samples at chroma 4 have larger hue difference than those at chroma 0.5. The samples of Colorcurve are based on cartesian coordinates, with samples at the corner of a square grid. Compared to the Munsell ‘Nearly Neutral Collection’, the ‘grey and pastel’ Colorcurve atlas furnishes samples of higher lightness, however the number of samples in both the atlases are approximately the same – 1 132 in Colorcurve and 1 100 in Munsell.

2.5.2 Representation of chroma/saturation The notations of chromatic amount are of three types – chroma, saturation and a combination of whiteness, blackness and chroma. Munsell chroma is independent of Munsell value. DIN saturation and Coloroid saturation are radically different in spite of the fact that both take into account the effect of lightness. In a perceptual uniform colour space, colours of equal Munsell chroma lie on the surface of a cylinder whereas colours of equal DIN saturation lie on the surface of a cone (Robertson, 1984). In the NCS and Ostwald-based Swiss Colour Atlas (SCA-2541) systems, the concept of chroma is tightly bound between two achromatic scales – the sum of the chroma, blackness and whiteness values is always constant. Smith and Billmeyer (1994) summarised that the three approaches to chromatic scales are not comparable. NCS and SCA-2541 chroma are more close to Munsell and OSA-UCS chroma than DIN and Coloroid saturation. Judd and Nickerson (1975) attempted to derive chroma-chromaticness conversion and postulated a simple proportionality, NCS chromaticness, c ≈ 5 × C (Munsell chroma)

[2.18]

Billmeyer and Bencuya (1987) found a still good approximation as C = Ac + B

[2.19]

where A and B vary with hue.

2.5.3 Representation of achromaticity There are two approaches producing substantial underlying scaling differences: 1 2

Lightness axis in Munsell, OSA-UCS, Colorcurve, Coloroid and DIN (opposite notation Darkness). Blackness-whiteness-chroma combined relationship of NCS and SCA-2541.

Billmeyer and Bencuya (1987) suggested the following relation for achromatic colours between Munsell Value (V) and NCS blackness (s).

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Colour measurement V = 10.03 – 0.1248s + 1.209 × 10–3s2 – 8.793 × 10–6s3

[2.20]

The relation for chromatic colours in Munsell and NCS systems is considerably complicated.

2.6

Accuracy of colour order systems

The accuracies of the NCS, DIN and OSA-UCS atlases have been studied (Smith et al., 1990a). It was found that the accuracies for the DIN and OSA-UCS systems are similar. Initially it was reported that these systems are on average 3½ times more accurate than NCS colour atlas samples. However it was corrected further (Smith et al., 1990b), saying that the errors of NCS atlas samples on average are approximately one ΔECIELAB unit. The error for the DIN and OSA-UCS systems varies between 0.11 and 6.48 ΔECIELAB units, whereas that of the NCS system varies between 0.04 and 16.21 ΔECIELAB units. The major source of inaccuracy for NCS samples present on the edge of the NCS colour solid. The samples of NCS blackness = 0 or NCS whiteness = 0 are highly inaccurate (Smith et al., 1991). However Döring (1995) pointed out that the accuracy of NCS samples are independent of chromaticness. In the outside gamut of the NCS solid, all colour samples deviate from their aim points systematically towards the centre of the colour solid. For chromaticness greater than 50, the accuracy decreases slightly. A visually ordered colour atlas permits selection of not only specific colours found in the set, but also of a way to specify many intermediate colours by visual interpolation. A set of 1 000 colours may allow one to visualise and specify 100,000 colours. The accuracies of visual interpolation for various colour order systems, namely Munsell, NCS and DIN 6164, were studied by Döring (1990). The uncertainties during visual interpolation had been found to be independent of colorimetric precision of the colour samples in the atlas. Döring also observed that the mean colour difference (ΔECIELAB) between colour notation by colour measurement and by visual interpolation were 2 ± 2.7 and 4 ± 3.9 respectively for the DIN and NCS systems which reduced to 1.2 ± 2.8 and 1.7 ± 2.6 for low to medium chroma samples.

2.7

Computer-based systems

Though colour atlases are convenient, portable, easy to understand and relatively cheap, there are several reasons for the increasing popularity of computer-based colour order systems (Rhodes, 2002): •

•

The cost of colour atlases, especially those containing tight-tolerance colour samples, are ever-increasing and a set of multiple atlases are not affordable for many users. On the other hand, computers and software are becoming a costeffective alternative. The physical atlases are inherently portable. On the other hand, LCD and other low-power displays have made portable computers a viable alternative.

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•

•

•

•

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The latter systems can be used anywhere, depending on the availability of the software. The availability of coloured chips in an atlas is limited by the practical constraints and costs and generally restricted to around 2 000 – far below the number of perceptible colours (a few million). Computer equivalents have no such limitation and any number of colours can be interpolated. An electronic or digital atlas can represent colours outside the colour gamut, but the accuracy of display may be questionable. Computer software can instantly convert colours from one notation system to another, while for physical samples even experienced observers need substantial time for specifying colours in a colour notation system. Physical coloured samples have a limited lifespan – they fade, scratch and soil easily. These limitations are not applicable to computer monitors and they can accurately represent colours if properly characterised and periodically calibrated. To avoid metamerism, physical colour standards are recommended to be viewed under a specific illuminant, which may not be portable. Monitor colours are self-luminous and such a problem does not arise. The greatest advantage of computer-based systems is that colours can be communicated globally through electronic networks, even in the absence of physical samples.

The monitors and printers follow device-dependent specification systems. In cathode ray tube (CRT) displays, colour television, and most computer video displays, colour stimuli are generated with three different types of phosphors after activation by electron beams. The three additive primary colours generated by such activation are orange-red, leaf-green and violet. A large number of colours can be created by their mixture. The two most common additive systems used in connection with computer displays are RGB (based on mixing three additive primary colours red, green and blue, produced by the phosphors of the display unit in cubic space) and HSB (hue, saturation and brightness in cylindrical form). RGB is a device-dependent colour space. Not all monitors or other RGB devices can produce the same range of colours. The term gamut is used to describe the universe of colours a given device or other range of colours can produce or describe. A better monitor, for instance, probably has a wider gamut than a cheaper one does and older monitors will have a harder time than newer ones, since their phosphors are starting to wear out. With today’s technology, a CRT monitor has a wider gamut than an LCD one does. If we feed 255, 0, 0 (pure red in RGB) to one monitor then we might get a more saturated red than another monitor is capable of. Each is doing its best to put out pure red, and neither can do a perfect job of it, but one may do better than the other. A colour space is a particular instance of a colour model that describes the specific colours one may get for each combination

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of numbers (red, green and blue in this case). Thus, a colour space differs from a colour model in that it maps specific values to specific colours while a colour model only determines that the values will be the red, green and blue components (or whatever) without saying how much of any given component is needed to get what specific result. Every RGB device (scanner, monitor) will have its own unique colour space even though they all share the same RGB colour model. When the exact chromaticities of the red, green and blue primaries are defined, the colour model then becomes an absolute colour space, such as sRGB (s = standard) or Adobe RGB (which has a significantly larger gamut). A set of primary colours, such as the sRGB primaries, define a colour triangle inside the chromaticity diagram. Only colours within this triangle (colour gamut) can be reproduced by mixing the primary colours. The chromaticity of illuminant (D65, D50 or C) is the white point. The chromaticity coordinates of red, green, blue and white point are (0.64, 0.33), (0.30, 0.60), (0.15, 0.06) and (0.31, 0.33) respectively. As of 2007, sRGB is by far the most commonly used RGB colour space, particularly in consumer grade digital cameras, high definition video cameras, computer monitors and high definition televisions, because it is considered adequate for most consumer applications. Having all devices use the same colour space is convenient in that an image does not need to be converted from one colour space to another (colour management) before being displayed. However, sRGB’s limited gamut leaves out many highly saturated colours that can be produced by printers or in film, and thus is not ideal for some high quality applications. The wider gamut Adobe RGB is being built into more medium-grade digital cameras, and is favoured by many professional graphic artists for its larger gamut. The mixed colour stimuli are represented in the RGB colour cube. The abbreviations (R, G, B) are used to represent, loosely, the three additive colour primaries used. The cube resembles the Benson cube (Kuehni, 2003) in which white and black are placed on two opposed corners of the tilted cube with yellow, pink and sea-green on the upper three intermediate corners and red, blue and green on the lower three. The centre of the cube is occupied by a medium grey. For a colour, the standard values of the three components in the RGB system range from 0 to 255. This gives us 256 different possible values for each primary colour which works well with the way computers store numbers. It is possible to generate 16.7 million different possibilities (256 × 256). As the cube is rotated, the white and black fall on a vertical axis, a version of a polar coordinate system is imitated and termed as HSB space. In this space, hue is expressed in hue angle in degree. Saturation is expressed in percentage – 0% at achromatic point (grey) to 100% at full saturation. Brightness is expressed as a percentage from 0% at black to 100% at white. Achromatic colours have identical values for the three components, while for chromatic colours they have different values. Both the spaces are regular but not uniform. The gamut or maximum chromatic range, possible to create, is dictated by the phosphor used. These systems are based on increments of colour stimulus and have no connection to perceptual scales (Kuehni, 2005).

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On a printer, a complete absence of any ink would leave the colour of the paper to be printed (usually white) unchanged. The RGB system does not work too well for printers since they have to combine various inks to get the desired colour and it is not possible to produce inks that are sufficiently pure in colour. Generally, black ink is added as a fourth colour to deal with the situation. Printers therefore work differently from monitors to produce colour and we most often use printer inks with colours different from the primary colours used by monitors. The CMYK colour model uses cyan, magenta, yellow and black inks (K is used to avoid confusion with blue) combined to produce various colours. A white colour has zero values for all components, while the grey scale differs in percentage of K. A chromatic colour may have percentage values in all four categories. The gamut of CMYK is usually smaller than the gamut for RGB because of the limited chroma of printing primaries. HSL and HSV are two related representations of points in an RGB colour space, which attempt to describe perceptual colour relationships more accurately than RGB, while remaining computationally simple. HSL stands for hue, saturation, lightness, while HSV stands for hue, saturation, value. The HSV model (Fig. 2.3(a)) forms a single hexacone colour space starting from black ‘K’ (S = 0, V = 0) with the grey scale run vertically and ends at white ‘W’ (S = 0, V = 1) with six corners with primary and secondary colours, namely red ‘R’ (H = 0°), yellow ‘Y’ (H = 60°), green ‘G’ (H = 120°), cyan ‘C’ (H = 180°), blue ‘B’ (H = 240°) and magenta ‘M’ (H = 300°). The HLS model (Fig. 2.3(b)) forms a double hexacone space in which the white point is stretched to form the upper hexacone at L = 1. In the former model, the white point lies in the centre of a hexagon, while in the latter it is the starting point of the upper hexacone. For three sets of RGB values, the corresponding HSL and HSV values are shown below: RGB (1, 0, 0) (0.5, 1, 0.5) (0, 0, 0.5)

HSL (0°, 1, 0.5) (120°, 1, 0.75) (240°, 1, 0.25)

HSV (0°, 1, 1) (120°, 0.5, 1) (240°, 1, 0.5)

The first device, on independent colour specification system for display users, was commercialised by Tektronix (1990). In the TekHVC system based on CIELUV, the hue (H) is offset (hUV – θ) by angle (θ) so that 0° corresponds with illuminantdependent ‘best red’ at u' = 0.7127 and v' = 0.4931. The chroma (C) is multiplied by a scaling factor, while V is identical with L*. It is a widely used, devicedependent, cross-platform colour notation system. However, like the CIELUV and CIELAB systems, physical embodiment is not available with the system. Various software packages also implement individual colour notation systems. Adobe Photoshop (www.adobe.com) displays colours in terms of RGB, HSB, CMYK and CIELAB values. In addition, the Adobe Colour Picker allows choosing custom colours from the Pantone Matching system, the Trumatch Swatching system, the Focoltone colour system, the TOYO Color Finder 1050 system, the ANPA-Colour system, HKS colour system, and the DIC Colour Guide.

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V G

Y

W

C

S

R H

B

M

K (a)

W

L

Y

G

S

C

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M

K (b)

2.3 (a) HSV and (b) HLS colour spaces.

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The Pantone matching system includes 1 114 solid colours (premixed ink) and 3 000 process colour (colour separable, CMYK ink) combinations. The Trumatch system, represented in a swatch book containing 2 000 process colours, is slightly more perceptually based than the Pantone system and allows computer users to select CMYK colour specifications according to the appearance of printed patches instead of the approximate colour represented on CRT displays. The user chooses the desired colour from the swatch book and uses CMYK values to colour the images, ignoring the colour displayed on the monitor. A colour profile is a file that the computer uses to understand what it needs to know about any given colour space. It does this by mapping colours in an internal colour space (usually CIE LAB or CIE XYZ) used by the colour management system (CMS) embedded in the operating system or other software. This internal colour space is known as the profile connection space and serves only as a way to map colours in one space to those in another. Standard ‘ICC-profiles’ are produced according to a norm of the International Colour Consortium (ICC), in order to reproduce colour files on diverse output devices with colour fidelity. The procedure can perfectly adjust colour files via ICC-profile, e.g. for offset print, provided accurately documented high value profiles are available. The ICC process for pictures containing many colours delivers good overall results. However, our naked eye may find better matching RGB/CMYK values for individual specific colour tones than the calculation does. An ICC profile contains between 400 and 1 500 interpolation points in which the comparison colours are mathematically interpolated. This is by no means enough to filter out the best-fitting field from the approximately 20,000 CMYK colour fields within a sensible atlas. Imprecise results may occur when an ICC profile is used to convert a specific RAL colour into RGB or CMYK.

2.7.1 Digital colour atlases Most of the material based atlases are now available in digitised form. ‘Colourtalk’ software system (Rhodes et al., 1992) incorporates both an on-screen visualisation of existing colour notation systems and also the transparent inter-conversion between them. The NCS Digital Atlas (www.ncscolour.com) is a colour atlas that visualises all the 1 950 NCS original colours specified in CMYK and positioned in the NCS colour space. The NCS colours can easily be selected from the digital colour palettes by clicking on the colour sample in the colour library of the software. The RAL system is also available for various CAD packages, through RAL Digital. The Coloroid Colour Plan Designer (1994) software generates very simple and user-friendly harmonic colour sets in computer monitors, and it can be applied in architecture, computer graphics, visualisation, product design, web page planning, in the paint industry and other fields, where harmonic colour sets are required.

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The Designer supports monochromatic, dichromatic and trichromatic harmonies, based on 1, 2 and 3 basic hues, respectively. The software takes the level of ambient light in consideration, using a colour appearance model, CIECAM97. Coordinates of colours, selected interactively by mouse or by defining coordinates, will be transformed in several colour systems, like CIE XYZ, xyz, Lab, Luv, Hunter Lab, display RGB with the corrected g values, and linear rgb in [0,1] assuming the sRGB primaries, and also all of Coloroid related data, like A, T, V, φ, additive components of s, w and p, and all of the hue angles and ‘A’ hue coordinates with highest harmony. A number of colour harmony rules like ‘Equidistant colour scales are always harmonic’ have been suggested in the software (Neumann et al., 2005). The Digital Colour Atlas 3.0 (www.dtpstudio.de) enables comparing colour tones from about 150 colour systems (e.g. Munsell, Pantone, RAL, etc.). Persuasive harmonies can be calculated quickly and every colour from every system can be imported into any software. 200 colour fans and CMYK-books were measured spectral photometrically and all the calculations are based on this huge CIELAB database (about 200,000). During colour comparison, the program searches for the colour which has the minimum colour distance (ΔECIELAB) to the input colour. The advantages of the Digital Colour Atlas are: •

•

•

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• •

The colour samples can be compared with reasonably high precision very quickly (maybe in a fraction of a second) as compared to hard copy comparison, which may take several minutes, e.g. CMYK values needed for RAL 3000 can be found in fractions of a second. A further advantage is the independence of such comparison from ambient light. On the other hand, paper or textile colour atlases are to be used in standardised artificial light. Many colour atlases are difficult to obtain or are no longer available. The digital colour atlas can specify colours in terms of several atlases and colour order systems in digital format even in their absence. Colour harmonies can be created on a CIELAB basis assuring accuracy to colour perception. Very fast visual communication in the trade.

In the software an approximate evaluation takes place according to the following conditions: ΔECIELAB Difference between two colours < 0.5 1 2 <5 < 10 10

not distinguishable distinguishable by expert distinguishable slightly different different, but still fairly similar different

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The eye is much more sensitive to bright, unsaturated areas than it is to dark or heavily saturated colour areas. In other words: ΔECIELAB = 2 leads to an obviously deviating colour when dealing with a pastel colour, whereas the same difference would be barely noticeable with dark colour tones. The eye is also more sensitive in the orange and yellow colour areas.

2.8

Universal colour language (UCL)

Many of the problems of colour technology could be more readily solved if everyone used a universal colour language that is understandable by all, at least in a general way. Such a language should allow colours to be described with different degrees of accuracy (by names or numerical notations), relate directly to the best known colour order systems, and provide meaningful translations of exotic or promotional colour names (Billmeyer and Saltzman, 1981). The NBS/ISCC colour naming system existed for over 15 years and was sold by the National Bureau of Standards (Washington, USA) in the name ‘Color: Universal Language and Dictionary of Names’ (special publication 40), but surprisingly it has not yet been widely adopted. It provides six levels of accuracy to describe colour by names (levels 1–3) or numerical designations (levels 4–6). In the first three levels of the UCL, everyday language is used to describe colours. Level four of the UCL is for the colour order systems, level five allows for interpolation between the colour samples of a colour order system, and, at level six, colorimetry provides precise specification. Level 1 Least precise, number of divisions of colour solids is only 13, represented by generic hue names and neutrals, e.g. brown. Level 2 The number of division increased to 29 by incorporating all hue names and neutrals, e.g. yellowish brown. Level 3 The number of colour samples increased to 267 as in the ISCC-NBS collection using all hue names and neutrals with modifiers, e.g. light yellowish brown (centroid # 76). Level 4 The collection of colour standards is increased to 943 (7 056 in higher version) based on systematic sampling within colour solid on the basis of the Munsell colour order system, e.g. 10 YR 6/4. Level 5 The colour discrimination can be increased by up to one million colours by visual interpolation of Munsell notations, e.g. 9½ YR 6.4/4¼. Level 6 The most precise, number of divisions of colour solid may be as large as five million on the basis of CIE (x, y, Y) or instrumentally interpolated Munsell notations, e.g. colour having specification as x = 0.38, y = 0.37 and Y = 34.7.

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The ISCC-NBS method of designating colours (Kelly and Judd, 1955) is based on the most comprehensive investigation done on relating colour names to areas or volumes of colour space. Thousands of visual estimates were made to relate colour names to Munsell notations and the range limits of each colour word was charted in Munsell space. The reference colours of the standardised language are called ‘centroid colours’. Because of non-linearity in our visual apparatus and irregularities in our natural-language system of colour names, not every hue has the full complement of modifiers. There are in fact only 267 centroid colours. That is a good practical number, small enough to be easily learned but large enough to make the distinctions needed for many applications. Since the eye and brain can only distinguish about 300 colours by memory, this system is of about the right size for distinguishing basic colours. A series of centroid chips are available which represent each of the colours. The chips are identified by descriptive colour names together with serial numbers. For example, school-bus yellow would carry the designation ‘Moderate Orange Yellow’. The system is useful for colour identification in design, architecture and art and about 7 500 colour names are listed and cross-referenced. Since its publication in 1955, thousands more names have been devised and one can presume that there is no end in sight. Simon (1995) regarded the colour ‘blue’ of level 1 as Munsell purple-blue and proposed a division of the area adding cyan/ turquoise. Since the CIELAB method is widely used for the numerical representation of colours, the Munsell system was mapped into this space by Pointer (1981), Hunt (1982), Nayatani et al. (1988) and others. However, it is not feasible to use a simple distribution of the metric hue angles to represent various Munsell hues around the circumference of a*b* space. Despite the limitation that the Munsell hues are not equally spaced in the CIELAB diagram, several computer programs have opted for simplicity and segmented the a*b* diagram into equal-angular divisions. This treatment leads to errors in two ways: 1 A plot of the true Munsell centroid versus the equal angle centroid shows varying hue angle difference for various chromatic colours. 2 Visual names do not match with the Munsell hue name, e.g. a colour with Munsell designation of 3.2 B 6.1/9.2 actually appears green and not blue. To correct the problems with equi-angle segmentation, unequal hue angle was obtained by mapping the ISCC-NBS named colour areas onto a*b* space (Simon, 1995). The hue angle boundaries, thus obtained, are shown in Table 2.1 against names under the universal colour language along with centroid colours. Colour zones, a flexible system for describing colours which links everyday language to colour order systems based on that of the Natural colour system (NCS), similar to the UCL system, is also proposed (GreenArmytage, 2002).

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Table 2.1 Universal colour names (level 1), centroid colours and hue angle boundaries Sr. No.

Universal colour names

Centroid colours

Hue angle boundaries

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Red Pink Orange Brown Yellow Yellow-green Green Olive Cyan/Turquoise Blue Purple/Violet White Grey Black

2.5 R 5/10 2.5 R 7/8 2.5 YR 7/10 2.5 YR 3/4 7.5 Y 8/12 5 GY 7/10 5 G 6/8 5 G 4/4 7.5 BG 5/6 2.5 PB 4/10 5 P 5/10

351–38 320–38 38–77 38–77 77–100 100–128 128–200 128–200 200–225 225–285 285–351

N/4

0–360

2.9

Future trends

There are relatively limited ranges of fibre types, yarn constructions, spinning methods and fabric forming techniques that form the basis for designing textile materials. By contrast, the range of colours that can be applied to these textile substrates is extremely wide. Though at least one million colours can be perceived, far less colours are produced on textile substrates. From a vertical production-led organisation in the 1980s, the textile industry has changed into a retail-specified manufacturing industry. The retailers now need to have a major input into the colour selection process as selection of an appropriate colour range is a means whereby both manufacturers and retailers can differentiate their products from those of their competitors. Selection of a proper colour range is equally important for other coloration industries too, e.g. paint (Park, 2007). Shade ranges, eventually known as colour palette, were traditionally developed by collection of samples from many sources on various substrates. These physical samples were then matched on appropriate textile substrates. This method resulted in several problems such as physical difference between target colour and dyed textile substrate, inaccuracy of visual assessment, etc. To overcome some of the problems, the coloration industries produced shade cards consisting of standard colours from production ranges. However, study showed that in a typical range of standard colours, 80% of sales were generated by 20% of the colours (Park, 2008). A further attempt to assist the colour selection process was the development of colour atlases by a number of textile-producing organisations in a more organised format. A major development in the colour selection process is the availability of colour specification products based on colours being uniformly distributed throughout

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the colour solid. They assist rapid communication of colour and larger swatches can be used as master standards. They are a valuable tool for quick generation of palettes, with reduction in laboratory matching and savings in time and cost. Many of these products are often less than perfect and atlases based on colour order systems are preferred. Physical standards are quickly soiled and the concept of using non-physical standards (NPS) based on reflectance measurement is slowly getting momentum. High repeatability and reproducibility of modern spectrophotometers with better inter-instrument agreement (variability as low as 0.3 ΔECMC unit) along with improved accuracy in computer colour matching and laboratory dyeing, have allowed NPS in the form of reflectance data. A major development in digital colour communication is the use of NPS for communication, visualisation, evaluation and manipulation of colours on screen. The research initiated at UMIST resulted in the Shade Master system (Haydon and Oulton, 1994), subsequently commercialised as Colorite (at present Envision) by Datacolor and a few others. The various probable steps of digital colour communication in future may be as follows (Park, 2007): 1 Precise colour generation on a calibrated screen by retailer (probably using digital atlas). 2 Request to the matching laboratory for virtual matching and prediction of approximate dye formulation. 3 Return of virtual matching to the retailer quickly by email. 4 Assessment of virtual match and modification, if necessary, by the retailer. 5 Laboratory dyeing of approved virtual match for keeping as ‘master standard’. 6 A sufficient quantity of ‘engineered standards’ is produced by matching with master standard. 7 Engineered standards may be produced on various substrates and are distributed by retailer to suppliers. 8 Production and laboratory dyeing conducted with engineered standard.

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Colour order systems are based on various principles and in most cases they are not compatible with each other. Each system serves some specific field or purpose. Several nations have adopted specific colour order systems as national standards: Germany (DIN); Sweden and other Scandinavian countries (NCS), Japan, Italy and many others (Munsell). There is no internationally accepted colour order system which is very necessary for quick global communication. Tonnquist (1986) suggested that the Munsell and NCS colour order systems can mutually benefit from each other. Judd and Wyszecki (1963) concluded that it is not possible to map equi-luminous colours uniformly in a plane surface. The perfect model for colour perception can be created only in non-Euclidean space and no colour space has achieved such perfection. As all samples cannot be collected, interpolation or extrapolation is necessary, but this may raise a point of controversy from observer to observer;

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hence equal visual spacing is very much essential. This spacing depends on illumination too; illuminating conditions are therefore to be specified. Digital colour communication based on existing colour order systems is extremely helpful for colour communication between manufacturers and retailers.

2.10

References

Berlin B and Kay P (1969), Basic Colour Terms, Berkeley, CA (USA), University of California Press. Berns R S and Billmeyer F W (1985), ‘Development of 1929 Munsell book of atlas: A historical Review’, Col. Res. Appl., 10, 246–250. Billmeyer F W and Saltzman M (1981), Principle of Colour Technology, 2nd edn, New York, John Wiley. Billmeyer F W (1985), AIC Annotated Bibliography on Colour Order Systems, Microform Services, Inc., Rear, 4805 Prince George’s Avenue, Beltsville, Md, 20705. Billmeyer F W (1987), ‘Survey of colour order systems’, Col. Res. Appl. 12, 173–185. Billmeyer F W and Bencuya A K (1987), ‘Interrelation of the Natural color system and the Munsell color order system’, Col. Res. Appl., 12, 243–255. Billmeyer F W and Loppnow G R (1988), ‘Accuracy of Munsell notations in two Japanese colour order systems’, Col. Res. Appl., 13, 235–242. Birren F (1983), ‘Book Review: Chromatigen 707’, Col. Res. Appl., 8, 262. Chroma Cosmos 5000 (1978), Tokyo, Japan Colour Research Institute. Coloroid Professional 1.1 (2004), Color Plan Designer, http://www.flexinform.com. Derefeldt G and Sahlin C (1986), ‘Transformation of NCS data into CIELAB space’, Col. Res. Appl., 11, 146–152. Derefeldt G (1991), ‘Colour appearance systems,’ Chapter 13 in The Perception of Colour, edited by P Gauras, Boca Raton, CRC Press, 218–261. Digital Colour Atlas 3.0 (2006) (www.colouratlas.com), dtp studio, Grünteweg 31, D-26127 Oldenburg, Germany. Döring G (1981), ‘Der Vergleich zweier neuer Farbsysteme (ACC und NCS) mit der DIN-6164-Farbordnung’, Farbe 29, 53–75. Döring G (1990), ‘Color notation by visual interpolation in color order systems: how accurate is it?’, Col. Res. Appl., 15, 99–110. Döring G (1995), ‘Letter to Editor’, Col. Res. Appl., 20, 358–360. Durrett H J (ed.) (1987), Colour and the Computer, Florida (USA), Academic Press. Fairchild M D (2006), Colour Appearance Models, 2nd edn, West Sussex (England), John Wiley. Gall L (1984), ‘The realisation of the CIELAB system in the Eurocolor atlas’ (in German), Farbe + Design, No. 29/30, 4. Graham L A (1985), ‘Color order systems, color specification and universal colour language’, in: Colour Technology in the Textile Industry, G Celikiz and R G Kuehni (eds), North Carolina (USA), AATCC, 135. Green-Armytage P (2002), Colour Zones – Explanatory diagrams, colour names, and modifying Adjectives, 9th Congress of the International Colour Association, Proceedings of SPIE Vol. 4421, 861–864. Greenville W C (1994), ‘The Colour Harmony Manual’, Col. Res. Appl., 19, 77–98. Härd A and Sivik L (1981), ‘NCS – Natural colour system: a Swedish system of colour notation’, Col. Res. Appl., 6, 129–138.

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Härd A and Sivik L (1983–4), The Forsius Symposium on Colour Order Systems and Environmental Colour Design, Colour reports F26 (1983) and F28 (1984), Stockholm, Scandinavian Colour Institute. Härd A, Sivik L and Tonnquist G (1996), ‘NCS, Natural colour system – from concept to research and applications’, Part I + II, Col. Res. Appl., 21, 180–220. Haydon S L and Oulton D P (1994), J. Soc. Dyers Col., 110, 104. Heila E (1988), ‘An artist’s preference for the Pope colour system’, Col. Res. Appl., 13, 260–263. Heila E (1991), ‘The chevreul color system’, Col. Res. Appl., 16, 198–201. Hering E (1872–4), Zur lehre vom lichtsinne, Vienna, Imperial Academy of Science. Hesselgren S (1952), Hesselgrens Färgatlas med kortfattad färglära, Stockholm, T. Palmer AB. Hesselgren S (1984), ‘Why colour order systems?’, Col. Res. Appl., 9, 220–228. Hirschler R (2008), ‘Book review – My Travel in the Realm of Colors by Antal Nemcsics’, Col. Res. Appl., 33 (3), 254–256. doi:10.1002/col.20412. Hunt R W G (1978), ‘Colour terminology’, Col. Res. Appl., 3, 79–87. Hunt R W G (1982), ‘A model of colour vision for predicting colour appearance’, Col. Res. Appl., 7, 95–112. Hunt R W G (1987), Measuring Colour, Chichester (UK), Ellis Horwood. Hunter R S (1942), ‘Photoelectric tristimulus colorimetry with three filters’, J. Opt. Soc. Am., 32, 509. Hunter R S and Harold R W (1978), Tolerance levels in the specification of appearance, Colour 77 (AIC conference), Bristol (UK), Adam Hilger. Imperial Chemical Industries (1969), ICI Colour Atlas, UK, Butterworth and ICI. Indow T and Watanabe M (1980), ‘Absolute identification of colors in Munsell notation: Trainability and systematic shifts’, Col. Res. Appl., 5, 81–85. Indow T and Aoki N (1983), ‘Multidimensional mapping of 178 Munsell colours’, Col. Res. Appl. 8, 145–152. Indow T and Romney A K (2008), ‘Reflectance spectra of Munsell standard chips and their appearance’, Col. Res. Appl., 33 (3), 229–237. Jacobson E (1972), Colour Harmony Manual, 4th edn, Chicago (USA), Container Corporation of America. Johansson T (1937), Färg, Stockholm, Lindsfors Bokförlag AB. Judd D B and Wyszecki G (1963), Colour in Business, Science and Industry, 2nd edn, New York, John Wiley & Sons. Judd D B and Nickerson D (1975), ‘Relation between Munsell and Swedish Natural Color System’, J. Opt. Soc. Am., 65, 85–90. Kelly K L and Judd D B (1955), The ISCC-NBS Method of Designating Colors and a Dictionary of Color Names, National Bureau of Standards (Washington, USA), Circular 553. Krewinkel H W (1979), ‘Color determination with the Acoat (ACC) system’ (in German), Defazet, 33, 312–315. Kuehni, R G (2002), ‘The early development of the Munsell system’, Col. Res. Appl. 27 (1), 20–27 doi:10.1002/col.10002. Kuehni R G (2003), Colour Space and its Divisions: Colour order from antiquity to the present, New Jersey, Wiley-Interscience. Kuehni R G (2005), Colour: An introduction to practice and principles, New Jersey, Wiley-Interscience.

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Kuehni R G (2008a), ‘Forgotten pioneers of colour order. Part I: Gaspard Grégoire (1751–1846)’, Col. Res. Appl., 33 (1), 5–9. doi:10.1002/col.20362. Kuehni R G (2008b), ‘Forgotten pioneers of colour order. Part II: Mattias Klotz (1748–1821)’, Col. Res. Appl., 33 (5), 341–345. doi:10.1002/col.20430. Land J H and Pinney J E (1955), ‘Empirical relationships with the Munsell value scale’, Proc. Int. Radio Engg., 43, 1137. Leblon C Jacob (reprinted in 1756), Colouritto or the Harmony of Colouring in Painting, Paris, English and French editions. Leonov Y P and Sokolov E N (2008), ‘The representation of colors in spherical space’, Col. Res. Appl., 33 (2), 113–124. doi:10.1002/col.20391. Lewis K and Park J (1989), ‘Colour specifier: a tool for quick response?’, J. Soc. Dyers Col., 105, 152–158. MacAdam D L (1935), ‘Maximum visual efficiency of coloured materials’, J. Opt. Soc. Am., 25, 361–367. MacAdam D L (1974), ‘Uniform colour scales’, J. Opt. Soc. Am., 64, 1691–1702. MacAdam D L (1978), ‘Colorimetric data for samples of the OSA uniform colour scales’, J. Opt. Soc. Am., 68, 121–130. Mearz A and Paul M R (1950), The Dictionary of Colour, New York, McGraw-Hill. Müller A (1962–5), ‘Swiss Colour Atlas 2541’, Chromos Verlag, Switzerland, Winterthur. Munsell A H (1905), A Colour Notation, 15th edn in 1988, Maryland (USA), Macbeth. Nayatani Y, Takahama K and Sobagaki H (1984), Formulation of a non-linear model of chromatic ‘adaptation for a light grey background’, Col. Res. Appl., 9, 106–115. Nayatani Y, Takahama K and Sobagaki H (1988), ‘Field trials on color appearance of chromatic colors under various light sources’, Col. Res. Appl., 13, 307–317. Nayatani Y, Umemura Y, Hashimoto K, Takahama K and Sobagaki H (1989), ‘Analyzing the natural color system’s color order system by using a nonlinear color-appearance model’, Col. Res. Appl., 14(2), 69–77. Nemcsics A (1987), ‘Color space of the Coloroid color system’, Col. Res. Appl., 12, 135–146. Nemcsics A (1993), Colour Dynamics – environmental colour design, Budapest, Akadémiai Kiadó. Nemcsics A (1994), ‘Spacing in the Munsell color system relative to the Coloroid systems’, Col. Res. Appl., 19, 122–125. Neumann L, Nemcsics A and Neumann A (2005), Technical report on ‘Computational Color Harmony based on Coloroid System’, TR-186–2-05–05 (revised), Institute of Computer Graphics and Algorithms, Vienna University of Technology. Newhall S M (1940), ‘Preliminary report of the O.S.A. subcommittee on the spacing of the Munsell colours’, J. Opt. Soc. Am., 30, 617–645. Newhall S M, Nickerson D and Judd D B (1943), ‘Final report of the O.S.A. subcommittee on the spacing of the Munsell colours’, J. Opt. Soc. Am., 33, 385. Newton I (1704), Opticks, London, Reprinted 1952 (New York: Dover). Nickerson D (1936), ‘The specification of colour tolerances’, Text. Res., 6, 505–514. O’Brien W J, Groh C L and Boenke K M (1989), ‘A one-dimensional color order system for dental shade guides’, Dental Materials, November, 371–374. Ostwald W (1915), Die Farbenlehre, Leipzig, Unesma. Ostwald W (1916), The Colour Primer, translated in 1969 by Faber Biren, New York, Van Nostrand. Pantone colour system, New Jersey (USA), Pantone Inc.,

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Park J (2007), ‘Engineered textile colour standards’, Color Technology, 123, 1–7. Park J (2008), ‘Colour selection, communication and assessment – getting the right colour right’, Colourage, supplement, LV (11), 27–31. Pointer M R (1981), ‘A comparison of the CIE colour spaces’, Col. Res. Appl., 6, 108–113. Pope A (1949), The Language of Drawing and Painting, Harvard University Press, Cambridge, Mass., Reprinted 1967 (New York: Russel and Russel). Rheinbolt W C and Menard J P (1960), ‘A mechanized conversion of colorimetric data to Munsell renotation’, J. Opt. Soc. Am., 50, 802–807. Rhodes P A, Scrivener S A R and Luo M R (1992), ‘ColourTalk – a system for colour communication’, Displays, 13 (2), 89–96, Butterworth, UK. Rhodes P A (1995), Computer mediated colour fidelity and communication, PhD Thesis, Loughborough University of Technology. Rhodes P A (2002), ‘Colour notation systems’, in Colour Engineering, P Green and L MacDonald (eds), pp 307–331, Chichester, England, John Wiley. Richter M and Witt K (1986), ‘The story of the DIN color system’, Col. Res. Appl., 11, 138–145. Robertson A R (1984), ‘Colour order systems: An introductory review’, Col. Res. Appl., 9, 234–240. Roy Choudhury A K and Chatterjee S M (1992), ‘Quantifying metamerism’, Rev. Prog. Col., 22, 42. Roy Choudhury A K (1996), ‘Colour order systems’, Rev. Prog. Col., 26, 54–62. Roy Choudhury A K (2008), ‘Colorimetric study of SCOTDIC Colour Specifier’, Color Technology, 124, 273–284. Scotdic Colour Book, Higashi-ku, Osaka 541 Japan, Kensaikan Ltd. www.scotdic.com, www.scotdic.co.in. Sikkens GmbH (1978), ‘Acoat Color Codification System: Handbuch für Farbgestaltung’. Simon F T and Frost J A (1987), ‘A new method for conversion of CIE colorimetric data to Munsell notations’, Col. Res. Appl., 12, 256–260. Simon F T (1995), Colour names for CIELAB space, ISCC-AATCC joint meeting, Greenboro (USA), April. Smith N S, Whitfield T W A and Wittshire T J (1990a), ‘The accuracy of the NCS, DIN and OSA-UCS Colour Atlases’, Col. Res. Appl., 15, 111–116. Smith N S, Whitfield T W A and Wittshire T J (1990b), ‘Research note on the accuracy of the NCS, DIN and OSA-UCS Colour Atlases’, Col. Res. Appl., 15, 297–299. Smith N S, Whitfield T W A and Wittshire T J (1990c), ‘Comparison of the Munsell, DIN and Coloroid colour order systems using the OSA-UCS model’, Col. Res. Appl., 15, 327–337. Smith N S, Whitfield T W A and Wittshire T J (1990d), ‘A color notation conversion program’, Col. Res. Appl., 15, 338–343. Smith N S, Whitfield T W A and Wittshire T J (1991), ‘The accuracy of the NCS Atlas samples’, Col. Res. Appl., 160, 108–113. Smith N S and Billmeyer F W (1994), ‘Comparison of the Colorcurve and SCA-2541 colour order systems using the OSA-UCS model’, Col. Res. Appl., 19, 363–374. Stanziola R (1992), ‘The Colorcurve system’, Col. Res. Appl., 17, 263–272. Taylor J M (1984), Multidimensional scaling of selected samples from the Optical Society of America Uniform Colour Scales, PhD Thesis, Rensselaer Polytechnic Institute, Troy, New York. Taylor J M and Billmeyer F W Jr. (1988), ‘Multidimensional scaling of selected samples from the OSA-UCS’, Col. Res. Appl., 13, 85–98.

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Tektronix (1990), Tekcolor color management system: system implementer’s manual, Tektronix, Inc. Tominaga S (1993), ‘Color notation conversion by neural networks’, Col. Res. Appl., 18, 253–259. Tonquist G (1986), ‘Philosophy of perceptive color order systems’, Col. Res. Appl., 11, 51–55. Whitfield T W A, Powell A S, O’Conner M and Wittshire T J (1988), ‘The conceptual NCS: an empirical investigation’, Col. Res. Appl., 13, 119. Wright W D (1984), ‘The basic concepts and attributes of colour order systems’, Col. Res. Appl., 9, 229–233. Wyble D R and Fairchild M D (2000), ‘Prediction of Munsell appearance scales using various color-appearance models’, Col. Res. Appl., 25 (2), 132–144. Wyszecki G and Stiles W S (1982), Colour Science: Concepts and Methods Quantitative Data and Formulae, New York, Wiley. Wyszecki G (1986), ‘Colour Appearance’, Chapter 9 in Handbook of Perception and Human Performance, New York, Wiley.

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Step 1. Classification of achromatic colours according to lightness

Black, greys, white (in the order of increasing lightness) Step 2. Classification of chromatic colours according to common names (principal hues)

Red

Yellow

Green

Blue

Purple

Step 3. Further classification of chromatic colours into intermediate hues

Yellow-Green

Green

Green-Blue

Step 4. Classification of chromatic colours with varying lightness (e.g. same red hue but in the order increasing lightness, very dark red to pink)

Step 5. Classification of chromatic colours with varying saturation of chroma (e.g. light bluish red with increasing chroma)

Plate IV Desert island experiment (classification of pebbles according to colour).

White Value

10/

Chroma

9/

5RP

8/

5R

7/

5P

5YR

6/

5PB

5Y

5/ 4/

5B

5GY

3/

5G

5BG

hue

2/ 1/ 0/

Black

Plate V Munsell colour order system. © Woodhead Publishing Limited, 2010

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1 2 3 4 5 6 7 8 9 10 1 2

(a)

W

05 10

20 30 40 50

C

60 70

90 80

80

70

60

90

50 40 30 20

R

0R

Y5

0R

Y4

0R

Y

G90Y

0Y

Y

G20

50

G3

Y

0Y

G G4

G80

G7 G6

(b)

0Y

Y6

0Y

0R

Y7

0R

Y80

Y

G10Y

Y90R

G

R

B90G

R10B R 20

G

0G

50

0G

G

B10G

B

B20

0B

B3

B4

0B

R7

R80

B

B

R6

B5

0B

R

B6

R4

0G 0G

B7

R3

0G

B80

R90B

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10

Y3

05

Y20

02

Y10R

S

Plate VI (a) NCS constant hue triangle, (b) NCS hue circle

© Woodhead Publishing Limited, 2010

0B

B

R

(a)

White a

s

s ne

ite

g sin

wh

rea

Inc

c

Eq

a c

a

e

ite

ne

g

ea

c

i

wh

e

ca

g

ui-

l

gc

e

n

ss

i

ga

s n

la

p

na

g

Full colour (13)

rie

l

ia

ge

p

se

Full colour (1)

pa pc

i

a

pe

li

c

Inc

l

rea

e

pi

g

sin

pg

ni

gb

lac

pl

n

i l

kn

es

pn

p

n

s

p

u Eq

p

g

i-b

lac

c

e

i l

n

a

k

s ne

ss

eri

es

Black

(b)

2 5

23

8

20

11

17 14

Plate VII (a) A vertical cross-section through Ostwald double-cone colour solid, (b) Ostwald hue circle.

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3

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Expressing colours numerically V. C. GUPTE, Advanced Graphic Systems, India

Abstract: Colour is an integral part of our daily life and impacts on many decisions that we make. There have been several attempts by colour scientists and artists to specify colour, but no specific language evolved to describe it. The Commission Internationale de l’Eclairage (CIE) standardized light sources/illuminants and defined the standard observer response to colour which resulted in specifying colour numerically. It specified colour in terms of unreal primaries, XYZ, referred to as tristimulus values, which are further converted to calculate chromaticity coordinates – x y z, providing additional information about colour. Describing colour in terms of tristimulus values was itself a significant achievement, but had its limitations as well, mainly it was a non-uniform system. To overcome its limitations, the CIE transformed the system into a more uniform one and specified colour in terms of L*, a*, b*, Cab* and hab, which formed the basis for most colorimetric calculations and helped colorists in handling colour applications. Key words: the CIE, standard illuminants, standard observer, tristimulus values, chromaticity diagram, CIELAB 1976 Colour Space, CIE chroma ‘C’ and hue ‘H’.

3.1

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Introduction

It is very difficult to imagine the world around us without colour as it has become an integral part of our daily life, be it textiles, paints, plastics or printing. All our daily necessities are packed in attractive packaging that tempt us to buy, even if we do not need the product. We cannot imagine our television without colour. The colour of the garments we wear projects our personality and the paint we select for our houses creates a pleasant atmosphere. We select the colour of our furnishings to match the paint in the room and it has even been proved that the colour depicts the nature and inner personality of the car owner. Colour has become a focal point of what we do every day. Despite being so important and so close to everyone’s day-to-day life, it is not possible to express colour in a unique or specific language. We remember colour while we look at it, but the moment we look away it gets erased from our memory. So colour scientists attempted to specify colour in an explicit universal language so that it could be understood by everyone involved with colour and colour reproduction. This led to effects to express colour numerically in a unique and unambiguous way.

3.2

Colour specifications

Many scientists put forward different systems to specify colour and these are described in Chapter 2. The Munsell1 colour order system, although over 100 years 70 © Woodhead Publishing Limited, 2010

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old, is still used in many industries today, for example in specifying the colour of instruments used in medicine and the colour of electric motors in engineering. Munsell was an artist and one of the first to state that colour cannot be specified in two dimensions, but requires three dimensions. He specified colour in hue, value and chroma. He defined them as: Hue (H) is by which we differentiate red from green, yellow from blue, etc. Or by which we refer to colour, like red, yellow, green, blue, etc. Value (V) is the lightness/darkness or in short the greyness of a colour. Chroma (C) is the saturation of colour. The Munsell system comprises 10 main hues – red (R), yellow (Y), green (G), blue (B), purple (P), yellow-red (YR), green-yellow (GY), blue-green (BG), purple-blue (PB) and red-purple (RP). Each main hue is preceded by the number 5 and each is further divided into 10, giving a total of 100 hues. The intermediate hues are graded on the main hues, such as 7.5RP, 10P, etc. When arranged in a circle, these form the Hue Circle. The Munsell value scale ranges from 1 for black to 10 for white. Black, white and the greys between them are neutral colours. These are all available as the Munsell Neutral Scale2 in a fandeck or in individual sheets in steps of 0.25 and every colour has value, whether it is a chromatic or neutral colour. The Munsell Neutral colour is specified as N V/. The grey colour used in most light booths is N 6/ or N 7/, although some specifiers recommend N 5/, a darker neutral grey. Standards like ANSI specify N 8/, while ASTM D1535 specifies N 7/ for light booths. It has not been possible so far to practically produce Munsell black N 1/ or white N 10/ using available white and black colorants. Munsell chroma is saturation. Imagine gradually adding a pure red hue, 5R, to a grey having a value of 6 until the original red is achieved. As the hue reaches a point when further addition does not change the shade, it is at saturation. Munsell referred to it as chroma and he prepared chips in such a way that there was uniform difference between two adjacent chips. The chroma is denoted by an even number preceded by ‘/’, with normal colours limited up to 20 and fluorescent colours as high as 30. Munsell prepared chips for all hues, values and chroma and arranged them huewise. This is the famous Munsell Book of Colors.3 The Munsell Book of Colors is available in glossy (1 488 chips) and matt (1 277 chips) versions. Each chip is denoted by Munsell notation – HV/C, for example 5R6/8. After the Commission Internationale de l’Eclairage (CIE) system was announced, the Munsell system was redefined and the revised spacing was defined in terms of the CIE Standard Observer and CIE source (2° observer and light source C) and the original Munsell notations of each sample were correlated with renotations from revised spacing. The Munsell system now has intermediate steps, such as 5.4R6.2/12.4. These are now referred to as Munsell renotations.

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Colour measurement

3.3

The Commission Internationale de l’Eclairage (CIE) system

The CIE is an international commission originally based in France, but now based in Vienna. In 1913 it took over the functions of the International Commission on Photometry (Commission Internationale de Photometrie) and established standards and measurement procedures for use in colour and appearance. It standardized light sources/illuminants, observer response to colour and also geometry for reflectance spectrophotometers. Using these inputs, the CIE specified colour in terms of additive primaries – R (red), G (green) and B (blue) – which expressed colour in the same way the human eye would see it. The red, green and blue primaries so selected have specific wavelengths – red at 700 nm, green at 546.1 and blue at 435.8.

3.4

The CIE standard light sources/illuminants

We know that the appearance of colour depends on the light incident on it. There are many lights around us, which are very different and hence make the colour assessment difficult. So it is necessary to have standard light sources characterized in numerical terms. The CIE standardized light sources based on their colour temperature, spectral power distribution and chromaticity coordinates. (Colour temperature of a light source is the temperature of a black body at which the light of the black body matches the light of the light source and is expressed in Kelvin – absolute temperature, for example + 273°C.) The CIE initially standardized three light sources which represented different phases of daylight. Light source A – 2856 K Light source B – 4874 K noon daylight Light source C – 6774 K north sky daylight

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Light source A can be simulated in a laboratory using a tungsten filament operating at 2856K. It is mainly used as a secondary light source for detecting metamerism (how an object will appear different when viewed under two light sources). Light source B represents noon daylight, but is not used so commonly in colour assessment. Light source C is the north sky daylight from overcast sky. The CIE recommended that the colour assessment be carried out under light source C and released the details of laboratory procedures to produce light sources B and C. However, it is not easy to simulate the daylight in a laboratory, particularly in the UV region, unless one adds the same amount of UV light to the laboratory light. It is further observed that light source C is not available throughout the day. It changes from place to place, season to season and it is not even observed in many countries during most periods of the year. The colour temperature of daylight varies significantly from 2800K to 25000K from sunrise to sunset – when it is

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clear blue sky, it reaches the maximum colour temperature. Every hour the colour temperature of daylight changes by 1000K. The CIE later introduced D illuminants representing various phases of daylight with different colour temperatures: D55 – 5500K D65 – 6500K D75 – 7500K These are referred to as illuminants. The difference between a light source and an illuminant is that while the former exists physically, the latter may or may not exist physically. The CIE defined D illuminants based on work by Judd4 and co-workers. Illuminant D55 is not used in any colour assessment, but its modified version, D50 is used in graphic art industries and photography for colour assessment. The reason for selecting 6500K and 7500K was that the average colour temperature of daylight, excluding sunrise and sunset, lies between 6000– 7000K. The D65 illuminant was also arrived at by taking into account measurements of total daylight in several countries. D65 and D75 are referred to as average daylight. However, there are no physical light sources which simulate the CIE daylight D-illuminants. The CIE recommended use of D illuminants for calculation of tristimulus values and colorimetric data in place of light source C in most applications in colour industries all over the world. Illuminant D75 is used by a few retailers in North America for colour assessment where blue phase of daylight is preferred. The main problem is that D illuminants cannot be closely produced in the laboratory. The CIE also took note of fluorescent lamps, which were then widely used in stores and standardized 12 such lamps. Of the 12 lamps, three are extensively used in the industry. F-2 – Cool white fluorescent (CWF) light with correlated colour temperature 4150K. It is known as a wide band fluorescent lamp and is deficient in the red region, hence suppresses reds, but enhances all other colours. F-7 – Daylight fluorescent lamp with correlated colour temperature of 6500K. The household daylight lamps commonly use single phosphor, while some use 3-phosphors and do not simulate daylight. This results in enhancing certain colours, but depressing other colours. The 7-phosphor daylight fluorescent lamps nearly simulate daylight. F-11 – Represents a TL84 fluorescent lamp with correlated temperature of 4150K and is known as a narrow band fluorescent lamp, as these produce narrow bands in blue, green and orange-red regions. Due to nearly equal energy in the visible region, most objects appear uniformly under these lamps. The CIE published relative spectral power distribution tables of all the CIE standard illuminants in the visible region spectrum 360–780 nm at intervals of 5 nm.

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Colour measurement

3.5

The CIE Standard Observer and unreal primaries

The standard observer represents how a human eye responds to spectrum colours wavelength by wavelength. The human eye perceives a colour due to a combination of red, green and blue sensitive cones which is explained by the different theories of colour vision, notably the trichromatic theory. Wright5 and Guild6 separately carried out experiments using red, green and blue lamps and a test lamp to produce spectrum colours wavelength by wavelength. They used one half of the field of a spectrum colour of a single wavelength and it was matched by the combination of red (R), green (G) and blue (B) primary lights in the other half. The red, green and blue lamps had measuring devices; it was therefore possible to match a single wavelength spectrum colour by knowing the individual amounts of red, green and blue lights. The experiments were carried out at intervals of 5 nm. The observer was kept at such a distance that he could see a two degree field of vision using the arrangement as shown in Figure 3.1. Wright used 10 observers while Guild had seven. The CIE combined the results of Wright and Guild to describe the CIE Standard Observer. The results were expressed as the tristimulus values for an equal-energy – spectrum using R, G and B primaries and the amounts were expressed as r–, g–, b equired to match each wavelength in the visible region.

Red

Green

Blue

Observer

Black partition

Yellow Test lamp

Masking screen

White screen

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CIE Standard Observer experiment

3.1 The CIE Standard Observer.

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3.5.1 The CIE unreal primaries It was observed by the CIE that the real primaries – red, green and blue – did not match certain spectrum colours. For example, a pure cyan colour could not be just produced by blue and green primaries alone. If we add red to blue and green, we may get a less saturated cyan. To match it correctly, we may have to add a red primary to a pure cyan colour. Mathematically, Cyan (C) + Red (R) = Blue (B) + Green (G) or Cyan (C) = – Red (R) + Blue (B) + Green (G)

In other words, to match certain spectrum colours, there may be negative values for one or more real primaries (we have to remove one or more primaries for matching these colours). The negative values are not desirable. This meant that although red, green and blue primaries produced most available colours, all colours could not be produced using positive mixtures of real primaries. To avoid negative values, the CIE recommended use of unreal primaries, X (red), Y (green) and Z (blue), as illustrated in Fig. 3.2.

[Z]

[B]

M

[R]

[G]

[X]

3.2 The real and unreal primaries.

© Woodhead Publishing Limited, 2010

[Y]

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Colour measurement

It can be explained using a two-dimensional figure with caution, as any colour requires three dimensions. If the primaries are red [R], green [G] and blue [B] (representing an equilateral triangle in Fig. 3.2), we can match any colour using proportions of R, G and B represented by r, g and b respectively. For example, R R+G+B G g= R+G+B B b= R+G+B r + g + b =1 r=

where

[3.1]

[3.2]

To match any point within the triangle, we would require, P = r.R + g.G + b.B where all values are positive. However, if we have to match a colour, M, we would require M = – r.R + g.G + b.B with appropriate proportions so that it meets equation 3.2. The colour M can be matched using positive values of blue and green, but would require a negative quantity of red. This is true for all colours lying outside RGB triangle. This would also be true for any three R, G and B primaries. To avoid these negative values the CIE recommended unreal primaries – X, Y and Z representing R, G and B respectively. The dotted triangle in Fig. 3.2 represents the gamut of unreal primaries wherein any colour will always be matched with positive values of X, Y and Z. The position of points X, Y and Z can be easily located using r, g, and b. The equilateral triangle RGB, which was used in the early days as a chromaticity diagram, was referred to as the Maxwell Colour Triangle, while Rigg7 has elaborately explained the concept of unreal primaries. The CIE adopted unreal primaries, X (red), Y (green) and Z (blue). The revised values x–, y–, z– form the CIE Two Degree Standard Observer and are always positive. It is also referred to as the 1931 Standard Observer. The standard observer is also referred to as colour matching functions. The Two Degree Observer in real and unreal primaries is illustrated graphically in Figure 3.3. The CIE realized that the two degree field of vision was too small for colour assessment. Stiles and Burch8 and Speranskaya9 used a much wider 10° field and repeated the experiments of Wright and Guild. The CIE averaged these results and released the new data, which is the CIE 1964 Supplementary Standard Observer, commonly referred to as the CIE Ten Degree Observer. It is referred to as x–10, y–10, z–10 just to differentiate from the 2° observer. It is noted that ‘y–10’ differs from ‘y–’ and hence it does not represent lightness of colour in the same way as the latter. It is also observed that x–,10 and y–10 have values throughout the region, while z–10 is practically zero beyond 550 nm. The CIE Ten Degree Standard Observer is used in most calculations. The Two Degree Observer is used in select applications, like calculating Munsell notations or in some whiteness index calculations.

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Expressing colours numerically (a)

77

b 0.3 r

Tristimulus values

g 0.2

0.1

0

–0.1 380

(b)

480

580 Wavelength, nm

680

780

680

780

2 z

Tristimulus values

1.5

y

x

1

0.5

0 380

480

580 Wavelength, nm

3.3 Graphical representation of (a) real and (b) unreal primaries.

3.6

Computation of tristimulus values

Using the spectral power distribution and the standard observer, a colour can be specified into the CIE primaries X, Y and Z when the reflectance of the object is known. The reflectance of an object is measured using a reflectance spectrophotometer. The CIE released the mathematical relations to compute X, Y and Z, commonly referred to as the tristimulus values. The initial equations

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involved integrations and the CIE subsequently noted that integration is approximated by summation in all practical calculations. With the product of reflectance, spectral power distribution and standard observer at a wavelength and further summation over the visible region 360–780 nm, and one can compute X, Y and Z values. The CIE released the revised procedures mathematically, 780

X=k Y=k Z=k

Σ Rλ.Eλ.–x λ.Δλ

360 780

Σ Rλ.Eλ.–y λ.Δλ

360 780

Σ Rλ.Eλ.–z λ.Δλ

360

[3.3]

with 780

Σ

k = 100/

360

Eλ.–y λ.Δλ

where X, Y and Z are the tristimulus values Rλ is reflectance at wavelength λ Eλ is spectral power distribution of the CIE illuminant at λ x– λ, y– λ, z– λ is the standard observer at λ. Δλ is wavelength interval. It is common to carry out summation for calculating X, Y, Z using normalized weight factors Wx, Wy, Wz as follows: Wxλ = k.Eλ.x– λ.Δλ W λ = k.Eλ.y–λ.Δλ y

[3.4]

Wzλ = k.Eλ.z– λ.Δλ for λ = 360 to 780 and 780

Σ

k = 100/

360

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Eλ.y– λ.Δλ

For given CIE illuminant, standard observer and wavelength interval, weight factor can be calculated once only and these weight factors are available in tabular form in standards like ASTM E308:08. The equations then become, 780

X= Y= Z=

Σ

Wxλ.Rλ.Δλ

Σ

Wyλ.Rλ.Δλ

360 780 360 780

[3.5]

Σ Wzλ.Rλ.Δλ 360

X, Y and Z are arbitrary values and have no units, while tristimulus values are numbers which represent how the human eye-brain responds to or sees a colour. Y defines the luminance factor and is normalized so that it lies between 0–100.

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The Y value for all illuminants is normalized to 100, while X and Z vary, Z more significantly than X. Theoretically, when a standard and a sample have the same X, Y, Z values under given illuminant and observer conditions, they are a matched pair. This is true in most cases for objects which do not have any physical attributes like gloss, texture, etc. Such an observation would also be true visually. However, when standard and sample differ in their physical attribute, their X, Y, Z might match mathematically, but their visual assessment would not. Both the standard and sample would appear different visually. The CIE has published tables for spectral power distribution and standard observer at 5 nm intervals. However, not many reflectance spectrophotometers measure reflectance at 360–780 nm, but at quite different ranges like 400–700 nm, 360–750 nm or 380–700 nm. There are no standard tables for such wavelength ranges. There are also a few spectrophotometers which report measurements at 5 nm intervals, but the bandpass of most spectrophotometers is 10 nm or higher. Most present generation commercial spectrophotometers report the measurement at 10 nm intervals. The ASTM E308 standard, (recent version ASTM E308-08) which is commonly used in most colour software, provides tables at 10 and 20 nm intervals. The colour software used with these instruments add the standard observer and spectral power distribution data to the first and the last wavelength data, for example 360–390 nm data is added to 400 nm data and 710–780 nm data to 700 nm data. Theoretically, this may appear correct, but Stearns10 has shown errors in calculating tristimulus values by such random addition. He has suggested a suitable method to overcome the problem. The X, Y, Z calculated using Stearns method provides the same results as would have obtained by using the CIE tables at 5 nm intervals in 360–780 nm. Stearn’s method can also be used to calculate correctly X, Y, Z values from any wavelength range or interval. If proper care is not taken it can cause major difference in X, Y, Z values and in subsequent colorimetric applications, like colour difference, etc. The proper method to check whether one has used the correct table is to calculate the Y value by inputting reflectance as 100% at all wavelengths. One must obtain the Y value as 100.

3.7

Reflectance measurement

X, Y, Z calculations require reflectance of the object and reflectance measurement is carried out using a reflectance spectrophotometer. Reflectance measurement is important as it is the only measurable parameter in the colour science and all other data are calculated from it. The basic components of a reflectance spectrophotometer comprise a light source, illuminating and viewing geometry, monochromator and a detector. The illuminating and viewing geometry of a reflectance spectrophotometer was standardized by the CIE. These geometries are 45°/0°, 0°/45°, d/8° and 8°/d, the first parameter indicating illuminating angle,

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while the latter indicates the viewing angle (or an angle at which reflected light is directed to the detector via a monochromator). A monochromator disperses the reflected light which further passes through an exit slit to the detector. The modern spectrophotometers provide reflectance data at 10 nm intervals, but the wavelength range varies for different models. The spectrophotometer and its functioning are described in Chapter 6. It must be pointed out here that the reflectance measurement must be correct as it is the basic data for all colorimetric calculations.

3.8

Chromaticity coordinates and chromaticity diagram

Tristimulus values X, Y, Z are further converted into chromaticity coordinates using simple mathematical relations, x=

X X+Y+Z

y=

Y X+Y+Z

z=

Z X+Y+Z

[3.6]

where x, y, z are the chromaticity coordinates and X, Y and Z are tristimulus values. It also means x + y + z = 1, or in other words when any two coordinates are known the third can be calculated; however, x and y are commonly used. The Y tristimulus value provides the third dimension. One can also specify colour in x, y and Y. When y is plotted against x for spectrum colours and all points are joined, it forms a horseshoe shape diagram – the chromaticity diagram. The two extremes – violet (360 nm) and red (780 nm) are connected with an imaginary line. All available colours lie within the diagram. The CIE illuminants lie at the centre forming a curve, light source A lying towards red and D75 towards blue, as illustrated in Figure 3.4. One can imagine chromaticity diagrams stacked along the Y axis. It is generally observed that one compares a colour in a two-dimensional chromaticity diagram and virtually ignores the Y value. The chromaticity diagram provides additional information about colour in terms of dominant wavelength and excitation purity. Though dominant wavelength gives indication of hue and purity provides chroma, it does not hold true for all colours due to the non-uniform nature of the chromaticity diagram. The diagram was used extensively early in the development of colour science applications. Wright,11 MacAdam12 and Stiles13 used the chromaticity diagram in describing ‘the just noticeable colour differences’. MacAdam’s famous perceptibility tolerance ellipses diagram work on the chromaticity diagram formed the basis for some important colour difference equations, like FMC-II and CMC (l:c). The chromaticity diagram is used in setting tolerances, for example signal light colours

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Expressing colours numerically y

81

520

0.8 540

560

0.6 500

Green

580

Yellow

0.4

600

Orange

770nm

Blue 0.2

Red 480

0

620 650

Violet

380nm

0.2

0.4

0.8

x

3.4 Chromaticity diagram.

(green, amber, red and white) in rail, road or life jacket lamps. The diagram is also used in gamut mapping – in colour monitor colours (R, G, B), for different dye classes, etc. – while chromaticity coordinates are used in many applications, e.g. whiteness index calculations.

3.9

Usefulness of the CIE XYZ system

The CIE system specified colour in numerical terms which was the one of the main objectives of the CIE. It helped in eliminating the subjectivity in colour assessment. Computation of tristimulus values X, Y and Z from reflectance measurement formed the basis of all colorimetric calculations. The CIE standardized illuminants in terms of colour temperature, spectral power distribution and chromaticity coordinates and these were useful not only in calculation of tristimulus values, but also in visual assessment. The development of the standard observer was one of the significant achievements and is the basic parameter for tristimulus calculation. The CIE also standardized illuminating and viewing conditions for spectrophotometers. Chromaticity coordinates provided more information in terms of dominant wavelength and purity, though these do not specify hue and chroma in the true sense. There have been a few additions

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since its introduction in 1931, like D illuminants, and ten degree observer, but the basic system remains unchanged and there is no possibility of replacing it altogether in the near future. The CIE system is very well accepted and successful in most colorimetric applications. Nevertheless there are some major limitations of the system which came to the fore as colorists started using it.

3.10

Limitations of the CIE system

The CIE specified colour in terms of tristimulus values X, Y and Z, but even an expert cannot visualize colour from given tristimulus values. The CIE ignores the physical or geometric attributes or, in simple words, the surface texture. For example, when a standard is glossy and a sample is matt their tristimulus values may match, but both the standard and sample will appear differently when observed visually. It is therefore necessary that one must have standard and sample with the same surface attributes to get any meaningful results. The CIE recommends the colour assessment – tristimulus values – to be calculated using illuminant D65 and ten degree observer or two degree observer. Both these observers represents average human response, but neither represents the individual observer and there may be differences in the observers. It is also not easy to simulate D65 in the laboratory as not many light booths simulate D65 correctly. Hence the calculated difference may appear differently if the illuminating conditions do not match. The other major limitation of the CIE system is its non-uniformity. Similar numerical differences in different areas of the visible region appear differently when assessed visually. For example, the same magnitude differences in X, Y, Z (ΔX, ΔY and ΔZ) or in chromaticity coordinates (Δx, Δy and Δz) in a green pair and a red or blue pair will appear differently. In the case of the green pair the difference may be accepted visually, but for the same difference in the red or blue pairs, the sample may not be accepted. This is due to non-uniformity of the CIE system. This system does not specify chroma and hue, which are very important colour parameters, although purity and dominant wavelength specify chroma and hue indirectly. Tristimulus values of colour are merely arbitrary numbers having no units and which represent amounts of three unreal primaries which when mixed additively would give the colour. But it is not true for all colours.

3.11

Transformation and improvement of the CIE system

Scientists and researchers started improving the non-uniformity by transformation of the CIE system and tristimulus values were converted into more uniform colour space. Hunter14 was the first one to convert X, Y, Z into L a b colour space, known as Hunterlab colour space. The space signifies the specification of colour into three-dimension. The other colour space which is worth mentioning is ANLAB – suggested by Adams15 and Nickerson.16 While Hunter used square and square

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root function for conversion, Adams and Nickerson used polynomial equations for transformation. By 1976, there were as many as 20 colour spaces put forward by different colour scientists. Instead of helping colorists, the availability of so many colour spaces confused them. These colour spaces were based on different colour concepts. In order to avoid confusion and bring in uniformity, the CIE stepped in again and introduced two new colour spaces – CIE 1976 L*A*B* and CIE 1976 L*U*V*. For transformation, cube and cube root functions were used to compute L*A*B* from X, Y and Z. Asterisk ‘*’ was used just to distinguish the CIE colour space from the other spaces. Of the two spaces, the CIE 1976 L*A*B* is used in textiles, paints, plastics, etc.; while the CIE 1976 L*U*V* is used in photography. We will discuss the CIE 1976 L*A*B* colour space and will not discuss the CIE 1976 L*U*V* colour space. The CIE17 released the mathematical equations for transformation of X, Y and Z into L* A* B* Cab* and hab. The original equations were revised by the CIE18 and are given below: L*= 116f(QY) – 16 a* = 500{f(QX) – f(QY)}

[3.7]

b* = 200{f(QY) – f(QZ)} where QX = X/Xn; QY = Y/Yn; QZ = Z/Zn and f(Qi) = Qi1/3 if Qi> (6/29)3 else, f(Qi) = (841/108)Qi + 4/29 if Qi ≤ (6/29)3 where i varies as X, Y and Z. The tristimulus values Xn, Yn and Zn define the tristimulus values of the standard illuminant with Yn equal to 100. C*ab and hab are calculated as follows: C*ab = {(a*)2 + (b*)2}½ hab = tan−1 (b*/a*) If b* = 0 then hab = 90 – 90 sign(a*) else, hab = 180 – (180/π) tan−1 (b*/a*) −90 sign(b*). The CIE L*a*b* C*ab hab colour space is illustrated in Fig. 3.5. Colour can be specified in CIE L*a*b* – lightness/darkness, red/green and yellow/blue. L* varies from 100 to 0: for perfect white, L* is 100 and for black, it is zero. When L* is 70, colour is light or light grey (when a and b are zero); when L* is 50, colour is medium or medium grey; when L* is 25 or less, colour is dark or

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Colour measurement White

Yellow +b

L* b* c* –a Green

h a

+a Red

–b Blue Black

3.5 The CIEL*a*b* 1976 Colour Space.

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dark grey. There is no colour having L* = 0 under SCI measurement, which is commonly used in most colour applications. L* can be near zero under SCE measurement or using 45/0 or 0/45 geometry. When a* is positive, colour is red or in red direction; when a* is negative, colour is green or in green direction. When b* is positive, colour is yellow or in yellow direction. When b* is negative, colour is blue or in blue direction. Colour can also be specified in LCH – lightness, chroma and hue. The chroma or saturation Cab* is the distance between the achromatic point and colour. Chroma can be explained from the a*b* plot (see Fig. 3.6). The longer the distance of a colour from the achromatic point, the higher is the chroma or brighter (more saturated) will be the colour. All bright yellows, oranges, reds, greens, blues and violets have medium to higher chroma values. The shorter the distance of colour, the lower will be the chroma or duller (less saturated) is the colour. Greys, olives and coffees have low chroma values. Figure 3.7 illustrates hues in the a*b* plot. CIE hab is measured in degrees starting with hab=0 in the red direction and increasing anticlockwise. All real hues fall within definite angles expressed in degrees: Red – 350 to 360 and 0 to 35 Orange – 35 to 70 Yellow – 70 to 105 Green – 105 to 195 Blue – 195 to 285 Violet – 285 to 350

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Expressing colours numerically b c = (a2 + b2 )½

c b

a

a

3.6 Chroma C calculation from a and b.

70º

105º

re G

35

º

e ng ra O

en

Yellow

350º

V i ol et

e lu B

195º

285º

3.7 Hue circle.

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L* a* b* are referred to as rectangular coordinates, L* Cab* hab are referred to as polar coordinates. In conclusion, the CIE system provides a numerical specification of colour in terms of tristimulus values X, Y, Z and chromaticity coordinates x, y, z. These are further transformed into more uniform colour space, L* a* b* Cab* hab. These numerical specifications form the basis for most colour applications.

3.12

Future trends

The CIE system is well established and despite its limitations, it enables in solving most colour applications quite satisfactorily. The CIE system is nearly eighty years old, but there have not been any major modifications to the basic system since its introduction and there is no possibility of the CIE system getting replaced by any other system in the near future. It is known that the CIE is not uniform due to which it has some limitations, so colour scientists and researchers modified basic parameters (L* a* b* Cab hab) to improve its acceptance with visual assessment. The recent colour difference equation, CIEDE2000, which is the result of such modifications, hopefully will meet most requirements of colour difference assessment. However, for visual colour assessment, one must have accurate simulation of D illuminants, particularly D65. This is still not possible to simulate very easily in a laboratory or light booths. One must have the same illuminant for visual assessment as well as for instrumental assessment. When it is available, it will overcome this major limitation. The other area which may require improvement is the standard observer, which is an average of the limited number of observers (2° and 10° observers are the averages of 17 and 67 observers respectively). The objective should be such that the standard observer truly represents the average observer.

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Colour measurement

References

1. Munsell, A.H. (1905) A Color Notation, 1st Edition, Munsell Color Company, Baltimore, MD. 2. Munsell, A.E.O., Sloan, L.L. and Godlove, I.H. (1933) Munsell Neutral Value Scale, J. Opt. Soc. Amer., 23, 394–411. 3. Munsell Book of Colors, X-Rite Inc. USA 4. Judd, D.B., MacAdam, D.L. and Wyszecki, G. (1964) Spectral distribution of typical daylight as a function of correlated color temperature, J. Opt. Soc. Amer., 54, 1031–1040. 5. Wright, W.D. (1928–1929) A re-determination of the trichromatic co-efficients of the spectral colours, Trans. Opt. Soc., 30, 141–164. 6. Guild, J. (1931) The colorimetric properties of the spectrum, Phil. Roy. Soc. (London), A 230, 149. 7. Rigg, B. (1997) The Colour Physics for Industry, Society of Dyers and Colourists, 90–93. 8. Stiles, W.S. and Burch, J.M. (1958) NPL colour-matching investigation: final report, Optica. Acta, 6, 1.

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9. Speronskaya, N.I. (1959) Determination of spectrum color coordinates for twentyseven normal observers, Optics and Spectroscopy, 7, 424. 10. Stearns, E.I. (1975) Weights for calculation of tristimulus values, Clemson Rev. Ind. Managem. Tex. Sci., 14, 79. 11. Wright, W.D. (1941) The sensitivity of the eye to small colour differences, Proc. Phys. Soc. London, 53, 93. 12. MacAdam, D.L. (1942) Visual sensitivities to color differences in daylight, J. Opt. Soc. Amer., 32, 18–26. 13. Stiles, W.S. (1946) A modified Helmholtz line element in brightness-colour space, Proc. Phys. Soc. London, 58, 41. 14. Hunter, R.S. (1942) ‘Photoelectric tristimulus colorimetry with three filters’, NBS Circular 429, U.S. Govt. Printing Press, Washington D.C., reprinted in J. Opt. Soc. Amer., 32, 509. 15. Adams, E.Q. (1942) X–Z planes in 1931 ICI System of Colorimetry, J. Opt. Soc. Amer. 32, 168–173. 16. Nickerson, D. (1950) Tables for use in computing small color differences, Am. Dyestuff Reptr. 39, 541. 17. CIE Publication No. 15, Supplement No. 2 (1978), Colorimetry (E1.3.1)(TC1.3) Paris, Bureau Central de la CIE. 18. CIE Publication No 15 :2004, Colorimetry, Central Bureau of the CIE, Vienna, 2004.

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Visual and instrumental evaluation of whiteness and yellowness R. HIRSCHLER, SENAI/CETIQT Colour Institute, Brazil

Abstract: Whiteness and yellowness are important characteristics of many industrial products and due to the uncertainties of visual evaluation instrumental measurement is the preferred method. This chapter describes the problems involved in the visual assessment: the lack of standardized illumination and the controversies due to the definition of a ‘preferred white’. Whiteness and yellowness indices based on instrumental measurements are widely used in industry, cosmetics and dentistry, but even they do not always yield unambiguous results. Key words: whiteness, yellowness, brightness, tint, fluorescent whitening agent (FWA).

4.1

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Introduction: whiteness and yellowness

White, like any other colour, is both an attribute of visual sensation and a characteristic of certain objects. We describe those objects as white which appear to be neither ‘coloured’ (i.e. their Munsell chroma is very low, usually less than a few tenths) nor ‘greyish’ (i.e. their Munsell value is very high, usually more than 9). According to the ISCC-NBS Method of Designating Colors (Judd and Kelly, 1967) white must have a Munsell chroma no higher than 0.5 for all hues, except for 4Y to 9Y where up to 0.7 is acceptable, and a Munsell value of at least 8.5. White occupies a very small area within the CIE chromaticity diagram. The limits of objects that may commercially be called ‘white’ according to the CIE (2004) definition are shown in Fig. 4.1 as compared to the limits of optimal colours (calculated from data of Wyszecki and Stiles, 2000). The optimal colour boundary shows the limits for the given luminance factor (CIE tristimulus value Y=90) within which the chromaticity of all imaginable (not necessarily real) nonfluorescent objects must fall. A significant part of the whiteness area can only be achieved using fluorescent whitening agents (FWAs). However, white is not really like any other colour. The perception of white depends not only on the spectral characteristics of the stimulus reaching the eye and the characteristics of the observer, but also on the spatial characteristics of the reflected light (diffuse or specular). Thus a mirror measured with the specular component included will show very high reflectance (see Fig. 4.2) in the specular included (SPIN) mode as if it were white, while it will show nearly zero reflectance in the specular excluded (SPEX) mode as if it were black. For comparison Fig. 4.2 also shows the reflectance curves of a glossy white tile with both measurement geometries. 88 © Woodhead Publishing Limited, 2010

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89

0.6

Optimal colours

y

0.5

0.4

Whiteness limits

0.3

0.2 0

0.2

0.4 x

0.6

4.1 Limits of white objects and optimal colours in the CIE 1931 chromaticity diagram for Y = 90. The X within the whiteness limits marks the position of the ideal white for illuminant D65.

100

Reflectance (%)

80 60

White tile SPIN White tile SPEX

40

Mirror SPIN Mirror SPEX

20 0 400

500

600 Wavelength (nm)

700

4.2 Spectral reflectance factor measurements of a glossy white tile and a mirror with the specular component included (SPIN) and excluded (SPEX).

Wittgenstein (1977) raises the questions: ‘Why is it that something can be transparent green but not transparent white? … Why can’t we imagine transparent-white glass, – even if there isn’t any in actuality?’ Evans (1949) discusses in much detail the definition of colour (referring to a report of the Colorimetry Committee of the Optical Society of America) according to which ‘color can be seen in any mode and has only the attributes of hue, saturation and brightness’. He then concludes, that ‘color seen in the aperture mode does not contain either white or grey. We are forced to the conclusion, therefore, that white and grey are not colors under the definition.’

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On the other hand whiteness is so much part of the colour sensation that some colour order systems, among them the very popular Swedish NCS – Natural Colour System (see Chapter 2), use ‘white content’ as one the three attributes of any colour (together with ‘black content’ and hue). We would thus be in a very difficult position indeed if we were to leave white out of the universe of colours, but we must always remember that it has a unique position in this universe. Yellow is a much simpler concept than white, being one of the five principal hues of the Munsell system and one of the four unitary hues of the NCS system (see Chapter 2). According to the ISCC-NBS Method of Designating Colors (Judd and Kelly, 1967) we may call ‘yellow’ (without a hue modifier) colours with Munsell hue 1Y to 7Y with chroma above 2 to 3 and value above 5.5. The concept and the measurement of whiteness are of great importance in a number of practical fields. We shall see in the following sections the possibilities for the visual and instrumental evaluation and the indices used for the numerical characterisation of whiteness. Yellowness as a characteristic of materials is used only in a limited way; it shall therefore be discussed only briefly.

4.2

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Colour measurement

Visual assessment of whiteness

The application of the concept of ‘whiteness’, as we could see above, is not limited to objects which are totally devoid of hue (i.e. whose Munsell chroma is zero), but also (in practice, mainly) to a wide range of slightly tinted ‘near-whites’ where the degree of whiteness is not simply a function of lightness. As soon as a combination of lightness, chroma and hue has to be evaluated, observers differ in their assessment of which sensation is to be called ‘whiter’. One of the major difficulties in visually assessing whiteness, determining which of two white objects is to be considered ‘whiter’ is the lack of a reference white. Before the advent of FWAs the idea of the ‘ideal white’ seemed to be very simple: an object diffusely reflecting 100% of the incident light throughout the visible spectrum. Even that could be questioned: for some observers a bluish (or violetish or even greenish) white would be preferred, i.e. considered whiter than a neutral white of equal or even somewhat higher luminance factor. For samples treated with FWAs there is no well-defined upper limit, and the possibility of having ‘metameric whites’, i.e. samples with different spectral power distribution (SPD) but the same degree of whiteness, is significantly greater. The decision would then have to be taken whether to prefer a white with a somewhat higher luminance factor, or one with lower chroma, or a third one with a different tint. According to Vaeck (1979): ‘At any given level of illuminance and for all normal observers, the chromaticity corresponding to the highest whiteness perception is never identical with the achromatic colour. It is always to the blue or purple side of the achromatic point (achromatic being taken here in the colorimetric sense).’ When ordering 20 white samples (all within the CIE defined range of ‘commercially white’) by 22 naïve observers, Jafari and Amirshahi (2008) found

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that observers can rank samples with a low whiteness index with much more consistency than those with a high whiteness index.

4.2.1 The effect of illumination In spite of the truly amazing capability of the human eye to adapt to changes in the illumination, visual evaluation of whiteness (just as that of colour) depends on the quality (and to a lesser degree, the intensity) of illumination. A white object will appear white even under extreme conditions, e.g. illuminated by monochromatic yellow sodium light (Wright, 1972). A white sheet of paper in the shade may emit less radiant power than a light grey sheet in sunlight, yet we shall perceive the former as white and the latter as grey. For the comparative evaluation of non-fluorescent objects the quality of illumination may not be critical, but, as we shall see later, the spectral radiance factor, and consequently the perceived whiteness of fluorescent objects, depends to a large extent on the SPD of the illumination. Therefore it is very surprising how little attention to the exact specification and recording of the SPD of the illumination was paid during the visual assessment and evaluation of white samples reported in the literature. Over 35 years ago Stensby (1973) complained that ‘many of the previous whiteness studies lack strict scientific meaning because experimental conditions and influencing factors were not carefully standardized and defined’. This was, in a way, understandable for the early work done on nonfluorescent samples, much less so for those involving samples treated with an FWA. MacAdam (1934) reported on the arrangement for the visual classification of 36 samples by 30 observers as follows: ‘A room was selected on the top floor of a building high enough to avoid reflection from neighboring buildings. This room had white walls and a northern exposure through large windows. The tests were carried out on completely overcast days.’ Judd (1936) was even less precise in determining the illumination conditions for his 15 observers: ‘The observations were carried out in the observer’s own laboratories under the customary illuminant, presumably daylight, and by his own method.’ Hunter (1958) reported on the work of the Inter-Society Color Council Subcommittee on Problem 19, White Surfaces, and stated that: ‘Standardized artificial light sources and inspection cabinets are popular, but natural daylight, preferably from the north sky, is still used for much if not most visual examinations.’ Since the 1970s, experimenters have become more careful in designing and defining the experimental conditions also as regards illumination, yet it should be noted how little we know of the exact conditions under which the basic experiments, leading up to the currently used whiteness formulae, were conducted. Probably the most carefully designed and best documented series of experiments in this field were those conducted by members of the CIE Subcommittee on Whiteness as reported in Die Farbe (Berger-Schunn, 1977; Berglund and Stenius, 1977; Mattiello and Lozano, 1977; Stenius, 1977) and in a series of articles by

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Ganz (1976, 1979a, 1979b), Ganz and Griesser (1981) and Ganz and Pauli (1995) in Applied Optics. From these publications we learn that indeed, the experiments were carefully designed, but, unfortunately, the technical details of the illumination are not sufficient to reproduce them if one wanted to. Berger-Schunn (1977) specified the following conditions: (a) ‘northsky light, behind glass’ and (b) ‘alternatively artificial daylight the irradiance distribution of which should be largely in accordance with D65’. Berglund and Stenius (1977) only state that the experimental conditions have to be standardized: ‘With regards to whiteness this means that the surround should be neutral with constant illuminance and constant SPD of the illuminant. The latter should moreover be of best obtainable agreement with the CIE standard illuminant D65 chosen for colorimetric analysis of the samples.’ Mattiello and Lozano (1977) illuminated their samples by ‘southern-hemisphere daylight of approximately 1000 lx’, while Stenius (1977) stated that: ‘The illumination was either natural daylight in front of a window, or the samples were placed in a viewing booth equipped with an illuminant claimed to correspond to the CIE standard illuminant D65.’ Swenholt et al. (1978) used ceiling illumination of Macbeth D65 fluorescent lamps (but gave no details of the characteristics of these). Ganz (1976) described in detail the importance of the control of sample irradiation in both visual and instrumental assessment, but also explained that: ‘For some industrial routine work such discrepancies are without consequence. Relative visual assessments and relative instrumental measurements of whiteness are sufficient for comparing the whitening effect of similar products, evaluating various application procedures, assessing fastness properties, etc.’ On the other hand, for critical work this may not be sufficient, as he himself described in an earlier work (Ganz, 1972): three whites W1, W2, W3 appear as increasing in whiteness under D65, whereas W2 appears less white than W1 under illuminant A (less UV) and whiter than W3 under unfiltered Xenon light (more UV). In their work on the assessment of tint Ganz and Griesser (1981) stated that the ‘visual assessments were carried out on a windowsill illuminated by natural north sky light through UV transmitting windows’. Recent research sheds some light on the influence of illumination on whiteness perception. Ayama et al. (2003) changed the illumination by using fluorescent lamps with correlated colour temperatures (CCT) from 2800 to 6700 K and observing the whiteness ranking of a neutral (N9.25) and eleven nearly white (Munsell value 9.25, Munsell chroma 0.5), non-fluorescent samples. They found significant changes in the ranking of different hues (some got a higher, others a lower ranking with the increase of CCT) in spite of the samples being nonfluorescent. Katayama et al. (2007) used exactly the same sample set, but only four fluorescent lamps, with CCT ranging from 3330 to 6160 K, and arrived at the conclusion that the ranking of different hues depended very little on the CCT of the illumination.

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While the importance of the SPD of illumination on the ranking of nonfluorescent whites may be open to discussion, for fluorescent whites this SPD is obviously all-important. What should be the reference illuminant? It depends primarily on the application in question: paper industry professionals have long argued that it must be ‘indoor daylight’, which has only recently been defined by the CIE (2009). For the textile industry it has for a long time been D65. For household appliances probably tungsten light (illuminant A) should be preferred, while for the evaluation of the whiteness of teeth any of the above might be used (from a purely practical point of view) as reference illuminant. The current most widely used whiteness and yellowness formulae (see 4.4) can only be applied to illuminant C or D65, thus these should be selected also for visual evaluation, provided they can be realized as practical sources.

4.2.2 Practical daylight simulators for visual evaluation Why is it that no ‘standard light source’ was used in any of the publications cited above? The relative SPD of the D65 CIE standard illuminant for daylight (CIE, 1986a) is based on experimental measurements of natural daylight, as reported by Judd et al. (1964). The D illuminants, recommended by the CIE (1964) as the SPDs best representing various phases of daylight, have not yet been, and probably never will be reproduced ‘exactly’, i.e. there are no physical sources having identical SPDs to D65 or any of the other daylight illuminants. Daylight simulators (i.e. practical sources with SPD similar to daylight illuminants) can be evaluated by a method published as a joint ISO/CIE Standard 23603 (2005). The basis for the assessment is the special metamerism index for change in illuminant, using eight pairs of virtual specimens: three pairs for the UV-range evaluation (consisting each of one fluorescent and one non-fluorescent specimen) and five pairs for the visible range evaluation. The colour difference between the members of the pairs is (by definition) zero for the reference illuminants (D50, D55, D65 or D75) and the average of that shown under the test light source is the UV-range metamerism index (MIuv) for the first three pairs and the visible range index (MIvis) for the set of five pairs. Based on these indices light sources (daylight simulators) are rated in five categories for the visible and five for the UV range, and the rating is given by two letters, the first for the visible and the second for the UV range, category A being the best and category E the worst. According to the Standard Practice for Visual Evaluation of Color Differences of Opaque Materials (ASTM, 2003): ‘For critical appraisal of colors and color differences, the category determined by that method shall be BC (CIELAB) or better. This rating ensures that the source provides ultraviolet and visible power in the right proportions to make both nonfluorescent and fluorescent materials look very nearly the way they would in the corresponding phase of natural daylight.’ There are currently three major technologies competing in the field: filtered incandescent (tungsten filament) lamps with an additional UV source, filtered

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xenon short arc and fluorescent lamps. According to Hirschler and Oliveira (2007) there exist Category BC sources (i.e. acceptable according to ASTM D 1729) available for each technology, but the vast majority of commercially available light booths and sources are far from being adequate. This, unfortunately, means that most of the visual evaluations of white (particularly fluorescent white) materials are not performed under conditions comparable to those of instrumental measurements, thus no close correlation may be expected between the two.

4.2.3 Sorting and ranking methods for visual whiteness determination Whiteness, as already described above, is a very complex concept. Even its dimensionality is not quite certain: according to some studies (Evans, 1964) achromatic colours are multidimensional. Lie (1969) reviewed the literature on this problem and stated that ‘as far as phenomenological descriptions are concerned, general agreement appears to exist … that the achromatic colours may be varied along two separate dimensions, from black to white and from dim to bright’. In the experiments leading up to the current CIE whiteness formula Berglund and Stenius (1977) arrived at the conclusion, that: ‘Perceptual whiteness may be regarded as unidimensional in the sense that a onedimensional solution correlates well with direct unidimensional scaling … [while a] more sophisticated analysis reveals that perceptual whiteness is to be considered multidimensional in the sense that independent perceptual components may in different combinations create the same perception of whiteness.’ The two most important forms of visual experiments leading up to the quantification of whiteness are either of the pair comparison or the ranking type. In the former the observer is shown only two samples at a time, and has to decide which of the two is ‘whiter’. In forced judgements the ‘they are equally white’ answer is not acceptable. The consistency of pair comparisons can be measured by the ratio of circular triads, where the same observer found sample A whiter than B, B whiter than C and C whiter than A. In the CIE experiments (Stenius, 1977 and Ganz, 1979a, 1979b) the individual number of circular triads ranged from 0 (for some of the professional colour matchers) to 62 out of the theoretically possible 120 triads. It is noteworthy that the average number was somewhat higher for the assessments performed under natural daylight and somewhat lower for those under artificial light. Ranking (placing a given number, in the CIE experiments 10, of samples in order according to increasing whiteness) is even more difficult, particularly if whites of different tint (i.e. not entirely neutral) are involved. Some professionals, who performed very well in the pair comparison experiments, simply refused to assess the samples by ranking, declaring ‘the task is impossible owing to the extreme differences in tint’ (Ganz, 1979b).

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Measuring techniques and instruments

In view of the difficulties in visual whiteness assessment, instrumental measurement and evaluation of white samples has long been the preferred method. The measurement of fluorescent samples, however, is far from being straightforward: differently from the spectrophotometric measurement of nonfluorescent specimens both the optical arrangement of the instrument and the quality of the illumination have a fundamental effect on the result. Figure 4.3 shows the total radiance factor (TRF – the sum of reflectance and fluorescence) measurements for a fluorescent white textile sample with two instruments. Even though the instrument with the ‘classical’ forward optics design (monochromatic illumination of the sample) has enough energy also in the UV range the TRF curve is completely wrong. The proper arrangement is polychromatic illumination and monochromatic viewing (‘reverse optics’), where we get the true TRF.

4.3.1 The effect of illumination In the spectrophotometry of non-fluorescent specimens the SPD of the light source has no influence on the results. The measured spectral transmittance or reflectance is a relative quantity, independent of the illumination. This, however, is not the case when measuring fluorescent specimens, where the SPD, particularly the UV/visible ratio of the illumination, is of primary importance. Figure 4.4 shows the TRF of a non-fluorescent and a fluorescent white textile sample measured with three positions of an adjustable UV filter (see 4.3.2). For the non-fluorescent specimen the UV content has no influence on the TRF, while

Total radiance factor (%)

140 120 100 80 60

Mono-poly

40

Poly-mono

20 0 400

500

600 Wavelength (nm)

700

4.3 Effect of instrument optics on the total radiance factor of fluorescent specimen measured with forward (mono-poly) and reverse (poly-mono) optics.

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100 80 Fluorescent – 100% UV

60

Fluorescent – 50% UV 40

Fluorescent – No UV Non-fluorescent

20 0 400

500

600

700

Wavelength (nm)

4.4 Effect of the UV content of the illumination on the total radiance factor of fluorescent and non-fluorescent specimens.

for the fluorescent one there is a significant difference in the 420 to 530 nm region and, consequently, on the whiteness indices, which increase from WI = 66 for the UV cut-off measurement to WI = 146 for the 100% UV measurement.

4.3.2 Practical daylight simulators for the instrumental measurement

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We have seen before that for visual work most of the currently available practical daylight simulators are not quite acceptable according to the relevant standards. With colour-measuring spectrophotometers the situation is somewhat different. Using filtered pulsed xenon illumination it is possible to achieve excellent simulation of the D65 illuminant. Hirschler et al. (2003) reported Category AB and Category BA ratings for different commercial instruments and stated that: ‘Modern single-monochromator spectrophotometers can have fully satisfactory daylight simulators as light sources both for the UV and the visible range when applied to fluorescent whites with excitation mainly in the UV region.’ The problem with these instruments is that the light source (nearly always a pulsed xenon lamp) ages and loses energy in the UV region (300 to 400 nm) as compared to the visible region (400 to 700 nm) of the spectrum. There are currently two technologies used to overcome this problem: the Gaertner-Griesser (1975) device and numerical UV control (Imura et al., 1997). The Gaertner-Griesser (1975) UV calibrating device consists of a UV adjustment filter with a steep absorption slope at about 400 nm which is inserted partially into the light path of the source (Fig. 4.5.) UV-free and UV-rich radiation are mixed inside the integrating sphere of the instrument illuminating the sample

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97

Integrating sphere

UV-free radiation UV-rich radiation

Mixed radiation

Xenon lamp

Sample

4.5 The Gaertner-Griesser UV calibrating device (Griesser, 1994). © 1994 John Wiley & Sons, Inc. Reprinted with permission of John Wiley & Sons, Inc.

with polychromatic light. When the lamp is new (high UV content) there is an excess of UV radiation which can be filtered out by inserting the UV-absorbing filter up to the optimum point. As the lamp ages and loses some of the UV (relative to the visible, i.e. longer wavelength) radiation, the UV filter is partially moved out of the beam thus recomposing the ‘original’ illumination (which is the best fit to D65). The degree of removal is determined by a calibration process (Griesser, 1994). The ‘UV calibration’ is changing the balance between the short and the long wavelength parts of the emitted light; the illumination inside the sphere is a linear combination of I1 (UV-rich illumination) and I2 (UV-free illumination): Ic(λ) = KI1(λ) + (1-K)I2(λ)

[4.1]

where Ic(λ) is the composite illumination and K depends on the position of the filter, is λ-independent. As can be seen in Fig. 4.6 the filter only changes the shape of the spectral curve in the 300–400 nm range and can thus ‘bring back’ the original UV content, it serves to compensate for the ageing of the lamp, but cannot ‘improve’ the D65 fit, it will not turn the source into a better daylight simulator than it originally had been. This method had been developed before pulsed xenon was introduced in spectrophotometers, but has been used since in most of the advanced industrial instruments. In 1997 Imura et al. patented a method called ‘numerical UV control’ or NUVC based on making two flashes for each measurement: one with a lamp with a UV-absorbing filter in front of it (‘UV excluded’) and another without such a filter (‘UV included’). In this method both lamps make one flash each (whose intensity is checked) for the total spectral radiance factor β1(λ) by illumination I1 (UV-rich) and for β2(λ) by illumination I2 (UV-free) and then the best linear combination of β1(λ) and β2(λ) is determined by calculation:

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[4.2]

where βc(λ) is the composite total spectral radiance factor and Kλ is determined by calculation for each fluorescent wavelength λ, may be λ-dependent (Imura, 2008). In this case the UV part of the illumination (and the resulting fluorescence) may be numerically calculated, in a sense ‘optimized’ between the two curves in the 300 to 400 nm region and thus the CIE rating showing the goodness of fit is normally very high. In the visible region (400 to 800 nm) the goodness of fit depends on the D65 filter, but in the case of modern instruments this is normally also very good, thus this technology ( just as the Gaertner-Griesser method) offers excellent daylight simulation in colour measuring spectrophotometers, adequate for most practical purposes.

4.3.3 Measurement of white and near-white samples The reflectance curves of some of the whitest non-fluorescent artefacts used as ‘white standards’ in spectrophotometry are illustrated in Fig. 4.7. Whiteness measurement, like colour measurement in general, is subject to measurement uncertainties and errors, and to a great extent these are due to the differences in illumination and measurement geometry of different instruments. In a detailed study conducted in the textile industry (Hardt et al., 2003), 8 non-fluorescent and 4 fluorescent textile samples were measured in 16 industrial laboratories, 5 public measuring laboratories and by 4 instrument manufacturers, and the results were disastrous. The difference between some of the non-fluorescent samples was as high as 34 Berger whiteness units; for fluorescent samples this was only somewhat worse (36 Berger units). Even within the public measuring laboratories group differences

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Spectral power distribution (%)

160 140 120 100 80 60

UV = 0% UV = 50% UV = 100%

40 20 0 300

400

500

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Wavelength (nm)

4.6 Three levels of UV radiation in an industrial reflectance spectrophotometer with UV calibration capabilities.

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Reflectance (%)

100

Spectralon

80

Barium sulfate Russian opal glass White ceramic tile 60 400

500

600

700

Wavelength (nm)

4.7 Reflectance curves of four white artefacts used for the photometric scale adjustment of reflectance spectrophotometers.

Table 4.1 CIE WI measured on four different instruments and compared to the nominal values given by the Hohensteiner Institute for the four textile samples and by BAM for the Halon standard

T1

T2

T3

T4

Halon standard

Filter

Nominal SF500 CM3720d

77.32 77.40 77.88

103.71 102.40 103.04

128.37 126.50 127.00

154.77 153.80 156.29

124.91 121.70 122.36

NUVC

Textile samples Instrument

CM3600d CM2600d

78.54 73.26

105.08 100.13

129.71 127.35

154.97 155.41

125.75 124.71

Source: Reproduced from Hirschler et al. (2003) by permission of the CIE Central Bureau, Vienna, Austria.

of 15 to 20 units were found. Willis (2002) reported differences of up to 20 CIE WI units between measurement results on the same samples on different instruments as ‘not unusual’. These differences may be reduced to a few CIE units with a proper calibration procedure. According to Hirschler et al. (2003) the differences between nominal values of a research institute’s BAM-traceable measurements and the BAM data can be as large as 9 Ganz-Griesser units, but, under carefully controlled conditions, different instruments can measure with a difference of only a few CIE WI units as shown in Table 4.1. Using proper calibration and adjustment procedures in a comparative trial involving 24 different instruments the standard deviation of the different whiteness value results was 0,7 whiteness units (ISO, 2004). It has to be emphasized here that these differences are due only to measurement uncertainties (instrument geometry, illumination, calibration procedure) and the

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situation becomes much worse when these are combined with the difficulties of translating measurement results into visually meaningful whiteness formulae.

4.4

Indices for whiteness and yellowness

Three years after the introduction of the CIE system of colour measurement MacAdam (1934) suggested a method for the numerical specification of whiteness based on brightness (‘a known factor of the value of Y’) and purity. Based on a study of 36 white textile samples, he arrived at the conclusion that for these samples, all having a dominant wavelength of 575±1 nm whiteness depends primarily on brightness when the purity was less than three percent; and on purity for samples having a purity greater than five percent. The first whiteness formula was suggested by Judd (1935) and it was followed by literally hundreds of others, which found their way into numerous fields of applications. References to most of these can be found in Sève (1977) and a brief description of some of the most popular ones in Puebla (2002).

4.4.1 Luminance factor Y Everything else (hue and chroma) being equal higher Y always means higher whiteness, but we know that for the large majority of practical samples we always have to consider the influence of all three dimensions. In some cases Y alone was used for the characterization of the whiteness of a specimen (Hunter and Harold, 1987), such as for grading raw cotton (where it is to be expected that the yellowness will be eliminated by bleaching) or in the measurement of soil removal for textiles.

4.4.2 Paper brightness

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For over 70 years the paper industry has been using ‘brightness’ as a useful measure to describe the optical characteristics of pulp and paper, particularly during the process of bleaching of pulp. (It is somewhat confusing that the word ‘brightness’ is used differently to the established CIE usage where it means the perceptual correlate of luminance – in this chapter we will restrict the usage to the special meaning as used in the paper industry.) Paper brightness is a weighted function of the spectral reflectance as defined by several national and international standards, but it is important to notice that the measurement conditions recommended are different, thus leading to different results. What has become known as TAPPI (or GE) brightness (TAPPI, 2008; ASTM, 2007) refers to bi-directional (45/0) measuring geometry. These standards define the SPD for a light source between CIE illuminants A and C as illustrated in Fig. 4.8. ISO brightness (TAPPI, 2006; ISO, 2009) is defined for diffuse/0 measurement geometry and illuminant C, while D65 brightness (ISO, 2008) for diffuse/0 measurement geometry and illuminant D65.

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Spectral power distribution (%)

120 ASTM

100

CIE A 80

CIE C

60 40 20 0 300

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450

400

500

Wavelength (nm)

Reflectance (%)/Brightness function

4.8 SPD of the light source recommended by ASTM (2007) and TAPPI (2008) (solid curve) for the measurement of TAPPI (GE) brightness as compared to that of CIE illuminants A and C.

100 80 Bleached Bleached and blued Brightness function

60 40 20 0 400

500 Wavelength (nm)

4.9 The brightness function with maximum at 457 nm and full width at half height of 44 nm as compared to the spectral reflectance of a bleached and blued paper specimen.

Whatever the geometry and the illumination, all paper brightness measurements refer to filtering the reflected light by a filter with maximum transmittance at 457 nm and a width at half height of 44 nm (see Fig. 4.9). In modern instruments the measurements are made spectrally and the same weighing functions applied to arrive – depending on the illumination and measurement geometry – at the TAPPI, ISO or D65 brightness value. The considerations of Bristow (1994a) made for ISO brightness are thus valid for all brightness measurements: •

[ISO] brightness is essentially useful as an arbitrary but sensitive measure of the progress of bleaching in a bleaching plant.

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•

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[ISO] brightness is not a direct measure of any visual property. It is not related to any system of colorimetric measurement, and it has no foundation in perceptual psychology.

Brightness measurements should thus never be used in the place of whiteness measurements. Figure 4.9 also shows the reflectance curves of two paper samples, one bleached and the other one bleached and ‘blued’ (tinted with a violet dye to achieve higher whiteness). Visually the blued sample appears to be significantly whiter and this is well reflected in the much higher whiteness index of the latter (CIE WI = 89.1) as compared to the first (CIE WI = 76.4), while the ISO brightness values are nearly the same: 88.9 vs. 87.9.

4.4.3 Traditional whiteness formulae (Berger, Hunter, Stensby, Taube)

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One of the best known traditional formulae was developed by Berger (1959). It is based on the Selling and Friele (1950) data with some new, FWA-treated samples added. The original formula was suggested in a form directly related to values obtainable by the then most popular tristimulus instruments: – – – WIBerger = f(Y) + (Z – X) [4.3] – – – where Y, X and Z are the measured instrumental values (with MgO=100 as white reference), from which the 2 degree CIE tristimulus values for illuminant C may be calculated by – – X = 0.783 X + 0.197 Z – Y=Y – Z = 1.181Z [4.4] – For f(Y) Berger suggested Y/3, the whiteness formula. Equation 4.3 thus became – – [4.5] WIBerger = (Y/3) + (Z – X) In various later publications this was then converted to CIE tristimulus values in the form WIBerger = Y/3 + k1Z – k2X

[4.6]

but, depending on who quoted the original publication (Berger, 1959), k1 and k2 took different values as shown in Table 4.2. Hunter (1960) suggested a very simple formula based on the difference of the Hunter lightness (L) and yellowness (b) values: WIHunter = L – 3b

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[4.7]

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Table 4.2 Coefficients in the Berger whiteness formula (Eq. 4.6) according to different sources; all values refer to CIE illuminant C Observer

Reference

– f(Y )

k1

k2

CIE 1931 CIE 1964 CIE 1931 CIE 1931 CIE 1931

Puebla (2003) Puebla (2003) Thielert and Schliemann (1972) Smith (1997) ASTM (2005)

Y Y Y/3 Y/3 Y

3.440 3.448 1.240 1.060 3.108

3.895 3.905 1.310 1.277 3.831

which, expressed in terms of CIE tristimulus values (ASTM, 2005) takes the form: WIHunter = 10(Y – 21)1/2(Y – 0.847Z)/Y1/2

[4.8]

Stensby (1967) modified the Hunter formula by adding a redness term: WIStensby = L – 3b + 3a

[4.9]

which, expressed in terms of CIE tristimulus values (Thielert and Schliemann, 1972) takes the form: WIStensby = 20.832 – 63.50Y + 55.12X √Y

[4.10]

Taube (1958) simply took the weighted difference between the blue and green reflectance: WTaube = 4B – 3G

[4.11]

which, according to Thielert and Schliemann (1972) takes the form WTaube = 3.97Z – 3Y

[4.12]

while the ASTM standard (2005) gives it as WTaube = 3.388Z – 3Y

[4.13]

Thielert and Schliemann (1972) compared the performance of the Berger, Taube, Hunter, Stensby and Stephansen formulae and found that they are all equally well suited for describing the visual impression of whiteness, except when the samples that differed strongly in hue or neutral white had to be compared with lighter but distinctly coloured samples. They found correlation coefficients above 0.97 for not too different samples, but for ‘complicated’ sample sets it was 0.78 or even much less. Figure 4.10 compares the hue preference of the four maybe best known conventional formulae. The different whiteness indices are plotted for a set of Munsell samples of different hue at chroma 0.3 and the neutral of the same Munsell value (9) in function of the hue. The Taube and Hunter formulae show no hue preference, which means that the P, PB and B samples are rated whiter, the G to R

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WI Stensby

100

80 70 60

80 70 60

50

50 5R 5RP 5P 5PB 5B 5BG 5G 5GY 5Y 5YR

5R 5RP 5P 5PB 5B 5BG 5G 5GY 5Y 5YR

Munsell hue

Munsell hue

100

100

90

90

WI Berger

WI Hunter

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Colour measurement

WI Taube

104

80 70

80 70 60

60

50

50

5R 5RP 5P 5PB 5B 5BG 5G 5GY 5Y 5YR

5R 5RP 5P 5PB 5B 5BG 5G 5GY 5Y 5YR

Munsell hue

Munsell hue

4.10 Comparison of the hue preferences of four conventional brightness formulae.

samples are rated less white than the neutral, the RP and BG hues are judged equal to the neutral. The Stensby formula (red preference) rates the R and RP hues whiter, the B as white as and the BG to YR less white than the neutral; while the Berger formula (green preference) rates the R and RP less white and the BG whiter, the G as white as the neutral.

4.4.4 Colour difference from a reference white

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A very logical whiteness index was mentioned by Judd and Wyszecki (1963) based on the colour difference from the ‘preferred white of whatever material is being studied’. Today we would define it as the colour difference (in CIELAB) from the perfect diffuser: W = 100 – √(100 – L*)2 + a*2 + b*2 [4.14] This index has never been much used, because it does not take into account the preference for bluish white as opposed to yellowish whites; a sample with the same L* and a* coordinates would be rated just as white with b* = 0.5 (yellowish) as with b* = – 0.5 (bluish) which, visually, is clearly not the case. In spite of its shortcomings this index appears in the literature for some applications.

4.4.5 The Ganz linear whiteness and tint formulae; hue preferences Contrary to the ‘traditional’ whiteness formula based on some kind of combination of the three tristimulus functions, Ganz (1972) suggested a family of linear whiteness formulae with the general form

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Visual and instrumental evaluation of whiteness and yellowness WI = f(Y) + g(x,y)

105 [4.15]

Based on systematic investigations of previously published L, a, b type (e.g. Hunter, Stensby) and B, G, A type (e.g. Berger, Taube) formulae his new, versatile, linear formula was: WIGanz = ∂W (Y + ω(px + qy)) + c ∂Y

[4.16]

in which p and q depend on the hue preference only, c serves to keep W = 100 for the ideal diffuser and with (∂W / ∂Y) = 1. The scaling of the formula may be adjusted and any combination of illuminant and observer may be selected by varying the coefficients in the formula. This formula is the basis for the Ganz-Griesser method described in 4.4.7. Based on visual assessments of the sample set prepared by Berger-Schunn (1977) for CIE TC-1.3, Ganz (1976, 1979a) later proposed a tentative formula for tint and new linear formulae for whiteness of neutral, green and red hue preference. The neutral hue preference formula for illuminant D65 and both CIE 1931 and 1964 standard colorimetric observers is: WIGanz, neutral = Y – 800(x – x0) – 1700(y – y0)

[4.17]

the supplementary formula of green hue preference: WIGanz, green = Y – 1700(x – x0) – 900(y – y0)

[4.18]

and the one of red hue preference: WIGanz, red = Y – 800(x – x0) – 3000(y – y0)

[4.19]

Figure 4.11a shows the very high correlation of the Ganz green preference formula (Eq. 4.18) with the Berger formula (Eq. 4.6), and Fig. 4.11b that of the Ganz red preference formula (Eq. 4.19) with the Stensby formula (Eq. 4.10). Two standard tint formulae were proposed for illuminant D65, one each for the CIE 1931 standard colorimetric observer: T = – 1000(x – x0) + 700(y – y0)

[4.20]

and for the CIE 1964 standard colorimetric observer: T = – 900(x – x0) + 800(y – y0)

[4.21]

Based on new visual evaluations these tint formulae were later slightly modified by Ganz and Griesser (1981), at the same time setting T = 1 for the perfect diffuser: TVGG(1931) = 1000(x0 – x) – 650(y0 – y) + 1

[4.22]

TVGG(1964) = 900(x0 – x) – 650(y0 – y) + 1

[4.23]

and

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90

R2 = 0.9982

Green preference Red preference

85 WI Ganz red/green

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80 75 70 65 60 60

65

70 WI Berger

75

80

4.11a Correlation of the Ganz green preference formula with the Berger formula (also showing green preference).

90

Green preference

R2 = 0.9806

Red preference

WI Ganz red/green

85

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80 75 70 65 60 75

80

85 WI Stensby

90

95

4.11b Correlation of the Ganz red preference formula with the Stensby formula (also showing red preference).

4.4.6 The CIE whiteness and tint formulae In the 1960s and early 1970s there were hundreds of whiteness formulae used in different applications, but none of them was proven to be much better than the others. A subcommittee was formed in 1969 within CIE Technical Committee

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TC –1.3 to start a systematic study on whiteness. Berger-Schunn (1977) prepared a set of 56 paper samples within a range of (what we now call) CIE whiteness 48 to 148 and CIE tint –10 to +5. Berglund and Stenius (1977) provided a methodological study on the evaluation of perceptual whiteness, and Mattiello and Lozano (1977) and Stenius (1977) reported on the psychophysical studies and the first results. Sève (1977) published an extensive bibliography on whiteness. In 1978 a new CIE task force was set up (Brockes, 1982) which, after much discussion, formulated a proposal for two whiteness and two tint formulae, one each for the 1931 and the 1964 standard colorimetric observer, based on the publications of the subcommittee cited above and later publications by Ganz (1976, 1979a, 1979b) and Ganz and Griesser (1981). The whiteness index adopted was the neutral (no hue preference) formula of Ganz (Eq. 4.17) and the tint formula was that of Ganz and Griesser (1981) (Eqs. 4.22 and 4.23) but without the additional term ‘+1’ at the end (thus for the perfect diffuser T = 0 again). The current form of the CIE whiteness and tint formulae for the CIE standard illuminant D65 was published in the latest edition of CIE Publication 15 (CIE, 2004) as: W = Y + 800(xn – x) + 1700(yn – y)

[4.24]

W10 = Y10 + 800(xn,10 – x10) + 1700(yn, 10 – y10)

[4.25]

Tw = 1000(xn – x) + 650(yn – y)

[4.26]

Tw, 10 = 900(xn,10 – x10) – 650(yn,10 – y10)

[4.27]

where Y is the Y-tristimulus value of the sample, x and y are the x,y chromaticity coordinates of the sample, and xn, yn are the chromaticity coordinates of the perfect diffuser, all for the CIE 1931 standard colorimetric observer. Y10, x10, y10, xn,10 and yn,10 are similar values for the CIE 1964 standard colorimetric observer. The application of the CIE formulae is restricted to samples that • • •

are called ‘white’ commercially; do not differ much in colour and fluorescence; and are measured on the same instrument at nearly the same time.

The CIE also set limits to the whiteness and tint limits. The values of W and Tw must lie within the following limits for the 1931 standard colorimetric observer: 40 < W < 5Y-280 and –4 < Tw < +2 and similarly for the 1964 standard colorimetric observer. These limits have been changed since the previous edition of CIE Publication 15 (CIE 1986b) where the tint limits were given as –3 < Tw < +3. Figure 4.12 shows the old and new tint limits for L* = 96 on the CIELAB a*–b* diagram, indicating the positions of the original whiteness samples (Berger-Schunn, 1977) used by the CIE subcommittee in the development of the formulae.

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20.00 Berger samples T=0 10.00

Old limits New limits

b*

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0.00

–10.00

–20.00 –5

0

5

10

a*

4.12 Old and new tint limits in the CIELAB a* – b* diagram at Y = 90 (L* = 96) as defined by the CIE (1986b resp. 2004) whiteness and tint formulae. Also shown are the data points of the original whiteness samples (Berger-Schunn, 1977).

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The restriction that the samples be measured ‘on the same instrument at nearly the same time’ limits the application of the CIE formulae to relative measurements, i.e. they are not supposed to be used when the samples have been measured in different laboratories (even of the same organization) and only differences – rather than absolute values – are supposed to be relevant. These restrictions are not always followed in an industrial environment, where whiteness and tint tolerance limits are very often set up and used in absolute terms. In the paper industry these practices are standardized with the provision that traceable reference white standards are used for the adjustment of the UV content of the measuring spectrophotometer; in the textile industry the Ganz-Griesser calibration procedure and the application of the related formulae gained popularity (see 4.4.7). AATCC (2005) permits using the CIE formulae also for the illuminant C / 1931 standard colorimetric observer combination using the appropriate values of xn and yn. ASTM (2005) gives the xn and yn values for illuminants C, D50 and D65 for both observers, but both standards carry the warning that conditions for illuminants C or D50 ‘are unofficial and should be used for in-house comparisons only’.

4.4.7 Instrument specific parameters; the Ganz-Griesser method The Ganz linear whiteness formula has been in use at the former Ciba-Geigy since 1971 (Griesser, 1981) in the general form:

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Visual and instrumental evaluation of whiteness and yellowness W = (DY) + (Px) + (Qy) + C

109 [4.28]

where Y, x and y are the colorimetrically determined values for the Y tristimulus value and the chromaticity coordinates; D, P, Q and C are the formula parameters whose magnitude determines the ‘whiteness bias’ of the formula. These parameters may be standard values for a given illuminant/observer combination, or instrument specific values determined through the Ganz-Griesser calibration procedure. The CIE whiteness formula is a specific case with the parameters given in Eqs. 4.24 and 4.25 for D65 and the two standard colorimetric observers, respectively. The Ganz-Griesser calibration method is based on one proposed by Levene and Knoll (1978) to calculate the formula parameters so that the calculated whiteness values • •

relate to the nominal values of any existing white scale, and are adapted to the illumination of the measuring instrument used (Griesser, 1981).

The tint formula uses the adjustable parameters m, n and k: TVnom(Ganz-Griesser) = mx + ny + k

[4.29]

also calculated through the calibration procedure. The method uses a set of calibrated whiteness scales incorporating the hue preferences and scaling for the Ganz whiteness formula (Eq. 4.28) and the GanzGriesser tint formulae (Eq. 4.29). Using these scales the industrial user or the equipment manufacturer performs the following steps (Griesser, 1994): 1

Adaptation of the illumination for the best UV/visible ratio that can be achieved with the particular instrument, with the light source using the Griesser-Gärtner UV-calibration device or numerical UV-control (see 4.3.2). 2 Calculation of the instrument-specific formula parameters D, P, Q and C. 3 Determination of the illumination check samples for the working instrument. 4 Periodic checking of the illumination conditions for the working instrument. Should the measured values of the illumination check sample exceed the predetermined tolerance limits the process has to be re-started from Step 1. According to Smith (1997): The combination of the methods of adjustable filtration and modifying the coefficients of the Ganz formula thus removes the restriction that specimens to be compared must be measured on the same instrument at nearly the same time. Although neither method has been accepted as a standard, the hardware and software required for the combination has been widely used in industry for several years, generally with good results.

Gay et al. (2004) reported some improvement in the comparison of four industrial spectrophotometers using the Ganz-Griesser calibration procedure as compared

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to using the CIE formula, but there still seems to be not enough evidence in favour of the former to have justified standardization.

4.4.8 Yellowness indices Yellowness is defined by ASTM (2005) as ‘the attribute of color perception by which an object color is judged to depart from colorless or a preferred white toward yellow’. According to Hunter (1981): ‘Yellowness is the result of a tendency of many materials (especially organic) to absorb more light in the blue end than in the rest of the visible spectrum.’ Traditionally yellowness was defined by visual scales and in the 1970s and 1980s there were ‘as many as 25 different scales for rating the yellowness of oils, resins, chemicals, solvents, plastics, fibers and so on, by visual comparison with color standards’. A typical such scale was the Gardner Scale (ASTM, 2004) consisting of 18 glass filters from colourless (Y = 80) to very dark reddish amber (Y = 4) as illustrated in Fig. 4.13. Yellowness is defined by ASTM (2005) as YI =

100(CXX – CZ Z) Y

[4.30]

where CX and CZ are coefficients defined for different illuminant/observer combinations as shown in Table 4.3. Very often the CIELAB b* yellowness coordinate is used as a measure of yellowness (the higher the b* value the yellower the specimen), but this does not take the lightness dimension into consideration. Hunter (1981) described the results of a study of four tristimulus scales most frequently used for measuring yellowness and came to the conclusion that bL (Hunter b) or b* (CIELAB), as well 100

14

12

10

80

16

9

17

8

60

15

18

7

b*

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13

11

6

40 5

4

20

3 2 1

0 –20

0

20 a*

40

60

4.13 Data points of the 18 Gardner filters (ASTM, 2004) in the CIELAB a* – b* diagram.

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Table 4.3 Coefficients of the ASTM E 313-05 YI for different illuminants and observers CIE standard illuminant and standard observer Coefficient

C, 1931

D65, 1931

C, 1964

D65, 1964

CX CZ

1.2769 1.0592

1.2985 1.1335

1.2871 1.0781

1.3013 1.1498

Source: Adapted from ASTM (2005).

0.6

Optimal colours

y

0.5

0.4

0.3

Whiteness limits

0.2

4.14 Yellowness index (ASTM, 2005) and CIELAB b* coordinate as a function of the Gardner Scale value.

as an earlier yellowness index of ASTM, were only satisfactory for the white-toyellow portion of the colour gamut, but did not work where the scales turn from yellow toward reddish amber. This is well illustrated in Fig. 4.14 showing the b* and the current ASTM (2005) Yellowness Index values for the original Gardner Scale, with the YI steadily increasing (as it should with increasing ‘yellowness’) while the b* value starts to decrease after scale point 11 (the lightness steadily decreases from scale point 1 through 18).

4.5

Applications in industry, cosmetics and dentistry

4.5.1 Textiles The textile industry was the first to use the concepts of whiteness and yellowness for product qualification. As early as 1931 Nickerson described procedures for the grading of raw cotton, by means of disk colorimetry, which provide an adequate description of the colour of the samples. Even today cotton grading is to a large extent made by an

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instrument called the High Volume Fiber Test System or HVI (Zellweger Uster, 1999) which measures the colour of cotton in +b (the yellow coordinate of the Hunter system) and Rd (reflectance), and only recently attempts have been made to convert these values into CIELAB coordinates (Thibodeaux et al., 2008). Most of the advanced colour measuring systems offered for textiles have UV-calibration capabilities for the measurement of fluorescent white specimens, using either the Gaertner-Griesser device or numerical UV control (see 4.3.2). Most of the spectrophotometers, however, have sphere geometry, only one supplier offers bi-directional (0/45) geometry with UV-calibration capabilities. Whereas the ISO-authorized laboratories use the CIE-recommended 0/45 geometry for the calibration of fluorescent standard reference material (SRM), the transfer to industry is done through research laboratories already using sphere instruments. In spite of its wide acceptance by industry the Ganz-Griesser method has not yet been standardized. The textile industry thus uses either the CIE whiteness formula (with or without some kind of UV calibration) or the Ganz-Griesser formula (based on transfer standards provided by not ISO-authorized laboratories). The number one unsolved technical problem in textile colorimetry today is probably that of the determination of the degree of whiteness of fluorescent (optically brightened) textiles, and the basic reasons for this are the following: •

•

•

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Visual assessments are generally performed under non-standard illumination conditions (acceptable D65 simulators for visual inspection are extremely scarce in the textile industry), and thus the results are not reliable. Even under the best-controlled conditions, the concept of ‘white’ or ‘whiter’ is subjective, observers within the same organization show significant disagreement in ranking samples according to whiteness. There are very few colour-measuring reflectance spectrophotometers in industry which are ideal for the measurement of fluorescent samples. Those which would be adequate are very often not properly calibrated, and even in the best possible cases the calibration process is not standardized. Strictly following CIE recommendations, whiteness indices can only be used as relative values – a solution which, for many of the applications, just does not suffice.

4.5.2 Paper and pulp Instrumental methods for evaluating whiteness, brightness and fluorescence have long been used in the paper industry (Parkes, 1989). TAPPI and ISO brightness has been widely used for process control in pulp manufacturing, e.g. in the control of colour removal in paper recycling (Popson et al., 1997). Whiteness and brightness are important quality attributes of office paper, and the paper industry has long been in the forefront of research and application of whiteness evaluation. According to Smith (2008) industry standard office paper has TAPPI brightness 92, but demand has increased also for brightness 96 or even

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98 in North America, and a minimum of CIE whiteness of 145 was also set. The whitest paper reported so far has CIE whiteness of 175 (Tindal, 2005). Bristow (1994b) reported the methodology developed in five authorized laboratories to establish and maintain a calibration chain for reference instruments and paper samples to achieve reasonable agreement between different instruments at the user’s site. The intention was to use the CIE whiteness equation and to avoid instrument-specific constants. The paper industry has thus not embraced the GanzGriesser method of whiteness determination so widely used in the textile field. The d/0 measuring geometry implemented in the standards and consequently in the measuring instruments for brightness and whiteness measurement of paper is unique (as opposed to the d/8 geometry used in all other fields). Another speciality of the paper industry is the use of illuminant C (e.g. TAPPI, 2005a, 2005b), which is no longer a CIE standard illuminant, but its SPD has been reproduced in the latest edition of the Colorimetry publication (CIE, 2004) ‘as many practical measurement instruments and calculations still use this illuminant’. A number of publications discuss the most appropriate illuminants (and sources) to be used in the paper industry (Jordan and O’Neill, 1991; Jordan, 2003; Jordan et al., 2003) as well as that of the most appropriate geometry (Singh et al., 2008), but the choice among the many standard conditions is left for the user. Bonham (2006) came to the conclusion that ‘if a single variable is used to rank the appearance of white papers, it makes little difference for a broad data set … whether one uses brightness or whiteness or whether one uses illuminant C or D65’. He warns, however, that ‘this statement should not be interpreted as saying that the choice of illuminant is irrelevant’.

4.5.3 Leather In the leather industry they very rarely try to obtain a really white surface (it would only be possible with a strongly hiding white pigment). Very often the ‘white’ obtained is a greyish white of rather low CIELAB lightness (L* around 80–85) and very low degree of whiteness (CIE WI around 55–60) (Defoe, 1993).

4.5.4 Food In the food industry whiteness formulae are rarely applied, probably because ‘white’ foodstuff (milk, flour, etc.) is seldom really white due to its natural coloration. Rankin and Brewer (1998) used L and b values for ‘whiteness’ and ‘yellowness’ in quantifying the effect of fermentation on the colour of milk. Avena-Bustillos et al. (1993) used the colour difference from the ideal white (Eq. 4.14) to characterise a whitish, dried appearance (white blush) on the surface of peeled carrot pieces. Gobbi et al. (2006) used the same index (together with L*) to measure the effect of an anti-oxidant treatment and the influence of cultivar on the change of quality – including whiteness – of minimally processed organically grown apples during shelf-life.

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4.5.5 Cosmetics In cosmetics the main product whose whiteness is of importance is talcum powder. The whiteness of talcum powders (CIE WI of 50 to 75) as a quality index for pharmaceutical uses was described by Soriano et al. (1998). In other applications CIELAB lightness (L*) is often used for ‘whiteness’ and b* for ‘yellowness’. An interesting combination of L* and b* is used for the characterization of skin type (Choe et al., 2006): ITAo = (arctan(L* –50)/b*) × 180/π

[4.31]

The individual typology angle is believed to be a quantitative and objective value, which can be used to classify an individual’s skin colour, but not even the category of ‘very light skin’ (ITAo > 55; L* ~ 72, b* ~10) can be considered white.

4.5.6 Dentistry

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In clinical practice the evaluation of tooth whiteness is still mostly done by visual comparison using one of the well-known shade guides (Bayindir et al., 2007; Paravina, 2008), but the dentistry literature is rich in articles referring to some kind of instrumental measurement (Joiner et al., 2008). In addition to spectrophotometers and tristimulus colorimeters, digital cameras are often used (Luo et al., 2007); and there are already quite a few instruments developed specifically for the measurement of tooth colour, such as the Shade Vision (X-Rite), the ShadePilot (DeguDent) and the SpectroShade (MHT). There are, however, a number of problems in the colour measurement of human teeth: the reproducibility of the measurement itself is very poor due to the irregular surface of the teeth, translucency influences the measurement accuracy, and in vivo measurements are only possible with special tooth colorimeters or digital cameras (whose precision is still not as good as that of industrial spectrophotometers). The evaluation of whiteness is also rather controversial: human teeth cannot be considered ‘white’ due to its low luminance factor and yellowish coloration. Typical values for human teeth are in the order of L* = 55 to 75 and b* = 6 to 12 (Joiner et al., 2008), and only the extreme case of L* = 89.5, a* = 0.3 and b* = 5.7 signifies WICIE = 48, just above the limit which may be considered white. Typical values for some of the shade guides were given by Lee et al. (2002): the ‘whitest’ specimens would get WICIE = –13 to –16. In spite of the human tooth not being ‘white’ in the colorimetric sense there are regular references in the literature to CIE whiteness (Eq. 4.24) with absurd values of –142.29 or similar. A special ‘tooth whiteness index’ has also been suggested (Luo et al., 2005): WIO = Y + 1075.012(xn – x) + 145.516(yn – y) but this also yields large negative values.

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The use of the Judd and Wyszecki (1963) colour difference type formula (Eq. 4.14) was also reported by Luo et al. (2007). In spite of the large negative values obtained for the CIE index and for WIO, and the extreme colour difference from the perfect diffuser (average values of over 48), reasonable correlation was claimed in comparing relative measurements with visual evaluations. Details on tooth colour measurement may be found in Chapter 14.

4.6

Future trends

4.6.1 New whiteness formulae Although the CIE whiteness and tint formulae have been widely accepted and used there is a search for ‘better’ formulae (which, according to their authors, correlate better with the results of visual evaluations or correct some of its shortcomings). The first ‘improved’ formula is a result of extensive work conducted by the Color Science Association of Japan, as reported by Uchida (1998). In fact there are two formulae, one for ‘in-base point samples’ (i.e. those whose CIE whiteness indices lie within the newly defined limits 40 < WCIE < 5Y–275) and another one for ‘out-base point samples’. For the former group the formula simply deducts twice the tint value squared from the CIE whiteness index: W10 = WCIE, 10 – 2 (Tw,10)2

[4.33]

For the ‘out-base point samples’ a new formula is suggested: W10 = Pw,10 – 2 (Tw,10)2

[4.34]

where

{

}

0.82 [4.35] Pw,10 = (5Y10 – 275) – 800[0.2742 + 0.00127(100 – Y10) – x10] + 1700[0.2762 + 0.00176(100 – Y10) – y10]0.82

Uchida found much better correlation between the visual and instrumental evaluation with the new formula than with the original WICIE. Jafari and Amirshahi (2007), however, could not confirm these results. In their investigations conducted with 113 different white fabrics they arrived at the conclusion that for both inside and outside boundary samples the CIE formula performed noticeably better than the one proposed by Uchida. Aksoy et al. (2003a, 2003b) proposed two new whiteness formulae, one corresponding to maximum whiteness for the perfect reflecting diffuser, the other based on the assumption that observers prefer a more bluish white (if it is not too blue). Coppel et al. (2007) proposed a further two formulae, WNEW and WeCIE which are modifications of those proposed by Uchida (1998) and Aksoy et al. (2003a, 2003b):

(

2 2 (a'−a1*) (b'−b1*) + WNEW = W0 0.5 c23 c42

)

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[4.36]

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where the a' and b' variables are the coordinates in a coordinate system aligned with the CIE whiteness, C3 is the distance in a' at which the function decays to half its maximum and C4 is the corresponding distance in b'. C3 was set as the maximum absolute a* in the whiteness region determined by the original CIE tint and whiteness limits. C4 and b*1 were determined by requiring WNEW = WCIE at maximum whiteness and at L*a*b* = {100,0,0}. An alternative (WeCIE) to the previous whiteness model is to keep the CIE whiteness within its region of validity and use a penalty function only for samples outside the limits (Coppel et al., 2007).

4.6.2 Open questions in the instrumental evaluation of whiteness There are four open questions of great practical importance in the instrumental evaluation of whiteness which need to be resolved in the future (Zwinkels, 2009): • • • •

measurement geometry, illumination, practical calibration of spectrophotometers, CIE whiteness limits.

Measurement geometry ISO standardizing laboratories use the CIE recommended 45/0 geometry for the measurement of fluorescent samples, including transfer standards, while authorized laboratories and industry use sphere geometry (typically d/0 in the paper industry and d/8 or d/t in the textile industry). A standardized procedure is needed for how to apply this geometric correction (Hirschler and Zwinkels, 2008). 1 2 3 4 5 6 7 8 9 40 1 2 43X

Illumination The currently used whiteness formulae (CIE and Ganz) have been developed and tested for illuminant D65. Out of practical necessity some standards (AATCC, ASTM) permit other illuminants (C, D50), and the paper industry is advocating, in addition to D65, C and also ‘indoor daylight’ defined only recently by the CIE. There is, however, no practical experience in using the whiteness formulae under these light sources, so further research and standardization is needed. Practical methods for the calibration of spectrophotometers For fluorescent specimens the quality (SPD) of the illumination in commercially available reflectance spectrophotometers has a strong influence on the measured

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total reflectance factor. When measuring white fluorescent specimens the UV content of the illumination in the instrument has to be regularly checked and adjusted (the instrument has to be ‘calibrated’). There are different methods used in the paper industry (based on paper specimens calibrated by ISO standardizing laboratories) and in the textile industry (based on the Ganz-Griesser method, which has never been accepted as an international standard). CIE whiteness limits The CIE whiteness formula establishes tint and whiteness limits (see 4.4.6) but many commercial papers and possibly also textiles perceived as white fall outside these (Coppel et al., 2007). The formula should be modified, probably by the introduction of a penalty function to handle white materials at the vicinity of the upper CIE whiteness limit.

4.7

Sources of further information and advice

Standards organizations AATCC: American Association of Textile Chemists and Colorists Research Triangle Park, NC, USA, www.aatcc.org ASTM: American Society for Testing and Materials West Conshohocken, PA, USA, www.astm.org CIE: Commission Internationale de l’Éclairage Central Bureau, Vienna, Austria, www.cie.co.at ISO: International Organization for Standardization Central Secretariat, Geneva, Switzerland, www.iso.org TAPPI: (Formerly) Technical Association of the Pulp and Paper Industry Norcross, GA, USA, www.tappi.org National standardizing/metrology institutions BAM: Bundesanstalt für Materialforschung und -prüfung Berlin, Germany, www.bam.de NIST: National Institute of Standards and Technology Gaithersburg, MD, USA, www.nist.gov NPL: National Physical Laboratory Teddington, UK, www.npl.co.uk NRC: National Research Council of Canada (ISO Standardizing Laboratory) Ottawa, Ontario, Canada, www.nrc-cnrc.gc.ca

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PTB: Physikalisch-Technische Bundesanstalt (ISO Standardizing Laboratory) Braunschweig, Germany, www.ptb.de

Instrument manufacturers Autoelrepho: d/0 spectrophotometers for the paper industry San Pedro de Alcántara, Marbella, Spain, www.autoelrepho.com Datacolor: d/0 spectrophotometers for the paper industry, d/8 spectrophotometers for other industries Lawrenceville, NJ, USA, www.datacolor.com DeguDent: dental colorimetry Hanau, Germany, www.degudent.com HunterLab: 45/0 and d/8 spectrophotometers Reston, VA, USA, www.hunterlab.com Konica-Minolta: d/0 and d/8 spectrophotometers Tokyo, Japan, www.konicaminolta.com Lorentzen & Wettre: d/0 spectrophotometers for the paper industry Kista, Sweden, www.lorentzen-wettre.com MHT S.p.A.: dental colorimetry Verona, Italy, www/mht.it Technidyne: d/0 spectrophotometers for the paper industry New Albany, IN, USA, www.technidyne.com X-Rite: industrial spectrophotometers, dental colour measurement Grand Rapids, MI, USA, www.xrite.com Industrial research and ISO authorized laboratories providing whiteness standards and/or calibration services

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Centre Technique du Papier: ISO authorized laboratory Grenoble, France, www.webctp.com FPInnovations – Paprican Division: ISO authorized laboratory Pointe Claire, Quebec, Canada, www.paprican.ca Hohenstein Institute Hohenstein, Germany, www.hohenstein.de KCL The Finnish Pulp & Paper Research Institute: ISO authorized laboratory Espoo, Finland, www.kcl.fi STFI-Packforsk AB Optical Calibration Laboratory: ISO authorized laboratory Stockholm, Sweden, www.stfi-packforsk.se

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Technidyne Corporation: ISO authorized laboratory New Albany, Indiana, USA, www.technidyne.com TITV: Textilforschungsinstitut Thüringen-Vogtland Greiz, Germany, www.titv-greiz.de

4.8

References

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Brockes A (1982) The evaluation of whiteness, CIE Journal, 1: 38–9. Choe Y B, Jang S J, Jo S J, Ahn K J and Youn J I (2006), The difference between the constitutive and facultative skin color does not reflect skin phototype in Asian skin, Skin Res Techn, 12: 68–72, doi: 10.1111/j.0909-725X.2006.00167. CIE (1964) ‘Official recommendations, Committee E-1.3.1 – Colorimetry’, in: Proceedings of the 15th Session, 1963, Vol. A, CIE Publication 11 A, Commission Internationale de l’Éclairage, Vienna. CIE (1986a), Colorimetric Illuminants, CIE S 001, Commission Internationale de l’Éclairage, Vienna. CIE (1986b), Colorimetry, CIE 15.2 – 1986, second edition, Commission Internationale de l’Éclairage, Vienna. CIE (2004), Colorimetry, CIE 15:2004, third edition, Commission Internationale de l’Éclairage, Vienna. CIE (2009), Indoor Daylight Illuminants, CIE 184:2009, Commission Internationale de l’Éclairage, Vienna. Coppel L, Lindberg S and Rydefalk S (2007), Whiteness assessment of paper samples at the vicinity of the upper CIE whiteness limit, Proceedings CIE 26th Session, Vol. 1, D1-10–14, International Commission on Illumination, Beijing. Defoe G A (1993), The psychological concept of whiteness in crust leather, Farbe, 39: 169–75. Evans R M (1949), On some aspects of white, gray, and black, J Opt Soc Am, 39: 774–9, doi:10.1364/JOSA.39.000774. Evans R M (1964), Variables of perceived color, J Opt Soc Am, 54: 1467–9, doi:10.1364/ JOSA.54.001467. Gaertner F and Griesser R (1975) A device for measuring fluorescent white samples with constant UV excitation, Farbe, 24: 199–207. Ganz E (1972), Whiteness measurement, J Col Appear, 1(5):33–41. Ganz E (1976), Whiteness: photometric specification and colorimetric evaluation, Appl Opt, 15: 2039–58, doi:10.1364/AO.15.002039. Ganz E (1979a), Whiteness formulas: a selection, Appl Opt, 18: 1073–8, doi:10.1364/ AO.18.001073. Ganz E (1979b), Whiteness perception: individual differences and common trends, Appl Opt, 18: 2963–70, doi:10.1364/AO.18.002963. Ganz E and Griesser R (1981), Whiteness: assessment of tint, Appl Opt, 20: 1395–6, doi:10.1364/AO.20.001395. Ganz E and Pauli H K A (1995), Whiteness and tint formulas of the Commission Internationale de l’Eclairage: approximations in the Lab color space, Appl Opt, 34: 2998–9, doi:10.1364/AO.34.002998. Gay J K, Melo C C and Hirschler R (2004), Instrumental Whiteness Evaluation – Practical Results Of Inter-Instrument Agreement Tests, Proceedings of the AIC 2004 Color and Paints, Interim Meeting of the International Color Association. Available at http://www. aic–colour.org/congr_archivos/aic2004proc.pdf Gobbi S, Genna A, Cerasi M, Kelderer M and Senesi E (2006), Influence of cultivar and dipping pre-treatment on quality of minimally processed organically grown apples, Paper presented at Joint Organic Congress, Odense, Denmark, 30–31 May 2006. Downloaded from http://orgprints.org/7557/ 14 March 2009. Griesser R (1981), Instrumental measurement of fluorescence and determination of whiteness: review and advances, Rev Prog Coloration, 11: 25–36.

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Griesser R (1994), Assessment of whiteness and tint of fluorescent substrates with good interinstrument correlation, Color Res Appl, 19: 446–60, doi: 10.1002/col.5080190605. Hardt P, Kahle V, Drenker K H, Lillotte W, Metz P, Mücke U, Müller B, Schlegel F, Schütze U, Seidl B, Tiedemann K, Mehlhorn A and Wurster P (2003), Are we being deceived by the instrumental measurement of whiteness? Melliand English, 84, E96 (534–8). Hirschler R, Gay J K, Oliveira D F and Gomes J C (2003), Practical daylight simulators for the colour measurement of fluorescent substrates, Proceedings CIE 25th Session, Vol. 1, D2-14–D2-17, International Commission on Illumination, San Diego. Hirschler R and Oliveira D F (2007), Visual colour control – are we standardized? AIC 2007 Color Science for Industry, Hangzhou, 12–14 July 2007 Proc. Guanrong YE and Haisong XU (eds.) pp. 14–17. Hirschler R and Zwinkels J (2008), ‘Use of CIE colorimetry in the pulp, paper, and textile industries’, in: Schanda J (ed.), Colorimetry: Understanding the CIE System, New York, John Wiley and Sons, 427–8. Hunter R S (1958), Description and measurement of white surfaces, J Opt Soc Am, 48; 597–605, doi:10.1364/JOSA.48.000597. Hunter R S (1960), New reflectometer and its use for whiteness measurement, J Opt Soc Am, 50: 44–8, doi: 10.1364/JOSA.50.000044. Hunter R S (1981), Conversion of visual to instrumental measurement of yellowness, JAOCS, 58: 608–12, doi:10.1007/BF02672375. Hunter R S and Harold R W (1987), The Measurement of Appearance, New York, John Wiley & Sons, 195–208. Imura K (2008), ‘Comments to TC1-44 Draft No. 3’, communicated to the CIE TC1-44 Practical daylight simulators for colorimetry (report to be published). Imura K, Imai K, Kawabata T and Makino M (1997), Measuring apparatus for measuring an optical property of a fluorescent sample, United States Patent 5,636,015, United States Patent and Trademark Office, Attlington. ISO (2004), 11475 Paper and board – Determination of CIE whiteness, D65/10 (outdoor daylight), International Organization for Standardization, Geneva. ISO (2008), 2470–2 Paper, board and pulps – Measurement of diffuse blue reflectance factor – Part 2: Outdoor daylight conditions, International Organization for Standardization, Geneva. ISO (2009), 2470–1 Paper, board and pulps – Measurement of diffuse blue reflectance factor – Part 1: Indoor daylight conditions (ISO brightness), International Organization for Standardization, Geneva. ISO/CIE (2005), 23603 Standard method of assessing the spectral quality of daylight simulators for visual appraisal and measurement of colour, Commission Internationale de l’Éclairage, Vienna. Jafari R and Amirshahi S H (2007), A comparison of the CIE and Uchida Whiteness Formulae as predictor of average visual whiteness evaluation of textiles, Textile Research Journal, 77: 756–63, doi: 10.1177/0040517507080688. Jafari R and Amirshahi S H (2008), Variation in the decisions of observers regarding the ordering of white samples, Color Technol, 124: 127–31, doi:10.1111/j.1478– 4408.2008.00132.x. Joiner A, Hopkinson I, Deng Y and Westland S (2008), A review of tooth colour and whiteness, J Dent, 36S: S2–S7, doi: 10.1016/j.jdent.2008.02.001. Jordan B D (2003), Accurate colorimetry of fluorescent paper, Proceedings CIE 25th Session, Vol. 1, International Commission on Illumination, San Diego.

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Jordan B D, Zwinkels J and McGarry P (2003), ‘The influence of the illuminant on the luminescent radiance factor spectrum of a reference fluorescent paper’, in: Proceedings of TAGA, Montreal, QC, 420–34. Jordan B D and O’Neill M A (1991), The whiteness of paper – Colorimetry and visual ranking, Tappi J, 74: 93–101. Judd D B (1935), A method for determining whiteness of paper, I, Paper Trade Journal, 100: 40–2. Judd D B (1936), A method for determining whiteness of paper, II, Paper Trade Journal, 103: 38–44. Judd D B and Kelly L K (1967), The ISCC–NBS Method of Designating Colors and a Dictionary of Color Names, NBS Circular, Washington: U.S. Department of Commerce. Judd D B, MacAdam D L and Wyszecki G (1964), Spectral distribution of typical daylight as a function of correlated color temperature, J Opt Soc Amer, 54: 1031–40, doi: 10.1364/JOSA.54.001031. Judd D B and Wyszecki G (1963), Color in Business, Science and Industry, 299, New York, John Wiley & Sons. Katayama I, Masumi K and Aoki T (2007), Quantitative evaluation of perceived whiteness under different illuminants, J Light Vis Env, 31: 24–32. Lee Y K, Yoon T H, Lim B S, Kim C W and Powers J M (2002), Effects of colour measuring mode and light source on the colour of shade guides, Journal of Oral Rehabilitation, 29: 1099–1107, doi: 10.1046/j.1365–2842.2002.00961.x. Levene R and Knoll A (1978), Determination of fluorescent whiteness: experience in using linear whiteness formulae, J Soc Dyers Col, 94: 144–9. Lie I (1969), Psychophysical invariants of achromatic colour vision: I. The multidimensionality of achromatic colour experience, Scand J Psychol, 10: 167–75, doi: 10.1111/j.1467–9450.1969.tb00024. Luo W, Westland S, Ellwood R and Pretty I (2005), Evaluation of whiteness formulae for teeth, Proc. 10th Congress of the International Colour Association, AIC 2005, 839–42. Luo W, Westland S, Brunton P, Ellwood R, Pretty I A and Naveen Mohan (2007), Comparison of the ability of different colour indices to assess changes in tooth whiteness, J Dent, 35: 109–16. MacAdam D L (1934), The specification of whiteness, J Opt Soc Am, 24, 188–91, doi: 10.1364/JOSA.24.000188. Mattiello M L F and Lozano R D (1977), A psychophysical study of whiteness, Farbe, 26: 47–61. Nickerson D (1931), A colorimeter for use with disk mixture, J Opt Soc Am, 21: 640, doi:10.1364/JOSA.21.000640. Paravina, R D (2008), New shade guide for tooth whitening monitoring: visual assessment, J Prosth Dent, 99: 178–84, doi: 10.1016/S0022-3913(08)60041–4. Parkes D (1989), Instrumental methods for evaluating whiteness, brightness, and fluorescence, Tappi Journal, 72: 95–100. Popson S J, Malthouse D D and Robertson P C (1997), Applying brightness, whiteness, and color measurements to color removal, Tappi Journal, 80: 137–47. Puebla C (2002), On whiteness formulas. Downloaded from http://mitglied.lycos.de/ whiteness/Reports/reports.html on 14 March 2009. Puebla C (2003), A whiteness primer. Downloaded from http://mitglied.lycos.de/ whiteness/Reports/reports.html on 14 March 2009.

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Rankin S A and Brewer J L (1998), Color of nonfat fluid milk as affected by fermentation, J Food Sci, 63: 178–80, doi: 10.1111/j.1750–3841.1998.00178.pp.x. Selling H J and Friele L F C (1950), Whiteness relations and their applications, Appl. Sci. Res. Section B, 1: 453–76. Sève R (1977), A bibliography on whiteness, Farbe, 26: 89–109. Singh J, Rowland C and Olsen E (2008), Diffuse versus directional brightness measurement comparison for paper, J ASTM Intnt, 5(2). Available online at www.astm.org, doi: 10.1520/JAI101370. Smith C (2008), Extra white, extra bright can be achieved cost-effectively, Pulp & Paper, 82(2), 34–36. Downloaded on 24 April 2008 from http://findarticles.com/p/articles/ mi_qa3636/ Smith, K J (1997), ‘Colour order systems, colour spaces, colour difference and colour scales’, in: Colour Physic for Industry, 2nd edition, edited by Roderick McDonald, Bradford: Society of Dyers and Colourists, 195–208. Soriano M, Melgosa M, Sánchez-Marañon M, Delgado G, Gámiz E and Delgado R (1998), Whiteness of talcum powders as a quality index for pharmaceutical uses, Color Res Appl, 23: 178–85. Stenius Å S (1977), Results of the visual assessment of the whiteness samples by pair comparison and ranking, Farbe, 26: 63–88. Stensby P S (1967), Optical brighteners and their evaluation, Soap and Chem Spec, 43(7): 80. Stensby P S (1973), Questions in regard to whiteness evaluation, J Col App, 2(1): 39–42. Swenholt B K, Grum F and Witzel R F (1978) Colorimetry of fluorescent materials: visual evaluation of fluorescent whites, Color Res Appl, 3: 141–5, doi: 10.1002/col.5080030312. TAPPI (2005a), CIE whiteness and tint of paper and paperboard (d/0 geometry, C/2 illuminant /observer), Test Method T 560 om–05, Norcross, GA, TAPPI. TAPPI (2005b), CIE whiteness and tint of paper and paperboard (45/0 geometry, C/2 illuminant/observer), Test Method T 562 om–05, Norcross, GA, TAPPI. TAPPI (2006), Diffuse brightness of paper, paperboard and pulp (d/0), Test Method T 525 om–06, Norcross, GA, TAPPI. TAPPI (2008), Brightness of pulp, paper, and paperboard (directional reflectance at 457 nm), Test Method T 452 om–08, Norcross, GA, TAPPI. Taube K (1958), Part of unpublished ‘Study of home-laundering methods’ (Housing and Equipment Laboratory, Institute of Home Economics, U.S.D.A., Beltsville, Maryland), referenced in Hunter (1960). Thibodeaux D, Rodgers J, Campbell J and Knowlton J (2008), Feasibility relating HVI color standards to CIELAB coordinates, AATCC Review, 8(11): 44–8. Thielert R and Schliemann G (1972), Korrelation zwischen visueller Bewertung und farbmetrischer Kennzeichnung optisch aufgehellter Proben, Die Farbe, 21: 113–30. Tindal A (2005), World’s Whitest Paper? With A Little Help From SAPPI & Clariant, 24 February 2005. Downloaded from www.pulpandpaperonline.com on 14 March 2009. Uchida H (1998), A new whiteness formula, Color Res Appl, 23: 202–9, doi: DOI: 10.1002/ (SICI)1520–6378(199808)23:4<202#drAID–COL4>3.0.CO;2–S. Vaeck S V (1979), Some new experiments on the colorimetric evaluation of whiteness, J Soc Dyers Col, 95: 262–9. Willis R F (2002), The color measurement of textiles which contain FWA’s, AATCC Proc International Conference and Exhibition, Charlotte, NC, American Association of Textile Chemists and Colorists. Wittgenstein L (1977), Remarks on Colour, Oxford: Basil Blackwell.

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Wright W D (1972), ISCC Fluorescence Conference summary, J Col Appearance, 1(5): 4–5. Wyszecki G and Stiles W S (2000) Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd edition, New York: John Wiley and Sons. Zellweger Uster (1999), Instruction Manual Uster HVI 900™ High Volume Fiber Test System, Zellweger Uster AG: Uster, Switzerland. Zwinkels J (2009), Evaluation of Whiteness, Report presented to the CIE Division 1 meeting, Budapest, Hungary, 2 June 2009.

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5 Use of artificial neural networks (ANNs) in colour measurement M. SENTHILKUMAR, PSG College of Technology, India

Abstract: An artificial neural network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. ANNs are used for modelling non-linear problems and to predict the output values for given input parameters from their training values. Most of the coloration processes and the related quality assessments are non-linear in nature and hence neural networks find application in colour science. The conventional approaches used for assessing and predicting the colour parameters are based on the approximations to the physical processes actually taking place and this leads to inaccuracy. It has been suggested that ANNs could be used to predict the colour parameters and recipe, controlling of dyeing process, classification of dyes, etc., based on the colorant concentrations and spectral reflectances. The formulation of colour parameters and assessments using ANNs is claimed to have better accuracy compared to any other models. Key words: feed forward neural networks, back propagation, recipe formulation, absorbance and reflectance value, colour difference.

5.1

Introduction

Artificial neural networks (ANNs) are used for modelling non-linear problems and to predict the output values for given input parameters from their training values. Most of the coloration processes and the related quality assessments are non-linear in nature and hence neural networks find application in colour science. Inspection of dyeing defects in the textile dyeing process, prediction of reflectance values from the concentration values of various colorants, colour matches on yarns dyed with different dyes, controlling of batch dyeing process of textiles, construction of reflectance curves of cotton fibres based on their tristimulus values, colour calibration, colour classifications in textiles, on-line colour measurements, prediction of dye absorbance from wavelength and concentration, predicting the concentration of fluorescent dyes, prediction of dyeing time and CIELAB values in textile dyeing are some of the areas where ANNs have been attempted. Since the early development of a computer colorant formulation method, computer recipe prediction has become one of the most important industrial applications of colorimetry. The determination of the amount of colorants, which are required in the application on substrate in order to produce the same colour as target, is the purpose of any colorant formulation. A relatively simple approach for 125 © Woodhead Publishing Limited, 2010

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relating concentration and reflectance was presented by Kubelka-Munk (Nobbs, 1985). This model, which is still commonly used in computer colorant prediction, is a source of error in colorant formulation. Some theories have been introduced for colour matching of blend of precoloured fibres, the most applicable being the Kubelka-Munk theory. This method is commonly used in colorant prediction (Amirshahi and Pailtorpe, 1994). The Kubelka-Munk theory allows the prediction of spectral reflectance for a mixture of components (colorants) that have been characterized by absorption K and scattering S coefficients. It has been shown that the Kubelka-Munk coefficients K and S are related to, but not equal to, the fundamental optical coefficients for absorption and scattering (Marjoniemi and Mantysalo, 1997a). The conventional 2-flux theory of Kubelka and Munk employed for computer colorant formulation reaches its limits in certain areas of coloration, suggesting the need to look at an alternative approach. More recently it has been suggested that ANNs may be able to provide alternative mappings between colorant concentrations and spectral reflectances (Bishop et al., 1991; Westland et al., 1991) and, more generally, are able to provide transforms between colour spaces (Kang and Anderson, 1992; Tominaga, 1993). Since most of the colour application processes quality assessments are non-linear in nature, neural networks find application. The formulation of colour parameters and assessments using ANNs is claimed to have better accuracy compared to any other models.

5.2

Artificial neural networks (ANNs): basic principles

5.2.1 History of artificial neural networks

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An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of a biological neural system (Hinton, 1992). Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived at least one major setback and several eras (Anderson, 1995). McCulloch and Pitts (1943) developed models of neural networks based on their understanding of neurology. These models made several assumptions about how neurons worked, their networks being based on simple neurons which were considered to be binary devices with fixed thresholds. Progress during the late 1970s and early 1980s was important to the re-emergence of interest in the neural network field. ANNs are now recognized worldwide as the most effective and appropriate artificial intelligence technology for prediction and pattern recognition. They offer solutions to a variety of classification problems such as speech, character and signal recognition, as well as prediction and system modelling where physical processes are not well understood or are highly complex (Rao and Rao, 1996).

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5.2.2 Artificial neural networks An ANN is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons (Haykin, 1999). One of the main problems with recipe prediction is that the application of exact colour theory is not computationally practical and an approximation to it has to be employed. It was expected that a neural network approach to recipe prediction would offer a novel and profitable new solution to this problem, since many problems in artificial intelligence (AI) involve systems where conventional rulebased knowledge is not perfect or the application of the pure theory is too computer intensive to be used in practical systems. In the field of recipe prediction it was hoped that a suitable network system would automatically learn relationships between colorants and colour, and hence learn to predict which colorants, and at which concentrations, need to be applied to a particular substrate in order to produce a specified colour.

5.3

Architecture of an artificial neural network

ANNs are typically composed of interconnected ‘units’ which serve as model neurons. The schematic diagram of a typical ANN is shown in Fig. 5.1. Outputs

Output layer

Hidden layer

……….

Input layer

……….

Input signals

5.1 Schematic diagram of an artificial neural network.

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Connection by weights

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5.3.1 Artificial neuron ANNs consist of a large number of neurons or simple processing units, also referred to as neurodes, and an artificial neuron mimics the characteristics of the biological neuron. Here, a set of inputs are applied, each representing an output of another neuron. Each input is multiplied by a corresponding weight, analogous to synaptic strengths and the weighted inputs are summed to determine the activation level of the neuron. The connection strengths or the weight represents the knowledge in the system. Information processing takes place through the interaction among these units (Fausett, 1994; Gurney, 1997).

5.3.2 Network layers The commonest type of artificial neural network consists of three groups, or layers, of units: a layer of ‘input’ units is connected to a layer of ‘hidden’ units, which is connected to a layer of ‘output’ units (Fig. 5.1). The activity of the input units represents the raw information that is fed into the network. Whereas, the activity of each hidden unit is determined by the activities of the input units and the weights on the connections between the input and hidden units. Similarly, the behaviour of the output units depends on the activity of the hidden units and the weights between the hidden and output units.

5.3.3 Types of network Feed-forward networks

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A general feed-forward network is illustrated in Fig. 5.1. This is a feed-forward, fully connected hierarchical network consisting of an input layer, one or more middle or hidden layers and an output layer. The internal layers are called ‘hidden’ because they only receive internal inputs and produce internal outputs. This network allows signals to travel only from input to output. There is no feedback (loops), i.e. the output of any layer does not affect that same layer. Feedforward ANNs tend to be straightforward networks that associate inputs with outputs. Feedback networks Feedback networks can have signals travelling in both directions by introducing loops in the network. Networks of this type operate by allowing neighbouring neurons to adjust other nearby neurons either in a positive or negative direction. Feedback networks are changing continuously until they reach an equilibrium point, where they remain until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent neural networks.

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129

Learning process

True human-like learning is beyond all artificial intelligence techniques, although some learning techniques have been developed which allow machines to mimic human intelligence. These techniques that allow computers to acquire information with some degree of autonomy are collectively known as machine learning. Neural networks exhibit the ability to learn in a similar fashion to animal learning: they have a given structure (topology and learning method), they are presented with stimulus (inputs), and they adapt to that stimulus. When the neural network produces an incorrect decision the connections in the network are weakened, so it will not produce that answer again. Similarly, when the network produces a correct decision the connections in the network are strengthened, so it will become more likely to produce that answer again. Through many iterations of this process, giving the network hundreds or thousands of examples, the network will eventually learn to classify all characters it has seen. This process is called supervised learning and it is critical that the data given to the network is very carefully selected to represent the information the network is to learn. There are generally three different ways to approach neural network learning (Pham and Liu, 1995): 1 2 3

supervised learning unsupervised learning reinforcement learning.

5.4.1 Supervised learning Supervised learning requires the programme to give the network examples of inputs and correct output for each given input. In this way the network can compare what it has output against what it should output and it can correct itself (Fig. 5.2). Back propagation, is the most widely used method for neural network training because it is the easiest to implement and to understand and it works reasonably well for most linear and nonlinear problems.

5.4.2 Unsupervised learning Unsupervised learning provides input but no correct output. A network using this type of learning is only given inputs and the network must organize its connections and outputs without direct feedback.

5.4.3 Reinforcement learning Reinforcement learning is a special case of supervised learning. Instead of using a teacher to give target outputs, a reinforcement learning algorithm employs a

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Input signal

ANN

–

Weight adjustment

+

Target value

Error value

Supervised learning

5.2 Supervised learning of ANN.

critic only to evaluate the goodness of the neural network output corresponding to a given input.

5.5

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Feed-forward neural network

Feed-forward neural networks are the most popular and most widely used models in many practical applications. They are known by many different names, such as ‘multilayer perceptrons’ (MLP). A feed-forward neural network is a biologically inspired classification algorithm. It consists of a number of simple neuron-like processing units, organized in layers and every unit in a layer is connected with all the units in the previous layer. These connections are not all equal, as each connection may have a different strength or weight. The weights on these connections encode the knowledge of a network. Often the units in a neural network are called nodes. Data enters at the input and passes through the network, layer by layer, until it arrives at the output as shown in Fig. 5.3. The input layer consists of just the inputs to the network. Then follows a hidden layer, which consists of any number of neurons, or hidden units placed in parallel. Each neuron performs a weighted summation of the inputs (eqn 5.1), which then passes a transfer/activation function, also called the neuron function. During normal operation there is no feedback between layers. n

vk = Σ wkjxj

[5.1]

j=1

5.6

Training of an artificial neural network using back propagation algorithm

In order to train a neural network to perform some task, the weights of each unit have to be adjusted in such a way that the error between the desired output and the actual output is reduced. This process requires that the neural network

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Output yk

Activation function

vk

Σ

wk1

wk2

wk3

×1

×2

×3

Summing function

……….

wkn

Synaptic weights

………. ×n

Input signals

5.3 Mathematical representation of a feed-forward neural network.

compute the error derivative of the weights. The back propagation algorithm is the most widely used method for determining the error derivative (Werbos, 1974; Rumelhart et al., 1986). In most learning networks the difference between the actual output and the desired output is calculated. This raw error is then transformed by the error function to match a particular network architecture. The artificial neuron’s error is then typically propagated into the learning function of another processing element. This error term is sometimes called the current error. The current error is typically propagated backwards to a previous layer. Yet, this back-propagated value can be either the current error scaled in some manner (derivative of the transfer function), or some other desired output depending on the network type. Normally, this backpropagated value, after being scaled by the learning function, is multiplied against each of the incoming connection weights to modify them before the next learning cycle.

5.6.1 Training phases The training of a multilayered feed-forward neural network is accomplished by using a back-propagation algorithm that involves two phases (Werbos, 1974; Rumelhart et al., 1986).

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Forward phase: During this phase the free parameters of the network are fixed, and the input signal is propagated through the network layer by layer. The forward phase finishes with the computation of an error signal (Ei) Ei = di – oi

[5.2]

where di is the desired output and oi is the actual output produced by the network in response to the input xi. Backward phase: During this second phase, the error signal Ei is propagated through the network in the backward direction. It is during this phase that adjustments are applied to the free parameters of the network so as to minimize the error Ei in a statistical sense.

5.6.2 Transfer function The behaviour of an ANN depends on both the weights and the input–output function (transfer/activation function) that is specified for the units. This function typically falls into one of three categories: 1 2 3

linear threshold logistic or sigmoid.

For linear units, the output activity is proportional to the total weighted output, while for threshold units, the output is set at one of two levels, depending on whether the total input is greater than or less than some threshold value. The sigmoid (logistic) function has a rich history of application as a cumulative distribution function in demographic studies and in modelling growth function (Balakrishnan, 1992). The particular functional form that is often used for the logistic or sigmoid activation function (eqn 5.3) is 1 2 3 4 5 6 7 8 9 40 1 2 43X

1 oi = f (neti) = ——— 1+e−neti

[5.3]

– [0,1]. This is shown in Fig. 5.4. which yields oi C The sigmoid function is so important and popular because for sigmoid units, the output varies continuously but not linearly as the input changes. Sigmoid units bear a greater resemblance to real neurons than do linear or threshold units.

5.7

Application of artificial neural networks to colour measurement

The application of ANNs to colour measurement and colour match prediction was first demonstrated by Westland et al. (1991), who concluded that this technique

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f (neti)

1

oi = f (neti) =

1 1+ e–neti

0

neti

5.4 The sigmoid activation function.

could be applied to colorimetric systems with complex behaviour. There are many types of ANN, one of the simplest and most successful of which has been described using colour problem solving; this is the multilayer perceptron (MLP). This network can be used to predict the reflectance values in the range of 400nm to 700nm (visible region) from the concentration values of various colorants; for example concentration values of cyan, magenta, yellow and black (CMYK) in a paper printer. Linear output functions are not used and would not lead to satisfactory results in these networks because the functional composition of several linear functions is itself a linear function. Before the networks can be used to solve a given task it must first be trained using known pairs of input and output vectors. For example, to train the system to convert a given reflectance curve to CMYK values, the output vectors would be a selection of CMYK values and the input vectors the measured reflectance curves of the samples printed with these values. Pairs of input and output vectors are presented to the input and output layers of the networks respectively. The weights between the neurons are adjusted so as to reduce the error between the calculated output of the last output layer and the desired output. Mathematical techniques such as ‘back propagation of the generalized delta rule’ are used for systematic optimization of the weights to minimize error (Bishop et al., 1991). The neural network with fuzzy logic technique was applied to control the batch dyeing operation of textiles by Smith and Lu (1993). McGregor et al. (1996) used the artificial neural network as a non-linear tool to predict colour matches on polyester yarns dyed using three different dyestuffs. Since neural networks perform a non-linear mapping, they should be able to predict more accurately the dye recipe of a three-component mixture than a linear model. In order to judge the effectiveness of the neural network, the results obtained were compared to results obtained using the traditional, linear algebra, method. The input to both methods were the K/S values obtained from the spectral reflectance data taken from each yarn sample using a spectrophotometer. The structure of the

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neural network was set up to have thirty-one input nodes for the K/S spectral values, fifteen middle-layer nodes, and three output nodes. The three output nodes yield the predicted concentrations of five each of the dyes for a particular spectral input. The neural network was set up to be trained using back propagation with momentum and an adjustable learning rate with the middle and output layers being set up to use a log-sigmoid function neuron. All of the neurons were fully connected. The traditional method uses individual, single dyeings as the input data to ‘train’ or establish the model and the dye recipe is expressed as a linear combination of the single-dyes. The problem with this method is that the dyes do not combine linearly. On the other hand, a neural network can model the non-linear interactions but requires a larger ‘training’ set to establish the model. The ‘training’ set for the neural network includes both the combination-dye data and single-dye data. In order to accurately compare the two methods of obtaining a colour match, an absolute error was calculated for each sample under each method. Fernández et al. (1995) developed a multilayer neural network in the modelling and prediction of colour of red wines. While a Hopfield network was used for colour image segmentation by Campadelli et al. (1997). Faes (1998) has shown how neural networks based on colorimetric data obtained by reflection measurement of the dyed material can be used as a reliable predictor of visual assessment. Gross et al. (1999) have used ANN to retrieve chlorophyll pigments in the near-surface of oceans from ocean colour measurements. This bio-optical inversion was established by analysing concomitant sunlight spectral reflectances over the ocean surface and pigment concentration. An artificial neural network with Bayesian regularization training technique was developed to predict colour appearance (from colorimetric attributes to colour-appearance attributes) by Xin et al. (2000). Xin et al. (2002) have developed a multilayer perceptron feedforward artificial neural network model with Bayesian regularization technique for training to predict the colour appearance from colorimetric values. Schettini (2001) has developed a method for approximating the colour appearance model CIECAM97s by means of feed-forward neural networks trained with the error back-propagation algorithm. The ANN technique was used to reconstruct the reflectance curves of cotton fibres based on their tristimulus values by Dupont (2002), while Alman and Ningfang (2002) have done an experiment with CRT colour calibration to explain the methods to avoid an over-trained condition in the ANN model development. A multilayered feed-forward neural network was developed to predict the reflectance spectra of yarn from the roving reflectance spectra using a back-propagation training algorithm by Thevenet et al. (2002). Xu (2003) has developed two models using neural networks, fuzzy clustering and fuzzy logic to colour classifications in textiles. The first application was the identification of colour patterns on a printed fabric. The self-organizing map and fuzzy clustering algorithm was used to automatically separate coloured patterns for independent evaluations. The second application was the colour classification of cotton fibres using fuzzy logic. He concluded that the fuzzy logic appears to

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be effective in dealing with ambiguity and uncertainty in cotton colour grading. Tandukar (2007) has developed a model to predict a recipe for reproducing the desired colour by using only three primary subtractive colours (cyan, magenta, and yellow), then be able to correct it dynamically for a reproduction close to the target. Blanco et al. (2007) have developed a multilayer feed-forward neural network with three hidden neurons to predict the colour of the final food products based on in-line measurements and they concluded that the predicted CIELAB values have an acceptable correlation with off-line colour parameters. In order to use the neural network to predict any type of value, it has to be trained with known values for the input and output parameters. The set of input and output parameters are known as the training pattern (Senthilkumar, 2007). So the colour of any material can be predicted using ANN by training the network with set of training patterns. The accuracy of the network depends on the number of training patterns used for training and the association of input parameters with the output parameters.

5.8

Recipe prediction

The conventional approaches to recipe formulation depend on equations resulting from the analytical treatment of the relationship between reflected light and colorant concentration. In general these equations are only approximations to the physical processes actually taking place and this leads to inaccuracies in recipe formulation due to a variety of reasons, for example: • • •

• •

Failure of the prediction theory to deal adequately with the non-linear relationship between dye applied from the dyebath and reflectance. Interaction between dyes, leading to dye uptake behaviour which is different from the uptake of the dyes when applied individually. Scaling-up problems, when recipes based on laboratory-scale dyeing ranges are applied in bulk machinery, and deliberate or accidental variations in the dyeing process compared with the calibration dyeings. Variations in physical structure of the substrate, compared with the calibration ranges. Mistakes in processing.

The failure to match the target may be detected either in a check dyeing carried out in the laboratory or in the dye lot processed under bulk conditions. In either case it is necessary to have some method of correcting the defective recipe to bring the colour closer to target. Similar problems arise in many sciences, and the need to tackle them has led to the application of the branch of artificial intelligence known as the artificial neural network (ANN) theory. In a conventional computer program, the computer carries out a set of specific instructions to complete a given task. Unlike a conventional computer program a neural network is designed to adapt and acquire knowledge over time in order to complete a certain task. ANN

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programs represent a different approach to problem solving which has strong parallels to the way the human brain is though to operate. Westland et al. (1991) have applied neural networks to recipe prediction with some degree of success. The network consisted of three input units (CIELAB values), 24 hidden units arranged in two layers of 8 and 16 units, and three output units (three dye concentrations). The training input consisted of a series of twodye recipe samples and single-dye recipe samples and the output vector was the measured CIELAB values of the samples. The training consisted of 55,000 epochs using 30 dyed samples, consisting of 12 single-dye and 18 binary mixtures. Predictions were then made to match these 30 samples plus another 21 samples not included in the training set. In the 54 predictions, 33 of the targets were binary mixtures and for these 78.8% gave errors less than 0.8 CMC(2:1) unit. Predictions of the remaining 21 targets dyed with single dyes were not so accurate, leading to an overall accuracy for the complete data set of 60% with errors less than one CMC(2:1) unit. They claim that the use of ANN offers several potential advantages over the conventional recipe prediction approach using absorption coefficients: • •

•

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Colour measurement

It is not necessary to prepare a special database of dyes in order to use the ANN method. The network can be trained on actual production samples. The network can continue to learn after the initial training period, since future production samples can be presented to the system and this knowledge incorporated into the network weights. This gives the network the potential to adapt to changes in factors such as water supply, change of substrate, change of dye strength and so forth. The network may be able to learn the behaviour of colorants for which the mathematical descriptions are complex. For example, fluorescent dyes and metallic paint systems are currently difficult to treat using the standard Kubelka-Munk theory.

The first paper to provide details about the application of ANN in colour parameters prediction was one by Jasper et al. (1993). They took three commercial dyes, namely Cibacron yellow G-E, Cibacron brilliant red 4G-E, and Cibacron blue TR-E, and mixed them in combinations of 0.00, 0.05, 0.10 and 0.15g/L. This gave them 62 dye solutions. Each dye mixture contained 50 g/L of NaCl, and all measurements were made at 27 °C. The full visible absorbance spectra (380–780 nm) of all the 62 dye solutions were measured. Three different methods were used to predict dye concentrations from the absorbances – Beer’s law, modified Beer’s law and a feed-forward neural network. Beer’s law proved to be very poor as a predictive tool and gave an average error of 51%. The modified Beer’s law gave good results when all the three components were present and gave an overall error of 9%. The neural network, in which the entire absorbance spectra were given as input, gave an error of only 2.6%. In a comment on this paper, Vangheluwe et al. (1994) pointed out that this error of 2.6% was the error of the training set. Indeed,

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the researchers had not kept any test set for validating the trained network. In a reply to the comments of Vangheluwe et al. the authors reported carrying out some cross-validation. A neuro-fuzzy technique was developed to colour recipe prediction by Mizutani et al. (1995). This relates surface spectral reflectance of a target colour to several colorant proportions. Marjoniemi and Mantysalo (1997a) have reported an experiment in which an Adaptive Neuro Fuzzy Inference System (ANFIS) was used to predict dye absorbance from wavelength and concentration. Two data sets were generated for this purpose, each of two dye mixtures. In data set 1, the concentration of yellow dye was kept at 50 mg/L, while in set 2 it was kept at 100 mg/L. The concentration of red dye was varied from 0 to 900 mg/L in both data sets 1 and 2, giving eighteen levels for each of them. Nineteen levels of wavelengths were recorded in the range 400–580 nm. The training data thus had 342 examples for each of the data sets. In later work, the same authors (Marjoniemi and Mantysalo, 1997b) have tried to predict the concentration of the red dye from wavelength and absorbance values. Bezzera and Hawkyard (2000) have reported an experiment in which they tried out four different feed forward neural networks for predicting the concentration of fluorescent dyes from the total spectral radiance factor (SRF), SRF curves (SRFC), XYZ, and L*a*b*, respectively. Some 283 samples were used for training and 28 for testing. They reported that the method used for this study is simple to apply, and requires only a representative database of fluorescent and non-fluorescent colorants, a commercial spectrophotometer adequately calibrated to measure SRF values and the software to create and train an adequate network able to learn the relationship between the colour parameters and dye concentrations. From the network types studied to predict dye concentrations, the one using SRF values as the input colour parameters proved to be the most appropriate way (an average error of 3.92%) to relate a fluorescent coloured sample with the dyes and the concentrations required to reproduce it. When the SRF-C network was used to predict dye concentrations for a sample, the only way to find out the difference in colour between that and the standard sample was to apply predicted dye concentrations to a substrate and then measure its SRF. Predicting SRF curves from concentrations also produced good results. Westland et al. (2001) have developed number of multilayer feed-forward neural networks and those networks were trained, using the back-propagation with momentum learning algorithm, to perform the mapping from colour to reflectance for a set of known paint samples (training set). Each of the networks used a single hidden layer of processing units and the number of units in that layer was varied between 3 and 15. Each network was trained using the full training set and alternative training sets (each being sub-sets of the full training set) in order that the performance of the networks could be assessed for different training set sizes. Furthermore, two types of networks were employed. The first

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type of network was a standard network that was fully connected. The second type was a hand-crafted partially connected system that was specially designed for the task of colour prediction. This second type of network was inspired by a consideration of the K-M model. The performance of all of the trained networks was assessed by computing CMC(2:1) colour differences, under D65/1964 conditions, between the predicted reflectances and the measured reflectances for the test set. Senthilkumar and Selvakumar (2006) have reported an experiment in which they predicted the dyeing time of cotton fabrics dyed with high exhaustion reactive dyes using a feed-forward neural network. For the study they selected three reactive HE dyes, namely Procion Brilliant Red HE 3B, Procion Green HE 4BD and Procion Brilliant Red HE 7B, and two different types of cotton fabric. The following training patterns were used to train the network. Input parameters • • • • • •

K/S values of the undyed fabrics %Total dye fixed on the fabric for a selected %shade Percentage shades NaCl concentrations Na2CO3 concentration K/S values of the dyed and washed samples.

Output parameters • •

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Time for primary exhaustion Time for dye fixation.

The network was trained using the input and output parameters and the training process of the neural network developed was started with 10,000 preliminary cycles to optimize the ANN prediction accuracy. These cycles were carried out with different network structures and different learning parameter values and the network training errors were obtained. The best structure is the one that gives the lowest training error and it was found to be 6/9/9/9/2 in the present study (the structure used in this study is given in Fig. 5.5). The training of the network was further continued in order to reduce the training error and the average training error of 1.0% was obtained when 1,00,000 cycles were used. Senthilkumar and Selvakumar concluded that the neural network developed could be used to determine the primary exhaustion time and fixation time for producing the expected depth of shade with high exhaustion reactive dye on cotton fabric. Senthilkumar (2007) developed a model using the ANN technique to predict the CIELAB value of vinyl sulphone dyed cotton fabrics. He used three dyes and two different types of cotton fabrics to develop a model using the following training patterns.

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Use of artificial neural networks (ANNs) in colour measurement Input layer

Hidden layers

139

Output layer

K/S values of the fabrics to be dyed Percentage total dye fixed on the fabrics Percentage shades NaCl concentrations

Primary exhaustion time Fixation time

Na2CO3 concentration K/S values of dyed samples

5.5 Schematic diagram of the ANN used for prediction of dyeing time.

Input parameters • • • • • •

Whiteness Index of the fabric to be dyed %Total dye fixed on the fabric Percentage shades Salt concentrations Alkali concentration Dyeing time.

Output parameters • • •

L* a* b*

The above training patterns were used to develop a network and the best structure that gives the lowest training error was 7/10/10/10/3. A training error of 2.0% was obtained when 85 000 cycles were used. The network was also tested with six different sets of input parameters and the error percentage was calculated (see Table 5.1). Senthilkumar also concluded that the neural network model developed could be used to optimize the dyeing parameters for producing the required depth of shade for any type of vinyl sulphone dyes. In order to formulate the dyeing recipe or optimize the process parameters, ANN can be used by selecting suitable input parameters (should be associated with output parameters) and training the network with sufficient cycles. There are limitations in the use of neural networks for colour recipe prediction. • •

An adequate number of samples must be prepared and presented for the network to learn the relationship between input and output parameters An increase in number of dyes/colours will increase the network topology.

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Table 5.1 Actual and predicted dyeing time % shade

0.25 1.00 1.75 2.50 3.25 4.00

Primary exhaustion time (min)

Fixation time (min)

Actual

Predicted

% error

Actual

Predicted

% error

15 15 15 15 15 15

14.90 15.21 15.05 15.33 15.34 14.96

0.67 1.40 0.33 2.20 2.27 0.27

15 15 15 15 15 15

14.91 15.20 15.06 15.32 15.33 14.95

0.60 1.33 0.40 2.13 2.22 0.33

Mean absolute error

5.9

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Colour measurement

1.19

1.17

Evaluation of the ANN method

In order to evaluate the prediction accuracy of ANNs, a set of input parameters has to be fixed to evaluate output parameter and the error percentage has to be calculated. Senthilkumar and Selvakumar (2006) have selected a twill fabric with unknown specifications and a dye namely Procion Brilliant Red HE 7B to test the prediction accuracy of the neural network developed. The K/S value of the fabric and the %total dye uptake of the fabric to be dyed with above dye were found out. Testing samples were produced with the %shades beyond the range used for training the network and the K/S values of these samples were found out. Followed by this, the input parameters were fed in to the neural network and corresponding output parameters, namely primary exhaustion time and fixation time, were obtained. These predicted timings, along with the actual timings, are given in Table 5.1. It can be observed that the mean absolute error with respect to prediction is around 1%. Senthilkumar (2007) has tested the model developed by calculating the two input parameters, namely %total dye fixed on the fabric and the whiteness index of undyed sample, experimentally. The rest of the input parameters were fixed arbitrarily and dyeing was carried out at various %shades using unknown vinyl sulphone dye. The above-prepared samples are considered as the control and using those input parameters, the output parameters (L* a* b*) were predicted. The actual and predicted values are given in Table 5.2. The colour difference (ΔE*) also calculated between actual and predicted values (Table 5.2).

5.10

Case studies

The developed ANN model was implemented in a textile based dyeing industry to predict the dyeing time. When goods are taken for dyeing, once the recipe and the conditions of dyeing for a given machine is fixed, the only parameter which needs attention to achieve the expected depth of shade is ‘the duration of the process’.

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Table 5.2 Actual and predicted L* a* and b* values and ΔE* value %

L*

a*

b*

Shade Actual Predicted % Actual Predicted % Actual Predicted % ΔE* error error error 0.25 0.75 1.25 1.75 2.25 2.75

66.03 59.44 56.54 54.53 53.37 52.34

65.02 60.32 55.76 55.09 52.92 52.66

1.53 1.48 1.38 1.03 0.84 0.61

Mean absolute error

1.15

44.34 50.88 54.16 56.59 58.05 59.40

45.27 49.93 55.09 55.89 58.65 58.98

2.09 1.87 1.72 1.24 1.03 0.71

13.53 16.53 18.22 19.59 20.46 21.30

1.44

13.93 16.06 18.73 19.19 20.83 21.03

2.96 2.84 2.79 2.04 1.81 1.27

1.43 1.38 1.31 0.98 0.83 0.60

2.29

The following input parameters were fixed for known reactive HE dye, fabric and target with six different depths of colour. • • • •

K/S value of the undyed fabric and target % total dye fixed was calculated % shade NaCl and Na2CO3 concentration based on percent shade.

All the above parameters were fed into the network, the dyeing time was predicted and samples were produced. The spectral reflectance curves of the samples produced with predicted timings and the target are given in Fig. 5.6. As these curves show insignificant difference with respect to various %shades, it can be said that the network developed can be implemented to predict the dyeing time for achieving expected depth of shade. Dyed textile materials are generally accepted when the ΔE* values are between 0 and 1.5 and the ΔL* values are between –0.7 and 0.4. If the ΔE* value is above 1.5 the colour difference between sample and control is very high and it is to be rejected. If the ΔL* values are less than –0.7 the samples are darker in shade and if greater than 0.4 the samples are lighter in shade compared to that of the control sample (Shah and Gandhi, 1990). The L*, a* and b* values of the sample to be dyed were predicted before dyeing by feeding input parameters to a developed ANN. Based on the CIELAB values predicted, the input parameters were adjusted and the fabric was taken for dyeing, then compared with the target. The spectral reflectance curve of those samples is shown in Fig. 5.7 for various %shades.

5.11

Future trends

The work reported in this chapter suggests that neural network techniques can be useful for solving recipe prediction problems. It has been shown that the Kubelka-Munk model has been approximated to develop the system. There is no

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100 Reflectance (%)

Reflectance (%)

100 0.25% shade

75 50 25 0 400

500

600

1.0% shade

75 50 25 0 400

700

500

Actual

Predicted

Actual

100

700

Predicted

100 Reflectance (%)

1.75% shade

75 50 25 0 400

500

600

2.5% shade

75 50 25 0 400

700

Wavelength (nm) Actual

500

600

700

Wavelength (nm)

Predicted

Actual

100

Predicted

100 Reflectance (%)

Reflectance (%)

600

Wavelength (nm)

Wavelength (nm)

Reflectance (%)

1 2 3 4 5 6 7 8 9 10 1 2

Colour measurement

3.25% shade

75 50 25 0 400

500

600

700

75

4.0% shade

50 25 0 400

Wavelength (nm) Actual

Predicted

500

600

700

Wavelength (nm) Actual

Predicted

5.6 Spectral reflectance curves of samples dyed with actual and predicted timings.

1 2 3 4 5 6 7 8 9 40 1 2 43X

reason to believe that similar neural networks cannot learn the relationship between colorant concentrations and colour coordinates for real coloration systems. In order for this approach to become viable it will be necessary to extend the training data to include a greater number of colorants. It remains to be seen how many training recipes will be necessary to enable the network to make accurate predictions when a larger number of colorants are used. It will also be necessary to be able to include information regarding illuminants other than D65; it is possible that this may be accomplished by entering colorimetric data for more than one illuminant or by using reflectance values for the target data during training. The ANN can also be used in colour science, coupled with fuzzy logic so the accuracy of prediction will be enhanced. In order to develop a universal solution

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70

L* value

60

50

40 0.25

0.75

1.25 1.75 % shade Actual

2.25

2.75

2.25

2.75

2.25

2.75

Predicted

70

a* value

60

50

40 0.25

0.75

1.25 1.75 % shade Actual

Predicted

25

b* value

20

15

10 0.25

0.75

1.25 1.75 % shade Actual

Predicted

5.7 Actual and predicted L*, a* and b* values for various % shade dyed material.

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for recipe prediction, a network has to be developed by training the network with the training pattern generated with all combination of dyes, dye depth, dyeing conditions, dyeing additives concentration, dyeing machines, substrates, etc.

5.12

Sources of further information and advice

Bishop C M (1995), Neural Network for Pattern Recognition, Oxford University Press, New Delhi. Chattopadhyay R and Guha A (2004), Artificial Neural Networks: Applications to Textiles, The Textile Institute, Manchester. Freeman J A and Skapura D M (1999), Neural Networks Algorithms, Applications and Programming Techniques, Addison Wesley Publishing Company Inc, USA. McDonald R (1997), Colour Physics for industry, Society of Dyers and Colourists, Bradford. Nelson M M and Illingworth W T (1991), A Practical Guide to Neural nets, AddisonWesley Publishing Company Inc, USA. Nigrin A (1993), Neural Networks for Pattern Recognition, The MIT Press, Cambridge, MA. Schalkoff R J (1997), Artificial Neural Networks, The McGraw-Hill Companies, Inc, New Delhi.

5.13

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Colour measurement

References

Alman D H and Ningfang L (2002), ‘Overtraining in back-propagation neural networks: A CRT color calibration example’, Color Res Application, 27, 2, 122–125. doi: 10.1002/ col.10027 Amirshahi S H and Pailtorpe M T (1994), ‘Application of the Kubelka-Munk equation to explain the color of blends prepared from precolored fiber’, Textile Res J, 64, 357–367. doi: 10.1177/004051759406400608 Anderson J A (1995), Introduction to Neural Networks, Cambridge, MA: MIT Press. Balakrishnan N (1992), Handbook of the Logistic Distribution, New York: Marcel Dekker. Bezzera C M and Hawkyard C J (2000), ‘Computer match prediction for fluorescent dyes by neural networks’, J Society of Dyers and Colorists, 116, 163–169. Blanco R V, Virdi A I S, Balke S T and Diosady L L (2007), ‘In-line colour monitoring during food extrusion: Sensitivity and correlation with product colour’, Food Res International, 40(9), 1129–1139. doi:10.1016/j.foodres.2007.06.008 Bishop J M, Bushnell M J and Westland S (1991), ‘Application of neural networks to computer recipe prediction’, Color Res Application, 16(1), 3–9. doi: 10.1002/ col.5080160104 Campadelli P, Medici D and Schettini R (1997), ‘Color image segmentation using Hopfield networks’, Image and Vision Computing, 15(3), 161–166. doi: 10.1016/S02628856(96)01121-3 Dupont D (2002), ‘Study of the reconstruction of reflectance curves based on tristimulus values: Comparison of methods of optimization’, Color Res Application, 27(2), 88–99. doi: 10.1002/col.10031 Faes G (1998), ‘Neural Networks in Colorimetry’, Melliand Textilberichte, 79, 462–465.

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Fausett L (1994), Fundamentals of Neural Networks, Englewood Cliffs, NJ: Prentice-Hall. Fernández M C O, Gutiérrez A H, Pastor M S S, Sarabia L A and Crespo M I (1995), ‘The UNEQ, PLS and MLF neural network methods in the modeling and prediction of colour of the young red wines from the Demonination orgin of Rioja’, Chemometrics and Intelligent Laboratory Systems, 28(2), 273–285. doi:10.1016/0169-7439(95) 80063-F Gross L, Thiria S and Frouin R (1999), ‘Applying artificial neural network methodology to ocean color remote sensing’, Ecological Modelling, 120(2–3), 237–246. doi:10.1016/ S0304-3800(99)00105-2 Gurney K (1997), An Introduction to Neural Networks, London: UCL Press. Haykin S (1999), Neural Networks, 2nd edition, Englewood Cliffs, NJ: Prentice Hall. Hinton G E (1992), ‘How neural networks learn from experience’, Scientific American, 267, 145–151. Jasper W J, Kovacs E T and Berkstresser G A (1993), ‘Using neural networks to predict dye concentrations in multiple-dye mixtures’, Textile Res Journal 63, 545. doi: 10.1177/004051759306300907 Kang H R and Anderson P G (1992), ‘Neural network applications to the colour scanner and printer calibrations’, Journal of Electronic Imaging, 1(1), 125–134. McCulloch W S and Pitts W (1943), ‘A logical calculus of the ideas immanent in nervous activity’, Bulletin of Mathematical Biology, 52, 1–2, 99–115. doi: 10.1007/BF02459570 McGregor R, Beck K R, Lee G K F, Smith C B and Jasper W J (1996), ‘Project S95-4: Real Time Analysis and Control of Batch Dyeing Processes’, National Textile Center Annual Report: November, 203–210. Marjoniemi M and Mantysalo E (1997a), ‘Neuro-Fuzzy Modeling of Spectroscopic Data. Part A: Modeling of Dye Solutions’, J Society of Dyers and Colorists, 113, 13–17. Marjoniemi M and Mantysalo E (1997b), ‘Neuro-Fuzzy Modeling of Spectroscopic Data. Part B: Dye Concentration Prediction’, J Society of Dyers and Colorists, 113, 64–67. Mizutani E, Jang J S R, Nishio K, Takagi H and Anslander D M (1995), ‘Coactive neuro-fuzzy modelling for colour recipe prediction’, Neural Networks, 5, Nov/Dec, 2252–2257. doi: 10.1109/ICNN.1995.487712 Nobbs J H (1985), ‘Kubelka-Munk theory and the prediction of reflectance’, Review of Progress in Coloration (SDC), 15, 66–75. Pham D T and Liu X (1995), Neural Networks for Identification, Prediction and Control, Verlag, London. Rao V and Rao H (1996). C++ Neural Networks and Fuzzy Logic, BPB publications, New Delhi, India. Rumelhart D E, Hinton G E and Williams R J (1986), Chapter 8 in Learning Internal Representations by Error Propagation, vol. 1 (eds D. E. Rumelhart and J. L. McCleland), Cambridge, MA: MIT Press. Schettini R (2001), ‘Approximating the CIECAM97s color appearance model by means of neural networks’, Image and Vision Computing, 19(9–10), 691–697. doi:10.1016/ S0262-8856(01)00041-5 Senthilkumar M (2007), ‘Modelling of CIELAB values in vinyl sulphone dye application using feed-forward neural networks’, Dyes and Pigments, 75(2), 356–361. doi:10.1016/j. dyepig.2006.06.010 Senthilkumar M and Selvakumar N (2006), ‘Achieving expected depth of shade in reactive dye application using artificial neural network technique’, Dyes and Pigments, 68(2–3), 89–94. doi:10.1016/j.dyepig.2004.12.016

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Shah H S and Gandhi R S (1990), Instrumental Colour Measurements and Computer Aided Colour Matching for Textiles, Mahajan Book Distributors, Ahmadabad, India. Smith B and Lu J (1993), ‘Improving computer control of batch dyeing operations’, American Dyestuff Reporter, September, 17–36. Tandukar J (2007), ‘Generic dyeing and color correction’, Journal of Theoretical and Applied Information Technology, 3, 4, 51–60. Thevenet L, Dupont D and Desodt A M J (2002), ‘Modeling color change after spinning process using feedforward neural networks’, Color Res Application, 28(1), 50–58. doi: 10.1002/col.10114 Tominaga S (1993), ‘Color notation conversion by neural networks’, Color Res Application, 18(4), 253–259. doi: 10.1002/col.5080180408 Vangheluwe L, Sette S and Pynckels F (1994), ‘Comments on using neural networks to predict dye concentrations in multiple dye mixtures’, Textile Res J. 64, 182–183. Werbos P J (1974), Beyond regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis, Harvard University, Cambridge, MA. Westland S, Bishop J M, Bushnell M J and Usher A L (1991), ‘An intelligent approach to colour recipe prediction’, J Society of Dyers and Colourists, 107, 235–237. Westland S, Iovine L and Bishop J M (2001), ‘Kubelka-Munk or Neural Networks for Computer Colorant Formulation’. Proceedings of SPIE: 9th Congress of the International Color Association, 4421, 745–748, Rochester, USA. Xin J H, Shao S and Chung K F (2000) ‘Colour-appearance modeling using feedforward networks with Bayesian regularization method. Part I: Forward model’, Color Res Application, 25(6), 424–434. doi: 10.1002/1520-6378(200012)25:6<424::AID-COL7> 3.0.CO;2-Q Xin J H, Sijie S and Chung K (2002) ‘Colour-appearance modeling using feedforward networks with Bayesian regularization method. Part II: Reverse model’, Color Res Application, 27(2), 116–121. doi: 10.1002/col.10030 Xu B (2003), Soft Computing in Textile Sciences, Physica-verlag GmbH Heidelberg, Germany.

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6 Camera-based colour measurement F. MARTÍNEZ-VERDÚ, E. CHORRO and E. PERALES, University of Alicante, Spain, M. VILASECA and J. PUJOL, Technical University of Catalonia, Spain

Abstract: For several decades, imaging sensors (CCD and CMOS) have been extensively used in many types of imaging capture devices (cameras and scanners). This first stage in any digital imaging workflow is very important in order to control the exact colour reproduction of images in subsequent applications (astronomy, television, cinema, printing, machine vision, mobiles, etc). However, there are many parameters (spectral sensitivities, white balance, dynamic range, etc) which can negatively influence accurate control of the colour reproduction of digital imaging devices. Nevertheless, if all these parameters are controlled, it is possible to transform a conventional digital imaging capture device into a versatile tele-colorimeter or even telespectrocolorimeter. In this chapter, the fundamentals and challenges of camerabased colour measurement will be explained, including several aspects of special interest, such as the control of raw RGB colour space, and the similarities and differences between spectral and colorimetric characterization and calibration. Finally, future trends with clear industrial applications will be described, including case studies focused on the spatial-chromatic dithering of texture images (textiles, ceramic tiles, natural stones, etc), and the pseudo-visualization of non-visible images from multi-spectral imaging capture. Key words: imaging sensor, digital imaging workflow, spectral sensitivity, white balance, exposure level, dynamic range, raw RGB colour space, colour gamut, calibration vs. characterization, luminance adaptation, multi-spectral imaging, spatial-chromatic dithering in texture images, pseudo-visualization of non-visible images

6.1

Introduction

With the invention in 1969 of the charge-coupled device (CCD) (Holst 1998) by Willard S. Boyle and George E. Smith – both awarded the Nobel Prize in Physics in 2009 – and its combination with photo-detection sensors, it was possible to expand many industrial applications, and even introduce new ones. In digital colour imaging, this invention was considered a significant milestone, together with the invention in 1993 of the CMOS or active pixel sensor by E. Fossum (Lee 2005; Nakamura 2006; Ohta 2008). Although both imaging sensors present similarities and differences, it is probable that the specific advantages and drawbacks of each in respect to each particular application will condition future trends in their application fields (see Fig. 6.1). In digital colour imaging these imaging sensors are essential elements for any digital imaging workflow (Jacobson et al. 2000; Saxby 2002; Sharma 2003; Peres 147 © Woodhead Publishing Limited, 2010

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Performance

1 2 3 4 5 6 7 8 9 10 1 2

High performance Low volume

CCDs

Low cost High volume

CMOS

Tubes

1960

1970

1980

1990

2000

Professional DSC Motion analysis Medical – Radiology – Digital endoscopy Low power space apps Automotive Computer video Consumer ESC Biometrics Optical micro Imaging phones Toys Bar code Security

2010

6.1 Evolution of CCD and CMOS application fields over the last fifty years.

2007; Trussell and Vhrel 2008; Langford and Bilissi 2008) focused on digital cinema, television, print media, telecommunications, and other applications (Lukac and Plataniotis 2007; Koschan and Abidi 2008), since they substitute the human eye position and role in any capture of a scene. Thus, for any digital imaging workflow we can define the main elements as follows: • •

• 1 2 3 4 5 6 7 8 9 40 1 2 43X

Scene: composed of several natural and/or artificial objects and light sources, arranged in different positions and orientations towards each other. Input devices: imaging capture devices (cameras or scanners) based on CCD/ CMOS sensors, with an optical system which enables the light coming from the scene to be focused on the photodetector plane (Johnson 2003), as in the human retina. Output devices: display and printing devices, primarily used for encoding and visualizing the information registered in the input devices, for editing and transference to different application fields.

Although there are many challenges in coordinating the colour encoding languages (namely, colour spaces) associated with each colour device in a digital imaging workflow, that is, colour management (Rodney 2005; Nelson 2007; Padova and Mason 2007), this chapter will only focus on the main issues related to the extensive use of imaging sensors for colour measurement, specifically the conversion of conventional digital cameras and scanners to colour devices, with features and performance similar to commercial colour instruments: tele-spectrocolorimeters, spectrophotometers and colorimeters (see Chapter 8, and Part II of this book) (Shevell 2003; Xin 2006). Therefore, given the general scheme of a digital imaging workflow, the following sections will mainly be focused on the interaction of the features of any scene with input devices, with special emphasis on the spectral

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and colorimetric information that these kinds of devices can provide before and after the corresponding digital colour encoding (Holm et al. 2002; Sharma 2003; Westland and Ripamonti 2004; Ramanath et al. 2005; Trussell and Vhrel 2008).

6.2

Principles of camera-based colour measurement

Imaging capture devices basically consist of an optoelectronic sensor, or analogical photosensor, and a device which converts analogical signals into a digital code. The sensor is a matrix of small cells modelled as a spatially uniform array. Each cell is a microscopic photosensitive element which has the ability to produce electrical impulses of different intensity regarding the incident light. This device, in spite of its photosensitivity, distinguishes light intensity variations, but not colours. To distinguish between colours it is necessary to use optical filters in order to separate red, green, or blue light into selected pixel sensors. Conceptually, the scene is codified by three spectral bands (red, green and blue), as in the human visual system. To analyse colour reproduction in imaging capture devices, one should bear in mind that the RGB colour space is dependent on the device, worsening the colour control rendering of these technologies. Due to the great variety of colour spaces, it is necessary to establish transforms which enable the RGB values associated with the imaging capture device to be converted into CIE-XYZ tristimulus values. Consequently, the device metamerism should be taken into account, that is, a colour stimulus with the same tristimulus values in the CIE-XYZ colour space can be encoded differently in the RGB colour space, and vice versa. Imaging capture devices are additive colour reproduction systems and therefore, in order to obtain an exact reproduction, their spectral sensitivities, or equivalently, their colour matching functions, should be an exact linear combination of the CIE-1931 XYZ colour matching functions. However, only a small group of input devices fulfils this important condition. Thus, from an initial and general point of view, all input devices are not colorimetric devices, so it is necessary to perform a preliminary colorimetric characterization process in order to convert them into colour measuring instruments. The main components of any imaging capture colour device consist of (see Fig. 6.2): •

•

An optical filter set and an optical system. An objective lens which excludes ultraviolet radiation (τUV, UV), an additional filter which cuts infra-red radiation (τIR, IR), and finally, the RGB colour filters (τR, τG, τB) to spectrally separate the photoelectrical information into three colour channels. An optoelectronic semiconductor device (CCD or CMOS) as a photodetector plane with a specific spectral sensitivity s(λ).

Mathematically, and given that the device performance is considered linear, the device responses (R, G, B) of any scene can be expressed as:

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R ∝ Σ ρ(λ)·S(λ)·sR(λ)·Δλ G ∝ Σ ρ(λ)·S(λ)·sG(λ)·Δλ

[6.1]

B ∝ Σ ρ(λ)·S(λ)·sB(λ)·Δλ where S(λ) is the spectral power distribution of the light source, ρ(λ) is the object spectral reflectance, and sR(λ) = τUV(λ)τIR(λ)·τR(λ)·s(λ), sG(λ) = τUV(λ) τIR(λ)·τG(λ)·s(λ), sB(λ) = τUV(λ)τIR(λ)·τB(λ)·s(λ) are the spectral sensitivities of the RGB channels respectively. As stated above, the colorimetric characterization of a digital colour camera implies knowing which CIE-XYZ tristimulus values are associated with the object from the RGB values encoded by the camera. This characterization enables the use of devices such as tele-colorimeters. However, a complete characterization model for cameras entails spatial, spectral and colorimetric characterization, which can be summarized as follows. •

•

•

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Spatial characterization (de Lasarte et al. 2007), which involves the application of a linear correction algorithm to compensate for the spatial non-uniformity of the camera sensor response when the object plane is homogeneously illuminated. Spectral characterization (Martínez-Verdú et al. 2002; ISO 17321-1:2006), which involves obtaining the RGB pseudo-colour matching functions of the camera, that is, the responses for each camera channel depending on the wavelength of the object being analysed. Colorimetric characterization (Martínez-Verdú et al. 2003; ISO 173212WD:2009), which implies obtaining the colorimetric profile, that is, the

6.2 Process of imaging capture of a scene: lighting, object and imaging device with zoom lens, colour separation (filters) and imaging sensor.

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matrix which enables the CIE-XYZ tristimulus values to be calculated from the RGB digital levels. A different method for colorimetrically characterizing a digital colour camera, without the need to apply the previous spectral characterization, is to use a direct transformation, which enables either the CIE-XYZ or CIELAB values to be obtained from the RGB digital signals (Hong et al. 2001; Sharma 2003). In this type of characterization, the tristimulus values of a colour stimulus are generally computed from a polynomial combination of its RGB signals. The polynomial modelling is obtained by taking into account the previous measurement of a training set with representative colour patches, from which both groups of values (RGB signals and CIE-XYZ or CIELAB values) are a-priori known. The Color Checker charts are two of the most widely used training sets for achieving this purpose (see Colour Plate VIII between pages 42 and 43). The classic Color Checker is a colour chart with 24 patches used for calibrating and evaluating colour reproduction systems. On the other hand, the digital Color Checker Semi Gloss (SG) is specifically designed to meet the needs of digital photography. Besides colorimetric characterization, it is also quite important to know the limits of the imaging capture device in order to obtain a perfect characterization, such as: •

•

•

Dynamic range: the limits of luminance values that a camera can capture, always smaller than the human dynamic range (from 10–3 cd/m2 under night illumination to 105 cd/m2 under daylight illumination). Spectral exposure level H(λ): the total amount of monochromatic light allowed to fall on the image sensor, which mainly depends on the spectral radiance of the colour stimuli Le(λ), the exposure time t, and the f-number N. White balance: the global adjustment of colour signals (typically red, green, and blue primary colours) to correctly render neutral colours, taking into account the chromaticity (correlated colour temperature) of the illuminant. Essentially, this procedure aims to replicate the human perception property known as colour constancy, using some basic algorithms from chromatic adaptation mechanisms in the human visual system.

6.3

Procedures of camera-based colour measurement

As mentioned earlier, the main purpose of using a digital colour camera as a colorimetric instrument is to transform the RGB signals corresponding to its device-dependent colour space, known in Digital Photography as raw RGB colour space (Steinmueller and Gubbins 2005; Andrews et al. 2006), to CIE-XYZ or CIELAB values. In order to achieve this, it is essential to set the camera settings which can alter the raw RGB colour space. In general, it is sufficient to set the exposure time, f-number, white balance and other gain and offset values specified

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by the manufacturer. The selected configuration may have an influence on the performance limits of the input device, and may only be useful for certain specific scenes or input conditions (colour and intensity level of illumination, position of the input device with regard to the scene, etc). Taking these initial instructions into account, the procedures for the colorimetric characterization of cameras will now be described. As stated above, the first option in order to obtain a complete characterization model for cameras entails spatial, spectral and colorimetric characterization. The first step is spatial characterization. If a digital camera is to be used as a colour measuring instrument, it must be borne in mind that they are not perfect detectors. There are various noise sources inherent to their performance that alter the digital levels corresponding to each pixel, distort the real image acquired in an unknown manner, and diminish the radiometric accuracy, the image quality and the resolution (Janesick 2001). Due to their origin and fundamental characteristics, frame averaging removes all noise sources except the spatial non-uniformity of the digital camera’s response (Healey and Kondepudy 1994), which must be corrected if the imaging system is to be used as a measuring instrument with high spatial resolution. The most commonly used technique for this is known as flat-field correction, and is based on calibrating the RGB channels of the detector by means of two images: a dark image and a uniform field or flat-field image. The spatially corrected image is the result of the linear combination of these two images with the image to correct (de Lasarte et al. 2007; Bellia et al. 2003; Berns 2001). The second step is spectral characterization, which consists in measuring the RGB signals provided by the digital colour camera when it is irradiated with a set of monochromatic stimuli with different radiances and wavelengths. Then, with a monochromator based experimental set-up (see Fig. 6.3), it is possible to obtain the spectral sensitivities of any digital imaging capture device (Martínez-Verdú et al. 2002) without knowing the spectral functions of their optical elements (zoom lens, colour filters, imaging sensor, etc). From these known spectral data,

Spectracolorimeter Halogen lamp

CCD camera Light diffuser PC unit

Monochromator

6.3 Monochromator based experimental set-up for measuring the spectral sensitivities of a digital imaging capture device.

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it is very easy to find a basic colorimetric profile (3×3 matrix) connecting the raw RGB and XYZ colour spaces, and to test the linear correlation between experimental and predicted RGB digital values under different lighting conditions for any scene. The third step is colorimetric characterization. For this purpose, several options are possible, including a colorimetric profile based on spectral sensitivities (MartínezVerdú et al. 2003), or direct polynomial modelling (Hong et al. 2001; Sharma 2003). Since the second method is the fastest and easiest to implement, it will be explained in more detail. In polynomial characterization or modelling, the colorimetric tristimulus values of a colour stimulus, such as the CIELAB coordinates, are calculated from the polynomial combination of their corresponding RGB signals. For instance, if a third order polynomial is used, the functions which relate both sets of values, that is, L* = f1(R, G, B, RG, …, RGB), a* = f2(R, G, B, RG, …, RGB), b* = f3(R, G, B, RG, …, RGB), can be expressed as follows:

1

Rn

[6.2]

Gn

Bn

... ...

RnGn

RnBn

...

R13 R3n

G13 G3n

B31 ...

R1B1

...

R1G1

...

B1

...

G1

...

b*n

...

...

V=

R1

...

1

a*n

...

L*n

b1* ...

...

D=

a1* ...

L*1

[6.3]

B3n

D=M·V

[6.4]

M = (mij)3×20 = ((Vt · V)−1Vt · D)t

[6.5]

where M is the matrix with the transformation polynomial coefficients characterizing the camera, {Rn, Gn, Bn} are the digital levels of the training colour patches measured by the camera and {L*n, a*n, b*n} are the CIELAB values of the training set measured by a spectrophotometer or other conventional colour instrument. Finally, transformation of the RGB values is achieved using the following equation: 1 R L*

G

a*

= (mij)3×20 · B

b*

[6.6]

RG ... B3

20×1

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It should be borne in mind that the coefficients which appear in the above equations are calculated by measuring the training set, such as the colour patches from the Color Checker charts.

6.4

Strengths and weaknesses

Nowadays colour characterization of input devices is a significant scientifictechnological challenge for various reasons. For example, despite the mission of the International Standardization Organisation (ISO), and the fact that some ISO standards do exist which focus on digital photography (ISO 12232:2008; ISO 14524:2009; ISO 22028:2006; ISO 15739:2003; etc), methods for determining transforms of raw RGB values from input devices to scene-referred image data (namely, absolute CIE-XYZ tristimulus values) do not constitute an active item of ISO work. Although there has been significant progress with the publication of ISO 17321-1:2006 (Graphic technology and photography – Colour characterization of digital still cameras (DSCs) – Part 1: Stimuli, metrology and test procedures), consensus among ISO experts in the search for the best method for determining these RGB-XYZ/L*a*b* transforms remains difficult to achieve (ISO 17321-2 working draft), as it depends on camera spectral sensitivities, the spectral radiances of the colours to be analysed, and various tradeoffs (e.g. colorimetric accuracy vs. noise amplification). However, regardless of the continuing confusion concerning digital camera colour characterization, even including terms and definitions (camera colour analysis gamut, etc), the use of digital cameras to determine scene colorimetry persists. Bearing these antecedents in mind, the main purpose of the following subsections is to provide some advice and ideas, which have proven valid and effective, for successfully carrying out the conversion of any input device as a colour measuring instrument.

6.4.1 Similarities and differences among camera characterization models 1 2 3 4 5 6 7 8 9 40 1 2 43X

Before describing the main similarities and differences between the input device characterization models described above, it is important to distinguish between characterization and calibration. The characterization procedure for input devices aims to select and apply a colour modelling or algorithm (with many parameters to be calculated) for use in the experimental set-up and data collection. In contrast, the purpose of the calibration procedure is to repeat the characterization procedure for obtaining new colour modelling parameters resulting from changes to the initial configuration (illumination change, f-number, exposure time, white balance, gain, offset, etc). This issue is particularly critical for colorimetric characterization, because the colorimetric calibration parameter set should not be applied to other camera and scene conditions, due to the risk of losing all initial performance in your input device converted into a colorimeter. Therefore, our advice is very clear: one scene and camera configuration, one characterization procedure.

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Turning to the complete characterization model, it is important to remember here that both spatial and colorimetric characterization procedures are always necessary. However, spectral characterization enables a relationship (3×3 matrix) between RGB and XYZ values to be obtained with ease. This is the basis for any colorimetric profile, such as that necessary for colour management workflows. Nevertheless, colorimetric characterization based on polynomial modelling usually performs well with test colours, which are very different to the training set. Therefore, although this increases computational cost and complexity, we recommend using several training sets with reduced chromatic ranges for each polynomial modelling (maximum number, three) until CIE colour space is completely covered. Obviously, these problems and challenges are not recent, so in the following subsections, we will focus on current alternative approaches for solving or minimizing them.

6.4.2 Concept of luminance adaptation model for cameras covering a high dynamic range Digital colour cameras have a limited dynamic range, where their response to exposure is practically linear. Generally, a change to the camera settings, such as exposure time, gain or offset, enables their dynamic range to adapt to the actual range of radiances. Nevertheless, it is very probable that digital responses for some of the image pixels are not located within the linear response zone of the imaging system due to the large radiance differences of the different objects imaged. Information loss is observed in highly illuminated areas, where all light variations are mapped onto the same value and thus become saturated, and in dimly illuminated areas, where information is overridden by sensor-noise. One way of overcoming this limitation is to use models for increasing the dynamic range of systems (Battiato et al. 2003). These techniques are mainly based on capturing sequences of images of the same scene taken under different exposure conditions (Reinhard et al. 2006; Mantiuk et al. 2007), and then merging them into a single image of increased dynamic range. For instance, luminance adaptation models (Martínez-Verdú et al. 2003; Pujol et al. 2006) transform the digital levels for each channel at a certain exposure time to virtual digital levels at a reference exposure time common to all pixels by means of a linear transformation. This enables all imaged samples to be mapped onto the same exposure time, and therefore, they are comparable and useful for metrological purposes, such as colorimetric measurements.

6.4.3 Introduction to multi-spectral capture systems: reconstruction of spectral reflectances from multi-channel colour values On the other hand, an alternative approach to improve the low accuracy of RGB digital colour cameras when measuring colour and which avoids the metamerism present in these systems is the use of multispectral capture systems (Hardeberg

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Table 6.1 Image capturing systems classification by their number of channels Number of channels

Description

1 3 4 to 9 10 to 100 More than 100

Monochromatic RGB or trichromatic Multispectral Hyperspectral Ultraspectral

1999; Hill 2002; de Lasarte et al. 2006). A multispectral capture system also consists of a digital camera but it records the scene through various acquisition channels with different spectral transmittance (see colour Plate IX as an example). Although this overall definition includes any device capable of sampling in frequency any incoming light, the ‘multispectral’ term is mostly applied to those systems that use more than the three conventional colour channels, but with fewer spectral bands than traditional spectrophotometers. In literature, it is common to find the classification given in Table 6.1 of image capturing systems depending on the number of channels or spectral bands used (van der Meer and de Jong 2001; Imai et al. 2003). A multispectral system is capable of providing instant information on the reflectance spectrum of a colour sample, and therefore, on its colour, from the corresponding digital response level for each pixel. This is achieved by means of spectral reconstruction mathematical algorithms such as principal component analysis (PCA) (Hardeberg 1999; Hardeburg et al. 2002; Tzeng and Berns 2005), the Moore-Penrose pseudoinverse (Vhrel et al. 1994; Vilaseca et al. 2004, 2006; Hardeberg 1999) and other higher-order polynomial fittings (Herzog et al. 1999; Hong et al. 2001; Cheung et al. 2005). Furthermore, in order to use multispectral systems for spectral reconstructions, it is essential to carry out prior training of the system so that digital responses can be related to the spectra that originated them, through the use of a reference colour patch training set with known spectra (de Lasarte et al. 2008a), similar to the process carried out for colorimetric characterization with polynomial modelling. The most conventional configuration of a multispectral capture system consists of a monochrome camera and a set of narrowband filters covering the whole visible range of the spectrum, which may be interference (de Lasarte et al. 2006; Vilaseca 2008) or tunable (Hardeberg et al. 2002; Tominaga and Tanaka 2008). However, other configurations comprising a colour camera and broadband absorption filters (Imai and Berns 2000; Vilaseca 2008) or newer ones with a monochrome camera and a set of narrowband spectral emitters, such as lightemitting diodes (Wenger et al. 2003; Yamamoto and Miyake 2007) are also possible. Figure 6.4 shows examples of transmittances associated with the acquisition channels of multispectral systems with different configurations. Due to the possibility offered by multispectral imaging systems of accurately estimating the reflectance spectrum at each pixel and, from this, the corresponding

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CIE-XYZ tristimulus values of a colour sample, the application fields of multispectral imaging systems have increased considerably in recent years. Some of these applications include accurate colour reproduction systems (Boosman and Hill 2004), the restoration and conservation of paintings and other artworks (Schmitt et al. 2005; Tominaga and Tanaka 2008), and the acquisition of high dynamic range images (Haneishi 2005). Furthermore, multispectral technology has also been applied to reconstruct and visualize spectra outside the visible range, such as infrared (Vilaseca et al. 2005, 2006).

6.4.4 Similarities and differences between spectral and colorimetric imaging based colour measurement Both the camera-based colorimetric and multispectral systems use imaging sensors. However, while there are three channels involved in a conventional

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colorimetric imaging configuration (RGB), the number of spectral bands used in multispectral systems is higher. Thus, colour assessment accuracy is much higher with multispectral systems, and the metamerism often associated with digital trichromatic cameras is avoided (de Lasarte 2008a, 2008b). Nevertheless, some authors have studied changes in multispectral system accuracy by increasing the number of channels, and have shown that from a certain number of spectral bands onwards (often less than 10 (Vhrel et al. 1994; Hardeberg 1999)), neither the accuracy of colour measurement nor the accuracy of spectral reconstruction improves significantly, which can be explained by the spectral properties of most surfaces, which are relatively smooth. On the other hand, drawbacks of multispectral systems include their slowness in scene acquisition through the various acquisition channels, and the cost of current implementations, which basically depends on the number and type of filters used: interference or broadband absorption filters may be cheap, although liquid crystal tunable filters are expensive. Compared to standard spectrophotometers and spectroradiometers, which use diffraction gratings in order to accurately sample the spectral data on the photosensors, colorimetric and multispectral camera-based systems do not have equally good levels of colour and spectral accuracy. However, they offer high spatial resolution and are relatively low cost; therefore, they may be acceptable for many industrial applications and thus have much potential.

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Case studies

Despite the foregoing, nowadays it is possible, on the international market, to find several industrial machine vision solutions which combine all the strengths of camera-based non-contact colour measurement. One example is the DigiEye™. Here we have a compact machine vision system which is mainly composed of a (fixed) lighting system which provides homogeneous cover of the target/scene, a calibrated digital camera, versatile, powerful digital image processing software for replicating the cortical performance of human visual perception, and a calibrated monitor and printer. Other industrial machine vision solutions prefer a free lighting system for adjusting to on-line processes, or free positioning of the calibrated digital camera for measuring the 3-D optical behaviour of materials (Tominaga and Tanaka 2008), both with normal or special effects. In the following pages, two interesting, open challenges will be described: the fundamentals of spatio-chromatic dithering in imaging capture, and the pseudovisualization of non-visible images from multi-spectral imaging capture.

6.5.1 Spatial-chromatic dithering in imaging capture: effects on visual appearance and measurement of textures In many cases, a trichromatic input device transforms the spectral information of a scene into three digital signals (RGB), compressing all initial spectral radiance

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data into three digital values. Consequently, the camera is also performing dithering when digitizing a scene. But depending on the distance between the camera and the target being digitized, relevant spatial information on colour can be dithered or not. This process is the same as when we use a tele-colorimeter or tele-spectrocolorimeter and its measuring spot (displayed in the ocular lens) covers spatially non-homogeneous samples (natural and artificial textures). Thus, both RGB pixels and measuring spots provide a spatially averaged colour signal inside the projected area in the scene. How then can we control the spatial issue before capturing any image and take into account the posterior effects on the final visual appearance? From a geometrical point of view, a digital camera’s sensor plane can be modelled as a spatially uniform array of small sensors. Camera sensor spatial resolution is determined by the sensor size p' and the distance x' between the object and sensor, according to equation 6.7: p' u = 2arctan — 2x'

[6.7]

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If the target/object is near enough to the camera sensor, the visual angle is smaller than the smallest detail of the object and hence the image is properly sampled. The borderline situation occurs when the visual angle subtended by the sensor exactly matches the visual angle subtended by a pixel, because this implies having identical spatial resolutions for both sensor and object. In this situation, the target will be at the appropriate distance x0 (see Fig. 6.5), and the area covered by the visual angle u is p0. Therefore, the distance x0 is fixed by the sensor size and the focal length of the optical system and x0 is the greatest distance from the image that does not cause spatial dithering in the sensor array. If the target is at a greater distance (x), which is k times the initial distance x0, the visual angle u takes k2 pixels of the original image. Given that, our aim is to simulate the appearance of an image placed at a greater viewing distance than the original one, without having to recapture the image at the new distance, averaging k2 pixels of the original image to calculate each pixel of the simulated

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image. The digital level values of the image could be used directly as averaged quantities, but this would be colorimetrically unsound, because these values do not predict colour appearance. Therefore, it was proposed to average CIE-XYZ tristimulus values, because these represent psychophysical encoding in colour science, necessary for calculating forward CIE L*a*b* values. Then, if the digital level values of k2 pixels are fitted by {Xi,j Yi,j Zi,j}i=1,. .k; j=1,…k, the new tristimulus values will be: 1 X=— k2

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where all tristimulus values have the same weight, regardless of position. From this theoretical approach, combining geometrical optics and colour science (Chorro et al. 2007), it is possible to predict different grades of spatiochromatic dithering of captured images by changing the value k, i.e. the viewing distance, or the size of the minimum spatial detail of target, in order to compare their effects through contrasting colour differences between images. However, as with other alternative versions of this approach such as spatial-CIELAB (Sharma 2003) or iCAM, it is necessary to find the direct relationship with true visual judgements by planning well-managed psychophysical experiments with human observers.

6.5.2 Multispectral capture system covering VIS and NIR radiation for textile applications

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The colour control process is a very important issue in the textile industry. In the past, these adjustments were often carried out visually, although obtaining results was time consuming and they were only valid for each specific evaluation. For these reasons, colour measurement devices are now used extensively in this field, although some drawbacks of this technology have still not been overcome. Examples of these are the colour measurement of very small areas, needed in patterned or printed fabrics, and the slowness usually associated with conventional colorimeters, which can only measure one integrated large uniform area at a time. Therefore, the use of colour digital cameras, or even multispectral systems, with high spatial resolution and a lower cost could be very useful and would speed up the control process, enabling faster production. Some preliminary attempts at using multispectral systems in the textile industry have already been carried out. For instance, a seven channel multispectral system has been used to perform colour measurements and spectral reflectance reconstructions of textile samples in the visible range of the electromagnetic spectrum (de Lasarte 2009). Specifically, the system was used to analyse 56 textile samples grouped in 28 pairs, made specifically to test the applicability of colour difference formulae to textile samples, particularly the CIELAB colour difference formula. Hence, these samples had quite similar spectra between pairs, making it

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difficult to distinguish between them (see Fig. 6.6). In this study, the multispectral imaging system developed was shown to be able to detect slight differences, therefore breaking the device metamerism, both in colour and in reflectance spectra between real samples, and making it useful for applications that require colour discrimination. Another multispectral system, in this case consisting of five spectral bands located at the near-infrared range of the electromagnetic spectrum (800–1000nm), was used to develop a pseudo-colour visualization system for the visual discrimination of textile samples (Vilaseca et al. 2005). This system used different

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colour space representations which associated the camera responses with the colour channels of a calibrated monitor. Samples with the same colour or appearance in the visible region, which are therefore indistinguishable to the human eye, can have different reflectance spectra in other parts of the electromagnetic spectrum, specifically in the near-infrared. Therefore, these samples can be discriminated by using the extra information provided by the five additional multispectral images in this region. Colour Plate X shows some blue and garnet textile samples, the appearance of which is exactly the same since they have equal visible spectra, but which nevertheless have different spectral features in the near-infrared. Using the pseudo-colour visualization system developed, the samples could be clearly differentiated using the five multispectral images provided by the system, and also with the pseudo-coloured images obtained as a combination of them, using different colour space representations. The methodology developed could have potential applications in the control of garment counterfeiting by adding special components with specific spectral characteristics to the conventional dyes utilized in the textile industry.

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Future trends

During the last decade, some advances in new imaging sensors and colour architectures (Super CCD, CMOS Foveon, HAD, etc) promise new frontiers in the applicability of camera-based non-contact colour measurement. On other hand, new challenges associated with new applications for special optic materials (luminescent, gonio, sparkle, glitter, etc) with varied measurement geometries will increase in importance and relevance. The use of camera-based systems for colour and spectral measurements is at an experimental stage, and therefore much remains to be done in order to extend their use commercially. In this context, only a few attempts have been made so far, such as the example of imaging spectrographs, which change an area scan camera to a spectral line imaging device that produces full continuous spectral information in each line pixel with high spectral resolution. Imaging colorimeters which capture complex luminance and colour distributions instantaneously constitute a further example. On the other hand, new trends and research directions in camera-based systems for spectral measurements include the use of new light sources (for instance LEDs), the development of spectral reproduction systems rather than colour matching devices, the combination of spectral and colour information, and the compression, management and visualization of multispectral data. Finally, and of no less importance, as we mentioned previously, it is to be hoped that experts will reach a consensus in order to recommend and publish new ISO standards related to versatile and complete input device characterization procedures. Given the main goal sought as regards camera-based non contact colour measurement, these new standards should not rule out certain issues related to the repeatability and

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reproducibility of performance tests between calibrated hyper/multi-spectral and trichromatic cameras.

6.7

Conclusions

Camera-based systems for non-contact colour measuring are less accurate than conventional colorimetric systems such as spectrophotometers and spectroradiometers. However, they have higher spatial resolution and are generally lower cost. This makes them suitable for several industrial applications where colour accuracy is not the main issue. Furthermore, these types of systems can easily be integrated into industrial production lines, or even into scientific and multimedia applications.

6.8

Sources of further information and advice

http://en.wikipedia.org/wiki/Charge-coupled_device http://en.wikipedia.org/wiki/Active_pixel_sensor http://www.123di.com/dpr.php http://www.cambridgeincolour.com/tutorials.htm http://www.color.org/info_profiles2.xalter#digitalphotography http://en.wikipedia.org/wiki/Hyperspectral_imaging http://www.specim.fi/products/spetral-imaging-products/imaging-spectrographs.html http://www.directindustry.com/prod/instrument-systems/spectroradiometer-57082377325.html

6.9

References

Andrews P, Butler Y J and Farace J (2006), RAW Workflow from Capture to Archives: A complete digital photographer’s guide to raw imaging, Focal Press, Oxford. Battiato S, Castorina A and Mancuso M (2003), ‘High dynamic range imaging for digital still camera: an overview’, J Elec Imag, 12(3), 459–469. Bellia L, Cesarano A, Minichiello F, Sibilio S and Spada G (2003), ‘Calibration procedures of a CCD camera for photometric measurements’, 20th Instrumentation and Measurement Technology Conference, 2003 (IMTC’03), Proc IEEE 1, 89–93. Berns R S (2001), ‘The Science of Digitizing Paintings for Color-Accurate Image Archives: A review’, J Imag Sci Technol, 45(14), 305–325. Boosmann T and Hill B (2004), ‘Estimation of Control Values for a 6-primary Display Considering Different Observers’, Proc IS&T Second European Conference on Colour in Graphics, Imaging and Vision (Aachen, Germany), 242. Cheung V, Westland S, Li C, Hardeberg J and Connah D (2005), ‘Characterization of trichromatic color cameras by using a new multispectral imaging technique’, J Opt Soc Am A, 22(7), 1231–1240. Chorro E, Perales E, Fez D de, Luque M J and Martínez-Verdú F M (2007) ‘Application of the S-CIELAB color model to processed and calibrated images with a colorimetric dithering method’, Opt Express, 15(12), 7810–7817. Haneishi H (2005), ‘Image acquisition technique for high dynamic range scene using multiband camera’, Proc 10th Congress of the International Colour Association (AIC Colour 05) 1, 183–187.

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Hardeberg J Y (1999), Acquisition and reproduction of color images: colorimetric and multispectral approaches, PhD Thesis, École Nationale Supérieur des Télécommunications, Paris. Hardeberg J Y, Schmitt F and Brettel H (2002), ‘Multispectral color image capture using a liquid crystal tunable filter’, Opt Eng 40(10), 2532–2548. Healey G E and Kondepudy R (1994), ‘Radiometric CCD camera calibration and noise estimation’, IEEE Trans Pattern Anal Machine Intell, 16(3), 267–276. Herzog P G, Knipp D, Stiebig H and König F (1999), ‘Colorimetric characterization of novel multiple-channel sensors for imaging and metrology’, J Elec Imaging, 8(4), 342–353. Hill B (2002), ‘Revolution of color imaging systems’, Proceedings of the IS&T First European Conference on Colour in Graphics, Imaging and Vision (Poitiers, France), 473–479. Holm J, Tastl I, Hanlon L and Hubel P (2002), ‘Color processing for digital photography,’ in Green P and MacDonald L, Colour Engineering: Achieving Device Independent Colour, Wiley, New York, 179–220. Holst G C (1998), CCD Arrays, Cameras and Displays, 2nd edn, SPIE Optical Engineering, Bellingham. Hong G, Luo M R and Rhodes P A (2001), ‘A study of digital camera colorimetric characterization based on polynomial modelling’, Color Res Appl, 26(1), 76–84. Imai F H and Berns R S (2000), ‘Comparative analysis of spectral reflectance reconstruction in various spaces using a trichromatic camera system’, J Imag Sci Technol, 44, 280–287. Imai F H, Wyble D R, Berns R S and Tzeng D (2003), ‘A feasibility study of spectral color reproduction’, J Imag Sci Technol, 47, 543–553. Jacobson R E, Ray S F, Attridge G G and Axford N R (2000), The Manual of Photography Photographic and Digital Imaging 9th edn, Focal Press, Oxford. Janesick J R (2001), Scientific Charged-Coupled Devices, SPIE Press, Bellingham WA, USA. Johnson M (2003), Photodetection and Measurement: Maximizing Performance in Optical Systems, McGraw-Hill, New York. Koschan A and Abidi M (2008), Digital Color Image Processing, Wiley-Interscience, New York. Langford M and Bilissi E (2008), Langford’s Advanced Photography, 7th edn, Focal Press, Oxford. de Lasarte M. (2009), Thorough Characterization and Analysis of a Multispectral Imaging System Developed for Colour Measurement, PhD Thesis, Technical University of Catalonia, Terrassa, Spain. de Lasarte M., Pujol J, Arjona M and Vilaseca M (2007), ‘Optimized algorithm for the spatial non-uniformity correction of an imaging system based on a CCD colour camera’, Appl Opt 46, 167–174. de Lasarte M., Pujol J, Arjona M and Vilaseca M (2008a), ‘Influence of the size of the training set on colour measurements performed using a multispectral imaging system’, Proceedings of the IS&T Fourth European Conference on Colour in Graphics, Imaging and Vision (Terrassa, Spain), 437–440. de Lasarte M., Pujol J, Arjona M and Vilaseca M (2008b), ‘Influence of colour ranges on colour measurements performed with a colorimetric and a multispectral imaging system’, Proceedings of the IS&T Fourth European Conference on Colour in Graphics, Imaging and Vision (Terrassa, Spain), 444–449. de Lasarte M., Vilaseca M, Pujol J and Arjona M (2006), ‘Color measurements with colorimetric and multispectral imaging systems,’ Proc SPIE 6062, 0F1–0F11.

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Lee H-C (2005), Introduction to Color Imaging Science, Cambridge University Press, Cambridge. Lukac R and Plataniotis K N (2007), Color Image Processing Methods and Applications, CRC Press, Boca Raton. Mantiuk R, Krawczyka G, Mantiuk R and Seidela H P (2007), ‘High dynamic range imaging pipeline: perception-motivated representation of visual content’, Human Vision and Electronic Imaging XII, Proc SPIE 6492, 212. Martínez-Verdú F M, Pujol J and Capilla P (2002), ‘Calculation of the color matching functions of digital cameras from their complete spectral sensitivities’, J Imag Sci Technol, 46(1), 15–25. Martínez-Verdú F, Pujol J and Capilla P (2003), ‘Characterization of a digital camera as an absolute tristimulus colorimeter’, J Imag Sci Technol, 47(4), 279–374. Meer F D van der and Jong S M de (2001), Imaging Spectrometry Basic Principles and Prospective Applications, Kluwer Academic Publishers, Dordrecht. Nakamura J (2006), Image Sensors and Signal Processing for Digital Still Cameras, CRC Press, Boca Raton. Nelson P (2007), The Photographer’s Guide to Color Management Professional Techniques for Consistent Results, Phil Nelson, Buffalo. Ohta J (2008), Smart CMOS Image Sensors and Applications, CRC Press, Boca Raton. Padova T and Mason D (2007), Color Management for Digital Photographers for Dummies, Wiley Publishing Inc, Indianapolis. Peres M R (2007), Focal Encyclopedia of Photography Digital Imaging, Theory and Applications, History, and Science, 4th edn, Elsevier, Amsterdam. Pujol J, de Lasarte M., Vilaseca M and Arjona M (2006), ‘High Dynamic Range Multispectral System for Wide Color Gamut Measurements’, Third European Conference on Color in Graphics, Imaging and Vision (CGIV’06), Proc IS&T’s, (Leeds, UK), 404–409. Ramanath R, Snyder W E, Yoo Y and Drew M S (2005), ‘Color image processing pipeline’, IEEE Signal Processing Magazine, 22, 1, 34–43. Reinhard E, Ward G, Pattanaik S and Devebec P (2006), High Dynamic Range Imaging Acquisition, Display and Image-based Lighting, Elsevier, Amsterdam. Rodney A (2005), Color Management for Photographers: Hands on Techniques for Photoshop Users, Focal Press, Oxford. Saxby G (2002), The Science of Imaging, An Introduction, IOP Publishing, Bristol. Schmitt F, Aitken G, Alquié G, Brettel H, Chouikha M B, Colantoni P, Cotte P, Cupitt J, Deyne C de, Dupraz D, Lahanier C, Liang H, Pilay R, Ribes A and Saunders D (2005), ‘CRISATEL Multispectral Imaging System’, Proc 10th Congress of the International Colour Association (AIC Colour 05) 1, 463–467. Sharma G (2003), Digital Color Imaging Handbook, CRC Press, Boca Raton. Shevell S K (2003), The Science of Color, 2nd edn, Optical Society of America and Elsevier, Oxford. Steinmueller U and Gulbins J (2005), The Art of RAW Conversion Optimal Image Quality from Photoshop CS2 and Leading RAW Converters, Steinmueller Photo. Available from http://www.outbackphoto.com/, http://www.outbackphoto.com/booklets/dop3002/ DOP3002-6_TOC.pdf Tominaga S and Tanaka N (2008), ‘Spectral image acquisition, analysis, and rendering for art paintings’, Journal of Electronic Imaging, 17(4), 043022. Available from http:// spiedl.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JEIME500001700000 4043022000001&idtype=cvips&gifs=

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Trussell H J and Vhrel M J (2008), Fundamentals of Digital Imaging, Cambridge University Press, Cambridge. Tzeng D-Y and Berns R S (2005), ‘A review of principal component analysis and its Applications to color technology’, Color Res Appl, 20 (2), 84–98. Vilaseca M, Mercadal R, Pujol J, Arjona M, de Lasarte M., Huertas R, Melgosa M and Imai F H (2008), ‘Characterization of the human iris spectral reflectance with a multispectral imaging system’, Appl Opt, 47, 5622. Vilaseca M, Pujol J and Arjona M (2004), ‘Illuminant influence on the reconstruction of near-infrared spectra’, J Imag Sci Technol, 48(2), 111–119. Vilaseca M, Pujol J, Arjona M and de Lasarte M. (2006), ‘Multispectral system for the reflectance reconstruction in the near-infrared region’, Appl Opt, 45(18), 4241–4253. Vilaseca M, Pujol J, Arjona M and Martínez-Verdú F M (2005), ‘Color visualization system for near-infrared multispectral images’, J Imag Sci Technol, 49(3), 246–255. Vrhel M J, Gershon R and Iwan L S (1994), ‘Measurement and analysis of object reflectance spectra’, Color Res and Appl, 19, 4. Wenger A, Hawkins T and Debevec P (2003), ‘Optimizing color matching in a lighting reproduction system for complex subject and illuminant spectra’, 14th Eurographics Symposium on Rendering, Leuven (Belgium), 249–250. Westland S and Ripamonti C (2004), Computational Colour Science Using MATLAB, John Wiley & Sons, Chichester. Xin J H (2006), Total Colour Management in Textiles, Woodhead Publishing and The Textile Institute, CRC Press, Abington. Yamamoto S and Miyake T Y (2007), ‘Development of a multi-spectral scanner using LED array for digital color proof’, J Imag Sci and Technol, 51(1), 61–69.

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Plate X Visible and near-infrared spectral reflectances of blue and garnet textile samples with the same appearance. Since they have different spectral features in the near-infrared, they can be differentiated in the five multispectral images (Im_F1-Im_F5) provided by the system. The pseudocoloured images obtained as a combination of the five independent images using different colour space representations are also shown.

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7 Colour shade sorting M. L. GULRAJANI, India Institute of Technology, India

Abstract: Shade sorting of the acceptable fabric lots is necessitated because the fabric swatches from these dyed lots, when placed together for stitching, show visual colour difference. Even though one can sort coloured samples visually, it is not in practice due to poor observer-to-observer correlation and inconsistent repeatability. Instrumental shade sorting is preferred and is considered more reliable. The first instrumental shade sorting method was evolved by Simon in 1961 and over the past 40 years many sorting methods have been developed that include 555 shade sorting, Clemson Colour Clustering, K-means clustering and adaptive clustering. A brief description of these methods of shade sorting has been covered in this chapter. Key words: 555 shade sorting, Clemson Colour Clustering, K-means clustering, sequencing, tapering, adaptive clustering, hierarchical clustering.

7.1

Introduction

Shade sorting is a process of segregating acceptable groups of dyed lots of fabric into subsets where the minute colour difference between the lots is not perceivable to the naked eye. This is necessitated because the fabric swatches from these dyed lots, when placed together for stitching, show visual colour difference. In view of this, more critical colour tolerances are required and the acceptable coloured batches need to be divided into subgroups with visually acceptable colour differences. Even though one can sort coloured samples visually, it is not in practice due to poor, observer-to-observer correlation. Instrumental shade sorting is preferred and considered more reliable. According to Li et al. (1998) four equally important prerequisites for efficient instrumental shade sorting are: • • • •

Effective data gathering: this includes both the colour measurement and data treatment process. Uniform colour space generated colorimetrically for allocating the coloured textile samples in coordinated form. Proper determination of the tolerance limit for dividing colour space and population of coloured textile samples. An efficient method for the separation of the colour space and population of colorimetric textile sample.

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a slightly modified form started during the mid-1960s (Aspland and Howard, 1997). During the last forty years many improved methods have been developed and are available in the form of software that can be integrated with the various other colour measurement and colour communication modules. Shade sorting methods are being mostly used by the garment industry. Even though they source the material that has been ‘passed’ by an instrumental method of ‘fail-pass’ as per the criteria set by them, still they find that some colour differences become visually perceivable on stitching of the garment from the ‘passed’ lots of the same shade. The perceptible difference in the colour of the ‘passed’ lots occurs due to various reasons, such as: • • •

The reflection of more light in one direction than in another resulting in lustre and appearance differences between two samples. Material might have been dyed with chemically different dyes that match in one source but appear different under another source of light. The ‘passed’ samples may be lying at the two opposite ends of the periphery of the pass-fail ellipsoid.

7.2

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(555) Fixed-grid shade sorting system

In the 555 shade shorting system as proposed in 1961, the colours were sorted on three-dimensional grids with lightness, chroma and hue as the coordinates in the UCC colour difference system (Hirschler and Zwinkels, 2007). The CIE chromacity coordinates, x, y, along with Y were calculated for each sample and used to quantify the colours for sorting. These were later replaced by more visually uniform CIELAB coordinates, L*, a* and b* or their equivalents in CMC and CIEDE2000 colour difference equations. In this system ‘standard’ shade is assigned the number ‘5’ for all the three colour axes, i.e. L*, a* and b* or L*, C*, H*. Therefore, the ‘standard’ shade is termed as ‘555’ and located at the centre of the 555 box. It is also known as the sort code of the sample. The first digit represents lightness or darkness, the second digit is for the redness-greenness (a*) or for saturation (C*), the third digit denotes yellowness-blueness (b*) or the hue angle (h) (represented by hue, H*, a function of hue angle) as the case may be. The 555 box is created around the standard with samples having ΔL*, Δa* and Δb* or ΔL*, ΔC* and ΔH* in the pre-defined tolerance limit. However, a sample may have a negative value of ΔL*, or Δa* or Δb* or ΔC* or ΔH* that is at the periphery of the box and another sample with a plus value of ΔL*, or Δa* or Δb* or ΔC* or ΔH* at the other end of the box, so the tolerance limit in the box becomes twice the pre-defined tolerance limit (Aspland and Howard, 1997). As shown in Fig. 7.1 the two samples having maximum acceptable negative and positive values of ΔL* will lie at points A and B, the colour difference in the case

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Colour shade sorting

h0

C*

L*

A

169

B

7.1 Tolerance ellipsoid and position of two accepted samples A and B.

919 915

911 511

111

955 855 559 525 755 558 535 7 5 5 545 655 556 555 455 565 575 355 585 255 551 595 155 515

999

199

151 191

7.2 Arrangement of ‘boxes’ of 555 shade sorting system.

of these two samples and standard at the centre will be acceptable. However, the colour difference between these two samples will be more than the limits set in the pass-fail criterion. So a garment stitched with parts from these samples will have perceptible colour difference. Having quantified the standard and having put it in the centre box, other boxes are created around it whose orientation is parallel to the three-dimensional axes of the opponent-colour scale system shown in Fig. 7.2. There is no dead space and no

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Table 7.1 Colour specification for apparel: block sizes for 555 shade sorting in Cartesian (L* a* b*) coordinate Colour

Lightness range

ΔL* allowance

Δa* allowance

Δb* allowance

Grey Grey/Black Black Blue Blue Navy Brown Brown Brown Green Orange Orange Red Red Red Violet Yellow Yellow

70–90 20–70 0–20 50 20–50 10–20 70–90 40–70 10–40 40 60 30–60 60 30–60 10–30 10–80 80 10–80

0.8 0.8 0.8 0.6 0.6 0.6 0.8 0.8 0.8 1.0 0.4 0.5 0.35 0.5 0.8 0.7 0.5 0.7

0.4 0.5 0.5 0.2 0.2 0.3 0.4 0.5 0.5 0.4 0.35 0.4 0.35 0.5 0.5 0.5 0.4 0.5

0.6 0.6 0.6 0.5 0.5 0.6 0.6 0.6 0.6 0.5 0.6 0.6 0.35 0.5 0.5 1.2 1.2 1.2

overlapping portion between the boxes. The 555 box is in the centre and other boxes have sort codes between 111 and 999. The dimensions of the three axis of the boxes are set as per the preset tolerance limits for the ΔL*, Δa*, Δb* or ΔL*, ΔC* and ΔH*. A guideline for setting up tolerance limits for individual colours in terms of ΔL*, Δa*, Δb* is shown in Table 7.1 (Li et al., 1998). These specifications have been worked out on the basis of the experience of colourists handling the shade sorting process and provide the specification for all three dimensions for the different regions of the colour space. However, one may use one’s own shade sorting criteria for adjusting the tolerance limit according to his or her experience. These specifications are applicable to the Cartesian (L*, a*, b*) coordinate. These are not entirely suitable for both the polar (L*, C*, h) coordinate and CMC microspace concept because both of these systems consider colour in terms of lightness, chroma and hue rather than lightness, redness-greenness and yellowness-blueness. In a subsequent study, Li et al. (1999) carried out regression analysis of twenty sets of coloured cotton knitted fabrics including eight main shade groups (red, orange, yellow, green, blue, violet, brown and black) dyed with reactive dyes, to determine the optimum colour tolerance level for instrumental shade sorting. On the basis of this study these investigators have concluded that although the performance of CIE94 and CMC were quite similar, the CMC equation is recommended for generation of micro-spaces for shade sorting processes since it gives a better overall performance over a whole set of batches.

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In 1986, Aspland and Jarvis introduced a graphical method for determining initial colour tolerance limits based on the CMC (l:c) formula. A tolerance limit of 0.5 CMC (2:1) or below as detailed in AATCC Test Method 173-1991 is generally used in creating boxes (Aspland and Jarvis, 1992). It also means that the maximum permissible colorimetric difference between coloured fabrics within a box will remain ≤1.0 ΔECMC units on either side (periphery) of the box. This also applies to the standard 555 box. Moreover, the coloured fabrics in a given box may ‘match’ each other but they may be several ΔE units away from the standard. Thus a dyed sample having shade number (sort code) 853 will be lighter (+L*) than the standard by 3 box units, will have the same redness-greenness (a*) as that of the standard and will be 2 box units bluer (b*) than the standard. However, in the case of the L*, C*, H* system the sample will be lighter (+L*), with the same degree of saturation as that of the standard (C*) and lower hue angle than that of the standard (h). The advantages of 555 shade sorting are its arithmetical simplicity and a welldefined relationship between the shade sorting blocks and the standard. These characteristics permit allocation of a new sample of a different production lot in the previously created relevant block. A simple method of calculating the sort code (shade number) is described by Sule (1997). According to this method, group limits (i.e. tolerance limits) have to be specified for a given set of samples to be sorted. These are the dimensions of the three axes of the boxes as per the preset tolerance limits for the ΔL*, Δa*, Δb* or ΔL*, ΔC*, ΔH* systems and they have to be lower than the limits set as the pass–fail criterion set for passing or rejecting the supplied samples. The group limits are denoted by the letter G as indicated below. GL – Group limit for lightness Ga – Group limit for redness/greenness Gb – Group limit for yellowness/blueness GC – Group limit for chroma GH – Group limit for hue. The next step is the calculation of the sort ratios (SR) by the following procedure: SR (L) = ΔL/GL, SR (a) = Δa/Ga, SR (b) = Δb/Gb SR(C) = ΔC/GC, SR (H) = ΔH/GH The sort code or the shade number of any given sample can then be calculated as illustrated by an example below. If for a given sample the colour difference as calculated by the CIELAB (1976) equation is ΔL* = 1.4; Δa* = –0.9; Δb* = 1.4 and the group limits are set as: GL* = 0.5; Ga* = 0.4; Gb* = 0.4, then the sort ratio will be,

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Colour measurement SR (L) = ΔL*/GL* = 1.4/0.5 = 2.8 SR (a) = Δa*/Ga* = – 0.9/0.4 = – 2.25 SR (b) = Δb*/Gb* = 1.4/0.4 = 3.5

The sort code for this sample will be obtained by adding 5.5 to the Sort Ratios and considering only the integer of the sum without rounding up. SC (L) = (2.8 + 5.5) = 8.3 = 8 SC (a) = (– 2.25 + 5.5) = 3.25 = 3 SC (b) = (3.5 + 5.5) = 9.0 = 9 Thus the sort code of the given sample will be = 839. For the 555 sorting method sort codes of all the supplied samples are calculated and the samples having the same sort code are grouped together and stitched so as to avoid any perceptible shade differences in the garment or any other made-up product. The 555 shade sorting method is also referred to as the fixed-grid method that relies on the closely packed array of boxes or blocks as discussed above. The dimensions of the three axes of the box depend on the preset tolerance resulting in rectangular or cubic blocks. Initially a suggestion was made that the best overall ratio between the three dimensions of the box is 4:2:1, that of the common (American) brick (Simon, 1983), however this was found to be unsatisfactory. In a cubic block sample, the centre of the cube will be 0.5 units away from all the six sides of the cube (i.e. the acceptable limit), but 0.87 units away from the corners. The distance along the longest diagonal in this case will be 1.73 units and 1.41 across the shortest diagonal. This lack of non-uniformity can be overcome by changing the shape of the box to a spherical shape with only one colour unit such as the ΔE colour tolerance unit for diameter such as 0.5 CMC (2:1). As discussed above, in that case all the samples within the sphere would have ≤1.0 ΔECMC units colour difference and hence will be in the acceptable limit. However, the spheres cannot be closely packed as shown in Fig. 7.3 and would cover only 52.3% of the original tolerance cube; resulting in the non inclusion of many acceptable samples in the specified tolerance space (cube). Another shape of the tolerance space that has been used is the truncated octahedron (TO) with 14 faces, six square and eight hexagonal faces, with each side having the same length as shown in Fig. 7.4(a) (Aspland and Howard, 1997). Truncated octahedrons can be closely packed as shown in Fig. 7.4(b). The numbers are integers in the case of 555, but include half-integers in the case of TO sorting blocks, e.g. 5.5, 4.5, 6.5. The truncated octahedron shade sorting programme was used in all shade sorting programmes supplied by Instrumental Colour Systems in 1984 (McLaren, 1987).

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Colour shade sorting

7.3 Packing of spheres.

(a)

7.4 (a) Truncated octahedron; (b) close packing of truncated octahedrons.

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Table 7.2 Indices of shade sorting effectiveness Geometry of block

Colour uniformity

Inherent efficiency (%)

Sphere Truncated octahedron Rhombic dodecahedron Cube

100 78.0 71.0 58.0

100 68.3 47.7 36.8

It is suggested that the relative colour uniformity of the samples in the chosen block may be assessed from the ratio of the minimum and maximum dimensions of the shade sorting blocks and the inherent efficiency, as a percentage relative to the volume of a sphere of the same diameter (Table 7.2) (Aspland et al., 1990). The use of shapes of blocks other than simple cubes, such as the rhombic dodecahedron and truncated octahedron shapes, improve relative uniformity but still suffer from the inherent disadvantage of the block systems. The block sorting system, even if refined, still does not optimise the sorting process to minimise the number of groups produced. They rely on allocation of samples into one of the blocks in the rigidly structured array around the standard. Block sorting systems may be satisfactory in a situation in which it is desirable to locate future production into previously established groups (Wardman et al., 1992). Another, drawback of the fixed grid is that of the closely matching samples falling on the periphery of the adjacent boxes resulting into more groups of samples adding to increased inventory, storage and handling problems.

7.3

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Clemson Colour Clustering

In a pioneering effort of developing an alternate method of shade sorting, Aspland and co-workers (Aspland et al., 1987) proposed a clustering method in 1985 and named it as the Clemson Colour Clustering (CCC) method. In this method a set of dyed samples is divided into a minimum number of groups such that the colour difference in each group is within the specified tolerance limit. Each group of dyed sample is called a cluster that is located in a sphere in colour space. The main difference between the CCC method and the 555 method is that in the 555 method the colour acceptability space (i.e. tolerance limit) is defined in a specified manner and the dyed samples falling within the acceptability space are ‘put’ into the boxes (cubes or polyhedra) while in the CCC method a minimum number of spheres (clusters) of specified size are created to house all the dyed samples. This method does not take into consideration the concept of colour acceptability space while creating the cluster. Some of the salient points of the CCC method are: •

The exact number and position of the clusters exclusively depends on the sample colorimetric data.

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• •

175

The spheres (clusters) that are created to ‘accommodate’ the dyed samples may not cover the entire acceptable colour space, however, all the dyed samples presented for sorting as a given lot are accommodated in one or the other sphere. The spheres created for accommodating the dyed samples may overlap each other but no dyed sample will be accommodated in more than one sphere. This method is independent of the equations used to determine the colour difference.

The clustering technique employed in the CCC method is known as the hierarchical agglomerative clustering technique or according to the authors, hierarchical complete linkage clustering technique (Aspland et al., 2000). In this technique, starting with one point (single sample) cluster the other clusters are recursively merged to the ‘parent’ cluster to create a large cluster until the termination criterion is reached. The choice as to which cluster is to be merged next is determined by the clusters of minimum colour difference. The colour difference between two clusters is quantified by the colour difference between those two points (samples), one from each cluster, that have maximum colour difference (ΔECMC). It is also referred to as the colour merge value (Aspland and Jarvis, 1992). The diameter of the sphere (cluster) is the function of the maximum colour difference between all pairs of dyed samples in that cluster. This process of recursive merger to the ‘parent’ cluster with other clusters to create a large cluster is illustrated in Fig. 7.5. In this figure four clusters have been recursively merged to produce a single cluster. The final number of clusters produced by this method determines the number and size of the shade sorting groups.

88, 1.04 265, 1.53 112, 0.79 177, 1.14

301, 2.18

65, 1.07

36, 0.99

7.5 CCC clustering process in which successive groups are being linked where blocks have shade number and colour difference. (Reproduced from Aspland et al., 1987.)

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Table 7.3 Comparing two populations sorted by the CCC and 555 methods Shade sorting geometry

Population sorted

Total number of sort groups

CCC

Blue twill

4

555

Blue twill

11

Groups

Percentage of samples in group

1 2 3 4 1 2 3 4 5–10

37 29 22 12 77 8 5 3 7*

*Percentage of all groups

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On the basis of the shade sorting of the 301 navy blue twill fabric samples by visual sorting by experts and by the CCC method into 10 and 11 clusters, respectively, it has been concluded by Aspland et al. (1987) that the samples in CCC clusters were more uniform without any exception. Moreover, they later reported that the CCC method normally classifies a given set of samples into less than half the number of groups than the 555 shade sorting method, as indicated in Table 7.3 (Aspland et al., 1990). These observations establish that the CCC method sorts a given set of samples into the minimum number of groups and the samples within the group have a more even distribution of samples of acceptable colour difference. When assessed by this criterion, the CCC method has been shown to be far superior to the 555 and other block methods of shade sorting. The CCC shade sorting system is a ‘dynamic’ system, in which the supplied dyed rolls (lots) or those in the stock grouped together initially may change when a new shipment is added to the inventory. This may change the number of groups and also the position of the initially grouped rolls from one cluster to another. As previously stated, it is the colorimetric values of the rolls which determine how they are grouped, not how they fall into a grid; while in the 555 system new rolls on evaluation fall into the previously created boxes (groups) on a grid. No need to re-group all the rolls of fabric. Figure 7.6 shows how the CCC programme should be used in order to best utilise its capabilities. Note that the inventory is shade sorted after new shipments arrive and after rolls have been removed for cutting. It is to the user’s advantage to update the clusters and repeat shade sorting after each of these operations. A limitation of the CCC clustering method is that the sorting is carried out without reference to a standard. This means that the groups produced are not coded according to their position in the colour space relative to the standard.

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Inventory

Shade sort

Cut rolls

New inventory

Rolls removed

7.6 Demonstration of CCC clustering for adoption by industry.

Table 7.4 Groups produced by various sorting methods Colour

Turquoise Pink Dark blue Yellow-orange Khaki Light blue Brown Blue Green

No. of samples

40 43 47 45 30 44 30 29 45

Number of sorted groups Visual

Clustering

Scotsort

555

4 2 5 4 3 2 2 3 5

4 2 4 3 2 2 2 2 5

6 3 3 3 4 2 3 2 5

12 6 9 8 6 5 4 5 15

Thus the colour difference between the samples within a group and the standard coloured sample cannot be assessed. A method to overcome this limitation of the CCC method has been proposed by Wardman et al. (1992). These investigators have suggested that initially a ‘primary’ cluster be created around the standard sample having samples with less than half of the value of the set tolerance limit of acceptability. In this way, all of the samples in the primary cluster will be an acceptable match to each other. The normal clustering method is then applied to the remaining samples that lie outside the primary cluster. This has been termed the Scotsort method. When sets of actual production samples were sorted by the visual, clustering, Scotsort and 555 methods (Table 7.4) it was observed that the Scotsort method was less efficient than the free clustering method, but only marginally so. In fact it gave the same number of sorted groups with three of the sets, and for the dark blue set actually sorted the samples into one group less. The Scotsort method was found to be more efficient than the 555 method.

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7.4

K-means clustering

This method has been used by Venkatraj, Gulrajani and Ramanujam (1994) to shade sort 307 olive-green dyed fabrics supplied by different dyeing units and accepted by field inspectors for stitching of army uniforms. These authors measured the L*, a* and b* values of all the samples and used the K-means clustering technique module of SYSTAT statistical and graphing software. K-means (MacQueen, 1967) is one of the simplest unsupervised learning algorithms that solves clustering problems. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The clustering modules operate using the following logic. Initially the (L*, a*, b*) data file is scanned by the clustering module and depending on the user defined clusters the seed points are created randomly. The samples having (L*, a* and b*) nearest to the seed point are picked up until no sample is left out. The first step is completed and initial clusters are formed. Subsequently new seed points are recalculated to create better fitting clusters. This iterative process continues until the seed points do not shift any further. Finally, this algorithm aims at minimising an objective function, in this case a squared error function. The objective function (equation 7.1) is k

J=Σ

x

||

||

2

Σ x(ij)–cj , j –1 i–1

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[7.1]

where ||x(j)i – cj||2 is a chosen distance measure between a data point x(ij) and the cluster centre cj, is an indicator of the distance of the n data points from their respective cluster centres. Distance between the seed point and an isolated point within the cluster is called mean distance (MD). Since the mean distance of various points in the cluster is nearly equal, an average mean distance for all the clusters has been calculated. Likewise, the overall mean distance of each cluster group of 4 to 20 clusters has been calculated and plotted against the number of clusters as shown in Fig. 7.7. The plot indicates that as the number of groups increase, the average mean distance decreases and becomes asymptotical. This indicates that grouping of data beyond some cluster groups is not the right solution. Tangents are drawn along the curve and an optimal number of clusters, 13 in the present case with an average mean distance of 0.3053, is obtained. The distribution of samples within a cluster is graphically represented in Fig. 7.8 for six major clusters. The graph has been plotted between sample numbers and their respective MD by scaling each MD by a factor of 1.00 to each successive cluster so that the isolated points within the cluster can be represented more clearly. Barring some isolated samples, all the samples lie within a very narrow range showing uniformity of the shade within a cluster. Li et al. (2001) have studied the performance of CCC and K-means shade sorting methods and observed that the performance of CCC shade sorting arithmetic is

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Colour shade sorting 0.8

Average mean distance

0.6

0.4

0.2

0

0

4

8

12

16

20

24

28

280

320

Number of clusters

7.7 Number of clusters vs. mean distance.

6

Mean distance (MD)

5

4

3

2

1

0 0

40

80

120

160

200

240

Sample number MD + 0, MD + 1, MD + 2, MD + 3, MD + 4, MD + 5

7.8 Graphic presentation of clusters.

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very effective, resulting in a good utilisation of fabric. However, the K-means shade sorting could do the same job with higher compactness of the sorted groups. They have stated that it is rather difficult to distinguish which is the more suitable for shade sorting.

7.5

Modified CCC shade sorting method

Li et al. (2001), having evaluated the CCC and K-means shade sorting methods and finding these methods to be equally effective, carried out modifications of the CCC shade sorting process by combining hierarchical and non-hierarchical algorithms into a single process to improve the effectiveness of shade sorting. In the modified CCC method, cluster seeds are located for non-hierarchical clustering. Subsequently, sequential K-means sorting is used to modify the outcomes obtained from the CCC shade sorting method. A comparative study was carried out to assess the performance of the original CCC and the modified CCC with sequential K-means shade sorting in terms of: • • • • •

formation of sorted groups, variation of colour within sorted groups, utilisation of coloured fabric, distribution of the population, and compactness of individuals in a sorted group.

This study indicated that the modified CCC method, originating from CCC sorting and subjected to additional sequential K-means clustering, shows better overall performance.

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Shade sequencing and clustering

Shade sequencing is a traditional process of manually arranging lots of dyed pieces so that the colour difference between the adjacent pieces is not visually perceptible before stitching a garment. The shades can be sequenced by using visual tapering method, i.e. arranging lighter to darker or vice-a-versa, which is a one-dimensional solution to the three-dimensional problem since colours are described in terms of three parameters, namely lightness, chroma and hue. It fits into the argument that, not withstanding the sophistication of instruments or the complexity of the mathematical formulae applied, there is always, at some stage of the process, someone who must look at the colour and decide whether to accept or reject it. The problem of visual sequencing becomes complex when slight variation of hue is encountered along with the shade depth variation. In a study conducted by Wills (1997) it was observed that disagreements between shade sorters as well as the repeatability of a single shade sorter increased when the variation in shade occurred due to hue, chroma and lightness-darkness of the shade (random

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variation). However, when variation occurred primarily in L* or C* (linear variation) the tapering and sequencing becomes easy with good agreement between sorters. Similarly when both L* and C* vary, tapering can be done smoothly, more so when the variation of one colour parameter is predicted by the second as in the case of dyed indigo denim, where L* and b* are taken into consideration for tapering as shown in Fig. 7.9. A solution to the linear tapering and random variations has been proposed by Wills. In the case of linear variation, the two parameters with largest variation (such as ΔL* and ΔC*) may be plotted by adding the minimum range of L*, a* and b* to each piece so that all the points lie in the first quadrant and the best-fit line can be worked out. Using the slope and intercept of this line, ΔEcmc between each point and intercept are calculated and arranged sequentially from highest to lowest along with the sample numbers to create a linear taper sequence. The samples so arranged may be divided into groups for stitching. For taper sequencing of samples with random variation of colour, Wills has developed a method called Minimum Path which mimics the type of pattern produced by human shade sorters. The acceptability of the taper sequence can be judged by the number of adjacent pieces that exceed the ΔEcmc value of 0.35–0.50.

L*

b*

7.9 Graphical representation of linear tapering of blue dyed sample from light to dark shade and the best fit line.

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Clustering of the tapered samples can be carried out by a pattern recognition technique known as the nearest neighbour technique. However, this technique cannot be applied when garment parts are cut at different times and then stitched or when multiple products must match each other. A further improvement in the combined sequencing and cluster technique has resulted in the development of the adaptive clustering technique. Adaptive clustering uses external feedback to improve cluster quality; past experience serves to speed up execution time. The algorithm carries out simultaneous n-dimensional clustering of multiple data observations. It first orders the observations by successive nearest neighbour, in the n-dimensional Euclidean sense, from a defined starting point. It performs clustering adaptively without any assumptions about the size, number, or statistical characteristics of the clusters. The adaptive clustering technique developed by SheLyn Inc for shade sorting combines clustering, sequencing and historical analysis. Initially ellipsoidal clusters are created based on user-defined ΔEcmc tolerance. The data of the pieces that do not fall into any cluster is maintained and when the new pieces arrive their data is compared with the left-out pieces. If found compatible, all these pieces are then used to create a new cluster which may be slightly shifted towards the centre of gravity of the cluster. The process is terminated when a sufficient number of pieces are added to the cluster. The pieces within each cluster are also sequenced, thus these clusters can ‘adopt’ based on evolving history. SheLyn Inc. has incorporated all these features in their Color iMatch Industrial software. This software is being used by GretagMacbeth.

7.7

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References

Aspland J R and Howard R W (1997), ‘Instrumental shade sorting: Past, present and future’, in: Colour Technology in Textile Industry, 2nd edition, Committee RA36 Colour Measurement Test Methods, AATCC, 121–130. Aspland J R and Jarvis J P (1992), ‘Shade sorting re-examined’, Text. Chem. Col., 24(9), 88–91. Aspland J R, Jarvis C W and Jarvis J P (1987), ‘An Improved method for numerical shade sorting’, Text. Chem. Col., 19(5), 22–25. Aspland J R, Jarvis C W and Jarvis J P (1990), ‘A review and assessment of numerical shade sorting methods’, JSDC, 106, 315–320. Aspland J R, Balasaygun K D, Jarvis J P and Whitaker T H (2000), ‘Alternative mathematical approaches to shade sorting’, Color Research and Application, 25(5), 369–375. Hirschler R and Zwinkels J (2007), ‘Use of CIE colorimetry in the pulp, paper, and textile industries’, in: Schanda J (ed.), Colorimetry: Understanding the CIE System, New York: John Wiley and Sons, 425. Li Y S W, Yuen C W M, Yeung K W and Sin K M (1998), ‘Instrumental shade sorting in the past three decades’, JSDC, 114, 203–209. Li Y S W, Yuen C W M, Yeung K W and Sin K M (1999), ‘Regression analysis to determine the optimum colour tolerance level for instrumental shade sorting’, JSDC, 115, 95–99. Li Y S W, Yuen C W M, Yeung K W and Sin K M (2001), ‘Modifying an existing numerical shade sorting system through cluster analysis’, Textile Res. J. , 71(4), 287–294.

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MacQueen J B (1967), ‘Some methods for classification and analysis of multivariate observations’, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297, Berkeley: University of California Press. McLaren K (1987), ‘Colour space, colour scales and colour difference’, in: Colour Physics for Industry, ed. R McDonald, Bedford: SDC, 114. Simon F T (1983), ‘Practical applications of colour control’, AATCC/ISCC, 22. Sule A D (1997), Computer Colour Analysis: Textile Applications, New Age International (P) Ltd, New Delhi, India, 118. Venkatraj R, Gulrajani M L and Ramanujam V (1994), ‘Shade sorting using a novel technique’, in Technological Conference, Resumé of Papers, BTRA, SITRA, NITRA & ATIRA, 35, 167–177. Wardman R H, Weedall P J and Lavelle D A (1992), ‘Some observations on the colour clustering method of shade sorting’, JSDC, 108, 74–78. Wills F B (1997), ‘Automated taper shading and clustering methods’, in: Colour Technology in Textile Industry, 2nd edition, Committee RA36 Colour Measurement Test Methods, AATCC, 131–134.

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Determining uncertainty and improving the accuracy of color measurement J. A. LADSON, Color Science Consultancy, USA

Abstract: We review the subject of uncertainty of measurement in spectrometric measurements of colored materials. Definitions of uncertainty components are offered, and methods of assessment and calculation of those components are revealed; this allows a user to prepare an uncertainty budget for their system. We address the practical applications of industrial measurement system uncertainty and example data sets are presented from measurements obtained from different industries. The reduction of uncertainty in color measurements can be addressed by training, the proper use of statistics, and reducing the systematic errors in spectrometers. Reducing systematic errors enables users to correlate global color values so that they will be nearly identical and can therefore be compared directly. This includes instruments manufactured by different manufacturers, and instruments manufactured by the same manufacturer. The methodology can be deployed on six different spectrometer modalities, including bi-directional geometries, that is, 45°/0°; hemispherical, either d/0° or d/8° geometries; or multi-angle geometries. Multi-angle geometries are used to characterize and assess gonioapparent colorants. Key words: colorimeter, colorimetry, diffuse reflectance, inter-instrument agreement, measurement uncertainty, spectrometer, spectrophotometer, uncertainty.

8.1

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Introduction to determining uncertainty

Measurement accuracy of color is an important parameter of color measurement, especially in today’s society. Looking around our world and being sensitive we see color everywhere we look as it is an important commercial parameter of many, many products. For instance, the sales of a particular brand of digital televisions (LCDs) are significantly influenced by their color reproduction accuracy; i.e., which one has ‘better color’ when viewed and compared in the store. The same accuracy in color measurement that is required here to make the best appearing color display is also present in other industries; such as colorants, coatings, food-stuffs, inks, paints, paper, plastics and textiles. In these industries and others, color reproduction accuracy is essential to transmit not only the perception of quality to the consumer but to reduce the manufacturing expense using process control methodologies. That means reproducing the same exact color consistently from batch to batch, from day to day, and from year to year. The best way to address the issue of color reproduction is to first access the error budget and then focus on reducing the single largest source of error. Let us begin with quantifying the uncertainty of measurement. 184 © Woodhead Publishing Limited, 2010

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Uncertainty

Technically, uncertainty analysis is the determination of the quality of the measurement result. It determines the range of values that can reasonably be attributed to the measured quantity and will most often be expressed as result ± uncertainty values. Previously published literature1,2,3 has generally referred to analysis of uncertainty components that would pertain at the level of a national standardizing laboratory. National laboratories promulgate the scales by which we measure reflectance and thereby color. We, the users, receive the standardized scales through the mechanism of a transfer standard(s) from the national laboratory, through the instrument manufacturer, and to our measurement system where they become a component of our uncertainty. The Guide to Measurement Uncertainty4 (GUM) is an international standard that outlines the methods by which laboratories may assess and report uncertainty. ISO 170255 requires laboratories seeking accreditation to report uncertainty with each accredited measurement. Because the teachings of the GUM do not entirely apply to the measurement of color, we offer the following as a reasonable alternative methodology for estimating the uncertainty of one’s measurements. There are several industrial applications in which an understanding of the total measurement system of uncertainty would be useful. For instance, here are a few examples. •

•

•

•

What product tolerances can we maintain in manufacturing? Certainly, no product tolerance can be maintained that is less than the uncertainty of the measurement system controlling it. Many times multiple manufacturing sites will produce the same or a similar colored product. Total measurement system uncertainty answers the question: How close in terms of total color difference, CIELAB ΔE, can we produce product standards? What contribution do differing measuring instruments in different locations, say quality control and manufacturing, make to the total measurement system uncertainty? Another frequent issue is: Color measurement varies over the surface of the specimen and from batch to batch. How many measurements on a sample do we need to make to obtain a value that is statistically representative of the product?

Quantifying the total measurement system uncertainty allows us to provide quantitative, definitive answers to these and other typical industrial questions. Answering these questions, and others not posed here, adds value to the enterprise by increasing the throughput of manufacturing, increasing product quality, and decreasing manufacturing costs. Measurement system uncertainty is, therefore, going to have a wave of future interest directed to it.

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Color measurement

The goal of this chapter is to provide an easy-to-use methodology for the user of a spectrometer (spectrophotometer) to assess the uncertainty of his colorimetric measurement and to provide some detailed examples applicable to industries. The term spectrometer replaces spectrophotometer, and is the accepted term defined by ASTM6 E-2847.

8.3

Definitions

Let us begin by defining several terms that will be utilized in order that we may discuss these concepts, with reader and writer agreed as to the ways in which the language of uncertainty is to be used. Measurement system – the entirety of variable factors that could affect a measurement’s precision, accuracy, or uncertainty such as the instrument, the operator, the environmental conditions, the traceability scheme of the calibration, the quality of the transfer standard, the specimen aperture size, as well as other factors. Instrument uncertainty conditions, of a measurement – conditions wherein the measurements are made repetitively as rapidly as is feasible, or desired, without replacement of the specimen being measured in the specimen port of the instrument. Operator uncertainty conditions, of a measurement – conditions wherein the measurements are made repetitively as rapidly as is feasible, or desired, with replacement of the specimen being measured by the operator completely withdrawing the specimen from the specimen port and replacing the specimen in the specimen port prior to the ensuing measurement so that the specimen aperture samples the same location on the specimen to the best of the operator’s ability to accomplish. Levelness uncertainty conditions, of a measurement – conditions wherein the measurements are made repetitively as rapidly as is feasible, or desired, with replacement of the specimen being measured to an entirely new location on the face of the specimen with the intent of sampling the entire surface of the specimen, or as much of the surface as is practical, by the end of the repetitive sampling run. Instrument uncertainty – the results of uncertainty analysis of a measurement system made under instrument uncertainty conditions. Operator uncertainty – the results of uncertainty analysis of a measurement system made under operator uncertainty conditions. Levelness uncertainty – the results of uncertainty analysis of a measurement system made under levelness uncertainty conditions. Levelness – the property of a colored specimen whereby the specimen presents itself to an instrument with an identical measurement over the entire surface to be measured. Discussion: Generally high levelness (the same colorimetric value all-over) is considered a salutary property, and low levelness is considered a deleterious property.

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When we measure levelness uncertainty, for instance, the two earlier uncertainties, instrument and operator are included as may be conceptualized by Fig. 8.1. If we measure each of the three kinds of uncertainty, we will be able to quantify each component by subtraction of the included components from the inclusive one and thereby obtain by calculation the value representing the total measurement uncertainty of the system. The measurement of uncertainty is done through the use of multiple measurements and by the determination using statistical techniques of the 95% confidence interval, which would be the equivalent to a two sigma expansion factor in a normal distribution. The range of that value when added and subtracted from the measured value gives a range in which 95% of the variation is expected.

8.4

Tables of results

In the tables, values may be found for the calculated (subtracted) uncertainties for the component values of CIE XYZ tristimulus values; X, Y, and Z, as well as the CIELAB component uncertainty values; L*, a*, and b*. These are given in columns from left to right under the following uncertainty components: calculated instrument uncertainty (CIU), calculated operator uncertainty (COU), and calculated levelness uncertainty (CLU). The total uncertainty is the vector length (the square root of the sum of the squares) of these three components. Because these components add under quadrature, the total uncertainty is the same as the largest component as in the case of Table 8.1, but this is not always the case. The units of the table are those of the value itself. That is, the uncertainty of the CIE X tristimulus value is expressed in the same un-dimensioned units as

Levelness

Operator

Instrument

8.1 Relationship of uncertainty parameters.

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Table 8.1 The uncertainty values in units of the value itself for a single yellow smooth glossy plastic plaque for Instrument 100

CIE X CIE Y CIE Z CIE L* CIE a* CIE b* CIE ΔE*

CIU

COU

CLU

Total uncertainty

0.04 0.04 0.01 0.03 0.01 0.05 0.06

0.00 0.00 0.00 0.00 0.00 0.03 0.04

0.40 0.47 0.56 0.32 0.35 1.39 1.46

0.40 0.47 0.56 0.32 0.35 1.39 1.46

the value of CIE X component unit is expressed, and the uncertainty value of component value CIE b* is in CIELAB units. In Table 8.1, the uncertainty values in units of the value itself for a single yellow smooth glossy plastic plaque for Instrument 100, we find the results of an uncertainty assessment of a yellow smooth glossy plastic plaque on two instruments of the same manufacturer and geometry. Instrument 100 has a sample aperture of about 7 mm and Instrument 500 has an aperture of 25 mm. Notice that the instrument uncertainties are both small and of the order that is reported to us by the instrument manufacturers at the time of purchase as the instrument repeatability specification. In the subsequent columns of the table, we take into account the operator’s ability to replace the specimen in the exact same location for each measurement. There is added uncertainty involved with this operation. Finally, we take into account color difference that may arise by the sample’s lack of levelness over the entire surface to be measured. We cannot determine where on the specimen the aperture might be placed if a duplicate measurement were to be made by another person. Consequently, measuring all over the surface is a legitimate component of uncertainty for any measurement. All expressed uncertainties in the tables are 95% confidence intervals determined from 30 measurements. While as few as ten measurements is generally sufficient to characterize these values adequately, the uncertainty values are a little larger when a small number of replicate measurements are used due to elements of uncertainty introduced by the fewer measurements. Beyond, say, 32 measurements, a point of diminishing returns takes over and no further additional number of measurements will improve the precision of the values obtained. In Table 8.2, we find the uncertainty components of a second plastic plaque. This time the plaque is a baby blue smooth glossy plastic. Similar results pertain. In Tables 8.3 and 8.4 we find the uncertainty values for the same plaques measured by the same operator on a different instrument. In this case, the instrument is Instrument 500 with a larger aperture. Variations in the results emphasize that the uncertainty results are dependent upon all elements of the measurement system and the way in which they interact.

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Table 8.2 The uncertainty values in units of the value itself for a single baby blue smooth glossy plastic plaque for Instrument 100

CIE X CIE Y CIE Z CIE L* CIE a* CIE b* CIE ΔE*

CIU

COU

CLU

Total uncertainty

0.00 0.01 0.01 0.00 0.01 0.01 0.02

0.07 0.15 0.19 0.11 0.20 0.02 0.23

0.23 0.15 0.34 0.10 0.29 0.47 0.43

0.24 0.21 0.39 0.15 0.35 0.47 0.49

Table 8.3 The uncertainty values in units of the value itself for the same single yellow smooth glossy plastic plaque for Instrument 500

CIE X CIE Y CIE Z CIE L* CIE a* CIE b* CIE ΔE*

CIU

COU

CLU

Total uncertainty

0.02 0.02 0.00 0.02 0.04 0.02 0.05

0.05 0.05 0.08 0.03 0.03 0.23 0.22

0.12 0.14 0.35 0.09 0.10 0.98 0.99

0.13 0.15 0.36 0.10 0.11 1.01 1.02

Table 8.4 The uncertainty values in units of the value itself for the same single baby blue smooth glossy plastic plaque for Instrument 500

CIE X CIE Y CIE Z CIE L* CIE a* CIE b* CIE ΔE*

CIU

COU

CLU

Total uncertainty

0.00 0.00 0.01 0.00 0.00 0.00 0.00

0.01 0.01 0.00 0.01 0.01 0.01 0.01

0.10 0.10 0.07 0.07 0.08 0.14 .016

0.10 0.10 0.07 0.07 0.08 0.14 0.16

One might expect that the larger aperture of Instrument 500 would average the color levelness of the baby blue plaque in the same way it appears to be doing to the yellow plastic. In both cases, this averaging appears to take place resulting in an approximate halving of the uncertainty measurement on the larger instrument.

8.5

Conclusions: determining uncertainty

In the case of these plastic plaques the major component of uncertainty of measurement is the levelness contribution. It is incumbent upon the spectroscopist,

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quality control and quality assurance personnel, colorists, and laboratory managers to assess the total measurement system uncertainty under their conditions. Having obtained the measurement uncertainty values one may decide to reduce the most significant error, which is usually a difference between instruments.

8.6

Improving accuracy: the absolute correction of instrumentally generated spectrometer values

Generally, for modern instruments that are close in performance, that is less than 1 CIELAB DE unit, 1 DE*ab, different from one another, one can expect that the errors will be reduced nearly an order of magnitude to an average of less than 0.10 DE*ab unit across 14 BCRA8 tiles. This agreement or correlation between two instruments can be relative, that is one instrument to another, or absolute, that is, correlating one or more instruments to a reference instrument or reference values. ISO9 and CGATS.510 recommend methods to improve inter-instrument agreement. The software uses traceable artifact standards to do the training. Implementing this program allows conformance to in-house certification programs, ISO requirements, and CGATS. This is of particular importance to those who utilize ICC profiling. Typically, there are multiple implementation methodologies available. For instance, software can be incorporated into your existing software through a Dynamic Linked Library (DLL). It can be used externally in post processing modes with an MS Excel spreadsheet, or it is usually available as part of a fullfeatured, quality control package. These programs operate transparently to the user. One can expect that the software, the support programs, and documentation required to utilize the program are included.

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Color measurement

Introduction to improving accuracy

The authors are professionally engaged in the instrumental measurement of color. The correlation of instrumental measurements is desired and yet has been elusive. There are at least five situations where improvement of instrumental spectrometer values would be beneficial. The first case involves customers with multiple manufacturing sites. In this case, customers want identical color values from multiple color measuring spectrometers around the globe so that data can be compared and analyzed. In many industries manufacturing facilities are off-shore while the central laboratories are located in the US, for instance. The second case involves customers who in this business climate of corporate acquisition acquire companies that have spectrometers of different modalities. For instance, there are six popular modalities: SIN11, SEX12, d/8°13, d/0°14, 45/0°15 and multi-angle16. Not included but also appropriate are a plethora of

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non-standard modalities. It is desirable to correlate values from any instrument with any modality to those values generated by a central laboratory instrument. The third case is customers who have large product databases. In this case, the customer wants to preserve the integrity of the database. Often they are told that the database has to be ‘scrapped’ because the values obtained with the new instrumentation do not correlate with the values obtained with the old instrument. Utilizing the old database values with the new instrumentation enables them to utilize standardized methods without introducing confusion. The fourth case involves customers who utilize a single universal database. Colorant manufacturers and large manufacturers require data compatibility from around the globe as they use the colorant information for computer color matching and product colorization. The fifth case involves companies who regularly utilize computer color matching or batch correction. These colorizing methods involve the preparation of multiple calibrations (let downs) for each colorant in a formula. Typically three to six colorants will be used to create each color. In this case the K/S values are calculated from measured spectral reflectance values at each wavelength. The K in the K/S represents the absorption component of the mixture and the S represents the scattering component of the mixture. A form of Kulbeka-Munk17 equations uses these values to compute the desired pigment concentrations. Statistical studies of the customer’s databases for these cases show a weak correlation between different modalities, configurations, and different manufacturer’s instruments. A model that could provide such a vehicle is of interest to the color community and has commercial value. Today as we engage in globalization and standardization, such as ISO, correlation becomes increasing important. The ability to utilize digital color values transmitted over the internet such as Gates described in Business @ the Speed of Thought18 has enormous competitive advantage by shortening the supply chain and reducing the time to market.

8.8

Experimental modeling

The parameters for a proprietary algorithm and their coefficients were put together in a computer program that allowed us to input data (such as a database or library), process that data, and analyze the results generated by the executable program. This allowed us to validate and adjust empirically if necessary the program, thereby optimizing the results. The results of the executable program are reported in two sets of specimens. The first set consists of less than (<) 1700 samples representing a library sampling a very large portion of reproducible color space. The distribution of samples in color space is shown in Fig. 8.2. This figure is the CIELAB data of the library plotted in the CIE a*–b* plane of CIELAB color space. The same sample set is shown in Fig. 8.3 in the L* versus a* plane of the CIELAB color system. The library samples most of color space extensively. We then plotted the spectral reflectance factor and Fig. 8.3 shows the

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Mean plus sample location in CIE a* - CIE b* space 120 100 80 60 40

CIE b*

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Color measurement

20 0 –60

–40

–20

0

–20

20

40

60

80

60

80

–40 –60 CIE a*

8.2 Specimen location in CIE a*–b* color space.

Sample test set CIELAB L* a* plane 120 100

80

60

40

20

0 –60

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–40

–20

0

20

40

8.3 Distribution of specimens in the CIELAB L* versus a* plane.

distribution of 255 samples randomly selected. The quantity of samples presented is a limitation of MS Excel. It is seen that the sample set contains high chroma samples. Samples with high chroma are difficult specimens to measure and correct because a small variation in the steep slope of the reflectance curve causes a large error in CIELAB color space. The sample set contained a large number of neutral and near neutral samples and these samples tested the efficacy of the photometric correction. The surface finish on the sample set is primarily matte and the samples are paint applied on a stiff paper substrate. There were no special precautions taken when performing the measurement of the library, but these were performed by experienced operators

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and those skilled in the art. The instruments were standardized at regular intervals throughout the measurement process in accordance with the manufacturer’s recommendations. All the measurements of a library on one instrument were made in one day and were performed under reproducibility conditions; that is, different operators, different sample sets, different locations, and different instruments performed on different days. Reproducibility data are the most difficult type of data to correlate since all known variables need to be considered. The effectiveness of this algorithm was tested by comparing values obtained using industrial spectrometers, the Macbeth (MCB) 7000™19 and the BYK-Gardner (BG) Color View™20. The MCB is a D/8° sphere operating in the SEX modality with a 10 nm bandwidth instrument reporting from 360 to 780 nm, with a 30 mm LAV aperture. The BG instrument is a 45°: 0° geometry, LAV 25 mm aperture reporting from 400 to 700 nm with a 10 nm bandwidth at 10 nm intervals. The spectral bandwidth and center band frequency are not constant over the wavelength region of interest. Both the instruments use a white tile for standardization supplied by the manufacturer, and were standardized according to the manufacturer’s instructions. The manufacturers’ calibrations are traceable to different national standardizing laboratories, resulting in significantly different full scale 100% settings, approximately 2%. No other adjustments to the spectrometers were performed. The spectral reflectance factor data for each sample was collected and analyzed by another program in MS Excel that converts the data from spectral reflectance values to CIELAB values for the CIE 10° Observer Function under Illuminant D65. We analyzed the resultant data by a technique called binning. That is, we created bins or buckets of 0.3 units CIELAB DE* from 0.00 to the maximum value. For the first bin we counted the number of samples whose values are between 0.00 and less than (<) 0.29 unit. For the second bin we counted the number of samples whose values were greater than (>) 0.30 to <0.59 unit, and so forth up to the maximum value. This is the data reported for the uncorrected values. Then we ‘trained’ the BG Color View to emulate the MCB 7000 unit. We analyzed the results and they are shown graphically in Fig. 8.4. The program could perform the reverse correlation, that is correlating the BG Color View to the MCB 7000 as well, with similar results. The data shows that in the corrected state >90% of the samples are in bins less than 1.2 (<1.2) CIELAB DE*ab units with a maximum value of 2.7 (<2.7) CIELAB DE*ab units. This is contrasted to the uncorrected state where approximately 90% of the samples are contained in bins less than 3.3 (<3.3) CIELAB DE*ab units with the maximum being 6.6 CIELAB DE*ab units.

8.9

Applications

The applications for this program are applicable to those who measure color in a variety of applications and who are searching for an implementation method, a

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Color measurement BYK-Gardner spectrogard 45:0 original values and corrected values binned using Macbeth 7000 SEX as reference

800

Original

Corrected

700 600

Quantity of occurrences

500 400 300 200

Corrected Original

100 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 4.2 4.5 4.8 5.1 5.4 5.7 6.0 6.3 6.6 6.9 7.2 7.5

Bin

Bin interval = 0.3 unit.

8.4 Original uncorrected values and corrected values versus bin size.

different method, or a different business model. This program will correct reported values by instruments made by different manufacturers, improve performance of instruments made by the same manufacturer, allow cross instrument modalities to be correlated, correct existing databases to conform values obtained from new instrumentation, and allow customers to preserve existing databases. The program is also appropriate for color measurement of objects, such as coatings, inks, paints, plastics, and textiles.

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Conclusions: improving accuracy

The model for the correction of absolute spectrophotometric errors has been developed using numerical methods and proprietary modeling techniques. The correction model is based in large part on the work that was begun in the 1980s. The math model deployed shows that systematic effects for known errors can be substantively reduced. The model shows that errors occurring in inter-instrument agreement, between a manufacturer’s family of instruments; instruments of different modalities; and intra-instrument agreement, different manufacturer’s instruments, can be substantially reduced thus providing the close agreement within or between factories around the globe. The Mean Plus™21 algorithm compensates for variances that occur as a result of design, calibration, and manufacturing. The sources of error include specular port error, photometric full scale error, photometric zero error, photometric nonlinearity, wavelength band pass, wavelength bandwidth, and sample aperture size. Any model deployed does not improve imprecision in the form of repeatability of an instrument or correct the effects caused by translucency blurring,22 which is an effect found in translucent materials. Improved correlation is shown between sampling apertures of different sizes used on the same instrument; these apertures sizes are called Large Area View, (LAV) and Small Area View (SAV). Models have been performing well industrially for many years.

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References

1 J. L. Gardner and R. B. Frenkel, Correlation coefficients for uncertainties in chromaticity, Metrologia, 21, 459–472 (1999). 2 J. L. Gardner, Uncertainty estimation in colour measurement, Color Res and App, 25, 349–355 (2000). 3 E. A. Early and M. E. Nadal, Uncertainty analysis for reflectance colorimetry, Color Res and App, 29, 205–216 (2004). 4 A guide to the expression of uncertainty in measurement. International Organization for Standardization; 1993. Available in the United States as US Guide ANSI/NCSL Z5402-1997. 5 ISO 17025, General Requirements for Accreditation of Laboratories. International Organization for Standardization, American National Standards Institute, New York (1999). 6 ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA, 19428-2959 USA. 7 E284. ASTM E12 Color Appearance Terminology. 8 BCRA tiles are obtained from CERAM Research, Great Britain. CERAM was formerly the British Ceramic Research Association (they may or may not have been calibrated by NPL). 9 ISO 13655 – ISO Central Secretariat, 1 rue de Varembe – CP 56, 1211 Geneva 20 Switzerland. 10 CGATS, NPES, The Association for Suppliers of Printing, Publishing and Converting Technologies, 1899 Preston White Drive, Reston, Virginia 20191-4367. 11 SIN is an acronym for Specular Included modality. 12 SEX is an acronym for Specular Included modality. 13 D/0° refers to diffuse illumination, viewing at 0 degrees relative to the sample normal. See ASTM 1331 for a complete explanation. ASTM International, West Conshohocken, PA, USA. 14 D/8° refers to diffuse illumination, viewing at 8 degrees relative to the sample normal. See ASTM 1331 for a complete explanation. ASTM International, West Conshohocken, PA, USA. 15 45/0° refers to bidirectional geometry. Refer to ASTM E 1349 for a complete explanation. ASTM International, West Conshohocken, PA, USA. 16 Multi-angle refers to instrumentation designed to characterize and measure gonioapparent coatings. See ASTM E 2194 for a complete explanation. ASTM International, West Conshohocken, PA, USA. 17 Judd & Wyszecki, Color in Business, Science & Industry, second edition, John Wiley & Sons, New York, 1952. 18 Business @ the Speed of Thought, Bill Gates, Warner Books, May 2000. 19 Trademark of X-Rite Corporation, Grandville, MI, USA. 20 Trademark of BYK-Gardner, Columbia, MD, USA. 21 Mean Plus is a registered trademark of Resource III, Tatamy, PA, USA. 22 Spooner, D.L, Translucent blurring errors in small area reflectance spectrophotometer & densitometer measurements. In: TAGA Proceedings 1991. Rochester, New York, USA: RIT Research Corporation, 1991, pp. 130–14.

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Colour measurement and fastness assessment M. BIDE, Department of Textiles, Fashion Merchandising and Design, University of Rhode Island, USA

Abstract: Changes in the colour or the transfer of colour from coloured items, especially textiles, is undesirable. Standard tests have been developed particularly by ISO and AATCC to reproduce the challenges that coloured items face and thus predict such poor colourfastness in use. Assessing the colour changes and staining that occurs in these tests is traditionally carried out visually by reference to standard grey scales, but instrumental assessment of colourfastness with equations that generate data corresponding to the grey scales is becoming more widely used. More rapid and accurate assessments are suggested by the use of camera systems and improved equations respectively. Key words: colour fastness testing, grey scales (gray scales).

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Introduction: colour and colourfastness

Humans have colour vision, appreciate the colour that occurs in nature, and use a range of technologies to produce colour in their constructed environment. Thus we paint our cars and houses, read coloured magazines and buy articles in coloured packaging, watch colour television and films, and dye textiles used for our clothes and household furnishings. This coloured environment can be generated by additive and subtractive colour mixing. With additive colour mixing (such as televisions, computer monitors, etc.) the colour is typically transient, and rarely is the image compared directly to real life. Poor control of colour is only obvious when the same image is seen simultaneously on several screens (in a store, or on an aircraft, for example). The colour is fleeting enough, and constantly regenerated, so that change is part of the picture, and any dissatisfaction with the results can be varied by the viewer with brightness and chromaticity controls. Subtractive colour mixing using light absorbing colorants is used to produce coloured objects with expectations of longer lasting colour. In some instances, the item is designed to have a very limited life (a coloured newspaper, or packaging material, for example) but often the lifetime may be months or years. In those cases, the consumer has an expectation that the colour will have some degree of permanence, the expectation varying with the cost of the item, the cost of replacing it (or its colour) and the effect of the colour loss on other items. The colorants used in subtractive colour mixing are, with the minor exception of interference pigments, chemical dyes and pigments whose structures efficiently absorb electromagnetic radiation in the visible region of the spectrum (i.e. light). 196 © Woodhead Publishing Limited, 2010

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These strongly coloured materials lend their colour to an item. Ignoring temporary changes of colour (if an item becomes wet, for example, or thermochromic and photochromic phenomena) the colour of the item may change, for one of two reasons: • •

the colorant remains in place but is changed or destroyed, or the colorant is removed. In this latter case, the removed colorant can generate unwanted staining elsewhere.

The distinction between dyes and pigments has notable implications when it comes to fastness and fastness testing. A pigment’s main characteristic is its insolubility: it exists as discrete particles, usually in the micron range, each particle consisting of billions of atoms or molecules. The insolubility will make its removal by dissolution in a solvent unlikely. If a few atoms or molecules on the surface of a pigment particle are removed or changed, the overall colour of the particle is little affected. For both these reasons items coloured with pigments inherently tend to have durable coloration. Pigments may be both organic and inorganic: the latter are particularly resistant to change. A dye is soluble in its application and often, too, in its ultimate use. It has substantivity for the substrate to which it is being applied. While mineral ‘dyes’ were used in the nineteenth century, dyes are almost exclusively organic. The solubility of a dye, and its presence in a substrate in monomolecularly dispersed form render it more susceptible to dissolution and removal. The (continued) substantivity of the removed dye renders it liable to stain other materials. Additionally, the destruction of any of the monomolecularly dispersed individual particles will have a greater effect on the colour than on a pigment particle. Textile items undergo a greater range of colour challenges than do most other coloured objects: textiles are flexible and clothes move with the body, and are rubbed against each other. Decorative items may hang in sunny windows. Clothes are regularly cleaned and must withstand the rigours of hot aqueous detergent solutions or dry-cleaning solvent. Dyes and textiles are, to a large extent, mutually inclusive. Dyes are little used for non-textile items, and pigments are less used for textiles than are dyes. Thus the problems of colourfastness are strongly associated with dyed textiles. The testing of colourfastness and the evaluation of the changes involved has been studied most widely for textile items and this chapter thus concentrates on colourfastness of textiles.

9.2

The use and usefulness of colourfastness testing

It is unrealistic to expect that an item will be serviceable without some form of assurance: no manufacturer or retailer will let an item be sold and trust that consumers will find it acceptable, for in doing so they risk loss of money, reputation and future business.

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The textile supply chain begins with fibre (natural or manufactured) and continues through yarn, fabric, finishing, garment construction and ultimate distribution and sale. A fibre may undergo many different processes, and see the inside of many different textile mills, perhaps in different countries. Ideally, inadequate goods should not proceed along the supply chain, and poor quality materials should not appear on store shelves. Given the pressure to get goods to market on time, this is obviously not always true! The most realistic means of determining acceptability is to produce sample garments and conduct a wear trial with selected consumers to see when and how failure occurs. Such real-life wear trials are occasionally performed. However, finding volunteers who are typical, organizing the distribution of items, writing good instructions to the volunteers, recovering the garments, analysing the results and drawing conclusions are highly expensive of time and money. Laboratory testing provides an economical and useful alternative. Standard test methods are developed that are designed to approximate to a single real-life property, and to predict how an item will respond when faced with that individual challenge. Such tests are typically economical to perform and provide results rapidly. A good product is not characterized by a single property, but a list of minimum requirements in several tests can come close to predicting overall satisfactory performance, and such a list forms a ‘product specification’ against which a product’s test results can be compared to determine general acceptability. Testing may be conducted for one or more of many reasons. At the various stages of the supply chain quality control testing is conducted on incoming raw materials, to monitor a given process, and on the finished product in a wellcoordinated quality assurance programme. Ultimately, all the various components are assembled and the finished product should be tested to predict its performance in use. At the product development stage, testing will avoid impossible fastness demands, and can assess a company’s product against those of its competitors. The use of hang-tags on garments that inform the consumer of a particular advantage in a garment must be backed by testing. Some unsatisfactory items do get made and appear on store shelves. When, inevitably, they are returned by the customer, testing is a valuable tool in analysing the cause of the failure. As discussed below, tests are developed in different parts of the world, and the choice of which particular test to employ to measure a property may depend not on where the test is being conducted, but where the ultimate customer is located. Thus textile items in India may undergo ISO tests if they are for export to Europe, or AATCC tests if they are for the US market. Issues of environmental, health and sustainability have come to the fore in recent years. Several organizations offer certification schemes that are designed to assure the consumer that an item has been produced in an environmentally friendly manner, that it does not contain (or will release) substances of concern, or that it is ‘organic’. Such certifications are largely based on testing, and many of

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the standard colourfastness tests are used in these schemes. Thus, for example, a range of colourfastness tests are included in the Oeko-tex 100 certification (Oekotex 2009), and in the Global Organic Textile Standard (GOTS 2009).

9.3

Colourfastness test method development

Test methods are developed to determine a particular property of interest, and are designed to reproduce in a laboratory some real life challenge that may result in loss or change of colour. The laboratory setting allows for carefully controlled conditions and in many cases achieves the colour change in an expedient way: tests are often ‘accelerated’. The match of a test to real life may be assessed by comparing results on a range of fabrics with some real-life wear trial. As new fibres, fabrics and finishes are developed, or new machines and technologies are introduced, new tests are introduced. Tests originate in many different ways. Commonly, a commercial company may develop a test in its own laboratories to resolve a particular problem or investigate a property. If the test is a useful one that has wider applicability, it will tend to be adopted by other labs. The original company may require its use by its suppliers. The test has broader acceptance when it is not proprietary and is not the property of a private concern. In order to avoid real or imagined bias based on the test’s origins, tests that are widely used typically become the provenance of an independent, non-commercial standards organization. When a test becomes standardized, lab to lab procedures are consistent and results can be fairly compared. The organizations are discussed in section 9.4, but the general method of working is common to all, and makes for interesting reading (Thiry 2009). The work of the organization in adopting and maintaining the test method is done by interested members, who volunteer their time to perform this valuable work. A proposed test is assigned to the relevant committee of the organization which will usually include experts in the area of the test: if not, additional expertise can be recruited. Rules ensure that no constituency (country, company) has more than one voting member of the committee. If the general concept is accepted, the test is performed at several different labs on a range of fabrics. Any problems in performing the test, and the variability in results on the same fabric, are considered as the test goes forward. When any problems are ironed out, the test is written in standard format and passed through the upper level committees of the organization for further comment or revision. At each stage of the process, it is important that the tests are accepted by more than a simple majority of those voting. A test that goes forward with reservations of a substantial minority is unlikely to achieve widespread acceptance and use. Consequently, most organizations require that a test be accepted by a substantial majority, and that negative votes and comments, if they have merit, are dealt with as far as possible. As discussed earlier, a good test should be valid: the property it measures should have some real life relevance, and the test should predict in-service

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suitability. Additionally, a good test should be simple in terms of how it is performed, and how easy the instructions are to understand. It should also be reproducible giving the same results from operator to operator and lab to lab.

9.4

Colourfastness test standard setting organizations

Formal colourfastness testing dates back to the early twentieth century, and as discussed in the introduction, its emphasis has been chiefly in textiles. Its development has been concentrated in the traditionally large industrial countries, chiefly those in Western Europe, USA, and Japan. Depending on the nature of the country’s governance, the standards-setting organizations have been either professional associations, or government agencies. The individuals who work in test development obviously need to be those with an interest in good tests, and so they might either act as members of those professional associations, or advise government agencies. Thus in the UK, for example, members of the Society of Dyers and Colourists worked to develop standard colourfastness tests that appeared under the auspices of the British Standards Institute, while in the USA, AATCC developed standards that were published under its own name, and recognized by the American National Standards Institute. With the rise in international trade, there has been a trend for tests, including those for colourfastness, to be organized more internationally, through the European Standards Organization (CEN) who develop European standards (EN) or International Standards under the auspices of the International Organization for Standardisation (ISO) (Smith 1994). The national standards bodies continue as member bodies of these larger organizations, and in turn provide the international standards within their own country in the appropriate language, and (to quote a typical national foreword to a published test method) these organizations ‘aid enquirers to understand the text; present to the responsible international/European committee any enquiries on the interpretation, or proposals for change, and keep the UK interests informed; monitor related international and European developments and promulgate them in the UK’ (BSI 2002). 160 countries are members of ISO: these fall into the categories of member bodies, correspondent member, and subscriber member, and ISO has a central secretariat in Geneva, Switzerland. Its tests cover a multitude of areas, each area being organized by a technical committee. ISO’s technical committee for textiles is TC38, and the subcommittee of TC38 responsible for colour fastness tests is SC1. These committees and subcommittees have a member body holding the secretariat (for TC38 it is jointly held by the national standard bodies of Japan and China, JISC and SAC respectively. The secretariat of SC1 is currently held by AATCC through its participation in the US standards body ANSI.) ISO standards are developed by working groups (WG). Many of ISO’s current standard tests originated with individual countries/organizations and have been adopted (and in some cases modified) by ISO. Where ISO has not adopted a

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particular test, the national test may continue in use: for example the German test for colourfastness to saliva, DIN 53160. Even where ISO does have similar tests, AATCC in the United States maintains its own versions and publishes them in English and in Chinese, reflecting the importance of the US market to exporters around the world. The emphasis here is clearly on textile colourfastness testing. Trade organizations and interest groups in non-textile areas may have their own tests, or modified versions of more widely standardized tests. These may reflect the use or particular challenges that are faced by, for example, an automobile interior, a paint, or a piece of leather.

9.5

Standard colourfastness test format

While the details may vary, a typical standard test includes the following information: •

• • • • •

• • •

• •

Name and number which usually includes the year in which the test was last reaffirmed, so that the tester may know that the latest version of the test is being used. This heading information may also include the original date of the test’s introduction, and a note of the committee that is responsible for its updating. Scope/purpose outlines the property being tested and any limitations of applicability. Principle – a simple outline of the test. Definitions of any terms used in the method that may not be immediately understood are included, or the reader may be referred to a glossary. Safety precautions that need to be addressed in conducting the test are described. The apparatus and materials used to conduct the test are listed. The apparatus may refer to a specific manufacturer, or if it is available from several makers, may be described generically. Test specimens – the size, shape, and number of specimens used in the test, and any special preparation required. The test procedure is described, step by step. In some tests a helpful flow chart may be included. Measurement. In the colourfastness tests that are the subject of this chapter, the procedure may generate a change in colour of the test specimen, or of another material included in the test. Thus the ‘measurement’ step in such a test is one that involves the assessment of a before–after colour difference. Since this is chiefly why this chapter should appear in this book, the discussion of ‘measurement’ is dealt with separately in more depth. Calculation and interpretation of the results may be required. Report. As well as the test result itself, the report may also indicate particular test conditions used.

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•

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Precision and bias. Standard tests are designed to measure a particular property of a material. In some cases several tests can provide the same information, and it may be recognized that one test gives a ‘true’ value, albeit with greater cost or complexity. Other tests may give values that vary from this true value in a systematic way, and can be said to have ‘bias’. In such a case, the tester should know about this bias, and this section of the test would include such information. In colourfastness testing, the property being measured will not have such an independently measurable ‘true value’ and thus colourfastness tests do not have a bias. Precision, however, is important and deals with the likely variability of the results when the test is repeated. The same test repeated will produce results that vary somewhat: precision is simply a measure of the extent of that variability, and a statement of precision informs the user of the test of how much an answer must differ (from a standard, or from a prior test, for example) before the difference can be considered significant. Not surprisingly, the variability of a test is least when a single operator repeats the test in the same laboratory. Variability increases (precision decreases) with multiple operators in the same lab, and further when the test is conducted in different laboratories. Such variability is usually established as the test is being developed via interlaboratory trials. Statistical analysis of the results will provide a precision statement, often in the form of critical values by which the test result must differ to be considered significant. These may be provided for different numbers of repetitions, and for within lab-single operator, within lab multiple operators, and between lab cases. The situation is made somewhat more complex in colourfastness testing because the two steps in the process (conducting the test, and measuring the results) each have their own level of precision, and contribute to the overall precision of the test. If the means of measurement can be conducted in different ways (visually and instrumentally, for example) the overall test precision is affected. The variability of grey-scale assessment and its contribution to the variability of a test method has been examined in detail for visual grey-scale assessment (Jaeckel 1980). It was found that the contribution varied with the grey scale value of the result. As the choice is made between assessment methods (discussed in section 9.7) this effect should be considered. Notes that provide supplementary information that will help in conducting the test.

It is noteworthy that colourfastness tests do NOT include the result (level of performance) that represents a pass or a fail, simply the way to carry out the test and get a result. The pass/fail level is a matter for the marketplace to determine, and would typically be given in a performance specification that a company develops based on its standards of quality, expectations of its customers, and its experience. Unlike test methods which, as discussed earlier, are of most usefulness when made public and shared widely, performance specifications are often proprietary.

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Testing for colourfastness: specific tests

As explained earlier, colourfastness tests are designed to reproduce the challenges that real life would impart to a textile. Certain tests relate to agencies that are met in the sequence of manufacturing processes that a textile item undergoes before it gets into the hands of the consumer. Most of the challenges, however, do occur after the item is sold. These challenges can conveniently be divided into those met when the item is being used, and those involved in the cleaning or refurbishment of the item. ISO’s tests for colourfastness are listed under ISO 105, with methods grouped according to a property designated by a letter as follows: A (General Principles), B (Light and Weathering), C (Washing and Laundering), D (Dry Cleaning), E (Aqueous Agencies), F (Adjacent Fabrics), G (Atmospheric Contaminants), H (Textile Floor Coverings), J (Colour Measurement), N (Bleaching Agencies), P (Heat Treatment), S (Vulcanising), X (Miscellaneous Agencies) and Z (Colorant Characteristics) (ISO 2009). AATCC’s methods are simply given a numerical designation (AATCC 2009).

9.6.1 Production processes Mill processes that can affect colour include scouring. Textiles woven from coloured yarns may need to be scoured or soda boiled (ISO105-X06). Multicolour textiles may include a white portion that requires bleaching, and thus a test for colourfastness to bleaching may be needed (ISO 105 N01–4 are the tests for fastness to hypochlorite, peroxide and mild and severe chlorite respectively). AATCC 101 describes the effect of a commercial hydrogen peroxide formulation on most textiles Cotton fabrics may be mercerized and affect the colour of yarns: ISO105-X04 tests this. Dry heat can cause colour changes (ISO105-P01). Wool undergoes a variety of wet treatments. Tests determine the colour changes caused when wool is carbonized with aluminum chloride or sulphuric acid (ISO105-X01,2), boiled in water (potting, ISO105-E09), bleached with sulphur dioxide gas (stoving, ISO105-N05) set with team under pressure (decatising, ISO105-E10), steamed (ISO105-E11) and most commonly, milled, the colourfastness challenges of which are reflected in the naming of certain acid dyes that will withstand this challenge as ‘milling acid dyes’. ISO105-E12 and E13 tests alkaline and acid milling fastness. After make-up, garments may be pleated (ISO105-P02) or hot pressed (ISO105-X11): these can cause thermochromic or sublimation based colour changes and are reflected in standard fastness tests. Silk fabrics may be degummed in hot alkaline soap solution, and a fastness test can predict any changes to coloured materials that undergo this process (ISO105-X08).

9.6.2 Consumer use In use, garments may be rubbed, and their colour transferred to another item. This is a common fault, and one that has potentially expensive consequences (blue

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jeans that transfer colour to a white leather sofa, for example) and the tests to determine this property are widely conducted. A white cloth (which may be dry or wet) is attached to a peg and under a load is rubbed back and forth (ISO105-X12, AATCC8) or in a rotary fashion (ISO105-X16, AATCC 116) on the textile under test. The stain on the white cloth is evaluated. Despite this apparent simplicity, the test requires some care in its performance, and is indicative of the need to control conditions even in a well-established and widely used test (Patton 1989). AATCC has developed a chromatic transference scale that is designed specifically for the evaluation of such stains. Its use is described in AATCC EP 8 and is discussed further below. A variety of wet agencies can change the colour of a textile. These tests may fall into two general categories. First, water or other liquid can leave a mark after it has evaporated. The application of a spot of the liquid, followed by drying, is the basis for test for water spotting (ISO105-E07, AATCC 104), solvent spotting (perchloroethylene, AATCC 157) or to acids and alkalis (AATCC 6, ISO105-E05 and 06). Alternatively, the test may involve soaking the material in the test liquid such as hot water (ISO105-E08) or boiling water (ISO105-E09). Other aqueous challenges include the possibility of transfer to a white material, and tests to predict the effects of water, sea water, perspiration (ISO105-E01, 2, 4; AATCC 107, 106, 15) typically involve moistening the test material and an adjacent fabric, sandwiching them under pressure between plates and storing them at a controlled temperature for several hours. Perspiration (acid or alkaline) and sea water are simulated by standard solutions. Chlorinated pool water is a different challenge, and is usually a dynamic test (ISO105-E03, AATCC 162). In the AATCC test a carefully standardized solution of chlorine is tumbled with a test specimen and the change of colour assessed. The test is subject to enough variation that a parallel test is carried out with a standard control fabric: the test is only considered valid if that fabric changes colour by a certain amount (GS 2–3 or 3). A variety of atmospheric contaminants prevalent in industrial areas and derived from combustion and sunlight are responsible for the change of colour of a textile. The main ones are ozone, and oxides of nitrogen (or ‘burnt gas fumes’). In the burnt gas test (ISO105-G02, AATCC 23) the specimen is exposed in a chamber in which a gas flame is burning. A two-step evaluation is used: a control fabric is included, and its change of colour is compared to a standard of fading (a fabric dyed to match the faded colour of the control). When the two are the same, the test specimen is removed and rated according to the grey scale. A similar test involves high humidities (in which some colours are notably sensitive to NOx), and uses NOx directly rather than a flame (ISO105-G04, AATCC 164). Similarly, fastness to ozone can be assessed under low and high humidities (AATCC 109 and 129 respectively, and ISO105-G03). These tests also involve a control fabric and a standard of fade. Colourfastness to light represents a particular challenge to assess in a standard test and has been the subject of much research. The main problem derives

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from the great variability of real life light exposure which includes direct and indirect light, sun vs. cloud, changes of humidity, height of sun in the sky, and so on. Nonetheless, real life exposure is included as one means of determining this important property (ISO105-B01, AATCC 16 Option 6). In addition to its variability, natural exposure often takes a long time to produce a meaningful change of colour, so instrument manufacturers have produced a range of exposure devices that shine bright lights onto materials to simulate the effect of real life light. To achieve a fade in a reasonable time requires intense light, and for many years a carbon arc lamp was the only means of doing this. Today, these have largely been superseded by xenon arc lamps, and in some devices by fluorescent tubes. The changes produced tend to be instrument-specific, and thus the tests are divided into subsections based on the kind of light used. ISO105-B02 and AATCC16 Options 3–5 describe xenon arc exposure. For certain situations such as car interiors, tests that use high temperatures of exposure are used (ISO105-B06, AATCC 181). The spectral power distribution of the light varies and can be further changed with the glass filters through which the light passes. As with atmospheric contaminants, the changes produced can also depend on the temperature and humidity of the exposure. Thus these are controlled in laboratory machines, and real life exposure may take place in dry or humid climates. Modern instruments can (at a certain wavelength) measure the amount of energy impinging on the specimen, and for a given light and filter combination, serve to indicate the total amount of energy over the whole spectrum that the test specimen is exposed to. Before this facility was commonplace, and even now that it is, the use of some standard fabric of known fading characteristics has been widespread. The standard fabric has been blue wool. However, two different series of blue wool standards have been in wide use for many decades, and are used somewhat differently. The two series both consist of eight standards, numbered sequentially, with each successive member of the series requiring approximately twice the exposure to fade to the same amount. The AATCC blue wool fabrics are created from mixtures of wool stock dyed with low fastness CI Mordant Blue 1 and high fastness CI Vat Blue 8 (numbered L2–L9). These are used primarily as an exposure check, for example, when standard L4 fades to 4 on the grey scale, the test specimen has received 20 AATCC Fading Units (AFU) of light. The specimen is exposed until it, too, has faded to the same extent, and the number of AFUs required to produce that change is quoted as the light fastness. Thus a sample may have a fastness of 40 AFU. Most machines would usually achieve 1 AFU in 1 hour of exposure, and thus colloquially a fastness of 40 AFU would be referred to as ‘40 hours’. In contrast, the ISO Blue Wool standards 1–8 consist of wool dyed with eight different dyes described in ISO105-B08. In a light fastness test, a specimen of each of the eight wool standards is exposed along with the test specimen. When the test specimen has faded to step 4, it is assigned the grade of the standard that has faded to the same amount. This ISO light fastness is given on a scale of 1–8: given the early dominance of European dye manufacturers, the

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light fastness in dye pattern cards is usually given on this 1–8 scale. A particular problem with the combined effects of light and perspiration on textile colour has been identified and is reflected in standard tests (ISO/FDIS105-B07, AATCC 125). The continued availability of the dyes used to produce both the AATCC and ISO blue wool standards is under question, and there is interest in the development of replacement standards (Guthrie et al. 1995, D’Andrea 2009).

9.6.3 Refurbishment Textile items become soiled, wrinkled, and require periodic refurbishment. The processes to be used in such refurbishing are given in a care label (the information on which should be derived from testing!), and they should restore the item to an acceptable level while not causing any damage. However, such refurbishment represents another set of challenges to the colour. Laundering is conducted in aqueous surfactant solutions with agitation. That said, the details can vary markedly. Variations include the detergent (type and amount used), the temperature, the amount of water and the extent of agitation (based on the type of machine used), the nature of the materials used to make up the bulk of the load (‘ballast’) and the presence or absence of bleach. The tests to assess colourfastness to laundering include many different details that reflect the variation in practice. The tests may involve real-time, full-scale launderings, which may be repeated three or five times before an assessment is made. This is a cumbersome procedure, however, since each different item should be tested individually to avoid possible confusion in results (did staining from another item contribute to the colour change?). More often, therefore, a specific sample size, often with a standard multifibre adjacent fabric, is agitated in standard detergent solution in a small (150–1000mL) cylinder for 45 minutes or so. The conditions are not the same as those in real life, but the effect on the colour has been shown to approximate to that obtained in five real cycles of laundering. The test is thus accelerated, and testing machines with multiple cylinders allow for many specimens to be assessed at one time. AATCC Method 61 for example, has five sets of conditions that represent the effects of hand washing, home laundering (with and without chlorine bleach) and commercial laundering (with and without bleach). ISO105-C06 similarly has 16 variations to simulate a wide range of conditions used in practice. The adjacent material must take account of the presence of chlorine and ISO105-C08, AATCC 190 are parallel tests that include an activated oxygen bleach. AATCC 172 and 188 involve full-scale launderings designed to assess the effect of non-chlorine and chlorine bleaches respectively. The appearance of some textile items is marred excessively by exposure to aqueous solutions and are cleaned by ‘dry cleaning’ in a solvent, usually perchloroethylene. A possible change of colour is the subject of tests ISO105-D01 and AATCC 132. As with some of the laundering tests, this accelerated test produces colour changes that would occur in three commercial procedures. The

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use of perchloroethylene raises health questions, and alternative methods of cleaning textiles distinct from laundering are being studied. These include other organic solvents, supercritical CO2, and ‘wet cleaning’ with minimal agitation. If any of these become widely adopted it is likely that new tests will be required. Clothes are ironed and this can affect colour. AATCC 133 includes variations to examine the effect of dry, moist, and wet heat: ISO105-P01 tests dry heat. ISO has developed combined tests that are designed to predict the overall change of colour that textile items might undergo. ISO/TR 12116:2008 describes four sets of colourfastness tests that simulate overall colour changes that occur in wear, applicable to sports clothing, shirt-like garments for use outdoors, indoor clothing and underwear, and military uniforms.

9.7

Colourfastness testing: assessment of results (colour measurement)

One particular characteristic of a colourfastness test that tends to distinguish it from non-colour tests is the means by which the results are assessed and reported. The assessment is obviously a measurement of colour and is the reason for this chapter’s presence in this book. Colour measurement can involve a single colour, or be concerned with the difference between two colours. In colourfastness testing the latter is important, since the tests are based on the difference between the original, untested material and the material that has undergone the test procedure, or the difference between an originally unstained (white, adjacent) fabric and the stained equivalent after testing. Colour difference measurement is a large subject. However, it has been studied more extensively for acceptability testing (the acceptance of a coloured item when compared to some standard colour) than it has for fastness testing (the determination of the results of a colourfastness test). Like colour, colour difference can be communicated in one of three ways: 1 2 3

words, by reference to physical colour standards, or in numerical terms based on numerical expression of light, objective and observer characteristics.

The expression of colour difference in colourfastness testing is no exception, but the emphases are somewhat different than in other applications of colour communication. Words are inadequate. While trained observers can reliably use words to communicate the quality of a colour difference (lighter, duller, bluer, for example), providing the difference is not too small (Revels 2007), they cannot communicate the size of a colour difference in a way that would allow for good decisions on acceptability to be made, or to indicate whether a fastness result is a pass or fail.

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9.7.1 Physical colour standards: grey scales The use of physical colour difference standard pairs against which to judge a colour difference provides a study in contrast between colourfastness testing and the judgement of the acceptability of a colour submitted for approval by a dyer. Historically, a standard-batch difference would be judged by the customer, and communicated to the dyer as a pass or a fail with comments (‘too dull’, ‘too blue’, etc.), since the colour difference has to be expressed more in a qualitative way, and only one quantity of difference (the pass-fail boundary) is involved. When a colour is produced across a range of formats, and in very large quantities (one may think, for example, of Coca Cola’s red colour that should be consistent on bottles, cans, trucks, advertisements and so on) it is reasonable to produce a standard colour sample along with ‘limit’ standards that represent the limit of lightness, dullness, blueness, etc., so that anyone supplying the colour may know if what they are making is acceptable. Most colours, especially those produced on textiles for fashion, are produced in limited quantities, so the development of limit samples is unrealistic, and the use of physical standards to define colour difference in pass-fail judgement of colour submits has been rare. In contrast, colourfastness testing is less concerned with the quality of the difference, and is more concerned with the size of the difference, and thus the use of physical standard colour difference pairs has long been standard practice in fastness testing. Furthermore, the critical size of difference from one situation to another may vary, so these physical standards need to include a range of difference pairs, from small to quite large. Thus the use of such physical standards of colour difference has been standard practice in colourfastness testing since the late 1940s as ‘grey scales’. They replaced verbal descriptions correlated with standard dyeings that had a range of fastness properties and against which the material under test could be compared. ISO introduced their grey scales in 1948 and they represented a significant technical breakthrough (SDC 1953). AATCC adopted these grey scales in 1954, and this version was distinguished by the inclusion of half-step difference pairs. The half-step version was adopted by ISO in 1974 (Hoban 1980). There are two distinct grey scales. One is used to assess a change of colour, and the second is used to assess the extent to which a white material is stained. They are defined and their general use described in ISO 105-A02 and ISO 105-A03, and in AATCC’s Evaluation Procedures (‘EP’) 1 and 2. The grey scales for colour change (see Fig. 9.1.) consist of pairs of neutral grey chips, one of the pair being a constant grey colour, with the second ranging from the same grey through a series of lighter greys arranged in order of increasing difference. The pair with no difference is labeled ‘5’, and the increasingly different pairs are 4, 3, 2 and 1. The half-steps in the grey scales are labelled 4-5, 3-4, 2-3 and 1-2. The Gray Scale for staining is similar, except that one of the pair is a constant white, and the second ranges from the same white through a series of increasingly

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9.1 The AATCC Gray Scale for Evaluating Change in Color (By permission, American Association of Textile Chemists and Colorists.)

darker greys. Again, the pair with no difference is labelled ‘5’, and the increasingly different pairs are 4, 3, 2 and 1. The half steps are labeled 4-5, 3-4, 2-3 and 1-2 (see Fig. 9.2). The half-steps in the grey scales are often quoted with a decimal place (4.5 and so on). Such a designation was warned against (Hoban and Stone 1974) and is somewhat controversial, since it implies that the numbers on these scales are validly subject to mathematical manipulation (averaging, and so on). This is arguable, since a DE vs. GS plot (used originally to derive the colorimetric values of the half-steps) is not linear, and the numbers on the grey scales are more properly regarded as labels only. Nonetheless, when multiple assessments are made, results

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9.2 The AATCC Gray Scale for Evaluating Staining. (By permission, American Association of Textile Chemists and Colorists.)

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are often quoted as a numerical average of the ratings, and are occasionally given as ratings that do not occur on the scale itself, such as 3.8, for example. The chips (and the differences between them) are carefully defined, so that the suppliers can perform quality control checks before they are sold, and users can check that their scales are still within tolerance and able to provide good data. Originally the differences were defined by their ANLab colour difference, but the development of the CIELAB formula in 1976 prompted its adoption by ISO and AATCC to define the differences in 1977 (Hoban 1980). In addition to the two basic grey scales, for colour change and for staining, AATCC has produced a ‘chromatic transference scale’ (see Fig. 9.3). This is used mainly in the assessment of the staining produced in crock fastness tests. These test specimens are stained in a circular pattern and the scale has several series of chips of different hues, each series starting with white and successive chips having greater chromaticity. Each chip pair has a circular opening through which the test specimen is displayed. Originally produced as a five-step scale and described by AATCC EP3, a nine step (with half steps included) scale was introduced in 1996 and is covered by AATCC EP8. While this scale may seem an additional complication in the grey scale scene, the evaluation procedure that describes its use makes clear that the scale is based on the grey scale for staining, and that in the event of disputes, ratings using the staining scale should prevail.

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9.3 The AATCC Chromatic Transference Scale. (By permission, American Association of Textile Chemists and Colorists.)

The correct use of each of these scales is described in the ISO and AATCC documents. The angle of viewing, the quality and intensity of the illumination, the masking of the test specimen/grey scale pair are specified to eliminate variables and increase inter-laboratory precision. Nonetheless, studies have shown that inter-laboratory results do vary systematically, especially from country to country. Over time, minor changes to grey scales are introduced and are properly subject to studies to ensure that the changes have no effect on their use. The grey scales were originally in a slide rule format, but in 1974 were changed to a fold-out scale that allowed more direct alignment of the scale with the colour difference being assessed. A study found that evaluations with the new format were equally accurate and reproducible, and the format was preferred by those in the study (Hoban and Stone 1974). More recently, a black background and sleeve has been joined by versions with grey backgrounds and sleeves. A study prompted by this change found that grey scale results were not affected by the background colour of the viewing cabinet. When specimens were judged with the background colour of the mask in view, the black sleeve produced significantly higher grades than the grey sleeve. However, when the grey scale pair and specimen pair were correctly masked by a sleeve of the same colour as that used with the grey scale, the sleeve colour does not affect the grades obtained (Westland 2001). While the use of physical colour difference standards relies on comparison by an observer and is thus partly subjective, it is more objective than purely visual judgements with the results communicated verbally. Such partly objective evaluations using grey scales has been the norm for fifty years. Meanwhile, the limited usefulness of physical samples in quantifying colour difference for

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making pass-fail judgements of colour submits was recognized even before the development of grey scales, and is reflected in the many efforts to develop numerical colour difference formulae. As reliable formulae (and better spectrophotometers) have been introduced, the colour acceptance world has moved to use them in preference to visual judgements, especially since they allow pass-fail judgements to be made over long distances. The colour fastness world, however, has largely continued with the use of grey scales and visual comparisons to express the results of a colourfastness test, despite the skill and experience required to perform the evaluations reliably and reproducibly.

9.7.2 Instrumental colour measurement of colourfastness test data As is implied by the definition of the grey scales themselves in colorimetric terms, a colour difference equation can reproducibly express the colour change or staining resulting from a fastness test. It would seem both simple and logical to adopt one such equation directly to provide completely subjective assessment of the colour change or stain occurring in a colourfastness test. However, given the extensive and well-established use of grey scales, it is essential that a colour difference equation used in this way generates the same results as do visual evaluations with the grey scales so that the visual and instrumental assessment methods can exist and be used side by side. Unfortunately, this is not the case, and general colour difference formulae do not provide close enough correlation with visual grey scale ratings. Part of the problem is the large range of colour differences that grey scales include and must be handled by such an equation, when compared to the small range of colour differences involved in acceptance testing. As the technology for colour measurement has become more widely available and more readily accepted, extensive studies have addressed this problem, and have derived and tested a range of formulae that provide closer correlation to colour differences in fastness tests, and which are fully objective and thus more precise. Obviously, given the two different grey scales, for colour change and for staining, two separate formulae are required. The underlying need for these formulae was recognized almost thirty years ago (Hoban 1980). The effect of different means of measuring the results of a colourfastness test on the test’s overall precision is discussed in section 9.5. The development of a colour change formula to correspond to grey scales was discussed in TC38 of ISO in the 1980s. Two formulae were considered in depth, the UK suggesting the use of CMC (currently ISO 105-J05, AATCC 173), and the Swiss delegation offering a purpose-made formula that eventually was adopted as the ISO standard 105-A05 in 1989. The formula was subsequently adopted by AATCC as EP 7 in 1995. The formula is based on first calculating the CIELAB Lch values for the reference specimen (L*R, C*abR and habR) and for the test specimen (L*T, C*abT and habT)

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to two decimal places using the CIE 1964 10° supplemental standard observer and illuminant D65, with the 1931 2° observer and illuminant C being permitted alternatives. The fastness colour difference ΔEF is then calculated using the formula: ΔEF = [(ΔL*)2 + (ΔCF)2 + (ΔHF)2]½ where ΔHF = ΔHK/[1 + (10 CM/1000)2] ΔCF = ΔCK/[1 + (20 CM/1000)2] ΔHK = ΔH*ab – D ΔCK = ΔC*ab – D D = (ΔC*ab · CM · e-x)/100 (subscript M refers to the mean of the reference and test specimens) x = [hM – 280)/30]2 if |hM – 280| ≤180 or x = [(360 – |hM – 280|)/30]2 if |hM – 280| >180. The grey scale grade for colour change can then be calculated using the formula: GSc = 5 – ΔEF /1.7 when ΔEF ≤ 3.4. GSc = 5 – Log (ΔEF/0.85)/Log(2) when ΔEF > 3.4. This ΔEf value requires interpolation to generate the appropriate one of the nine possible grey scale values. Alternatively, the grey scale value can be read directly from a table of ΔEF values. A later study compared the performance of this formula, the original UK proposal, plus CIELAB, and two new formulae developed by Teraji and co-workers (referred to as Nc# and Fc) that give grey scale values directly (Sato et al. 1997a). The paper found that the existing ISO formula and the Fc formula were equally the best. Interestingly, it found that CMC(1:1) was better than CMC(2:1), suggesting that fastness testing is perceptibility based, rather than acceptability based. A proposal for an ISO staining scale formula was put forward in 1983 (Anon 1983) and was reported to give 90% agreement (i.e. the formula provided a rating closer to the average observer rating than a given individual judgement). It was subject to a field trial in which is was found that the results had a systematic dependence on the hue being assessed, and that overall, the formula produced satisfactory results in less than 60% of the samples examined by 14 observers in four locations, and it was suggested that further modification was required (Kuehni 1984). An alternative formula proposed by a German committee was found to be more satisfactory and was adopted as ISO 105-A04 in 1987. It is currently under consideration by AATCC as an evaluation procedure. The formula is based on first measuring the reflectance of the stained and unstained materials and calculating the CIELAB color difference ΔE* and the

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magnitude of the lightness difference ΔL* between the stained and unstained reference to two decimal places, using the CIE 1964 10° supplemental standard observer and illuminant D65, with the 1931 2° observer and illuminant C being a permitted alternative. The Gray Scale difference (ΔEGS) is then calculated from the following equation: ΔEGS = ΔE* – 0.4 * [(ΔE*)2 –(– (ΔL*)2]½ The staining-scale grade (SSG) is then calculated to two decimal places using the following equation: SSG = 6.1 –1.45 * ln(ΔEGS) If SSG is greater than 4, it is recalculated using the following equation: SSG = 5 –0.23 * ΔEGS The Gray Scale for Staining Grade (GSs) to be reported is determined from Table 9.1. Work continues to find better formulae. As with the ISO colour change formula, it has been compared to the UK formula, CIELAB, and two more recently derived staining formulae, Ns and Fs (corresponding to Nc and Fc referred to above) (Sato et al. 1997b). The Fs formula was found to be marginally the best based on the data used in the study. While these studies suggest that improvement is possible, a modified formula would probably need to provide substantial improvement and be subject to widespread trials before it is adopted.

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Table 9.1 Gray scale for staining step values Calculated SSG

Reported GSs

5.00 to 4.75 4.74 to 4.25 4.24 to 3.75 3.74 to 3.25 3.24 to 2.75 2.74 to 2.25 2.24 to 1.75 1.74 to 1.25 Less than 1.25

5– 4–5 4– 3–4 3–0 2–3 2– 1–2 1–0

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unevenness of the staining, and the length of time (per specimen) needed to make spectrophotometric measurements when compared to visual assessments. A colour measurement system based on a digital camera (DigiEye) has been developed. The proposed advantages of such a system include speed, the ability to select for measurement the most uniform area of a stain, and the ability to assess multiple colours within a printed pattern, for example. Additionally, in one captured image may be multiple measurement areas that can give results, instead of carrying out multiple spectrophotometric measurements. It has been shown to measure colour and colour difference in a highly satisfactory manner. The extension of its use to measure colourfastness test results has been described (Cui et al. 2003a). In this study a range of colour change and staining sample pairs were measured both with a spectrophotometer and with the DigiEye, and also evaluated visually by multiple observers and multiple laboratories. The study found that the colour change pairs (many of which were heavily textured) gave different results when DigiEye and spectrophotometer results were compared, with the DigiEye results corresponding more closely to the visual observations. The DigiEye gave results that were close to the inter-observer error, and slightly worse than the interlaboratory error, but this was achieved with single measurements: measurement averaging and optimization of measurement area selection would be expected to improve the performance. In the stained pair assessment, the DigiEye and spectrophotometer agreed well. A further observation from this study was that the current ISO formula for the instrumental measurement of staining did not correlate well with visual data, suggesting that the formula is in need of revision. The study examined a simple corrected formula that performed better. A subsequent paper described in more detail the development of an improved staining scale formula (Cui et al. 2003b). Three formulae were developed and compared using three data sets with a total of 601 staining sample pairs. The conclusion was that a formula based on CIEDE2000 was the best, with errors of prediction less than interlab and inter-observer, and repeatability close to that of both the DigiEye and spectrophotometer. Its overall performance was 60% better than the current ISO formula. Given the promise of this work, further studies (Cui et al. 2004a) examined the ISO colour change formula using the same technique of comparing inter-observer and inter-lab visual assessments with spectrophotometric and DigiEye instrumental measurements on a large data set. A new and simpler colour change formula was developed, like the proposed new staining formula described above, based on CIEDE2000. The SDC’s Technical Coordination committee (TCI/81) sponsored an interlaboratory study (12 labs and 38 observers) to examine these two new formulae, referred to as GRS and GRC, and the use of the digital imaging system (Cui et al. 2004b). Both formulae performed well, and the errors in digital measurement were encouragingly smaller than inter-observer errors. Based on this work,

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ISO/CD 105-A11 (‘Determination of colour fastness grades by digital imaging techniques’) is under development.

9.8

Conclusions

Colour is an important property of many items, and its durability in items with a life expectancy beyond a few weeks is a matter of concern. Textile colorants have the greatest potential to produce unwanted changes in the textiles they colour, and in adjacent materials, and textiles are subject to the greatest range of potentially damaging agencies. Thus colourfastness testing is most widely studied and performed on textiles. The tests reflect real life challenges met in textile processing, in use, and in care, and many of them are accelerated. ISO and AATCC are the most active organizations in the development of such tests. The tests necessarily include a measurement of the changes produced by the test challenge, and the stains caused by removed colour. For half a century, the measurement has been based on visual comparison with standard grey scales. Compared to other situations involving colour assessment, the adoption of instrumental methods to assess fastness results has been slow. Standard methods for such instrumental assessments are in place, if yet to be widely adopted. Meanwhile the use of systems based on digital cameras have prompted further study, indicating that rapid, efficient, objective and precise fastness assessment is within reach, and, incidentally, suggesting better formulae for use in such assessments.

9.9

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References

AATCC 2009. https://www.aatcc.org/testing/tm_main/index.htm – accessed July 2009. Anon, 1983. Instrumental Assessment of the Staining of Adjacent Fabrics: ISO Test Under Consideration, J.S.D.C., Vol. 113 (3), 101. BSI 2002. British Standard BS EN ISO 105-X12:2002 Textiles – Tests for colour fastness – Part X12: Colour fastness to rubbing. Cui, G. et al. 2003a. Grading textile fastness. Part 1: Using a digital camera system, Coloration Technology, Vol. 119 (4), 212. Cui, G. et al. 2003b. Grading textile fastness. Part 2: Development of a new staining fastness formula, Coloration Technology, Vol. 119 (4), 219. Cui, G. et al. 2004a. Grading textile fastness. Part 3: Development of a new fastness formula for assessing change, Coloration Technology, Vol. 120 (5), 226. Cui, G. et al. 2004b. Grading textile fastness. Part 4: An inter-laboratory trial using DigiEye systems, Coloration Technology, Vol. 120 (5), 231. D’Andrea, C. 2009. Colorfastness to light: finding an L4 equivalent, AATCC Review, Vol. 9 (6), 43–47. Guthrie, J. et al. 1995. A novel approach to lightfastness testing, JSDC, Vol. 111, 220–222. GOTS 2009. Global Organic Textile Standard. http://www.global-standard.org/ – accessed July 2009. Hoban, R.F. 1980. Color measurement in fastness evaluation, Text. Chem. Col., Vol. 12, 33.

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Hoban, R.F. and Stone, R.L. 1974. New Gray Scales simplify colour assessments, Text. Chem. Col., Vol. 6 (9), 38. ISO 2009. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_tc_browse. htm?commid=48172 Jaeckel, S.M. 1980. The variability of grey scale assessment and its contribution to the variability of a test method, JSDC, Vol. 96 (10), 540. Kuehni, R. 1984. Field trial of the British ISO proposal for the instrumental assessment of the degree of staining, Textile Chemist and Colorist, Vol 16 (4), 22–23. Oekotex 2009. International Oekotex Association. http://www.oeko-tex.com/OekoTex100_ PUBLIC/index.asp – accessed July 2009. Patton, J. 1989. Crock test problems can be prevented, Text. Chem. Col., Vol 21 (3), 13. Revels, C. 2007. Describing color differences: How good are your color comments? AATCC Review, February, 40–44. Sato, T. et al. 1997a. Comparison of instrumental methods for assessing colour fastness. Part 1 – Change in colour, JSDC, Vol. 113 (1), 17. Sato, T. et al. 1997b. Comparison of instrumental methods for assessing colour fastness. Part 2 – Staining, JSDC, Vol. 113 (12), 356. SDC 1953. Colour Fastness Test Coordinating Committee, JSDC, Vol. 69, 404. Smith, P. 1994. Colour fastness testing methods and equipment, Review of Progress in Coloration, Vol. 24, 31–40. Thiry, M. 2009. The anatomy of a test method: The development of AATCC test methods, AATCC Review, February, 26–31. Westland et al. 2001. Effect of sleeve colour and background colour on change in colour assessments, JSDC, Vol. 117, (3), 123–126.

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Part II Colour measurement and its applications

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10 Colour measurement methods for textiles N. S. GANGAKHEDKAR, Compute Spectra Colour Pvt. Ltd., India

Abstract: In textile dyeing application one has to produce consistent lots of dyeing. Colour measurement technique helps us to quantify colour in terms of numerical values. The need for a numerical pass/fail program arises from the fact that visual evaluations of shade difference are not consistent when establishing the fine line between acceptable and unacceptable dyeing. In this chapter, we will discuss the role of colour measurement in textile application with reference to colour measurement techniques, acceptability limits for pass or fail, importance of spectral match, use of on-line and non-contact colour measurement systems, colour of dry and wet fabrics, and inspection of colour of finished fabrics. Key words: colour measurement techniques for textile application, numerical pass/fail program for textile dyeing, non-metameric matches, colour of dry and wet fabrics, inspection of colour of finished fabrics.

10.1

Introduction

In the production of textile products generally a designer creates designs using available colour palettes and the colours in the design are converted into easily communicable numbers. These are then supplied to textile mills or production units along with physical samples for producing the required quantities of material in the specified colours. The textile production unit then creates lab dips that are sent for approval where the criteria for pass/fail based on possible colour differences in the dyed lots are worked out and agreed upon. For production of lab dips the production unit uses the recipe prediction methods and also works out the recipes of lowest cost with the least or no metamerism. The bulk supply is then tested as per the agreed pass/fail criteria, using the specified colour difference equation, and the fabric is then sent to the converters for production of products that may be garments or made-ups, etc. The converter uses shade sorting methods to classify the supplied fabrics to avoid perceptible colour differences in the final textile products. Hence, for the textile industry it is important to understand how to measure colour so as to specify it in terms of communicable numbers, work out the least metameric recipes and quantify colour difference and set up a pass/fail criteria. Some of these aspects have been covered in previous chapters. This chapter will mainly focus on the colour measurement techniques for textiles, on-line colour measurement for in-process quality control, colour of dry and wet fabrics, expression of colours as numbers and its specification, problems of metamerism, measurement of colour difference and setting up of pass/fail criteria and present a case study. Metamerism is one of the main reasons for not obtaining an acceptable 221 © Woodhead Publishing Limited, 2010

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matching and we will see how to get a non-metameric match. We will discuss recent advances in on-line and non-contact colour measurement systems which are being used for colour of wet fabric. As we know that finishing chemicals and processes affect the final colour of the fabric in production batch, colour variations are noticed between lab dip sample and production batch. We will present a case study which explains the role of the finishing process.

10.2

Colour as numbers

Colour is light. Colour is dye. Colour is eye. Colour is sensation. Colour is information. Colour is mystery. Colour is subjective. Poor colour memory, eye fatigue, colour deficiencies (colour blindness) and viewing geometries cause problems in visual colour assessment. Visual judgement of colour is based on physical, physiological and psychological aspects. Munsell was the first who specified colour in terms of three attributes: hue, value and chroma (Munsell 1929). Our eye cannot detect differences in hue, chroma or lightness equally, which means our eye is not equally sensitive to all colours. The purpose of colour science (Wyszecki and Stiles 1967) is to express colour as numerical values. For arriving at these colour numbers, we have a triplet composed of the light source, the object and the observer. Measured colour of the object is a function of light source (E), the characteristics of the material of the object (%R) and colour response functions (r, g, b) of the observer. Colour = f(E, R, r-g-b)

[10.1]

where E = energy of the light source R = reflectance of the object r-g-b = tristimulus response of the human observer.

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Objective measurement of colour is now based on quantification of light source (E), reflectance of the object (%R) and colour response functions r-g-b of the observer (Wright 1958). This triplet is the basis of colour science and CIE mathematics describes colour in terms of tristimulus values (X,Y, Z) derived from the source-object-observer triplet and CIE L,a,b or CIE L,C. h values express colours in terms of colour as numbers. This is discussed in detail in earlier chapters. The colour attribute of material is nothing but reflectance and which is re-emission ratio of re-emitted energy (ER) and incident energy (EI). Colour Attribute %R = (ER/EI).

[10.2]

It is the fingerprint of a colour and every colour has its characteristic reflectance signature. Two colours are perfectly matched if their reflectance curves are exactly the same or the tristimulus values are the same. The Munsell system (Munsell 1929) is a visual system and the CIE system is independent of observers. The correlation is now found between the two and Munsell hue, value and chroma

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can be correlated to CIE dominant wavelength, lightness and purity (Billmeyer and Saltzman 1981). This colour theory is discussed in details in earlier chapters.

10.2.1 Colour and appearance The overall appearance of any object is a combination of its chromatic attributes and its geometric attributes (like gloss, shape, texture, shininess, haze, and translucency). Thus, both types of attributes should be measured and accounted for when making visual or instrumental assessments of colour and appearance (Hunter 1975).

10.2.2 Chromatic attributes Chromatic attributes are those attributes associated with colour. They are divided into three components: lightness – L, hue – h and saturation – C. A spectrophotometer measures the chromatic attributes of appearance, but the chromatic attributes of an object can never be completely separated from its geometric attributes.

10.2.3 Geometric attributes Geometric attributes are those attributes associated with distribution of light from an object. For instance, a flat cotton weave fabric is very different geometrically from corduroy. Gloss, haze and directionality are three main geometric attributes. Orientation effect in yarn and loose fibres and different weave structure cause colour assessment problems and it is the characteristic of a textile sample which causes it to look different, depending in which direction it is turned.

10.2.4 Colour versus appearance Specular reflection (light directed in an angle that is exactly opposite to the incident light or when the angle of incidence is equal to the angle of reflection) is generally less than 4–5% of the total and is perceived by an observer to be the ‘shininess’ or ‘glossiness’ of the sample. However, in order to see the apparent colour of the sample, the observer must move their eye away from the specular and concentrate on examining the diffuse (scattered) reflectance from the sample. These necessary viewing conditions have led to standardized test methods such as ASTM D 1729 ‘Standard Practice for Evaluation of Colour Differences of Opaque Materials’, where 0°/45° (or 45°/0°) (lighting/viewing) geometry, which avoids the specular, is recommended (Hunter 1975). Therefore, the appearance of the colour has been affected by the scatter of the specular light. To a normal observer, a high gloss specimen would appear to have more chroma and be a darker colour than an identically pigmented specimen with lower gloss or increased surface texture.

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The role of a colour measuring spectrophotometer is critical and one has to look into the design features and select a suitable instrument for textile application. Many materials used as standards for shade matching appear glossy when viewed from a particular angle. These materials include shiny paint chips, plastic panels, and magazine pages among others. When these types of materials are measured for new shade formulation or when samples are measured behind a glass plate, it is important to exclude the glossy reflectance from the sample measurement. The glossy reflectance – or specular gloss – is automatically removed when using a 45/0 instrument and can be removed from a diffuse/8 instrument by use of a specular gloss port. The gloss port is located at an eight degree angle opposite the lens port and opens automatically when selected during the calibration routine on most software programs. The specular gloss can be safely included when comparing two identical materials but may be excluded when comparing two materials with significantly different gloss levels as long as the same measurement technique is used.

10.3

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Colour measurement

Colour specification

All colours can be characterized by hue, that is, the dominant shade; by saturation, that is, how much colour of any hue is present; and by lightness, that is, the degree of lightness or darkness of a particular colour (Billmeyer and Saltzman 1981). Therefore, it is necessary to describe a different hue, saturation and lightness for each unique set of illuminant or observer conditions. By using a standard illuminant and a standard observer, the amount of light reflected from any one object can be converted into the hue, saturation, and lightness descriptions for any colour. Additionally, a sample can be compared to any standard with these same three attributes. In 1976 the CIE adopted a standard method of calculating colour attributes, known as 1976 CIE L*a*b* (or CIE Lab) Colour Space (CIE 1976a). There are two other terms that are occasionally used to describe colour: a red/ green colour difference or a yellow/blue colour difference. CIE Lab Colour Space assigns the designation Da* for a difference in red/green value and the designation Db* for a difference in yellow/blue value. The total colour difference between two samples is termed DE* and is equal to the square root of the sum of the squares of DL*, Da* and Db*. It uses the designations Dh* to signify a hue difference between a sample and a standard, DC* to signify a difference in saturation (or chroma) between a sample and a standard, and DL* to signify a difference in lightness between a sample and a standard. Thus, by using these three terms – h (hue), L (lightness) and C (saturation) – we can describe the attributes of any colour, or the difference between a sample and a standard.

10.3.1 CIE Lab colour space In the CIE L*a*b* Colour Space the colour coordinates in this rectangular coordinate system are:

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L* – the lightness coordinate a* – the red/green coordinate, with +a* indicating red, and –a* indicating green b* – the yellow/blue coordinate, with +b* indicating yellow, and −b* indicating blue. The CIELAB colour difference, between any two colours in CIE 1976 Colour Space, is the distance between the colour locations. This distance is typically expressed as DE*, where: DE* = [DL*2+Da*2+Db*2]1/2

[10.3]

DL* being the lightness difference, Da* being the red/green difference and Db* being the yellow/blue difference.

10.3.2 CIE LCh Colour Space CIE L*C*h* Colour Space is three-dimensional, with colours located using cylindrical coordinates as follows: L* – the lightness coordinate (the same as in L*a*b*) C* – the chroma coordinate, the perpendicular distance from the lightness axis h* – the hue angle, expressed in degrees, with 0° being a location on the + a* axis, continuing to 90° for the + b* axis, 180° for – a*, 270° for – b*, and back to 360° (or 0°). Many CIE system users prefer the L*C*h* method of specifying a colour, since the concept of hue and chroma agrees well with visual experience. For those preferring to express colour differences in the CIE L*C*h* system, the following terms are utilized: DC* being the chroma difference Dh* being the hue angle difference DH* being the metric hue difference. The metric hue difference (DH*) is the colour difference, in distance units, due to the hue angle (Dh*) difference. DH* is used in the total colour difference computation, where all terms are distances (not angles), as follows: Chroma C* = [a*2 + b*2]1/2 and Hue angle h = tan-1 (b*/a*)

[10.4]

CIE 1976 DE* and DL* differences are the same for any pair of colours whether using CIE L*a*b* or CIE L*C*h*. The CIE L*C*h* system describes a colour in the three-dimensional CIE 1976 Colour Space, based on the L*, C*, and h* coordinates. Colour acceptability management using this system is similar to using CIE L*a*b*, except that chroma (C*) and hue angle (h*) are used in place of the (a*) and (b*) coordinates. The CIE L*C*h* acceptability volume conforms better to the visual evaluation ellipsoid than is the case to CIE L*a*b*. Alignment of the acceptability volumes are typically the same although the shapes differ significantly. Colours near the

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edges of the L*C*h* solid will often be calculated as acceptable although visually judged unacceptable.

10.3.3 CMC colour difference and tolerances The CMC colour difference formula is based on the colorimetric principles of the CIE 1976 system. It is typically employed as a colour tolerancing system in industrial applications. CMC colour difference (DECMC), a modification (transformation) of CIE L*C*h* colour difference, has proven to be a useful measure of the commercial acceptability of coloured products (McDonald 1987; The Society of Dyers and Colourists 1981). CMC colour difference is often employed in pass/fail colour production applications, where a single numerical tolerance can be established and utilized to make acceptability decisions. An important advantage of CMC is that once a tolerance has been successfully implemented for a product, the same tolerance may prove applicable for other colours produced under similar commercial conditions.

10.4

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Colour measurement

Metamerism

Metamerism (CIE 1976b) is the phenomenon where colours of two objects are perceived to be the same under one illuminant, such as daylight (CIE D65), but are not the same under a different illuminant such as incandescent (CIE A). The goal of most manufacturers and suppliers is to minimize metamerism. Imagine that you have a shirt where the sleeves and body are metameric? In daylight the shirt appears as a single colour, but in cool white and incandescent the sleeves are a ‘different’ colour from the body of the shirt. To accurately evaluate the presence of metamerism in sample pairs, the samples must be evaluated in multiple illuminants. This can be done both visually and instrumentally (Alian Chrisment 1998). For instrumental assessments there are methods for calculating the degree of metamerism using the Metamerism Index (MI). There are four types of metamerism: • • • •

Source metameric: two objects do not match when source is changed (D65, A, C, CWF). Object metameric: when dye combination is different in sample compared to standard. Observer metameric: when one observer says ‘match’ and another says ‘no match’. Instrument metameric: when you get different colour co-ordinates from two different instruments.

Metamerism always involves a pair of objects. The two objects can be described as ‘metameric objects’, or a ‘metameric pair’ and are sometimes said to be ‘metameric’, ‘exhibit metamerism’, and/or be ‘metameric matches’.

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10.4.1 Degree of illuminant metamerism: Indices CIE Special Metamerism Index: Change in Illuminant – the colour difference (DE) between a pair of objects, under a test illuminant, assuming that the objects match (DE=O) under the reference (primary) illuminant.

10.4.2 Metameric and non-metameric matches Colour is matched in daylight but it is not matched in artificial light. In Fig. 10.1 you can see that the X, Y, Z values are matched in D65 but not matched TRISTIMULUS VALUES OF THE 10˚ OBSERVER A

D65 Std

Sample

Std

Sample

X

31.28

31.28

47.66

52.66

Y

20.28

20.28

27.57

31.41

Z

12.71

12.71

4..04

4.42

%R

100

50

Sample Std

0 400

Wavelength in nm

700

Spectral curve

Metamerism

10.1 Metameric match: colour is matched in daylight, but not matched in artificial light. The X, Y, Z values are matched in D65 but not matched in Illuminant A. There is no spectral match.

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in Illuminant A. Also look at reflectance curves of the standard and the sample. As reflectance curves are not matched colours are metameric. Figure 10.2 and Fig. 10.3 show the metameric matches as the reflectance curves are not perfectly matching even though colour difference (DECMC) is 0.61 (Fig 10.3). Even though it is a very small colour difference and within acceptable limits, visually colours are not acceptable. This shows that metamerism plays an important role and one should always get a spectral match. Metameric objects exhibit the following: • • •

They have different spectral reflectance values (spectral curves). They match under at least one combination of illuminant and observer. They do not match under at least one combination of illuminant and observer.

Reflectance in %

Spectral reflectance graph

50

Sample B

Sample A 0 400

500 600 Wavelength in nanometer

700

10.2 Metameric match: it is not a reflectance match.

Spectral data

dCIELab/CMC: D65-10

-FAWN STD

Yellow

STD.

BATCH

Red

% R (or %T)

80

Green

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1.0 0.8 0.5 0.3 0.0 –0.3 –0.5 –0.8 –1.0

-BATCH

100

60 40 20 0 360 400

Blue –1.0–0.8–0.5–0.3 0.0 0.3 0.5 0.8 1.0

500

600

700 750

Wavelength (nm)

ILL 1D65-10

I:c Ratio

2.00

Standard name: FAWN STD

L* a* 61.21 4.20

b* 5.51

C* 6.93

h° 52.70

Trial name BATCH

L* a* b* 60.91 4.31 6.04

C* 7.42

h° DL* Da* Db* DC* DH* DEcmc DE* 54.45 –0.30 D 0.11 R 0.52 Y 0.49 B 0.22 Y 0.62 0.61

10.3 Non-metameric match of fawn colour fabric.

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Spectral data

dCIELab/CMC: D65-10

-DK GREY STD -BATCH

Yellow 100

STD.

BATCH

Red

% R (or %T)

80

Green

0.8 0.6 0.4 0.2 0.0 –0.2 –0.4 –0.6 –0.8

60 40 20 0 360 400

Blue –0.8 –0.6 –0.4–0.2 0.0 0.2 0.4 0.6 0.8

500

600

700 750

Wavelength (nm)

ILL 1 D65-10

I:c Ratio

Standard name: DK GREY STD

L* a* 29.23 1.52

b* 1.65

C* 2.25

Trial name BATCH

L* a* 28.71 1.19

b* 1.89

C* h° 2.23 57.69

2.00

h° 47.39 DL* –0.52 D

Da* –0.33 G

Db* 0.23 Y

DC* –0.01

DH* 0.40 Y

DEcmc 0.64

DE* 0.66

10.4 Non-metameric match of dark grey fabric.

Spectral data

dCIELab/CMC: D65-10

-OLIVE STD

Yellow

-BATCH

100

STD.

BATCH

Red % R (or %T)

80

Green

1.3 1.0 0.8 0.5 0.3 0.0 –0.3 –0.5 –0.8 –1.0 –1.3

60 40 20 0 360 400

Blue

500

600

700 750

–1.3–1.0–0.8–0.5–0.3 0.0 0.3 0.5 0.8 1.0 1.3

Wavelength (nm) ILL 1 D65-10

I:c Ratio

2.00

Standard name: OLIVE STD

L* a* 43.06 0.65

Trial name BATCH

L* a* b* C* h° DL* Da* Db* DC* DH* DEcmc DE* 42.89 0.83 6.40 6.46 82.58 –0.17 D 0.18 R –0.55 B –0.53 D –0.24 R 0.62 0.60

b* 6.96

C* 6.99

h° 84.63

10.5 Non-metameric match with two crossings.

10.4.3 Instrumental test for metamerism 1 Using a spectrophotometer, measure the objects, and confirm that the objects match under a specific illuminant/observer combination (DE = 0). 2 Compare their reflectance curves. If the curves differ, and cross each other at least three times, then the objects are metameric (Fig 10.2). You can see from Fig. 10.5 that reflectance curves are not matched but colour difference is acceptable (DECMC = 0.60). Also see Fig. 10.6. It is a reflectance match and

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Spectral data

dCIELab/CMC: D65-10

-DK BRWN STD

Yellow

-BATCH STD.

100

BATCH

80

Red

% R (or %T)

1.2 0.9 0.7 0.5 0.2 0.0 –0.2 –0.5 –0.7 –0.9 –1.2

Green

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Colour measurement

60 40 20 0 360 400

Blue –1.2–0.9 –0.7–0.50.2 0.0 0.2 0.5 0.7 0.9 1.2

500

600

700 750

Wavelength (nm)

ILL 1D65-10 I:c Ratio

Standard name: DK BRWN STD

L* a* 29.12 4.63

b* 3.51

C* h° 5.81 37.21

Trial name BATCH

L* a* 29.18 5.04

b* 3.86

C* 6.35

h° 37.46

DL* 0.06 L

Da* 0.41 R

2.00

Db* 0.34 Y

DC* 0.53 B

DH* 0.03 Y

DEcmc 0.55

DE* 0.54

10.6 Spectral match – non-metameric match with DE 0.54.

Spectral data

dCIELab/CMC: D65-10

-GREY STD -BATCH

Yellow

100

0.8

STD.

0.6

BATCH

80

–0.2

Red

0.0

Green

0.2

% R (or %T)

0.4

–0.4

60 40 20

–0.6 0 360 400

–0.8 Blue

500

600

700 750

–0.8 –0.6 –0.4 –0.2 0.0 0.2 0.4 0.6 0.8

Wavelength (nm) I:c Ratio

ILL 1D65-10

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Standard name: GREY STD

L* a* b* 31.03 0.05 –0.99

C* 0.99

Trial name BATCH

DEcmc L* a* 0.89 30.18 0.40

b* C* h° –1.35 1.41 286.62

2.00

h° 272.89 DL* Da* –0.85 D 0.35 R

Db* DC* DH* –0.36 B 0.42 B 0.28 R

DE* 0.99

10.7 Spectral match but visually not acceptable.

colour difference is DECMC = 0.54. It is a good match. Figure 10.7 shows that it is a spectral match but colour difference DECMC is 0.99. It is a little higher and not acceptable. Visually the match is not acceptable as DL value is negative (DL = – 0.85). The colour being darker, it is not acceptable even though hue is matched. Spectral match is a non-metameric match, generally a good match but colour difference should be within tolerance limit. All above mentioned examples are of very small colour differences. A single-number pass/fail (DE) will not help us to decide acceptance or rejection. Spectral

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match and acceptable colour difference will correlate with visual assessment of colour (Gangakhedkar 2004b). One has to see the metamerism and compute its amount, by calculating colour differences (DE > O) under different illuminant/observer combinations.

10.4.4 Reducing the effects of metamerism Metamerism is a potentially important consideration in any colour control application involving the colour matching of two objects. Its effect can be minimized (or eliminated) in most colour production application (Gangakhedkar 1986b) by: 1 Utilizing exactly the same dyes in the formulation of the production object that have been used to produce the standard (target). 2 Selecting a production formulation that minimizes metamerism, whenever it is not possible to use the same dyes as were used to make the standard. 3 Substituting ‘working standards’ (made from the production formulation) for the original metameric standard, whenever possible in the control and acceptance processes. 4 Correcting production colours without adding any ‘new’ (different) dyes to the product. In textile dyeing, this problem is the most common for getting a perfect match. One should not substitute or replace any dye in the original recipe.

10.5

Reasons why colours do not match

This is a question always raised. The following are the main reasons for not getting a good match. •

•

• • • • •

Colour is subjective: colour is a sensation, just like touch, and the colours you see are purely subjective, as interpreted by your visual system and your brain. Lighting affects colour: the colour of an item will vary depending on the light, so it will look different under incandescent light, fluorescent light, and daylight. Colours affect colours: your perception of colour will change, depending on the colours around it, an effect called simultaneous contrast. Identical colours can be metameric: two colours are the same under one light but look totally different under another light. Different observers perceive colour differently: colour is within us and colour impressions are different for two different observers. The human eye is different: from spectrophotometer, camera, video display unit or scanner. Different devices have different colour gamut: colour monitors (video display unit of the PC) show colours that printers cannot print.

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•

Colour measurement

Different applications have different methods of colour matching: printing application is different from textile/paint application; colour television application is different from camera or scanning. Different devices use different colour models: printer, monitor, scanner or camera use different colour models such as RGB or CMYK model.

10.6

Visual versus numerical pass/fail

In “textile dyeing application” one has to produce consistent lots of dyeing. Colour measurement technique helps us to quantify colour in terms of numerical values, and using internationally accepted colour difference equations we can arrive at acceptable tolerance limits. A numerical pass/fail program is essential for communication between dyer and customer. The need for this program arises from the fact that visual evaluations of shade difference are not consistent when establishing the fine line between acceptable and unacceptable dyeing. Acceptability limits are to be defined using colour difference equations such as CIE DE2000 or CMC. The practical approach is to use acceptability limits defined by statistical analysis, correlated with visual acceptable matches. A sample that is approved by one person may be rejected by another simply because they perceive colour differently. This is true not only for the dyer and a customer, but for dyers on different shifts, and even for the same dyer day to day. The end result is delay in the decision making process, not to mention the uneasiness produced when a borderline shade is approved. You can see from Fig. 10.5 and Fig. 10.6 that the colour differences are small and acceptable (DE < 1 unit) but visually samples are rejected. Samples in Fig. 10.3 and Fig. 10.4 show metamerism, so a numerical pass/fail is the objective solution for the colour matching problem (AATCC 1996; X-Rite 1994). Another important question asked is what should colour tolerances be like? The solution is to use colour difference equations which quantify hue, brightness, and chroma. CIE LCh formula is to be preferred and one should opt for CMC or CIE DE2000 formulae (Luo et al. 2001). Once a customer has accepted a formulation (a lab dip sample) and production is started, it is necessary to control the process variables so as to maintain and deliver the colour desired. The decision whether the process is in or out of control demands the establishment of tolerances (lighter/ darker, redder/greener and yellower-bluer). Acceptability limits are to be defined using colour difference equations CIEDE 2000 or CMC. It is established that the CIELAB colour difference equation is inadequate for many purposes – equal sizes of DE* correspond to different perceptual differences in colour. There is strong evidence to show that most of the modern optimized equations (such as CMC, M&S, BFD, and CIE 94) are more uniform than CIELAB. The CMC (2:1) formula allows for the use of a single tolerance for all shades, so a pass/fail program based on CMC will be easy to implement or understand. The CMC tolerance will also correlate with visual assessment regardless of the

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colour being evaluated, making CMC an ideal choice for an instrumental pass/fail program (McDonald 1987). The practical approach is to use acceptability limits defined by statistical analysis, correlated with visual acceptable matches. To successfully implement an instrumental pass/fail program, one must note that instrument pass/fail results must correlate with the visual perception of those involved in initially establishing an acceptable pass/fail tolerance and pass/fail results must be reliable and repeatable. To ensure that the tolerance formula that is actually implemented is accurate, it should be based on batch history gathered using repeatable techniques. Once an accurate pass/fail tolerance has been established, one can completely rely on numerical pass/fail (Ken Butts, datacolor). Acceptability tolerances are usually established between supplier and customer, based on historical experiences and commercial factors. The CIELAB system is often utilized to help order and quantify the acceptability tolerances for each customer and colour combination.

10.6.1 Setting the pass/fail value The pass/fail limit depends upon the equation that is used, but more importantly it also depends upon the application. The correct pass/fail value can only be determined from experience – pragmatically, the correct pass/fail limit is such that all pairs of samples with a colour difference less than this limit will be accepted by the customer. When establishing acceptability tolerances, it is usually best to determine separate tolerances for DE* DL*, Da*, and Db* (or DE*, DL*, DC*, and Dh*). The separate tolerances allow the CIE system to be employed in acceptability applications, even as the customer acceptability criteria deviate from the uniform perceptibility of CIELAB Colour Space (Ken Butts, datacolor).

10.6.2 Effectiveness of CIELAB colour differences Unfortunately several evaluations of CIELAB have shown that DE* is not a particularly good measure of the magnitude of the perceptual colour difference between two stimuli. The relatively poor ability of DE* to predict the magnitude of perceptual colour differences has led to more complicated ways of computing a colour difference from the CIELAB coordinates of two samples and some of these measures have been shown to be more reliable than the simple DE*. Our eye cannot detect differences in hue, chroma or lightness equally, i.e. our eye is not equally sensitive to all colours. If we ask visual assessment of two closely matched shades of yellows, blues, reds or greens, we will find that our acceptance limits are different for different hues. The author (Gangakhedkar 2003) collected two closely matched samples of different hues such as yellow, green, blue, purple. All these samples were visually passed by different observers responsible for colour

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Table 10.1 Acceptable limits of DE varying from hue to hue: a case study – pass/fail numerical standards for yarn/threads Visually-close samples

DE CIELAB

DECMC

P. Green T-854 Violet F-850 Red T-22 Maroon T-722 Pink T-695 Yellow F/511 Golden Yellow F/533 Lt. Grey T-821 Blue T-401 Tur. Blue F/629 Orange F/629 N. Blue F/627 Black (1) Black (2)

0.67 1.05 0.96 0.66 1.07 1.84 3.82 1.03 0.28 1.29 1.07 0.65 0.55 0.42

0.31 0.70 0.42 0.51 0.45 0.67 1.30 0.45 0.15 0.67 0.53 0.64 0.58 0.48

assessment. When the same samples were measured and the CIE 1976 DE* value was obtained, it was noticed that acceptable limits varied from hue to hue (0.6 to 2.5 units, see Table 10.1). In the case of CMC formula DE, values were more or less close to 0.6 to 0.7 units. Generally, the observer will see the hue differences first, chroma differences second and lightness differences last. This is the main reason for opting for colour difference formula based on L, C, h co-ordinates. CMC, M&S, BFD, CIE 94 and CIE DE 2000 equations are offering closer correlation with visual assessment as they are based on LCh colour space. If you would like to use CIELAB or FMCII colour difference equations, then you have to fix three-dimensional tolerance limits. It works well. Some of the auto-tolerancing programs of colour system manufacturers refine the limits with statistical control based on input of visual judgement. The author has used such a program and found it satisfactory. 1 2 3 4 5 6 7 8 9 40 1 2 43X

10.6.3 Deciding which colour difference equation to use It is established that the CIELAB colour difference equation is inadequate for many purposes – equal sizes of DE* correspond to different perceptual differences in colour. There is strong evidence to show that most of the modern optimized equations (such as CMC, M&S, BFD, and CIE 94) are more uniform than CIELAB. It is not clear, however, whether any one of these new equations is significantly better than the others. The CMC equation is a British Standard (BS 6923) and is being considered as an ISO standard. Although there are several colour space options available for implementing a pass/fail tolerance program – none of which are absolutely uniform – results of observations indicate that tolerances based on (DECMC) produce computer pass/

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fail decisions that most closely duplicate visual assessment. To ensure that the tolerance formula that is actually implemented is accurate, it should be based on batch history gathered using repeatable techniques. Once an accurate pass/fail tolerance has been established, much of the guesswork may be taken out of day to day shade assessment. It is to be noted that one has to look at reflectance curves for the non-metameric match. Luo and co-workers carried out extensive work to test the performances of four colour difference formulae (CIELAB, CIE 94, CMC and CIEDE 2000) and their results showed that CIEDE 2000 formula outperformed CIELAB, CMC and CIE 94, and it is also more accurate than panels of observers (Luo et al. 2001).

10.6.4 The M&S equation In the 1980s Marks & Spencer, in conjunction with Instrumental Colour Systems, developed their own in-house equations that are used in the textile industry. Research shows that there is little to choose between the CMC and M&S equations in terms of overall performance. The fact that the M&S equations have never been published has restricted their use (McDonald 1987). Colour tolerances are specified using the M&S colour difference equation M&S 89 with appropriate variations from the norm for particularly critical merchandise. All dyed colours are measured after conditioning in the Vindon Ultrasonic ‘conditioning cabinet’, a refrigerator-like device that standardizes the fabric to 65% relative humidity, 20° centigrade, with 30 minutes of exposure to D65 daylight. M&S also specify the lighting standards at the retail level so as to assure colour consistency throughout the process. M&S asks suppliers to submit a reflectance match between the given standard and the lot supplied, i.e. they would like to have a non-metameric match. M&S provide digital standard by giving reflectance values the supplier has to use these digital standard to meet the numerical specification of M&S. However, if the supplier is not using the same spectrophotometer which is used by M&S, it will create problems of instrument metamerism. M&S philosophy is to have a numerical specification as an essential base. But virtual or digital sampling may never replace all physical samples, although it can help to eliminate a great deal of potentially wasted effort by concentrating and focusing attention on what they really want to buy.

10.6.5 Tips for instrumental pass/fail Fred W. Billmeyer and Saltzman (1981) gave the following tips for adopting instrumental pass/fail. 1 2 3

Select a single method of calculation and use it consistently. Always specify exactly how the calculations are made. Never attempt to interconvert between colour differences calculated by different equations through the use of average factor.

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Use calculated colour differences only as a first approximation in setting tolerances, until they can be confirmed by visual judgements and customer acceptability. Always remember that nobody accepts or rejects colour because of numbers: the way the sample looks is most important. Follow CIE recommendations. Look and Think.

10.7

Colour measurement techniques for textiles

The most accurate means of measuring colour requires a device called a spectrophotometer that detects small colour differences which are not registered by the eye. As we can accurately measure colour, measurement matters. Colour measurement is colour management and determines the colour attributes of the material. It is an instrument which specifies colour in terms of a reflectance curve. Spectrophotometers measure light reflected from an object at each wavelength. This spectral data can be displayed numerically or in graph form (% R vs. wavelength). Spectrophotometers are ideal for measuring metamerism, the phenomenon in which two colours look the same under one light source, but different under another. Perfect colour match is a spectral match. If it is not a spectral match then it is called a metameric match. One can only identify this difference by looking at spectral curves of the standard and the batch. The performance of any colour-matching system depends on the quality of the spectrophotometer used for the measurement for colour. Buyers and sellers of colour products are talking of numerical standards (reflectance values) and expecting very tight colour tolerance limits (DE). In a colour measuring spectrophotometer, there are three components – light source, light splitting device (grating) and light detecting technology (photo diodes). In earlier generation instruments, manufacturers were using different technologies. Today, almost all of them are using more or less the same technologies. One can measure reflectance or transmission: generally, we measure colour by measuring reflectance and only for dye solution do we use transmission mode.

10.7.1 Parameters for colour measuring instruments When you are presenting any colorimetry data, you must mention (Gangakhedkar 1986a, RPI 1978a & b) the following instrument parameters • • •

Instrument model – make. Geometry of instrument: d/8 or 0/45. Specular component included (SCI) or Specular component excluded (SCE) mode. The gloss covers a complex set of effects related to surface

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• • • • •

• •

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structure. The samples with intermediate gloss exhibit different measurement problems compared to those with very high gloss or no gloss, respectively. Colour matches of highly glossy samples are to be performed without gloss (SCE mode) and for SCI measurement, one uses gloss correction by subtracting the effect of gloss. Aperture size (large, small or ultra small area view). UV included. UV out. UV filters: 400 / 420 / 440nm. Gloss compensation (automatic/manual); surface correction (many programs use Sauderson’s correction – using different values of internal and external reflection coefficients). Instrument diagnostics. Instrument metamerism.

Comparison of instrumental measurements is meaningless unless all above mentioned parameters are the same for the measurements being compared. There are instrument variables such as lamp technology, measurement interval, geometry, gloss, type of photo detectors and electronics. These parameters are mainly responsible for instrument metamerism.

10.7.2 Instrument considerations Before measuring samples on the spectrophotometer, one must perform the following tests (Reid Clonts et al. 2006; RPI 1978a): 1 Instrument calibration with white and black tiles. 2 Calibration with white (100%) and black (0%). 3 Wavelength calibration is performed by the use of filters, exhibiting sharp absorption peaks, and a set of known wavelengths. The calibration method depends on the chosen spectrophotometer. The check of photometric scale is performed by the use of neutral filters of known optical density. Photometric precision should be less than 0.1%. 4 Short term drift test: How well does the instrument measure what it supposes to measure? Influenced by the quality of the wavelength scale, optical components and photometric and wavelength calibration. Repeatability DE should be less than 0.15. 5 Long term drift test: How well does the spectrophotometer repeat the measurement of the same sample, usually over the time period of minutes and hours? Reproducibility should be less than 0.25. 6 BCRA tile test: At least once a month to check performance of instrument. Use of BCRA tiles or opal glass or didymium filters for determining the performance of any instrument will decide the performance quality of any instrument.

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7 Inter-instrument agreement: How well do different spectrophotometers of the same manufacture and model measure the same sample? This is very important while exchanging the data with the customers. Instruments of same type and same make should not show not-acceptable colour difference. There are large differences when instruments of different types are compared. 8 Sensitivity: Sensitivity of light for light and dark colours is to be checked properly. 9 Thermochromism : One has to check thermochromic effect if samples are heated due to light source such as tungsten lamp or photochromic effect due to UV filters for minimizing effect of UV energy of Xenon lamp. Red and orange dyed samples are especially sensitive to temperature variations and may cause difficulties in precise colour measurements. The author has found a number of dyes and pigments showing this phenomenon during field experience. Generally, increasing the temperature of a sample caused a fall in reflectance values. It is worth pointing out that the reflectance usually decreases as the temperature increases. Yellow, orange and red dyed samples are found to be affected most, leading to large colour differences, whereas grey samples usually do not show any clear thermochromism. Because of thermochromism, new recommendations for colour measurements require the temperature during the measurement to be set to a predetermined temperature, e.g. (25+/−1) degree Celsius. 10 Absolute accuracy of instruments: Absolute accuracy is essential concerning the determination of tristimulus values.

10.7.3 Sample types: fibre, yarn, fabric and knitted sleeves Loose fibre

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Loose fibre is especially difficult to measure repeatedly. A mass of fibre placed at the port of a spectrophotometer tends to protrude into the sphere in much the same way as too many layers of a sheer material. Not only does this introduce error into the reflectance measurement, but there is also the risk of loose fibre falling into the instrument and interfering with the measurement process. One technique to eliminate these errors is to place a piece of optically clear glass against the view port before measuring (Fig. 10.8). It is critical that the instrument be configured to read in specular excluded mode (SCE mode) to remove the glossy reflectance due to the glass (Hunter 1975, Hunterlab 2009). Yarn The sample form that allows the quickest yet potentially most variable measurement is yarn. The most common method for measuring yarn is to obtain a small skein and simply place it against a small measurement port and rotate the sample for two or three measurements.

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Compression cell

10.8 Sample holders: yarn holder, card winder, fibre/powder sample holders card wound and compression cell.

Other useful techniques for yarn measurement include winding the yarn around a card or tab (Fig. 10.7) and using specially designed devices with springs that clamp the yarn securely to a plate. Yarn tension is a concern in either case and must be controlled from sample to sample to prevent measurement errors. Knitted sleeves Knitted sleeves provide the most repeatable measurements because of their uniform construction and size. The samples can be read very easily with the spectrophotometer’s largest view port. A typical measurement technique involves four measurements using four layers of material. Tests may show that as few as two reads are sufficient with fewer layers, but keep in mind that the deviation when repeating the sample measurement should be less than 0.15 (DECMC). A repeatable measurement technique includes specification of the number of layers of material to use, the positioning of samples, the number of measurements to make, instrument settings, and clear communication with the instrument operators. It is known that one has to have multiple layers of samples so that colour data is consistent and not varying.

10.7.4 Sample preparation method The author suggests the procedures and guidelines given by Hunterlab, a leading instrument manufacturer. You can see the details on their website. AATCC recommendations for textile materials are to be followed and are very useful standardization guidelines. Yarn can be measured by using one of several sample preparation methods. One method for both bench top and portable colour measurement instruments is to measure the cone or package directly. In this case no sample preparation is

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required; however a positioning device should be used at the instrument measuring port. A second method is to prepare a wad of randomly oriented yarn large enough so that no light can penetrate through it and place it inside a container with a window in the bottom. Place a specified weight on top of the yarn and measure through the glass. A more consistent method would be to use a compression cell (see Fig. 10.8) in which a specified amount of yarn is placed in the container and a specified pressure is applied to a piston to compresses the yarn. Since the yarn is being measured through a glass interface, the instrument will measure the sample as a duller colour than it actually is. Another method that often gives measurements that correlate well with visual assessment is to wind the yarn closely and parallel on a card to a sufficient thickness to prevent show-through. Commercial card winders (see Fig. 10.8) are available for this purpose to ensure consistent tension and a constant number of windings. The card can be made of cardboard, plastic or metal and should be a uniform, neutral colour. Ensure that it does not contain fluorescing agents. Skeins of yarn can be prepared by clamping or taping one end to the edge of a rigid backing material, stretching the skein across the form keeping the strands of yarn parallel and clamping or taping to the other edge of the form. Commercially available skein holders can be used for this purpose. The yarn can also be knitted into a sock for measurement. Since a knitted sock is flat, it can be measured without a support or compression device. When necessary, to increase the opacity of the sample, the knitted sock can be folded such that multiple layers are produced. The number of layers can be increased until the reading does not change significantly. One can see that colour data (L, a, b values) for two, four or six layers will change significantly and will become constant after six or eight layers depending on the nature of the material. In the case of yarn measurement, sample directionality is the primary area of concern and one should rotate samples by 45, 90, 180 and 270 degrees and take average readings. If a cone or package of yarn is being measured directly, and the sample is curved and does not lie flat on the sample port, it is necessary to get reproducible readings with proper positioning technique. One can see the effect of orientation of yarn on colour values from Table 10.2. Table 10.2 Effect of yarn orientation on colour values Orientation Taupe Horizontal Taupe Vertical Orange Horizontal Orange Vertical

L* 46.4 45.0 57.5 56.4

a* 3.6 3.5 30.9 30.4

b* 9.3 9.1 53.4 53.1

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The best colour measuring instrument has no value if the samples presented for measurement are not accurate. •

•

• •

If we prepare the samples the same way each time, then each sample will be the same as all previous and all subsequent ones. This is called the reproducibility of making samples. In no case did the duplicate samples exactly match one another. You may get the colour difference in the range of 0.04 to 0.6 CMC units. We must realize that in setting colour specification we must have good repeatability of sample making. Sample surface differences: If there is a difference in surface appearance (e.g. gloss), then colour measurement will be inaccurate, gloss, texture and surface irregularity will create problems in colour measurement. Differences in surface gloss account for the colour difference. Samples made with different equipments will show large variations. Samples made by different operators will show considerable variations. Understand the limitations of sample preparation procedure. This should not be the cause of gloom. Instruments are unintelligent machines, which see only what we give them to see. In most cases an improperly prepared sample or sample preparation procedure has more errors in it than we realize. Careful sample preparation is a must and a prerequisite for correct colour measurement.

In textile colour measurement, orientation effect, unlevel dyeing, weave structure, light transmitting due to thin fabric, the sample size creates measurement problems and one has to look into sample preparation procedures (see AATCC 1996, Hunterlab 2009, Brockes et al. 1964).

10.7.5 Sample presentation method Measurement technique Before any permanent samples are measured and stored into the computer database, a repeatable measurement technique must be established and observed. Samples should always be measured multiple times with the largest area view available on the spectrophotometer being used as long as the samples are large enough to completely cover the viewing area. Sample conditioning should also be considered because variations in temperature and moisture content can contribute to variations in measurement data. • • • • •

All textile materials must be consistent and standardized for presenting to the port of the spectrophotometer. Fibrous materials must be carded into a suitable pad. Top and tow should be straighten by combing treatment. In case of yarn, it should be card wrapped, using a precision card winder. For fabric samples, sufficient layers should be made to prevent transmission of light through a composite specimen.

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Sample thickness Two to four layers will be sufficient for most knitted and woven materials to achieve an opaque sample for presentation to the instrument. If the material is not opaque, light will pass through the sample and reflect off the backing material or sample holder and produce misleading reflectance data. Sample positioning Sample rotation and repositioning will reduce measurement variability due to fabric construction, directionality of yarns, and unlevel dyeing. A common practice in sample measurement is to place the sample at the instrument port and simply rotate the sample for four or more measurements. This technique is quick, but it will not account for variations due to unlevel dyeing and should be avoided. Developing a repeatable technique An optimum measurement technique has been established when a sample can be measured, removed from the instrument, and re-measured with a variation of less than 0.15 DE(CMC) units. Higher variation will decrease the confidence level in the quality of the stored data and lead to less accurate match predictions. Measurement repeatability is especially critical as it affects computer pass/fail programs in use in the quality control and final inspection areas. If a sample is measured and the DE(CMC) is calculated as 0.80 but the measurement variability is 0.30, then the true reading can average from 0.50 to 1.10. This may mean the difference between ratings of pass or fail if the pass/fail tolerance is less than 1.10. It is suggested that one should follow these recommendations:

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• • • •

Wherever possible, the large aperture should be used to increase accuracy. Aperture can be selected depending upon sample size. Measure the sample at four different positions by rotating it by 90 degrees for obtaining accurate average reflection values. Reproducibility of measurement should be within a colour difference of DECMC =0.2 units.

10.8

On-line colour measurement

On-line colour measurement systems are recent systems for quality control of colorimetric features, production certification, centre-selvedge/head-tail measurements and for lot formation. The measurements are implemented without interfering with the production process: an integrated system is able to implement, directly on production line, all the measurements relevant to the fabric colour in order to point out the processing mistakes as colour homogeneity, colour deformed areas, colour differences of ‘head–tail’ and centre-selvedge (see websites of X-Rite and Hunterlab).

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All leading instrument manufacturers such as Hunterlab, X-Rite, Orintex offer latest on-line colour measurement systems with different features. The author has worked on the Orintex on-line colour measurement system (Fig. 10.9) consisting of a GretagMacbeth spectrophotometer and Orintex software. This product is now discontinued. Table 10.3 gives the data of a few samples for centre-selvedge. It gives all the colour parameters and remarks on pass/fail based on the tolerance limits specified. Change in hue angle is to be watched if colour differences are very small and visual perception does not agree with instrumental pass/fail. Salient features of any on-line colour measurement system are: •

•

Continuous, real-time measurement of any product on a process line, which allows us to respond to product colour changes when they happen, as they happen. Runs can be continuously monitored to identify colour variation and out-ofspec product. This information helps us make permanent process improvements,

10.9 On-line colour measurement: production equipment. Table 10.3 Colour measurement of centre-selvedge Sample TAN STD TAN Left TAN Centre TAN Right Khaki STD Khaki Left Khaki Centre Khaki Right Grey STD Grey Left Grey Centre Grey Right Illuminant D65

L* a* b* 47.75 2.97 6.76

h 66.26

DE

DL

DC

DH

P/F

h

0.29 0.28 −0.07 −0.00 Passed 0.29 −0.22 −0.05 0.17 Passes 0.13 −0.04 0.06 −0.10 Passed 53.66 5.01 19.17

75.35 0.12 0.10 0.03 0.06 Passed 75.49 0.07 −0.07 0.01 −0.02 Passed 75.31 0.08 0.06 −0.04 0.03 Passed 75.42

38.47 1.61 −1.62

314.70

0.43 0.10 −0.13 0.39 Passed 0.13 0.02 0.00 −0.13 Passed 0.31 0.17 −0.14 0.22 Passed Colour Difference DE (CMC) P/F Limit 1.00

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while providing assurance that our product is within specification and allowing us to make shipment and allocation decisions. On-line systems help us in four major ways: • • • •

Monitoring the finished product quality. Making decisions about allocating product to a customer. Monitoring the process variables that affect colour. Helping us to control process variables.

10.8.1 Map control system The latest on-line colour measurement system is a ‘map control’ system (Fig 10.10). The measurement instrument is a special spectrophotometer that is able to acquire remote reflectance spectrum of the fabric surface to be controlled. The information data are sent to a PC that controls the plant and appraises the colorimetric data and gives all the necessary parameters to the operator. Advantages of On-line colour measurement are: • • •

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quality control can be programmed per client/product automatic remote measurements elimination of stop time

10.10 Map control.

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automatic tolerances according to colour shade operator’s objective testing via instruments and not subjective testing testing according to international standards documentation and certificates of colorimetric quality control.

It is possible to consider the colour difference at all measured points or to check the head-tail and/or centre-selvedge difference of the lot. The types of control that can be carried out by the system can be personalized by the user, as well as the various options of quality control, together with formulas CIELAB, CMC, DE2000. • •

The operator can have complete history of measurement. The user can also see at a glance the progress of measurements carried out and deduces the presence of the zones, which are beyond tolerance.

10.8.2 Hunterlab SpectraProbe XE This on-line colour measuring instrument has the following notable design features. It measures colour both across the product’s width and along its length. The colour of a product can vary in shade between width and length, which causes significant product quality concerns. SpectraProbe XE increases your colour monitoring capability dramatically. Hunterlab’s leading-edge technology gives this remarkable colour measurement system the ability to simultaneously traverse the product and measure colour both across its width and along its length. It then reports any shade variation against a standard, giving you more product colour information than has ever before been available. In addition to the commonly used side-centre-side configuration, many other traversing patterns are also available. Due to its highly advanced optical design, the SpectraProbe XE spectrophotometer can measure the appearance of colour independent of sample surface texture, directionality and gloss.

10.8.3 Non-contact spectrophotometers Leading manufacturers such as KonicaMinolta and X-Rite offer new generation non-contact spectrophotometers. They are very useful for wet colour materials, especially wet paint samples. In textile application they are used for on-line colour inspection, but as of date they are extremely costly and not much used in industry. However, on-line inspection is finding more interest. Some of the models available in the market are described below. Minolta CF 1440 spectrophotometer It is a unique non-contact spectrophotometer (Fig. 10.11) with large area view (40 mm), multi-point measurement and high speed. The optical system is highly

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10.11 Minolta non-contact spectrophotometer.

adaptable to vertical movement or tilting of objects. Built-in auto-calibration functions guarantee stable measurements, even under long term use. Multi-point measurements are carried out in 0.6, 1.2 and 1.5 seconds and inter-instrument agreement for 12 BCRA tiles is De 0.3 units. Measuring distance is 100 mm from the bottom of the instrument, which uses a standard xenon arc lamp. The illumination light is also measured during measurement of the specimen, to correct the influence caused by minute fluctuation in light quantity and spectral characteristics of the illumination light. The instrument is ideal for wet colour matching and widely used in paint application. Minolta has also introduced some more models. You are advised to see product details on the supplier’s website. 1 2 3 4 5 6 7 8 9 40 1 2 43X

VeriColour Spectro This new generation of on-line colour measuring instrument is offered by X-Rite and is accurate and affordable. The VeriColour Spectro is compact and durable for harsh industrial manufacturing applications and an important feature is no special lighting or shrouding required with this all-inclusive solution. It improves quality control and reduces operating expense, provides absolute spectral and colorimetric data for process control, and is easy to set up and manage. The VeriColour® Spectro system enables colour control in real time to contain and eliminate colour problems without disrupting production. X-Rite is also offering a wide range of models with many unique features. Notable models are VeriColour Solo, VeriColour System, and the TeleFlash 130 non-contact spectrophotometer. All the details can be obtained from the X-Rite website.

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Colour of dry and wet fabrics

One can now measure wet samples and correlate them to dry sample measurements. This is a very recent development with notable success in the paint industry. It can be effectively used for textile materials, however one has to find out the correlation between wet and dry measurements taking into consideration dyeing parameters such as the substrate-dye-process combination. Measurement of wet sample and colour quantification is not a problem. It is well known that immersion of any dyed fabric into water creates a deeper shade. In order to explain this phenomenon, several researchers (Hero Tada Lida 1970, Goldfinger et al. 1970, Allen and Goldfinger 1971, Dalton et al. 1995) tried to solve the problem of colour appearance of fabrics in wet and dry states. They confirmed that differences between wet and dry colour are due to the presence of the moisture content. Recently, extensive research was carried out at the UICT laboratory, Mumbai on correlation between wet and dry colour samples. Modification of the Kubelka-Munk equation for colorant formulation for prediction of dry colour of a textile sample from its wet state was reported by Jahagirdar and co-workers (Jahagirdar et al. 2002). This study is very useful for understanding the correlation between the dry and wet colour measurement. In another study (Jahagirdar and Tiwari 2007), they reported prediction of dry colorimetric properties of dyed polyester fabric from its wet reflectance values. Polyester fabric was dyed with six different disperse dyes at various concentrations. The reflectance values of dyed polyester fabrics were measured over the visible range of 400–700 nm at two different conditions, viz. in wet and dry states. Using the reflectance data, a numerical relationship between the wet and dry reflectance was established. In this work, an attempt was made to predict the colorimetric properties of dyed polyester in the dry state directly from the corresponding wet reflectance values. Jahagirdar and Tiwari predicted dry colour parameters of dyed polyester fabric from its wet reflectance values. Figure 10.12 shows how one can use wet colour readings and predict reflectance values of dry fabric by employing a 3D polynomial function. Reflectance spectra of wet (Rw) and dry (Rd) are shown in the figure and reflectance predicted from wet reflectance values (Rd3D) using a 3D polynomial function is plotted and shows that it is very close to dry reflectance value. Jahagirdar and Tiwari developed an empirical relationship between dry and wet reflectance values for the dyed polyester fabrics, so that the colour parameters of any dyed polyester sample in dry condition can be found out easily from their respective wet reflectance values. From their investigation, it was observed that the colour parameters of any dry polyester samples dyed with disperse dyes can be easily found from its wet reflectance values by using the established 2-degree and 3-degree polynomial equation. But overall, the 3-degree polynomial equation holds good in comparison to the 2-degree polynomial equation. The empirical relationship between wet and dry reflectance values can be applied as an analytical tool in order to display the dry colour of the polyester fabrics by monitoring its reflectance values after dyeing.

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0.3 Rw Rd Rd-3D Reflectance

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0.2

0.1

0.0 400

450

500

550

600

650

700

Wavelength (nm)

10.12 Reflectance spectra of wet (Rw) and dry (Rd) predicted from wet reflectance values (Rd3D) using a 3D polynomial function: polyester fabric dyed at 5% disperse blue 366.

The work reported in their paper is found to be valid and tested only for polyester fabrics dyed with disperse dyes. Also, one can generalize such a relationship for any substrate by using the general equation: Rd = AR3w + BR2w + CRw + D

[10.5]

where A, B, C and D are the variables and calculated from a curve fitting method for the selected substrate. Rd and Rw are the reflectance values of dry and wet fabric. It is now possible to find correlation between dry and wet fabric and predict the colour of the dry fabric from the wet fabric reflectance measurement. One has to test these results for cotton dyeing with reactive and vat colours. 1 2 3 4 5 6 7 8 9 40 1 2 43X

10.10 Inspection of colour of finished fabrics: a case study Generally, after the dyeing colour is assessed for pass/fail, finishing treatments are applied to the fabric such as enzyme wash, etc. It is noticed that there are considerable changes in the colour of finished products, as many times there is no correlation between original dyed fabric and fabric treated with finishing agents. One has to find out the effect of the finishing agent on the colour of fabric and adjust the colour. It is very difficult to correct the colour if the finishing agent or finishing process is affecting the colour of a dyed fabric which was already approved before the finishing treatment was applied. Sometimes, it creates a problem of metamerism. Portable instruments or on-line measurement will help in controlling the colour of finished goods.

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The author was associated with much work carried out at one of the leading textile mills in India. Some of the findings are given here for understanding the role of finishing agents on final colour. It was noticed that many international buyers approve the colour of the textile material before the finishing treatment is given. A considerable colour change was noticed after the finishing treatment was given to the fabric, which resulted in a not-acceptable matching which was not possible to correct as it had gone to the point of no return. Table 10.4 gives details of enzyme wash and the Table 10.5 gives the colour data on textile colour samples – standard (un-wash) and trial (enzyme wash). These are Table 10.4 Details of enzyme wash MLR Desize Bactosol PHC Wetting agent Temperature Ph/Time

1:5 I GPL 0.25 GPL 50 deg C 5.5–6.0/ 15 minutes

Enzyme Bactosol ADF PDR Ph Time

1 % OWM. Or 10 gm per kg 4.5–5.0 50 deg C

WASH Cold wash Hot wash Cold wash

5 minutes 60–70 deg C / 10 minutes 5 Minutes

Finish Ceraperm MW Ph Time

15 GPL 5.0–5.5 10 minutes

Table 10.5 Finishing (enzyme wash) effect on colour data of textile material Illuminant D65 Colour Khaki Std Khaki Trial Grey Std Grey Trial Khaki 2 Std Khaki 2 Trial Carmel Std Carmel Trial Olive Std Olive Trial

(L:C)

2.00

L a b C h 53.71 2.87 10.06 10.46 74.06 58.22 2.61 9.77 10.11 75.06 64.98 2.91 5.44 6.17 61.84 67.07 2.52 5.21 5.79 64.21 55.96 3.54 12.22 12.69 74.22 56.36 3.27 11.95 12.39 74.71 56.07 8.81 18.30 20.31 64.30 59.17 7.91 17.65 19.36 65.77 53.90 −0.56 6.34 6.36 95.03 54.75 −1,09 5.38 5.49 101.45

Note: Std = Un-wash

DL

Da

Db

DC

DH

DE(CMC)

4.51 −0.27 −0.29 −0.35 0.18 2.04 2.10 −0.39 −0.23 −0.38 0.25 1.01 0.40 −0.19 −0.27 −0.31 0.11 0.33 3.11 −0.86 −0.65 −0.95 0.51 1.63 0.85 -0.53 −0.96 −0.87 0.66 1.29

Trial = Treated with enzyme wash

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the materials supplied to international buyers, but the buyer specifications are very strict and they only accept the material if CMC colour difference is less than 0.7 to 0.8. The author has collected this data from JCT, Phagwara, Punjab, a leading textile process house in India exporting textile material to Europe and the USA. All the standards were within acceptable limits before giving enzyme wash except the sample Khaki #2 and all other samples are showing more than one unit of CMC colour difference. It is in the range of 1.01 to 2.04. When one makes visual assessment, it is noticed that there is a considerable change in hue. One can see that olive and grey samples show considerable change in hue angle. DL, Da, Db values are showing directional changes. Total colour difference is not at all within tolerance limits and we noticed considerable spectral changes which clearly indicate that metamerism is introduced in samples after giving enzyme wash. It is not possible to make any colour correction at this stage. It can be concluded that finishing agents and the processes are to be carefully selected so that the metamerism problem is not introduced.

10.11 Future trends As of date, on-line colour measurement and non-contact instruments are very costly, but new advances in optics and electronics will make spectrophotometers cheaper. New internet based computer colour matching systems will be available shortly and all the required colorant data will be stored on a central server, so match prediction of customer colour will be possible instantly. Low cost portable spectrophotometers integrated with internet based colour matching software will change the concept of colour production and colour marketing. ‘Colour on demand’ will be the marketing concept and using internet technology colour will be produced instantly. Acceptance of colour (pass/fail decision) will be communicated by the buyer and seller by ‘colour mail’ using the internet but the role of colour measurement will remain critical. 1 2 3 4 5 6 7 8 9 40 1 2 43X

10.12 Conclusions In this chapter, we have discussed the role of colour measurement in textile application with reference to colour measurement technique, relation between colour and appearance, on-line colour measurement systems, colour of dry and wet fabrics, inspection of colour of finished fabrics and how to get non-metameric matches. We have found the answers to frequently asked questions such as, ‘Why colours don’t match?’ and ‘What is the relation between colour and appearance?’ We discussed in detail various aspects of sample preparation methods, measurement techniques, instrument considerations and various parameters to be controlled for developing a repeatable technique for fibre/yarn/fabric samples. We also discussed various aspects of visual assessment of colour and instrumental pass/fail criteria.

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Generally, colour is assessed for pass/fail after dyeing. For the majority of time, finishing treatments are given to fabric such as enzyme wash, etc. It is noticed that there are considerable changes in the colour of dyed fabric after the finishing treatment and one cannot make any correction if metamerism is introduced due to the process and finishing agent.

10.13 Sources of further information and advice See web sites of colour instrument manufacturers such as datacolour, Hunterlab, GretagMacbeth (now X-Rite) and of the Musell Colour Laboratory of RIT, USA. Also refer to international journals such as Colour Research and Application and Journal of Society of Dyers and Colourists as well as various conference proceedings of AATCC, ISCC, AIC, CIE, SDC and different national colour societies. Research the Central Bureau of CIE, Kegelgasse 27, A -1030, Vienna, Austria (Email : [email protected]).

10.14 References AATCC (1996), ‘1996 Technical Manual of the American Association of Textile Chemists and Colourists’, Volume 71, AATCC Test Method 173-1992: ‘CMC: Calculation of Small Colour Differences for Acceptability’, p. 315. Alian Chrisment (1998), Colour and Colorimetry, Editions 3C, Paris. Allen E H and Goldfinger G (1971), ‘The influence of moisture content on the colour appearance of dyed fabrics’, Text. Chem. Col., 3, 289. Billmeyer, Jr., F W and Saltzman M (1981), Principles of Colour Technology, 2nd Edition, New York, Wiley. Brockes A, Stroca D, Berger A S (1964), ‘Colour Measurement in textile Industry’, Bayer Farben Revue Special Edition No 3E, Farbenfabriken, Bayer-Leverkusen, Germany, Berger-Schunn. CIE (1976a), ‘CIE Recommendations Uniform Colour Spaces, Colour Difference Equations and Metric Colour Terms,’ Supplement No. 2 to CIE Publication No. 15, May 1976. CIE (1976b), ‘CIE Recommendation for Special Index of Metamerism’, Supplement No. 1 to CIE Publication No. 15. CIE (1995), ‘CIE Technical Report: Industrial Colour Difference Evaluation,’ CIE Publication No. 116, Vienna, Austria, Central Bureau of CIE. Dalton P M, Nobbs J H and Rennell R W (1995), ‘The influence of moisture content on the colour appearance of dyed textile material, Part 1’, J.S.D.C., 111: 285–287. Gangakhedkar N S (1986a), ‘Colour Measuring Instruments (New & Old),’ Colourage, November 13, 1986. Gangakhedkar N S (1986b), ‘Metamerism and Colour Difference’, Colourage, July 1986. Gangakhedkar N S (1991), Understanding Computer Colour Matching, Mumbai, Rutu Prakashan. Gangakhedkar N S (2003), Understanding Science and Technology of Colour, Mumbai, Rutu Prakashan. Gangakhedkar N S (2004a), ‘Colour difference-single number shade passing versus three dimensional colour tolerance’, Jour. Col. Tech.& Mgmt., 1(1): 31.

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Gangakhedkar N S (2004b), ‘Colour clinic: metamerism’, Jour. Col. Tech. & Mgmt., 1(2): 33–37; 1(2): 51; 1(3): 32–33. Goldfinger G, Goldfinger H S, Hersh S P and Leonard T M (1970), ‘The colour of absorbing scattering substrates, I The colour of fabrics’, J. Polymer Sci., C31 25. Hero Tada Lida (1970), Senryo To Yakuhin, 3: 15. Hunter R S (1975), The Measurement of Appearance, New York, John Wiley and Sons. Hunterlab (2009), Colour Measurements: Methods and Effects of Sample Presentation, Hal Good, Reston, VA : http://www.hunterlab.com Jahagirdar C J, Deshpande V D and Tiwari L B (2002), ‘Modification of Kubelka-Munk Equation for Colorant Formulation for Prediction of Dry Colour of a Textile Sample from its Wet State’, Colourage, 9: 51–58. Jahagirdar C J and Tiwari L B (2007), ‘Prediction of Dry Colorimetric Properties of Dyed Polyester Fabric from its Wet Reflectance Values’, Colourage, Volume LIV No. 1, pp. 37–44. Ken Butts, ‘Colour Tolerances for Consistent Pass/Fail Decisions, Datacolor,’ http://www. datacolor.com Luo M R, Cui G, and Rigg B (2001), ‘The Development of the CIE 2000 Colour Difference Formula,’ Colour Res. Appl. 26: 340–350. McDonald R (1987), Colour Physics for Industry, Society of Dyers and Colorists – Dyers Company Publication Trust. Munsell A H (1929), Munsell Book of Colour, Baltimore, Maryland, Munsell Colour Company. Reid Clonts, Ranjith Thangavelu, David Hinks, Jennifer Dunn, Patricia Guzman, Ann Laidlaw, Warren Jasper (2006), ‘Inter-instrument agreement in the colorimetric measurement of textile materials’, AATCC Review, 6(8): 45–48. RPI (1978a), Rensselaer Polytechnic Institute, Advances in Colour Technology, Topics, Experiments, Reprints, Bibliography, Rensselaer Polytechnic Institute, Troy, New York 12181. RPI (1978b), Rensselaer Polytechnic Institute, Colour Technology for Management, Reprint, Bibliography, Department of Chemistry Rensselaer Polytechnic Institute, Troy, New York, 12181. The Society of Dyers and Colourists (1981), ‘Colour Difference Measurement, A Reliable basis for Quality Control’, Papers of Conferences held at the University of Bradford on May 12. Wright W D (1958), The Measurement of Colour, London, Hilger & Watts Ltd. Wyszecki Gunter and W S Stiles (1967), Colour Science, Concepts and Methods, Quantitative Data and Formulas, New York, John Wiley and Sons, Inc. X-Rite (1994), A Guide to Understanding Colour Tolerancing, X-Rite, Inc. Publication, X-Rite, http://www.x-rite.com

© Woodhead Publishing Limited, 2010

11 Grading of cotton by color measurement B. XU, The University of Texas, USA

Abstract: Cotton color is an important measurement in judging fiber’s spinning quality in the current cotton classing system. This chapter first briefly reviews the history and major factors of cotton color grading, and then discusses the instrumental methods, including HVI colorimeter and color imaging system, that has been developed for objective and reliable grading of cotton color. Key words: cotton color, HVI colorimeter, imaging colorimeter, neural network, fuzzy logic

11.1

History of cotton color grading

The color of raw cotton is an important indicator of its overall quality because the lint color is related to processing performance and yarn quality.1, 2 Normally, cotton has a bright, white color but continued exposure to weathering and the action of micro-organisms may cause white cotton to lose its brightness. If frost or drought stops cotton growth prematurely, cotton may have a yellow color that varies in depth. The action of insects, fungi, and soil stains may result in cotton discoloration.3, 4 Traditionally, cotton colors are classified by human classifiers who are trained to compare raw cotton samples with the cotton reference standards, i.e. the USDA universal standards, to determine color grades on the samples.4 Because of the subjective nature and inconsistency in visual color classifications, the need for instrumental measurements of cotton color has been highly demanded since the early days of cotton color classing. In the 1930s, the USDA began developing instrument color measurements for cotton. The measurements of color Rd (grayness) and color +b (yellowness) became the standard cotton color attributes with the first evaluations of the Hunter Colorimeter in 1950.5 In the 1970s, colorimeter technology was brought in for use in classification, and was fully integrated into High Volume Instrument (HVI) systems by the end of the 1970s. Since then, 100% of U.S. cottons were tested by the HVI colorimeter and classifiers, but the official color grade was assigned by the classifier. This practice maintained until the 2000 classing season. Since 2000, the official color grades based on the Rd and +b measurements of the HVI colorimeter have been approved for use in determining on USDA classed Upland cotton.5, 6

11.2

USDA cotton color grades

In the USDA universal standards, cotton color grades are divided into five major categories: white, light spotted, spotted, tinged, and yellow stained, and each is 253 © Woodhead Publishing Limited, 2010

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Table 11.1 Color grades of American Upland cotton

Good middling Strict middling Middling Strict low middling Low middling Strict good ordinary Good ordinary Below grade

White

Light spotted

Spotted

Tinged

Yellow stained

11* 21* 31* 41* 51* 61* 71* 81

12 22 32 42 52 62 – 82

13 23* 33* 43* 53* 63* – 83

– 24 34 44 54 – – 84

– 25 35 – – – – 85

* Physical standards. All others are descriptive.

further divided into eight subcategories: good middling, strict middling, middling, etc..4 Cotton color notation consists of a double-digit number, e.g., 21, 32,… . The right digit indicates the five major categories, and the left one indicates the eight subcategories. Table 11.1 lists the color grades for American Upland cotton. Note that in the USDA universal standards, there are only seven physical standards for cotton in the white category, five in the spotted category, and three in the tinged category. The other two categories, light spotted and yellow stained, are presented by descriptive standards.

11.3

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The HVI colorimeter is an instrument that measures the reflectance Rd and yellowness +b of cotton and provides official color grading in the U.S. based on the Nickerson-Hunter color diagram,1, 4 which illustrates the relationships between HVI color measurements and color grades. In the HVI colorimeter, cotton samples are pressed against a window, under which a light source and two photo sensors are installed. The photo sensors are chosen to detect lights in the selected wavelength bands, and to output the Rd, +b and corresponding color grade. Although the HVI colorimeter can yield repeatable cotton color grades, its disagreement with cotton classifiers has been recognized since the beginning of its use in the classing system. Cotton Incorporated conducted a survey on colorimeter-classifier color grade disagreements for 1995’s cotton at the USDA cotton classing offices, and found such disagreements exist commonly in all the classing offices. The disagreement was as high as 33.8%. The most noticeable disagreement is the discrepancy between ‘white’ and ‘light spotted’ categories. It is more likely for an HVI colorimeter to assign a ‘white’ grade to a sample labeled as a ‘light spotted’ grade by a classifier, which could reach 35.4%. The HVIclassifier disagreement on color grade can result in a significant economic impact on both cotton producers and buyers.7

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During the 1998 classing season, the USDA Cotton Program conducted a pilot project in conjunction with the National Cotton Council Quality Task Force Committee for the purpose of making the HVI color grade determination closer to the Universal Cotton Standards and to classifiers’ official color grades. An adjustment in color grade divisions was made to better match the Rd and +b measurements with classifiers’ color grades on the Nickerson-Hunter color diagram for the 1986 cotton crops. The Cotton Program evaluated the overall impact by analyzing all of the white and light spotted grades assigned during the 1999 classing season. The data revealed that 87% of the classifier color grades and 90% of the HVI color grades were classified as white. In addition, 12% of the classifier grades and 10% of the HVI grades were classified as light-spotted. The reproducibility for both methods remained consistent at approximately 74–75% for both the classifier and the HVI color grades.5 As a result of the study, the National Cotton Council Quality Task Force Committee made a recommendation in early 2000 to adopt the HVI color grade as the official color grade. The National Cotton Council’s Board of Directors voted in favor of the recommendation and the cotton industry agreed to accept the decision. The HVI color grade became the official color grade effective July 1, 2000.

11.4

Factors affecting cotton color grade

Redness, yellow spots, and trash particles are three major factors that can influence the instrumental measurements of cotton color.7

11.4.1 Factor one: redness It can be readily noticed from the Nickerson-Hunter color diagram that a ‘white/ good middling’ sample can have the same yellowness as a ‘spotted/strict good ordinary’ sample. This means that the yellowness +b cannot exclusively reflect the chroma of the cotton color. The third color attribute, redness a, should be taken into account in the instrumental grading of cotton color. The color values of the 15 USDA physical standards were measured using the imaging colorimeter, and the distributions of Rd, b and a against color grades are displayed in Fig. 11.1. Although Rd and b have clear trends to decrease with color grades both within and among color categories, a shows significant changes only among color categories. In the same color category, a appears to be almost invariant with sub-categories (good middling, strict middling, …) except the point at grade 34. Each color category has a certain level of a, which does not overlap with its neighboring categories (Table 11.2). This uniqueness of a among color categories would make it a useful role in the cotton color grading. To quantify the contributions of a to cotton chroma C,8 the percentage of a in C for each physical standard was calculated and displayed in Fig. 11.2. The a’s contributions take 10–16% of C for cotton in the white color category; 21–26% in

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12

b 10

8

6 6 5 4 a

3 2

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1 0

11 21 31 41 51 61 71 23 33 43 53 63 34 44 54 White

Light spotted

Spotted

11.1 Rd, b and a of the USDA physical standards.

the spotted category, and 28–33% in the tinged category. Overall, the percentage of a increases as the color grade goes down. The redness content in C suggests that the a’s contribution to cotton color cannot be ignored, and should be included in the color grading system.

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Table 11.2 The a range for each color category Color category

a

White Light spotted Spotted Tinged Yellow stained

<1.5 1.5~2.5 2.5~4 4~5.2 >5.2.

% 35 30 a C

25 20 15 10 11 21 31 41 51 61 71 23 33 43 53 63 34 44 54 Standard

11.2 Contribution of a to cotton chroma C.

11.4.2 Factor two: spots Locally yellowed areas in a cotton sample are regarded as spots. The existence of spots in a cotton sample may make a classifier call the sample ‘light spotted’. What is the influence of spots when the cotton’s color is measured by a colorimeter? In general, the influence depends on the depth of the spot’s color as well as the size of the spots in the viewing area of the colorimeter. When the sizes of spots are not negligible relative to the viewing area of the colorimeter, the existence of the spots may cause appreciable change in color measurements and thus in the cotton color grade. An experiment was conducted to explore the sensitivity of cotton color change to the spots present in the sample (see Table 11.3). Four light-spotted cottons, their classifier’s grades 22, 32, 42, and 52 respectively, were selected in an effort to keep the amount of spots unchanged as the cotton brightness varies, and a Minolta colorimeter CR-210 was used to measure the colors of two selected areas on each sample, the areas with or without a spot. The viewing area of CR-210 is circular with a diameter of 5 cm. The change in one color attribute (Rd, a or b) caused by a spot is quantified by the relative difference Δ defined as: Δ = (x – x0)/x0 * 100%

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Table 11.3 Influence of spot on color measurement with CR-210 colorimeter No Classifier With spots grade Rd a b 1 2 3 4

22 32 42 52

71.9 73.9 61.0 62.4

Without spots Grade

1.8 12.1 23–3 1.4 9.4 31–4 3.2 10.6 53–4 1.9 8.7 52–2

Δ (%)

Rd

a

b

Grade

Rd

a

b

75.6 75.9 69.9 64.7

1.5 1.2 2.2 1.7

11.2 9.1 9.9 8.0

22–1 31–3 42–1 51–4

5.2 2.8 14.5 3.7

–19.0 –10.7 –31.6 –13.9

–7.2 –3.1 –7.0 –8.4

where x is one color attribute of the cotton sample without spots, and x0 is the same color attribute of the sample with spots. A positive Δ (x > x0) indicates an increase in the color attribute without a spot being present. Table 11.3 summarizes the Δ in Rd, a or b of the samples. It can be seen that all the color attributes of these four samples are affected by the presence of spots, with the change in a being the maximum. The Δ(Rd) values show a positive change and Δ(a) and Δ(b) show negative changes, suggesting that the spots make the samples appear darker and more chromatic. As a result, the color grades of the samples are lowered when a spot is present inside the viewing area. Although the color change does not shift the color grade of sample 2, its sub-grade has changed from 31-4 to 31-3. Depending on the color and size of the spot, the influence on cotton color varies. Overall, the colorimeters with smaller viewing areas will be more sensitive to the existence of spots.

11.4.3 Factor three: trash particles

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Trash particles, such as leaf, bark and grass, are foreign matters in cotton that have substantially different colors from lint. They will affect the output of a colorimeter if they are not either physically or computationally removed from the scene as the colorimeter takes the measurement, as the color alteration depends on the type and amount of trash particles that the sample contains. Five cotton samples that have the same classifier’s color grade but different leaf grades were selected to test the influence of leaf on the color measurements made by the CR-210 colorimeter. A higher leaf grade means a higher leaf content. Table 11.4 shows the color values and grades of the samples before and after the leaves were manually removed from the samples. After the removal of leaves, the Rd of the samples increases, a decreases, and b shows only a slight increase. These tendencies are clearer when the leaf grade increases. The change in a is greater than in b. This reveals that leaves contribute more a components to the cotton chroma. For the samples whose leaf grades are not larger than 3, the color differences caused by leaves are not significant enough to alter the samples’ color grades. For the samples whose leaf grades are larger than 3, the color differences bring

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Table 11.4 Influence of leaves on color measurements with CR-210 colorimeter No

1 2 3 4 5

Leaf Classifier With leaf grade grade Rd a

b

Grade

Rd

a

b

Grade

2 3 5 6 7

7.5 7.9 8.5 7.6 7.4

41–2 41–4 31–3 41–2 51–1

72.6 71.8 74.9 73.5 71.4

0.72 1.44 0.88 1.03 0.95

7.7 8.0 8.6 8.0 7.5

41–2 41–4 31–4 41–1 41–2

41 41 41 41 41

71.7 71.6 74.5 72.2 69.6

Without leaf

0.84 1.48 1.04 1.13 1.24

Press foot Cotton sample

Window Flasher Lens

CCD camera

11.3 Schematic set-up of the imaging colorimeter.

about some changes in the color grades, especially for sample 5 whose leaf grade is 7. The leaf content of a high leaf grade sample becomes more noticeable in the viewing area of CR-210. Hence, CR-210, or any other colorimeter with a small viewing area, is not suitable for grading the color of a heavily contaminated sample. The CR-210 color grades without including leaves are closer to the classifier’s grades. This is because classifiers are trained to make color grades independent of trash particles.

11.5

Color measurement using color image analysis

Advanced imaging devices have been used to construct a new image-based colorimeter to measure both cotton trash contents and color.9 As depicted in Fig. 11.3, the imaging colorimeter uses a 3-chip color CCD camera (JVC KY-F55B) and a xenon flash light (Vivitar Electronic Flash 1900) to capture images of cotton fibers over a much larger area ((8.47 × 6.35 cm2) than traditional colorimeters. This camera has a high resolution and convenient ways to adjust white balance and other settings. The flash can uniformly illuminate the entire viewing area with high-intensity, short-duration pulses. It was found that the overall variation

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in intensity in a grabbed image of a white paper over the same viewing area is 2.3%.9 A specially designed circuit was added to the Vivitar flasher to obtain a steady flashing intensity each time the camera captures the image, and therefore the repeatability of color measurement can be ensured. The computer triggers the flasher when an image is being grabbed. The schematic set-up of this computer vision system is presented in Fig. 11.3.

11.5.1 Identification of irregular regions Chromatic areas, such as trash, spots and shadows, in the color image of a cotton sample are called irregular regions, which should be excluded from cotton color measurements (Fig. 11.4). The imaging colorimeter uses not only lightness, but also other color attributes to identify these irregular regions before taking color pixel counts for cotton fibers. Fig. 11.5 shows the typical CIE L*C*h distributions of the cotton sample shown in Fig. 11.4. The peaks of these three curves correspond to the L*C*h values of white cotton lint, which has a brightness around 90, a chroma of around 9, and a hue angle of 85° (yellow regions in a color wheel). The typical L*C*h values of trash particles, spotted and shadowed areas can be obtained by manually selecting a number of these features from various images, and the thresholds for screening these regions can then be selected. Although one threshold in either lightness or chroma is inadequate to discern these features from white lint, using thresholds in both dimensions and combining the two criteria with a logical ‘AND’ can achieve this goal more effectively. That is, if a pixel satisfies: L* < L*0; and C* > C*0 the pixel will be assigned to the irregular regions. Here, L*0 and C*0 are a set of the threshold. Image a in Fig. 11.6 shows identified trash, spots, and shadows

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a

Shadow

Shadow Spots

11.4 Irregular regions in cotton image.

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Frequency

Grading of cotton by color measurement 0.5

0.5

0.5

0.25

0.25

0.25

0

0 0

20

40

60

80

100

261

0 0

L*

20

40

60

80

100

C*

0

60 120 180 240 300 360

h (degree)

11.5 L*C*h distributions of cotton sample.

(a (a) a)

(b)

(c)

(d)

11.6 Identified irregular regions; (a) all, (b) spots, (c) shadows, (d) trash particles.

in the sample (Fig. 11.4). The irregular regions can be further separated by using a multi-dimension thresholding algorithm9, 10 illustrated in Fig. 11.7. L*1, C*1, h1 and C*2 are a set of threshold levels that are determined from the averages and standard deviations of L*C*h values in these regions. Images (b), (c) and (d) in Fig. 11.6 are the maps of separated spots, shadows and trash particles.

11.5.2 Color measurement The imaging colorimeter system (IC) provides a comprehensive function for measuring cotton colors and distributions in various color coordinate systems.11 Twelve cotton samples (S1–S12) with various trash contents were used as the

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C* > C1*?

N

N

C* < C2*? Y

Y Spot and trash

Shadow and trash

L* > L1* and h > h1? Y

L* > L1* and h > h1? N

Spot

N Trash

Y Shadow

11.7 Multi-dimension thresholding.

80 IC

CR-210

SPL

MCI

75 70 Rd 65 60 55 S1 S8 S5 S9 S4 S11 S6 S10 S7 S12 S2 S3 11 10 9 +b

8 7

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6 5 4 S7 S11 S5 S2 S9 S8 S1 S12 S10 S6 S3 S4

11.8 Rd and +b comparisons of the samples.

experimental materials. For each sample, five images were captured at different locations of the sample, and the results from the five images were combined for the final report. The samples were also tested by Spinlab HVI (SPL), and Motion Control HVI (MCI) and Minolta colorimeter CR-210 (Fig. 11.8). The results exhibit a high consistency between the systems, although the Rd and +b readings of the Minolta CR-210 are systematically lower than the corresponding readings of

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the other colorimeters. This is because any difference in light source, color sensor and set-up geometry may contribute to the differences in the results that colorimeters output. This figure indicates that the imaging system (IC) has been adjusted to generate Rd and +b values very similar to those of SPL and MCI colorimeters. The system is also able to yield the distributions of the color attributes since it measures color of every pixel in a relatively large area. Figure 11.9 shows the Rd ab distributions of S1, indicating the dispersions of the color measurements in the sample. The red-green attribute a concentrates in a range from –5 to 5 with larger distributions being in the positive range. A negative a indicates a green constituent in the sample. Usually, the average a of a cotton sample falls in the range 1–2, and the average b in the range 6–10. Because of a relatively small portion of a, only Rd and +b are taken into account in the previous cotton color measuring systems.

11.6

Using neural networks12

The disagreements of HVI and classifier color grades were examined specifically in the major and sub-categories of cotton colors. Table 11.5 shows the distributions of the disagreements among the five major categories. A substantial amount (44.3%) of the samples were graded ‘white’ by the HVI, but disputably graded ‘light spotted’ by the classifier. However, almost no ‘light spotted’ samples graded by the HVI were graded ‘white’ by the classifier. Hence, there is a biased trend in the disagreements between the white and light spotted categories. The disagreement in these two categories is a determinant (about 82%) in the total disagreement.

Frequency

0.4

0.6

0.2

0.4 0.2

0.1 0.2 0

0 0

20

40

60

80 100

–10

0

10

Rd

20

30

0

40

a

10

20

30

40

50

+b

11.9 Rdab distributions of a cotton sample. Table 11.5 Disagreement (%) in the major categories Classifier HVI White Light spotted Spotted Tinged Yellow stained

White

0 0

Light spotted

Spotted

44.3

0 0.2

Tinged

0.08

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Table 11.6 Disagreement (%) in the subcategories Classifier HVI GM SM M SLM LM SGO GO

GM

SM

M

SLM

LM

SGO

GO

2.79 0

3.34 1.07

2.19 0.56

0.52 0.12

0

GM: good middling, SM: strict middling, M: middling, SLM: strict low middling, LM: low middling, SGO: strict good ordinary; GO: good ordinary.

Between the light spotted and spotted categories, the disagreements are nearly negligible and unbiased. The disagreements among other categories are not available from this sample set, but they are expected to be low. Table 11.6 shows the distributions of the disagreements among the seven subcategories. Overall, the disagreements in the subcategories are much lower, and more widely spread than in the major categories. The HVI has a slight tendency to give higher grades to the samples than the classifier in the subcategories. The possible sources attributable to HVI–classifier disagreements can be both systematic and random. Systematic disagreements mainly occur among the major color categories, particularly between ‘white’ and ‘light spotted’, and are the dominant component in the total disagreements. A neural network classification provides an effective solution for solving this disagreement problem.

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11.6.1 Multilayer perceptron (MLP) A neural network (NN) is a computational system that can provide sophisticated mappings from a set of input variables to a set of output variables according to the relationships learned from the training data.13, 14 An NN usually contains massive processing units (neurons) organized in successive layers. The neurons between two adjacent layers are connected with adjustable parameters governing the form of the input–output mapping. To perform an explicit mapping, the connections of neurons must be feed-forward. One of the most common feed-forward networks is a multilayer perceptron (MLP), which normally composes one input layer, one or more hidden layers and one output layer (Fig. 11.10). To design an MLP for solving a specific classification problem, the developer needs to determine the inputs, outputs, number of hidden layers, number of neurons in each layer, and the training algorithm that is suited for the problem.

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(n)

(1)

W ij x1 x2

(1) y1

(n) y1

y 2(1)

y 2(n)

(1) yM 1

(n) yM n

W ij

(n+1)

y1

(n+1)

y2

(n+1)

xM0

Input

Hidden

yM

n+1

Output

11.10 MLP topology.

11.6.2 Neural network classifier for cotton color grading To classify cotton color, the inputs of the MLP should utilize the statistic information, such as the means and standard deviations, of Rd, a and b of samples, and the imaging colorimeter is capable of measuring these data. In this research, however, we were unable to obtain enough cotton samples that had been graded by the classifier and HVI for us to use the imaging colorimeter to collect these data needed for training the NN classifier. We had to use only the Rd and b means from the HVI as the inputs. The principle and procedure established by using these two inputs are directly applicable to the one using more inputs. Normally, the MLP uses one output neuron to represent one respective category, such as a color grade. Since there are 25 official color grades and five belowgrades in the USDA universal standards, a neural network should have 30 output neurons to differentiate these grades. However, the color grades of U.S. cotton heavily concentrate in the white and light-spotted categories. The sample set randomly selected for this research cannot equally represent all the color grades. There would be a negative bias over less represented grades if all the grades were judged simultaneously. Therefore, a two-step approach was adopted in developing a neural-network-based classifier. This classifier consists of multiple neural networks that perform the classifications of the major and sub-color categories separately (Fig. 11.11). The color data of a sample are first classified by an MLP to determine the main color category, and then sent to a separate MLP to determine the sample’s subcategory within the identified main category. The classifier has one main MLP that has two inputs, two hidden layers, and five output neurons corresponding to the five main categories (1–white, 2–light spotted, …). The two hidden layers have six and twelve neurons, respectively. For each main category, there is a subMLP that may have three to eight output neurons depending on how many subcategories (1–good middling, 2–strict middling, …) are available in this main

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1 2 3 4 5 6 7 8 9 10 1 2

Main Sub category category 1 2 3 4 5 12345678

Sub-MLP1 Sub-MLP2 Sub-MLP3 Sub-MLP4 Sub-MLP5 1 2 3 4 5 Main MLP Rd b

11.11 The neural network classifier.

category. A sub-MLP also uses a two hidden-layer structure, with six neurons being on the first layer and 15 neurons on the second layer. Each output of the main MLP is also used to control a switch that permits the color data to be sent to the corresponding sub-MLP when it is turned on. After two categories are identified, a color grade is generated by placing two digits together. The connecting weights Wij(n) of two adjacent layers in each MLP were determined through a supervised training procedure called the error back-propagation algorithm.13, 14

1 2 3 4 5 6 7 8 9 40 1 2 43X

11.6.3 Cotton color grading results by NN classifier The training data used should be those obtained from the universal standards for Upland cotton. Unfortunately, the universal standards do not include the physical samples (biscuits) for all the color grades, whose colors can be measured by an instrument. We had to use the classifier’s color grades as targeted grades in the network training, since they are currently ‘official’. The same sample set (2489 color data of 1996 crops) was used as the training set. 1385 more samples from 1997 U.S. crops were used as a validation set to check the generalization performance of the classifier. It was found that the NN classifier reduced the machine–classifier disagreements from 54.08% to 16.35% for the training set. The NN–classifier disagreement seems to have reached a minimal level (around 20%), because the classifier’s reproducibility is generally 80%. Figure 11.12 presents the distributions of the

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% 100 90 80 70 60 50 40 30 20

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as Cl

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11.12 Distributions of color grade disagreement: (a) HVI–classifier, (b) NN–classifier.

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color grade disagreements of the training samples between HVI–classifiers (a), and NN–classifiers (b). Compared with the HVI–classifier disagreements, the NN–classifier disagreements are much smaller and more evenly spread across all the grades. That means that the NN classifier can drastically reduce systematic disagreements with the classifier. The NN–classifier disagreements of the validation samples decrease from 62.09% to 22.89%, which are consistent with those of the training samples. The result from the validation set shows that the NN classifier provides a good generalization for new cotton color data.

11.7

Using fuzzy logic15

11.7.1 Boundaries of cotton color grades The partition of the color space in the Nickerson-Hunter (Rd, +b) diagram was based on the experimental data of cotton crops in the 1950s. The boundaries between color blocks may not accurately represent color differences in today’s cotton. The crisp, abrupt separations of color grades in the diagram do not reflect the clustering nature of cotton color classes, which often have blur boundaries and neighboring classes always overlap to some extent. Thus, the belongingness of a sample point in an overlapping region is inherently ambiguous. The above analysis can be further evidenced by the color data of 2489 bales of cotton selected from the 1996 crop. To facilitate the discussion, we focused on two major color classes, white and light spotted, which are also the two most disputable color grades. Fig. 11.13(a) shows the distributions of the white and light spotted classes labeled by classifiers for 2489 bales of cotton selected from the 1996 crop. Both the white and light spotted classes seem to follow a two-dimensional Gaussian distribution. Note that the real boundaries separating three major color classes, white (W), light spotted (LS) and spotted (S), were also drawn on the Rd–b plane. Although the two classes have distinct populations, they overlap extensively and their intersection does not seem to coincide with the W–LS boundary. It is evident that the W–LS boundary does not provide a realistic separation between the white and light spotted classes. This mismatch brings a systematic error into the HVI’s color grading, as shown in Fig. 11.13(b). The clear split between the white and light spotted classes arises from the crisp boundary used by the HVI. However, the W–LS separation by the HVI does not indicate the natural grouping of the cotton color data in these two classes. It is logical to consider that the two peaks of the distribution represent two separate populations in the color data as seen in Fig. 11.13(a). The HVI, however, did not allocate these two populations properly. This is the reason why the HVI tends to grade cotton colors for the white class more likely than for the light spotted class. In order to make the machine grading more realistically reflect the natural grouping of cotton colors, the Cotton Program of the USDA and the cotton community agreed to adjust the boundaries of the Nickerson-Hunter color diagram in 2000.

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o − White * − Light spotted

0.2 Frequency

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o − White * − Light spotted

Frequency

0.2

0.1

0 90 80

W

70 Rd

LS

S

12 10

60

8 6

b

4 (b)

11.13 Distributions of white and light spotted colors: (a) graded by classifier, (b) graded by HVI.

However, the modified color diagram still does not deal with problems associated with overlapping boundaries of color classes.

11.7.2 Fuzzy inference system (FIS) for cotton color grading Fuzzy logic uses the fuzzy set theory and approximate reasoning to deal with imprecision and ambiguity in decision-making.16–19 It provides intuitive, flexible ways to create fuzzy inference systems for solving complex control and

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classification problems. For classification applications, fuzzy logic is a process of mapping an input space into an output space using membership functions and linguistically specified rules. In this study, we take the output of the HVI colorimeter, the Rd, b data, as the input, and color grades as the output. Our discussion in this paper will be limited to the classification for five major color classes, white (W), light spotted (LS), spotted (S), tinged (T) and yellow stained (YS). Figure 11.14 presents a schematic diagram of the fuzzy inference system (FIS) for cotton color grading.

11.7.3 Fuzzy sets and membership functions Elements in ordinary or crisp sets have full membership in one set and zero membership in others. A fuzzy set contains elements only with partial membership ranging from 0 to 1 to describe uncertainty for classes that do not have sharply defined boundaries. For each input and output variable of an FIS, fuzzy sets are created by dividing its universe of discourse (entire space) into a number of sub-regions and are named in linguistic terms. Fuzzy sets’ linguistic terms are useful in establishing fuzzy rules. In designing an FIS for cotton color grading, five fuzzy sets were selected for the input variable Rd and six for b. The fuzzy sets for Rd represent five levels of brightness varying from very low (I), low (II), median (III), high (IV) to very high (V), and the fuzzy sets for b represent six levels of yellowness ranging from very low (I) to extremely high (VI). Table 11.7 lists the ranges and other distribution parameters of the input fuzzy sets. Each fuzzy set overlaps with its adjacent fuzzy sets. The reason for adding one more fuzzy set for b is that b seems more critical than Rd in determining major cotton

Input (Rd, b)

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Colour measurement

Fuzzy rules

Fuzzification

Defuzzification

Output (W, LS, S, T, YS)

11.14 Fuzzy inference system for cotton color grading.

Table 11.7 Parameters for the fuzzy sets of the input variables Fuzzy set

Very low (I) Low (II) Medium (III) High (IV) Very high (V) Extremely high (VI)

b

Rd Range

m

σ

Range

m

σ

40–52.5 45–65 54–75 60–82.5 67.5–87.5

46.5 55 64 71.5 77.5

3.5 3 3 3.5 3.5

4–7 4–11 7–12.5 9–17 11–18 14–18

4.0 7.2 9.5 12.4 15.1 18.0

1.00 1.00 0.80 1.19 1.19 1.19

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color classes (white, light-spotted, etc.). In general, the more intermediate levels are used, the higher accuracy the classification would be. However, increasing the fuzzy sets will significantly increase the number of fuzzy rules in the next step. The final selection on the number of fuzzy sets and their range may be determined by trial and error. Since this FIS was designed to classify five major color classes, the output variable was split by five fuzzy sets named as white (1), light-spotted (2), spotted (3), tinged (4) and yellow stained (5). The range of the output variable was equally divided into five sections for the five fuzzy sets. Once the fuzzy sets are chosen, a membership function for each set should be created. A membership function is a curve that maps an input element to a value between 0 and 1 showing its degree of belongingness to a fuzzy set. The curve can have different shapes, such as bell (Gaussian), sigmoid, triangle or trapezoid, for different types of fuzzy sets.16, 17 In this study, the Gaussian distribution curve was used to build the membership functions for the input fuzzy sets Rd and b: 2

µ(x) = e–(x−m)2/2σ

where m and σ are the mean and the standard variation of one fuzzy set in x (Rd or b). Finding the right parameters for the functions is a major task, which may be selected arbitrarily and then tweaked by using a known set of input– output data. The m and σ values used in this FIS are included in Table 11.7, and the membership functions are displayed in Fig. 11.15. The extent of overlap between the membership functions of two adjacent sets indicates the nature of the soft boundary between two color classes. For the simplicity of defuzzification, a triangular shape was used to construct the membership functions for the output fuzzy sets:

{

= 0, µ(x) = = (x – a)/(b – a), = (c – x)/(c – b),

x < a or x ≥ c a≤x
The shape and size of the triangular function depend on the values of a, b and c. To make the output clear and unbiased, the symmetric, non-overlapping and equal-size membership functions were used for all the output sets (Fig. 11.16).

11.7.4 Fuzzification Fuzzification is a step to determine the degree to which an input data belongs to each of the appropriate fuzzy sets via the membership functions. For a given input point (Rd0, b0), the memberships of all the fuzzy sets are calculated, and only the fuzzy sets with non-zero memberships are forwarded to the next steps. In Figure 11.15, an example of determining the relevant fuzzy sets was shown for an input data (Rd0, b0) = (67.5, 9.0). Rd0 belongs to the medium (III) and high (IV) sets of Rd with the memberships being 0.51 and 0.52, while b0 belongs to the low (II) and

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II Low

I 0.5 Very low

16

b

11.15 Membership functions of input fuzzy sets.

1

0.5

2 Light spotted

1 White

0

0

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10

15

3 Spotted

20

25

5 Yellow stained

4 Tinged

30

35

40

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50

11.16 Triangular membership functions of output fuzzy sets.

medium (III) sets of b with the memberships being 0.20 and 0.82. There are four combinations with the selected fuzzy sets: 1 2 3 4 5 6 7 8 9 40 1 2 43X

[µIII (Rd0), µII (b0)] = [0.51, 0.20], [µIII (Rd0), µIII (b0)] = [0.51, 0.82], [µIV (Rd0), µII (b0)] = [0.52, 0.20], [µIV (Rd0), µIII (b0)] = [0.52, 0.82]. These four combinations will be evaluated by fuzzy rules to determine the output fuzzy sets and the weight of each rule influencing the output.

11.7.5 Fuzzy rules In an FIS, fuzzy rules provide qualitative reasoning that links input fuzzy sets with output fuzzy sets. They are a collection of linguistic rules of the form:18

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273

i = 1, 2, …, k

where Ai, Bi and Ci are the fuzzy sets for the inputs Rd and b and the output color in the ith rule Ri, and k is the number of the rules. The values of Ai and Bi are the linguistic terms such as very low (I) and very high (V), and the values of Ci are the linguistic terms such as white (1) and light spotted (2). An example of such a rule may be given as follows: If Rd is very high (V) AND b is very low (I), then color is white (1). The if-part of the rule is called the antecedent, and the then-part of the rule is called the consequent. Since the antecedent in this FIS always involves two conditions (one for Rd and one for b), fuzzy operators are needed to specify the relationships of the fuzzy sets in the antecedent. AND (intersection), OR (union) and NOT (complement) are the three common fuzzy operators. Because Rd and b should be simultaneously observed in selecting a color class, the fuzzy operator in all the antecedents must be AND. For two fuzzy sets A and B, the fuzzy AND is defined as:16,18 A AND B: min{µA(x), µB(x)}. Fuzzy AND aggregates two membership functions by outputting the minimum value at a given input x (Rd or b). The result of fuzzy AND serves as a weight showing the influence of this rule on the fuzzy set in the consequent. The fuzzy rules should be established based on both visual grading experience and the basic relationships between color data and color grades in the HVI color diagram. Since there are five fuzzy sets in Rd and six fuzzy sets in b, there are 30 possible combinations in the antecedents when only fuzzy AND is applied. Thus, the maximum number of the fuzzy rules that can be established is 30. To simplify the fuzzy rule expressions, we designed a chart that illustrates (Fig. 11.17). In the chart, the five sets of Rd are arranged vertically and the six sets of b are arranged horizontally. A knot (a black dot) between a horizontal line and a vertical line indicates an antecedent formed by the connecting fuzzy sets from Rd and b, and leads to a consequent that in turn gives an output fuzzy set. The aggregated membership of the antecedent is then used as a weight factor to modify the size and shape of the membership function of the output fuzzy set in a way of either truncation or scaling. Truncation is done by chopping off the triangular output function, while scaling is done by compressing the function. In this FIS, the truncation operation was used. If the membership function of an output fuzzy set is µ(x) and the weight generated from the antecedent is w, the truncated functions are: µT(x) = max{µ(x), w}. As given previously, the input fuzzy sets containing sample (Rd0, b0) have four combinations, which satisfy the four following rules:

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R1: R2: R3: R4:

Colour measurement If Rd is median (III) If Rd is median (III) If Rd is high (IV) If Rd is median (IV)

AND AND AND AND

b is low (II), b is median (III), b is low (II), b is median (II),

then color is white (1); then color is spotted (3); then color is white (1); then color is light spotted (2).

The four output fuzzy sets are circled in Fig. 11.17. The weights of the rules on the four outputs are 0.20, 0.51, 0.20 and 0.52, respectively. The truncated membership functions of the output fuzzy sets are presented in Fig. 11.18.

11.7.6 Defuzzification After all the fuzzy rule evaluations are done, the FIS needs to output a crisp member to represent the classification result (color classes) for the input data. This step is called defuzzification. As seen in the (Rd0, b0) example, one input data may generate several weighted output fuzzy sets. The multiple sets need to be aggregated into a single set in preparation for the defuzzification. If those output fuzzy sets are different, the aggregation can be done simply by placing all the truncated functions together to form the final fuzzy set. If two of the output fuzzy sets are identical, they can be combined by using fuzzy OR, which is defined as:16,18 A OR B: max{µA(x), µB(x)}. Fuzzy OR gives the maximum value of the two membership functions at any given point. For example, sample (Rd0, b0) has two ‘white’, one ‘light spotted’ and

b

II (b0 =9.0) III (b0 =9.0) IV

I

VI Output

Rd

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I

1 2 3 4 5

II

1 2 3 4 5 1 2 3 4 5

III (Rd0 =67.5)

1 2 3 4 5

IV (Rd0 =67.5)

1 2 3 4 5

V

11.17 Fuzzy rules.

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Grading of cotton by color measurement 1

1

1

1 White

1 White

0.51

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0.5 Centroid 0

5

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G

20

25

30

11.19 Aggregation of the weighted output functions.

one spotted output functions. After the fuzzy OR operation, the two trapezoidal ‘white’ functions merge into one so that the aggregated curve becomes the one shown in Fig. 11.19. The most popular method for defuzzification is the centroid calculation, which returns a grade weighted by the areas under the aggregated output functions. Let a1, a2, … an be the areas of the truncated triangular areas under the aggregated function, and c1, c2, … cn be the coordinates of their centers on the x-axis. The centroid of the aggregated area is given by:17,19 n

G = Σaici i=1

/

n

Σ ai i=1

The location of the centroid indicates the color class to be designated to the input data. For sample (Rd0, b0), G is 17.1, which falls in the light spotted set (see Fig. 11.19). The location of this centroid inside the identified fuzzy set can suggest a finer rating.

11.7.7 Cotton color grading results by FIS The constructed FIS was tested using cotton samples in 1996, 1997 and 1998 crop years. Since the majority of the U.S. Upland cottons are ‘white’ and ‘light spotted’ and the most disputable grading occurs between these two classes, samples only in these two classes were selected for the experiment. There were totally 2489 samples from the 1996 crop, 1375 samples from the 1997 crop and 658 samples from the 1998 crop in the selection. The samples were firstly graded by official cotton classifiers, and then measured by the HVI colorimeters. The (Rd, b) data

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Table 11.8 Disagreements between different grading methods Color class

White

Crop year

1996

1997

1998

1996

1997

1998

By classifier (C)

1240 (49.8%) 2350 (94.4%) 1161 (46.6%) 1347 (54.1%) 154 (6.2%)

416 (30.3%) 1200 (87.3%) 355 (25.8%) 846 (61.5%) 87 (6.2%)

464 (70.5%) 628 (95.4%) 447 (68.0%) 337 (51.2%) 40 (6.1%)

1249 (50.2%) 139 (5.6%) 1328 (53.4%) 9 (0.4%) 234 (9.4%)

959 (69.7%) 175 (12.7%) 1020 (74.2%) 14 (1.0%) 144 (10.5%)

194 (29.5%) 30 (4.6%) 211 (32.0%) 3 (0.5%) 72 (10.9%)

By HVI By FIS C-HVI disagreement C-FIS disagreement

Light spotted

were converted into color grades using the HVI color diagram and the FIS. We used the classifiers’ grades as a reference to check the consistency of the HVI and FIS data, since the classifiers’ grades were the official grades at that time and they coincided with the natural grouping of the color data. Table 11.8 presents the data showing the disagreements among the testing methods. Percent disagreement was calculated by dividing the disagreement count with the number of the tested samples. For the white class, 54.1% of the samples in the 1996 crop, 61.5% in 1997, and 51.2% in 1998 were graded differently by the classifiers and the HVI. The amount of disagreements between the classifiers and the FIS has decreased considerably, and the FIS results are consistent for consecutive years. The classifier– FIS disagreement was much more evenly spread between the white and light spotted classes, suggesting that there is no systematic bias in the FIS’s grading. Figure 11.20 shows the distributions of the white and light spotted color classes of the 1996 and 1997 samples classified by the FIS. The two color classes in both years’ data were reasonably separated. Based on the two newly separated data clusters, a best-fit polynomial curve was calculated (curve 2 in the figure). This curve provides a new boundary that more properly reflects the segregation between the white and light spotted color classes. The distribution for the 1998 crop was not included because the number of the available samples from that year was not sufficient for drawing an effective distribution.

11.8

Conclusions

Color is an essential indication of the overall quality of raw cotton. Colorimetry has replaced human classifiers to become an official method for cotton color grading in the current U.S. cotton classing system. The HVI colorimeter performs the grading based on the Nickerson-Hunter color diagram, which illustrates color grade divisions in a two-attribute (Rd, +b) color system. For more precise and

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1– W–LS boundary in HVI 2– W–LS boundary in FIS 0.2

0.2

0.1

0.1

0 90

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Rd 70

2 1 60 50

4

6

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70 Rd

2 1

60 50

4

6

10 8

12

b

o— White (W) *— Light spotted (LS)

11.20 Distributions of white and light spotted colors classified by the FIS.

reliable results, the influence of other factors, such as the third attribute – redness, localized discoloration, and extraneous matters – should be taken into account. Color imaging technology seems to be an ultimate solution to solve the sophisticated problems that still remain in cotton color grading, especially when coupled with advanced classification methods (e.g., artificial neural networks, fuzzy logic) which can better interpret a human’s understanding of cotton colors.

11.9

References

1. Nickerson, D., Color Measurements of Standards for Grades of Cotton, Textile Research Journal, 16, 441–449, 1946. 2. Xu, B., Fang, C., Huang, Y. and Watson, M.D., Chromatic Image Analysis for Cotton Trash and Color Measurements, in press. 3. Anthony, W.S. and Mayfield, W.D., ‘Cotton Ginners Handbook’, USDA, December 1994. 4. USDA, The Classification of Cotton, Agricultural Handbook 566, Washington D.C., 2001. 5. Knowlton, James L. Improving Cotton Color Classification, 2004 EFS® Systems Conference Presentations, Cotton Inc. 2004. http://www.cottoninc.com/2004Conferen cePresentations/. 6. Earnest, Darryl W. HVI Color Grade and Other Cotton Program Issues, 2000 EFS® Systems Conference Presentations, Cotton Inc. 2000. www.cottoninc. com/2000ConferencePresentations 7. Xu, B., Fang, C. and Watson, M.D., Investigating New Factors in Cotton Color Grading, Textile Research Journal, 68, 779–787, 1998. 8. Minolta Co., Ltd, ‘Precise Color Communication’, 1994. 9. Xu, B., Fang, C., Huang, R. and Watson, M.D., Chromatic Image Analysis for Cotton Trash and Color Measurement, Textile Research Journal, 67(12), 881–890, 1997. 10. Jain, A. K., ‘Fundamentals of Digital Image Processing’, Prentice-Hall, Inc. 1989. 11. Xu, B., Fang, C., Huang R. and Watson, M., Cotton Color Measurements by An Imaging Colorimeter, Textile Research Journal, 68(5), 351–358, 1998.

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12. Xu, B., Su, J., Dale, D. and Watson, M.D., Cotton Color Grading by Neural Network, Textile Research Journal, 70(5), 430–436, 2000. 13. Bishop, C. M., ‘Neural Networks for Pattern Recognition’, Clarendon Press, Oxford, U.K., 1995. 14. Lin, Chin-Teng and Lee, George C.S., ‘Neural Fuzzy Systems’, Prentice Hall, Upper Saddle River, NJ, 1996. 15. Xu, B., Lin, S., Dale, D. and Watson, M.D., Cotton Color Classification by Fuzzy Logic, Textile Research Journal, 72(6), 504–509, 2002. 16. Cox, E., ‘Fuzzy Systems Handbook, 2nd Edition’, Academic Press, Chestnut Hill, MA, 1999. 17. Jang, Roger J.S. and Gulley, Ned, ‘MATLAB/Fuzzy Logic Toolbox,’ MathWorks, Inc. Natick, MA, 1997. 18. Hguyen, H.T. and Walker, E.A., ‘A First Course in Fuzzy Logic, 2nd Edition’, Chapman & Hall/CRC, Boca Raton, FL, 1999. 19. Lin, Chin-Teng and Lee, George C.S., ‘Neural Fuzzy Systems’, Prentice Hall, Upper Saddle River, NJ, 1996.

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12 Colour measurement of paint films and coatings N. S. GANGAKHEDKAR, Compute Spectra Color Pvt. Ltd., India

Abstract: Paint quality is colour quality. We specify colour in terms of three numbers (X, Y, Z) and closeness of the match is described in a single number (ΔE). A colour computer system is used for quality control of colour, formulation, production batch correction and quantifying colour related properties such as whiteness and yellowness indices, opacity, contrast ratio, pigment load and hiding power. Preparing a good sample for colour measurement is very important and internet based colour management systems were recently introduced for point-of-sales and are operating successfully. A non-contact spectrophotometer for wet paint colour matching and a multi-angle spectrophotometer for matching metallic in automotive paints are now available. Key words: quality control of paints, preparing good samples for colour measurement, colour management systems for paint shop, wet-paint colour matching, measurement of metallic colours in automotive paints.

12.1

Introduction

In paint making, product quality is colour quality and one of the major items in colour quality is closeness of the match. We specify colour in terms of three numbers (X, Y, Z or L, a, b) and closeness of match is described in a single number ΔE. In instrumental pass/fail of colour, we have to set-up three-dimensional tolerance limits (ΔL, Δa, Δb) or a single number colour difference (ΔE). We can also quantify whiteness index, yellowness index, colour strength of pigment, opacity, contrast ratio and the hiding power of paint. Appearance of the paint sample is mainly affected by gloss and we have to understand the importance of colour measurement with or without gloss. Quality control programs determine lot-to-lot variations in pigments and finished paints as measured against the standard and colour strength of in-process tinters (bases)/colorants. A computer colour matching system consisting of a spectrophotometer–computer combination is used for quality control of incoming pigment, pass/fail decision of outgoing paint product, colour formulation, production batch correction, quantifying colour related properties such as pigment load and opacity. How to prepare a good sample for colour evaluation is very important. We will discuss various aspects such as quality control of pigment, selection of colour measuring instrument for wet-paint colour matching, colour matching systems for paint shop, internet based colour management system for point-of-sales, measurement of metallic colours and roll of special effect pigments, measurement techniques for automotive paints and advances in colour instrumentation. 279 © Woodhead Publishing Limited, 2010

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12.2

Quality control of paints

Essentially, colour is something which we feel or perceive. Colour is a sensation and the eye is the real detector which gives us the total picture and feel. Perception of colour will vary from person to person. However, it is possible to quantify colour using a reflectance measuring spectrophotometer. From a purely physical point of view, colour is measured by spectral curves of reflectance plotted as a function of wavelength in the visible spectrum 400–700 nm. The physical quantities important in specifying colour are: spectral power distribution of light source (Energy, E), the spectral reflectance of the sample (%R), and the spectral response of the eye in the form of colour matching functions (CIE Standard Observer, designated by colour matching functions r, g, b). One has to reduce the spectrophotometric data into colorimetric data using CIE mathematics (Judd and Wyszecki 1952: Wyszecki and Stiles 1967). This special physical analysis is designed to give colour as numbers (tristimulus values, X, Y, Z) correlating with visual impressions of the three colour response mechanism of the human eye (Billmeyer and Saltzman 1966).

12.2.1 Colour as numbers Based on the fact that the human eye has three different types of colour sensitive cones, the response of the eye is best described in terms of three stimulus (tristimulus) colour matching functions r, g, b derived from three sensitivities. Using the r, g, b colour matching functions, the X, Y, Z (tristimulus values) system was introduced by CIE in 1931 (see Fig. 12.1). It is based on scientific data (Jude and Wyszecki 1975; Wyszecki and Stiles 1967). Only three numerical values X, Y, Z are sufficient to describe colour: X=ΣE×R×r Y=ΣE×R×g Z=ΣE×R×b where X, Y, Z are tristimulus values E is spectral energy distribution of light source (400–700 nm) R is spectral response curve of the object (400–700 nm) r, g, b are colour matching functions of a human observer. Standard Observer

0

× 400

700

Wavelength nm

Energy

D65

× 400 Wavelength nm

700

Sensitivity

100 %R

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[12.1] [12.2] [12.3]

X = 62.04

b g

=

r

400

Y = 69.72 Z = 7.34

700

Wavelength nm

Tristimulus values

12.1 Colour as numbers: computation of tristimulus values – CIE mathematics.

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12.2.2 Recommendation CIE 1976 (L*, a*, b*) colour space The CIE 1976 uniform colour space (CIE 1976) is a simplified version of the Adams Nickerson space. It is produced by plotting in rectangular coordinates (Fig. 12.2) the quantities L*, a*, b* defined by L* = 116 (Y/Y0)1/3 – 16Y/Y0 > 0.01 a* = 500

[(X/X0)1/3

–

[12.4]

(Y/Y0)1/3]

[12.5]

b* = 200 [(Y/YX0)1/3 – (Z/Z0)1/3]

[12.6]

The tristimulus values X0, Y0, Z0 define the colour of the normally white objectcolour stimulus. Usually, the white object-colour stimulus is given by the spectral radiant power of one of the CIE standard illuminants, for example, D65 or A, reflected into the observer’s eye by the perfect reflecting diffuser. Under these conditions, X0, Y0, Z0 are the tristimulus values of the standard illuminant with Y0 equal to 100. The total difference DE* between two colours each given in terms of L*, a*, b* is calculated from ΔE *= [(ΔL*)2 + (Δa *)2 +(Δb *)2]1/2

[12.7]

12.2.3 Recommendation CIELCH colour space When seeking to identify the components of colour differences in terms of approximate correlates of lightness, chroma, and hue, or to express colour specifications in terms of such approximate correlates, the following colour terms White + +Yellow

L*

b*

c*

Green –

h a*

+Red

–Blue

Black

12.2 CIELAB colour space and CIELCH colour space.

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should be used: metric lightness (L*), metric chroma (C*), metric hue-angle, and metric hue-difference (ΔH*). These terms are defined by using the parameters L*, a*, b* of the CIE 1976 (L*a*b*) colour space, and when these terms are used, it must be made clear which of these two colour spaces has been chosen (see Fig. 12.2).

12.2.4 Colour tolerance Note that tolerance limits are different for different applications. We have to have acceptable limits for each product or else decide on the perceptibility limit of the human being. For industrial application, we must opt for acceptable limits for colour difference. We can opt for single number shade passing (DE) or three-dimensional tolerance limits based on L, a, b or L, C, h colour space. Colour tolerance is an allowable deviation in colour from the standard sample and is expressed in terms of colour space dimensions or colour difference units (DE) (CIE 1995). In CIELAB colour tolerancing, we use three-dimensional colour tolerances: ΔL lightness, Δa (Red/Green) and Δb (Yellow/Blue). We may not have uniform tolerance limits for all the parameters. Let us say we have fixed ±Δa and ±Δb limits, which will give us a tolerance box which will be rectangular (see Fig. 12.3). All samples c*

b*

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• • • ••• • • ••• ••• •• • S

a*

12.3 CIELAB colour co-ordinates: tolerance limits are threedimensional. Tolerance limits for a and b form a rectangle: all samples inside the rectangle are passed while those outside are failed.

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which will fall within the box limit will be numerically passed. On the other hand samples which are within the ellipse are visually accepted (see Fig. 12.4) and those samples which are outside the ellipse but still in the rectangular tolerance block are failed (not acceptable). This is one of the reasons that the CIELAB (1976) colour difference formula fails and does not agree when compared with visual tolerance. In the case of CIELCH, the difference limit for ΔL* (lightness), ΔC* (chroma), and Δh (hue) are considered (see Fig. 12.5). This method gives a wedge-shaped tolerance box (non-rectangular) and offers better correlation between visual assessment and instrumental pass/fail (Clark et al. 1984). The JPC-79, CMC and CIEDE 2000 formulae are based on L, C, h and they offer better correlation with visual assessment (Luo et al. 2001; Luo 2001). This leads to single number shade passing (only ΔE value) and we can do away with three-dimensional tolerance settings. It is confirmed with statistical data that the CMC colour difference unit based on CIELCH colour space is more uniform and independent of any hue and agrees very well with visual assessment. The CIELAB 1976 formula has given very serious problems in paint sample evaluation and the only solution was to fix the three-dimensional tolerance limits for each colour. It has been successfully used in industry and it is still quite popular. If you want to use single number shade passing then you have to use

in ge an

• • • ••• • • •• ••• •• •

Ch

b*

hu

ea

ng

le

c*

S

Hue angle a*

12.4 CIELCH tolerance limits. Elliptical tolerance limit: C and h – all samples falling outside ellipse will be failed as hue changes are not acceptable to eye. In this case, samples which are failed in CIELAB tolerance limit are passed in CIELCH and vice versa.

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12.5 CIELAB vs. CIELCH – CIELAB tolerances are rectangular in nature and CIELCH tolerances are elliptical. Samples outside the ellipse are not passed as hue changes are not acceptable to the eye and this agrees with visual observations.

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CIELCH formula (McDonald 1987) and if you want to continue to use CIELAB 76, then use three-dimensional tolerance limits for pass/fail. Colour difference tolerances are designed as boundaries in colour space within which acceptable colours of the product fall. The boundaries do not necessarily correlate with perceptibility of difference but rather with the limits of acceptability. The standard colour may not be in the centre of the bounded region but may be displaced to one side. For example, where subsequent yellowing may occur, the tolerance of the yellow-blue dimension might be +0.1, –0.8 units. For defining such colour difference tolerances, a three-dimensional description of the colour difference allowed can be considerably more helpful than a one-number tolerance such as ΔE (total colour difference). A colour difference expressed in terms of ΔL, Δa, Δb, provides a better guide to the needed formulation correction. Such threeterm tolerances are readily reducible to graphs for showing acceptability. A very small change in a tristimulus value, particularly in the red-green dimension, as indicated by the X-value, can represent a fairly substantial colour difference.

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For many colours of practical interest a 0.02 variation (in X) causes a 1.0 CMC unit change. We use the tristimulus system (X, Y, and Z) to represent visual colour difference evaluations. Before the introduction of the CMC and CIE 94 formulae, the author had worked on a decorative paint system based on alkyd resins. The single number shade passing was not giving satisfactory results or good visual correlation and the CIELAB 1976 formula was not very satisfactory, although three-dimensional tolerance limits using FMC-II formula were acceptable. After measuring a number of batches and correlating with visual observations, three-dimensional tolerance limits were fixed for each colour. The tolerance limits for a few colours are given in Table 12.1. We can see from the tolerance limits of different colours that each colour has specific preferred tonal variations (Gangakhedkar 2003). It is observed that: •

•

In paint applications FMC-II agrees well with visual assessment. For small colour differences, CIELAB is not in agreement with visual assessment. For yellows and bright reds, FMC-II offers a better solution. Now we can use CMC, CIE 94 or CIE DE 2000 colour difference equations which are much better. Some more work is needed in this area. The single number shade passing concept (total ΔE) based on CIELAB does not work properly. We must use three-dimensional tolerance limits while using the CIELAB equation. Table 12.1 A case study – acceptance tolerance limits (FMC-II) S. no.

Colour

DCRG

DCYB

DL

1

P.O. Red

2

Golden Brown

3

Satin Blue

4

Royal Ivory

5

Oxford Blue

6

Jade Green

7

Brilliant White

8

Olive Green

9

Pale Rose

10

Off White

0.80 −2.50 0.20 −1.00 0.80 – 0.20 −0.50 0.80 −1.00 2.00 – 0.60 −0.20 0.50 −0.50 1.80 −0.30 0.20 −1.00

0.50 −0.60 1.00 – 0.10 −0.80 1.00 −0.20 0.80 −0.20 0.50 0.50 0.50 −0.50 0.50 −0.50 0.60 −0.40 1.00 −0.10

0.20 −0.80 0.50 −0.50 0.20 −1.00 0.50 −0.10 – −1.00 0.50 −0.50 0.50 −0.50 0.50 −0.50 0.50 −0.50 – −1.00

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12.2.5 Colour strength The strength of any colorant (dyestuff / pigment) is related to the absorption property. We measure reflectance, not absorbance and it is known that when reflectance is more, absorbance is less. The Kubelka-Munk theory gives the relation: {K/ S = {(1- R)2 / 2 R}

[12.8]

where R is reflectance, K is the absorbance and S is the scattering. The K/S curve always has characteristics of every colorant and colour strength is defined as Strength = [{(1- R)2/ 2 R} batch / {(1-2R)2/ 2R} standard] × 100

[12.9]

We can determine colour strength using the following method: 1

2

3

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Colour measurement

5

R min (absorbance maxima) We can find the lowest value of R (which is the maximum value of absorbance) and obtain K/S values of the sample and standard and compute the strength. This is generally accepted and more or less agrees with visual observation. In commercial software packages, it is automatically done by the program. At given wavelength When comparing two dyestuffs or pigments, they may have different R minima. In such cases we have to compute the strength based on the wavelength of the standard having lowest reflectance values. This will give correct assessment of the strength. Based on tristimulus values X, Y, Z, X & Y (average), Z & Y (average) If samples are Red/Green, then use the ratio of X-tristimulus value and if samples are Yellow/Blue, then use ratio of Z-tristimulus values. If we want to consider the light/dark property then it is best to use combinations such as average of X and Y (for Red/Green) and Z and Y (for Yellow/Blue). Integrated wavelengths Commercial software packages use an integrated wavelength approach for computation of colour strength. In this case, strength is calculated at each wavelength and the average is taken as real strength of the colorant. This is now widely used in the paint industry. However, sometimes it may not give the correct strength of the colorant. Strength and colour difference relationship We have to look at the strength and colour difference ‘as it is’. Strength may be acceptable but colour difference is too much or strength may not be acceptable but colour difference is within the tolerance limit. Generally, when the strength of the pigment is high, we can adjust it by loading less. Taking this practical approach into consideration, mathematically we can predict the reflectance/ absorbance curve of any given sample and predict the expected colour difference ‘as it is’ and strength and colour difference ‘after adjustment of strength’.

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Figure 12.6 illustrates the computer output for strength (Gangakhedkar 2003). We can see that the strength of the pigment is 110.69 with a colour difference of 1.97. After adjusting to 10.69% of the strength, the colour difference will reduce to 0.95 units from 1.97. This may be acceptable. Sometimes, we may find an increase in the colour difference value after adjustment of strength. We must remember to take into consideration both the parameters, strength and colour difference. Table 12.2 illustrates the strength calculations using the different methods mentioned above (Gangakhedkar 2003). Jade Green Standard and batch samples of 1% concentration were measured and strength was calculated based on different methods. In this case, variation was not significant but it is noticed that the strength value will be different for different methods. The author’s experience indicates that strength at Rmin is always a correct representative.

12.2.6 Tinting strength of white pigments In principle, instrumental tinting strength (TS) measurement may be achieved by varying the weight of colorant used with the standard until it matches the sample in lightness, now considered as R∞. In practice, it is easier to use the same weight of colorant with both standard and sample and calculate by K-M equations (from reflective values) the weight needed to produce a match. This concept is LAV

Illuminant : D65

Colour Difference : CIELab

Observer : 10deg

Color

Wavelength(nm)

%R

K/S

Green Std

420

7.18

5.4656

Green Batch

420

7.13

6.0499

As Is

Adjusted

110.69

100.00

Illu/Obs

DE

DL

Da

Db

As is Colour Difference

1

1.97

–1.73

–0.83

–0.43

Adjusted Colour Difference

1

0.95

–0.22

–0.84

–0.40

12.6 Computer output of colour strength ‘as it is’ and ‘after adjustment’. Strength – Rminima.

Table 12.2 Strength calculations using different methods Calculations based on

Strength

DE ‘as it is’

DE ‘after adjustment’

Rmin(600 nm) Selected wavelength (580 nm) X value Y value Z value

84.95 86.67 84.62 84.47 83.54

2.32 2.32 2.32 2.32 2.32

0.85 0.47 0.25 0.25 0.25

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used in the ASTM method and the basis for the calculation is discussed in the Pigment Hand Book, Vol. III (Paton 1973). For an opaque film consisting chiefly of vehicle, the white pigment under test and a little tinting colorant, the S-value arises (essentially) from the white pigment and the K-value from the colorant. Therefore the TS of the white pigment is proportional to its S-value. That is, TS white pigment = [{(1-R)2 / 2R} STD / {(1-R)2 / 2R} Batch] × 100

[12.10]

For the colorant, the magnitude of the K is proportional to the amount (weight) present. That is, remembering the symbols for weight of colorant used. The tinting strength is defined as [{(1- R)2/ 2 R} Batch / {(1-2R)2/ 2R} STD.] × 100

[12.11]

This equation shows that the TS of white pigments may be measured instrumentally by making use of calculations while still maintaining the classical concept of the visual TS (Committee on Colorimetry 1953). The use of an instrumental TS method has many advantages over the classical visual method besides requiring only a single preparation of sample and standard. The instrumental test is objective. There are sometimes advantages to defining this TS value as equal to its S-value measured in the same paint without the colorant. This produces TS values of the test sample in the same physical units used for the S-value, thus allowing direct calculation of the hiding power from tinting strength measurements.

12.2.7 Whiteness and yellowness indices

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As with any other colour, three numbers are necessary for the complete identification of any white. When using L, a, b, it is almost always shown that the –b dimension measuring yellowness is the most critical of the three. In fact, the use of bluing, which decreases L but increases blueness, produces a visually whiter product. In studies, –b is typically found to be three or four times as important as L, which is next important. The -a dimension is the least important in normal practice. White colours are to be represented on graphs having the two dimensions (L and b). Whiteness as a single number index rather than three colour scale parameters is preferred for whiteness assessment. Various whiteness indices exist and are used for numerous applications (Hunter 1958 & 1960). The most common whiteness index available is WI E313, which is defined by ASTM Designation E313–73 (Reapproved 1993), ‘Standard Test Method for Indexes of Whiteness and Yellowness of Near-White, Opaque Materials’. This index may be applied to most opaque materials. Whiteness values assigned to colored items are meaningless. WI CIE is described in CIE Publication 15.2 (1976), pages 36–38. It is recommended for use with CIE illuminant D65 and may be used with either the 2° or the 10° observer. This index is often used by the textile, paint and plastics industries for measuring product whiteness.

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The CIE Whiteness The CIE Whiteness formula is to be used for comparisons of the whiteness of samples evaluated for CIE standard illuminant D65/10° measured on the same instrument at nearly the same time. * WI = Y + 800 (xn – x) + 1700 (yn – y)

[12.12]

which involves three dimensions (CIE Y, x y) of colour of specimen, with xn and yn being the chromaticity co-ordinates of the achromatic point for the chosen observer (2° and 10°). The higher the value of W, the higher the whiteness and a perfect reflecting diffuser will have WI = 100. For Fluorescent Whitening Agent (FWA), W >> 100. T = 900 (Xn – X) – 650 (Yn – Y) ¾ Tint. In 1987, the CIE Whiteness formula was incorporated in ISO, Part J02 and in 1989 it was written into AATCC Test Method 110 – 1989. The CIE Whiteness index generally seems to agree well with visual assessment but different researchers reported different formulae (Ganz 1975). One can use the formula based on agreement between buyers and sellers of colour products. ASTM (E313) and ASTM 1925 are the most popular whiteness indices used in industry (Hunter 1958 & 1960). Yellowness index Yellowness is a property important in paint industry. One can always find yellowing of paint, particularly white paint. The purity of white pigments may be determined based on the amount of yellowness present. Also, some paints degrade and yellow with exposure to sunlight, temperature, or other environmental factors during use. Thus, yellowness has become an important variable to measure in the paint industry. There are different types of yellowness indices available, depending on the type of product being measured. Two of the most common are ASTM D 1925 Yellowness and ASTM Designation E313-73 (Reapproved 1993), ‘Standard Test Method for Indexes of Whiteness and Yellowness of Near-White, Opaque Materials’. ASTM D 1925 yellowness YI (1925) = {(128X – 106Z)}/ Y

[12.13]

ASTM E313 yellowness YI (ASTM-313) = 100 (Y-100Z/Z0) / Y

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[12.14]

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where Y and Z are tristimulus values and Z0 is the Z value of a perfect diffuser. This is applicable to nearly white or colourless samples. (For details of whiteness and yellowness measurements see Chapter 4.)

12.2.8 Opacity/contrast ratio and hiding power Opacity The opacity of a material is an indication of how much light passes through the material. The higher the opacity, the lower the amount of light that can pass through. Generally, opacity is calculated from reflectance measurements of the material with a black backing and the material with a white backing. Opacity = [Yblack backing / Ywhite backing]

[12.15]

where Y is the CIE tristimulus value. Contrast ratio (CR) The ratio of luminous reflectance of a specimen backed with black material (RB) specified reflectance to reflectance of the same specimen backed with white material (RW) specified reflectance is CR = RB/ RW.

[12.16]

Hiding power Determination of the hiding power of non-coloured paints

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The hiding power (HP) of paint is understood to be its ability to eliminate the contrast between a black and a white substrate to the extent that the reflectance obtained over a black substrate is 98% of that obtained over a white substrate. The hiding power of paint measures its ability to obscure a background of contrasting colour. White pigments scatter incident visible light at all wavelengths whereas coloured pigments absorb incident visible light at characteristic wavelengths. Measurement of hiding power for white versus coloured paints We have to get HP for pastel and dark-coloured paints. It is easier to absorb light with a coloured pigment (and thus gain hiding) than it is to increase the amount of light scattered in a white paint. In fact there is little interest in measuring the HP of very dark-coloured paints because they have more than enough HP for conventional usage. Only TiO2 will produce high-hiding white paints whereas many coloured pigments will produce high-hiding, dark coloured paints. Computation of hiding power of white paint is given in detail in the Pigment Hand Book, Vol III (Paton 1973). It is based on computation of scattering

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coefficient (S) of TiO2. Using K-M equations, we can calculate absolute scattering (S) and absorbance (K) coefficients. We can determine the hiding power of white (TiO2) based on S which is representative of the hiding power of white. The mathematical formulae are available in the above mentioned book.

12.2.9 Pigment load For pigment load calculation (Parker 1973) we have to use the concept of absolute K and S (not relative). Contrast ratio, thickness and pigment load decide the quality of paint or plastic material and optimization is very important for determining the loading of pigment. This is taken into consideration in the pigment loading program based on calculations of absolute K and S (see Paton 1973). Table 12.3 gives the hiding power data of two white pigments taking into consideration thickness and contrast ratio. In answer to ‘How to arrive at the optimum concentrations of pigments so that the desired shade gives complete hiding or opacity?’, pigment load calculations can be used (Gangakhedkar 1992; Paton 1973).

12.3

Sample preparation for colour measurement

Tips for preparing paint samples (Gangakhedkar 2003) for colour measurement are given below. •

• •

• • • • •

The pigment must be fully developed, preferably by pre-dispersion in concentrated form. Be on the lookout for streaks, specks and other indications of incomplete dispersion. For colour measurement by instrument, an opaque sample is recommended. Side by side comparison with the standard, either by drawdown or by press-out, is preferred. This is even more important if transparent samples are being measured so that differences due to film thickness are minimized. Make certain that there are no surface blemishes, bronzing or differences in gloss. Be extremely cautious about the effects caused by temperature. Be on the lookout for indications of incompatibility such as flocculation, crazing or slight solubility of pigment. Design the test to examine one property of the pigment. Use one test to examine colour. Use another test to examine for ease of dispersion and so on.

Table 12.3 Hiding power calculation (white paint) Sample

RG

RB

RW

(A) White 7100 9000 9140 (B) White 7250 9100 9250

Tmil

CR

a

2.5630 2.0582

0.9851 0.00334 0.9770 1.00052

b

ST

S

0.08181 12.1559 4.7426 0.03413 10.6121 5.1555

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HP (sq/lit) 775.04 735.74

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• •

• • • • • • • •

•

Colour measurement

Note that coloured samples are subjected to the vagaries caused by normal sample preparation errors. Take the time to prepare duplicate samples. First check for reproducibility and repeatability. Measure the colour differences and strength of duplicate samples. While fixing the tolerances, consider the variations observed in the duplicate or reproduced sample. Even the most carefully conceived, simplest, and most carefully run test will exhibit some difference in sample preparation. Duplicate samples had an average measured colour difference of 0.2 units. In no case did the duplicate samples exactly match one another. Despite our best efforts to control variations, we will find that strength differences between duplicates are as much as two or three percent. Test sample surfaces must be the same with regard to gloss, texture and surface regularity. Surfaces must also be free from fish eyes, pinholes, bronzing, crazing, etc. Differences in surface gloss account for the colour difference. The unthinking instrument might be showing us where we have gone wrong. We must remember that instruments are intelligent machines which see only what we give them to see. In most cases, the source of the problem is an improperly prepared sample or a sample preparation procedure that has more errors in it than we realize.

12.4

Pigment quality control

Note the following tips for application techniques: •

• 1 2 3 4 5 6 7 8 9 40 1 2 43X

•

• •

Always use a film applicator to achieve uniform film thickness. Apply standard and sample side by side. Check repeatability and reproducibility of laboratory technique. Make visual observations and compare with physical (instrumental) measurements. Establish tolerance limits for each hue, i.e. red, green, blue, etc. Always remember ‘Look and Think’. The smaller the tolerance limit, the more problems in reproduction. Be reasonable and select the tolerance limit based on supplier and manufacturer information. Take maximum care before presenting samples for spectroscopic measurements. As a computer reading can be taken only after complete drying, the approval time is considerably increased. The solution to this problem is to compare sample and standard side by side during wet or semi-dry conditions. Approval time can be reduced if comparison is made under identical conditions. Use of the IR drying technique is recommended as it does not produce colour drift.

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•

293

Laboratory data is to be statistically analysed and correlation between laboratory and production batches is to be established by creating analytical history. For coloured pigments, determine the strength at maximum absorption (Rmin). In case of yellow and red pigments, the absorption region is a flat spectral curve. So, we may select the danger point. We cannot correct for the deviations in chromaticity of incoming pigment but can always correct for strength by adding required pigment or clear. Use strength correction factors for assembling the production batches (Gangakhedkar 2003).

In the paint development laboratory, we have to test pigments and make primaries of colorants for the database required for the match prediction. We should also remember the following points. • •

•

•

• •

•

Remember, you are not matching formulae you have already established. Your objective should be to get the least metameric and low cost formulation. Primaries for database: We have to prepare separate primaries based on the production process, i.e. separate primaries for sand mill processing and ball mill processing for the same pigment (1 pass–2 pass). We have to make new primaries if colour development (strength and chromaticity) is very much affected due to the process. Then we have to collect the data of optical properties for each pigment based on the pigment-medium-process combination. Water base paints: We can use the pastes of unknown pigmentation, but care should be taken while mixing with the black: in no case is bronzing to be observed and clear or proper pigmentation is to be prepared. The concentration of the mix with white is to be selected so that reflectance of the mix with black will not be more than that of the mix with white. This will depend upon the strength/depth of colour paste. In the case of colours where the TiO2 percentage is at 4%, a separate white base is to be prepared. Specular excluded (SCE) mode: Gloss plays a very important role. In a computer program, proper calibration values of SCE are to be stored and after getting the correct data we can use this SCE mode of operation to run a match program with SCE data. Metamerism/Non-metamerism: We will see the colour difference (ΔE) in A, D and CW F illuminants and look at Metamerism Index. Colour difference unit: Our eye is non-linear and equal colour differences in different hues are not equivalent to each other. In one of the author's case studies, we selected three paints of three different hues (red, yellow and green). Visually matches were very close between the sample and standard of these three different hues, but instrumental measurements indicated large deviations (see Table 12.4). This case study indicates why there are differences in visual and instrumental assessment of colour. Colour difference in metallics: If we measure the metallic colour at four different places and compare with the standard, considerable colour differences

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Table 12.4 Color difference in FMCII and CIELAB unit for three different hues

1 2 3

•

•

Shade

Visual observation

FMCII

CIELAB

P.O. Red G. Yellow Green

Close match Close acceptable Close match

1.6 1.8 3.1

0.8 0.6 1.4

are noticed. While fixing tolerances, we should note down these influences and deviations. If we prepare the samples the same way each time, then each sample will be the same as all previous and all subsequent ones. This is called reproducibility of making samples. We must realize that in setting colour specification, we must have good repeatability of sample making. Most carefully run tests will exhibit some differences in sample preparation. In no case did the duplicate samples exactly match one another. We may get a colour difference in the range 0.04–0.6 CMC units. The pigment manufacturers usually allow a maximum batch to batch strength variation from standard of plus or minus three percent and total colour difference ΔE = 0.5 CMC units. This is to be strictly controlled.

12.4.1 Pigment evaluations For proper evaluation of pigment, bases and white, note the following points (Gangakhedkar 2003). •

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•

•

•

Quality of pigment or bases is based on pigment dispersion. The shade or mass tone of any pigment will often vary significantly, depending upon how well the pigment is dispersed. The maximum strength of pigment can be obtained with complete dispersion but the presence of streaks and specks indicate incomplete dispersion of pigment. This will result in inaccuracies in colour measurement. There is a relationship between shear viscosity and processing temperature which affects pigment colour development. For example, phthalo blue milled at 275°F and 300°F will have significantly different tone and strength. System failures: Flocculation, bleeding, spewing, crazing, mottling and migration are due to system failures. Colour measurement will be a futile exercise if system failures are observed in pigment dispersion and paint making. Sample surface differences: If there is a difference in surface appearance (e.g. gloss), then colour measurement will be inaccurate. Gloss, texture and surface irregularity will create problems in colour measurement while differences in surface gloss account for the colour differences.

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12.4.2 Uncertainties in sample preparation To avoid uncertainties, we must note the following. • • •

• • •

•

Prepare a standard sample along with a batch sample together to minimize small variations. In the case of paint/pigments, samples have the same drawdown and should show complete hiding. In the case of paint samples, wide variations are observed in duplicate drawdown when drawdown is made at either fast or slow speed. This is due to the different film thickness of the drawdown. We can reduce the variations to a minimum by using opaque samples. Samples made with different equipment will show large variations. Samples made by different operators will show considerable variations. In paint applications, we have to remember variations occur due to different variables such as dispersion technique, temperature effect, system failures, sample surface differences and anomalous pigment behaviour. Geometric metamerism plays an important role in metallic colour matching.

12.5

Problems in match prediction: paint applications

Variables in paint processing make the problem complicated and areas of poor control are to be considered along with the evaluation of process areas (Gangakhedkar 2003). Some of the factors affecting the prediction of matches are: • • • •

application errors in sample preparation brushing/spraying/human errors, role of dispersion and dispersibility (machine variables – resin/pigment behaviour), role of white TiO2 and extender in hiding, orientation of metallic flakes.

Variables such as changing standards, efficiency of grinding machines with respect to required dispersions, colour and strength variations in in-coming pigment batches, floating, flooding, flocculation, settling of pigments, skinning, strength of tinters (bases and whites), substrate variations on thickness developed, paint application methods such as brushing, spraying, stoving create colour matching problems.

12.6

Computer colour matching for paints

Colorant formulation or what is commonly but inaccurately termed colour matching, is the determination of colorant concentrations required in the application on a substrate so that the combined colour of these will be the same as that of the standard. Recent developments in the chemical technology of dyes and pigments have given the modern colorist an almost endless number of colorants

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with which nearly any colour that can be perceived can be reproduced on a variety of substrates. If this is to be done faster and at lower cost, then we have to replace the trial and error method by a more scientific method based on colour measuring instruments integrated with personal computers. Davidson and Hemmendinger (1966) revolutionized the concept of colour matching by introducing the analogue Colorant Mixture Computer (COMIC) in 1965. In computer colour matching, we have to first make an attempt to quantify colours by virtue of a unique reflectance pattern that each colour exhibits and then to match this unique pattern by a blend of various pigments. The blend that gives an identical reflectance pattern is an exact match for the desired colour. For this, we have to collect the spectral reflectance data for both the standard colour and the pigments. This data is then to be analysed by using the Turbid-Medium theory (Kubelka and Munk 1931). K-M equations do work well when properly applied, depending upon the type of the system. Optical properties of pigments are described by famous Kubelka-Munk equations which are used to determine the absorption coefficient (K) and scattering coefficient (S) of the colorants used. The basic relationship of the Kubelka-Munk equation can be expressed in the following equation: K/S = [{(1 – R)2/ 2 R}]

[12.17]

where R is the reflectance value of samples at a given wavelength. This equation is valid for an opaque paint film at any wavelength in the visible region. K-M equations do work well when properly applied, depending upon the type of the system. For example in a paint system, while applying the K-M theory, we have to make an assumption that the total absorption coefficient for a paint film is the sum of the absorption coefficients of each colorant weighted by its concentration and similarly for the scattering coefficients. We have to determine separate K and S values and use the two constant Kubelka-Munk theories. Using additive concept, we can write:1 2 3 4 5 6 7 8 9 40 1 2 43X

K = C1 K1 + C2 K2 + C3 K3 + Cw Kw

[12.18]

S = C1 S1 + C2 S2 + C3 S3 + Cw Sw

[12.19]

where C1 C2 C3 are concentrations of coloured pigments K1 K2 K3 are absorption coefficients S1 S2 S3 are scattering coefficients Cw is concentration of white pigment Kw and Sw are absorbance and scattering coefficients of white. Using characteristic optical properties of colorants (K and S) we can compute the colorant concentrations in a desired colour and colour mixture theories based on different mathematical models are available (Kuehni, 1975). An enormous amount of calculations are involved in solving colour matching equations but a digital computer provides the fastest solution to this problem because it can be interfaced directly to the colour measuring instrument.

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The basic equations used in computer colour matching are based on the Tristimulus Match and Two Constant Theory. Match prediction mathematics is given in details by Eugene Allen (1966) and Rolf Kuehni (1975), in his book Computer Colorant Formulation, reviewed all the earlier work and presented the complete mathematical know-how for the colour matching problem. Gangakhedkar (1991, 1992), in his two books, worked out the practical problems and explained how to write the software program based on all earlier works. We have to prepare calibration samples for creating a database for match prediction and the colour computer system is a powerful tool for predicting paint formulation based on low cost and least metameric matches. The ‘right first time match’ is the dream of any paint formulator and new developments in the computer colour matching (CCM) technique offer a complete solution to colour matching problems.

12.7

Colour control system

A colour control system consists of: • • • •

spectrophotometer, computer hardware and system software, colour programs, and application technology.

The real backbone of a colour control system is the colour software and application technology required for specific colour problems. The colour software necessary for the solution of most colour problems is relatively complex and there are a number of commercial colour systems available on the market based on different spectrophotometers and colour matching software. Computer colour matching (CCM) today is quite different from what CCM was in the 1970s and ‘computer tinting’ is a reality at the point-of-sale (POS) terminal. One can make any given colour quickly by using automatic paint dispensing machines interfaced with colour matching computer systems, a most important trend in the paint industry. Portable spectrophotometers are also now available with very high accuracy and at reasonably affordable costs. ‘Do it yourself’ is a concept used in the paint industry in the USA while a ‘POS’ system equipped with a mobile computer colour matching system is now a reality and will help to expand the coating business.

12.7.1 Colour measuring instruments A spectrophotometer, colour measuring instrument can make the same kinds of judgement that human observers do. Our main objective in the use of a colourmeasuring instrument is to find a meaningful correlation with visual perception, but please note that an instrument has nowhere near the versatility of the eye.

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Design features of modern colour spectrophotometers are discussed in detail in other chapters, but we will mention some important points which will help us in the proper selection of an instrument.

12.7.2 Today’s colour measuring instruments It is to be noted that the race between competing designs seems to be over – at least for the time being. Today’s industrial instrument is most likely to have a pulsed xenon flash source and a replica grating monochromator with diode array detector. The most important characteristics of the instruments are: • • • •

precision (repeatability, reproducibility and accuracy) measurement geometry and the size of measured area UV calibration of the light source different sample holders.

Additional features desired are: • • • • •

automatic identification of aperture 45/ 0° geometry with UV calibration facility LED based instrument numerical gloss control numerical UV control.

We have to make the choice of instrument based on real needs and type of application. Most of the top of the line instruments have excellent agreement with the National Physical Laboratory and the Hemmendinger Colour Laboratory (RPI 1978; AIC 1981).

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The author has experienced many difficulties as his customers were pigment suppliers and paint manufacturers with multiple production plants using colour measurement methods for quality assurance. Users were very critical for setting-up numerical tolerance limits for pass/fail (paint manufacturers as buyer and pigment manufacturers as sellers). Performance and functioning of instruments at each location was critical and we had to look into problems related to: • • • • • •

standardization instrument calibration short term repeatability long term repeatability inter-instrument agreement sample preparation procedures.

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12.7.4 Problems of instrument metamerism If a company with multiple plants uses different models and different makes of instrument and uses colorimetric data for quality assurance, instrument metamerism can cause serious problems. It is related to geometry, aperture size, light source, design of integrating sphere, dispersing elements, detector system, standards used in calibration and the manufacturer’s design approach. Ideally speaking, we should have the same instrumentation everywhere if colorimetric data is to be transferred and used in multiple plants. BCRA tile data will indicate the performance of instruments but cannot identify the problem of instrument metamerism. The problem is more serious if instruments are of different make or different models from the same manufacturer. It is to be noted that the role of instrument metamerism confuses the language of digital communication, but nowadays, inter-instrument agreement is fairly good and manufacturers are supplying special programs for this. There are problems when using instruments from different manufacturers for the reasons given above.

12.8

Measuring colour properties of wet paints

Wet-paint colour matching is a new trend in paint matching. A non-contact spectrophotometer is effectively used for wet-paint colour matching with suitable software for quality control and match prediction. We have to find out the correlation between wet and dry paint and there are some limitations. However, colour measurement of wet paint is not a problem. How to make a database of wet-paint and find correlations with dry-paint measurements is a major concern. Accuracy will be based on preparation of the database and storing of the wet and dry standards. We also have to make colour files for each product line, which is tedious. Wet-paint colour matching certainly saves considerable time and increases productivity but some more work is needed for industrial use. The most important component of a wet-paint colour system is the non-contact spectrophotometer for wet-paint measurement.

12.8.1 Non-contact spectrophotometer Recently on-line colour measuring instruments were made available for wet-paint colour matching. The Minolta CF-1440 is an ideal non-contact spectrophotometer widely used in the paint industry (one can get details from the manufacturer’s website). Konica Minolta recently introduced a new portable non-contact model CR-241 which measures colour without touching the sample. X-Rite offers a range of instruments, namely the TeleFlash 130, TeleFlash 445 and VERY COLOR spectrophotometers.

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12.8.2 Wet-paint colour matching A number of wet-paint colour matching systems are in operation in industry with latest new generation software for wet-paint application, quality control and formulation; the description of the wet-paint software is the same as for dry-paint matching except for the non-contact spectrophotometer (Gangakhedkar 2003). Some of the features are as follows: • •

• • •

• • •

Wet-paint software is suitable for oil based alkyd paints/water based paints and industrial paints. Wet-paint software is used for quality control, first trial matching, and correction of a batch. It is necessary to use a non-contact spectrophotometer, which measures colour from a distance of 10 cm. One has to prepare the colour file (data file) on a wet basis and standards must be measured on a wet basis. Wet matching helps save time, control paint product and increase productivity. On the contrary, with traditional methods by contact, for an oil paint to be controlled after its application on the substrate, we must wait for at least 12 hours for drying and may have to do up to two corrections. If everything is OK, still production stops for 24 hours, i.e. three working days of eight hours each. With wet-paint matching, we may match the shade in one hour. Precision and accuracy will depend on the data files and standardization, i.e. preparation accuracy. It is possible to work with all types of paint products. We make colour files by the same methods. Note that if the colour file is performed on wet then the standard must be measured in wet and all controls must be performed in wet.

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Instant colour matching at the paint shop

A computer colour matching system is a must for any paint company for handling day-to-day colour problems of formulation, batch correction and perfect matching. This system is required for plant matching new colours, production correction and quality control. It is called the ‘mother’ system and has a complete database. A ‘daughter’ system with portable low cost instruments and limited colour software is installed at the paint dealer’s shop, commonly known as the Point of Sales (POS) system. Some of the features of mother/daughter colour systems are given below. • •

Any mother system software consists of a complete package for formulation, batch correction and quality control. Formulation is based on calculation of load of pigments in consideration of desired covering for the applied thickness.

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•

•

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Correction of the first imitation is expressed in addition and reformulation. Automatic saving of the formulation in the Formula Book and automatic search. Choice of spectrophotometers: One can select any low, medium or top-of-the range spectrophotometer from a leading manufacturer for the mother system and also a portable, low cost spectrophotometer for the daughter system. For example, the Tethered spectrophotometer from X-Rite is lightweight, flexible, simple to use, and allows the user to take spectral measurements. It is based on 45°/ 0° geometry and is ideal for lower-volume paint retailers (see Fig. 12.7). The PocketSpec Spectrophotometer is a colour reader used by Benevue Colour Management System. It is accurate, low cost and reliable instrument (see Fig. 12.7). Wet-paint colour matching is a new trend in paint matching. It is necessary to buy a non-contact spectrophotometer and software suitable for wet-paint colour matching. Hardware and software interface with the POS system: Mother system software should be integrated to any tinting machine. There are a number of

Tethered spectrophotometer (X-rate)

035/067/149 035/063/144 +0 – 4 –5 %diff = 3.59

Pocket spectrophotometer

12.7 Low cost spectrophotometers for POS.

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• •

•

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commercial paint dispensing systems on the world market. Mother system software and tinting machine hardware and software integration is a must for any good system. A POS system consists of three components: (1) base paints – whites and colorants, (2) tinting machines and (3) portable colour measuring instrument. POS tinting machines (CORAB/FLUID, etc.): Canister management and dosing management are made available by tinting machine manufacture, but one needs the ‘LINK’ package for integration and shakers for proper mixing. POS spectrophotometer: For on-spot colour measurement, select the required instrument for on-spot colour measurement of custom colour. In a POS system, the Formula Book with automatic search program is stored in the computer along with data of colour cards, individual colour chips, and fan decks. The mother system has a complete database which is transferred to the POS (daughter) system either by transferring the required database or through internet connectivity. Any given colour can be made quickly by using an automatic paint dispensing machine interfaced with POS colour matching systems.

12.9.1 Internet based colour matching at point of sale (POS)

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There is an innovative development in the POS colour system based on internet technology (Benevue 2009). It is not based on normally used conventional K-M theory for match prediction. In this new POS colour system, you need a colour sensor and access to the web page of the service provider. There is no need to determine optical properties (K and S coefficients) of the bases and colorants. You have just to supply the existing formulae of the fan deck colours in electronic form and the service provider’s software will take care of matching any given custom colour. There is secrecy surrounding the software package as the manufacturers do not want to disclose the mathematics of colour matching. It is mainly database driven and for every colour, there is a formula. Every colour has a unique colour specification such as X, Y, Z or L, a, b. This is correlated to the colour formula. Once you have a large number of colours covering the full colour gamut and you have the established formula bank, you can predict the tint formula for any custom colour by using database and powerful mathematics. Benevue has done a lot of work in this field and the author knows this product’s development. The main features of this system are: • •

Zap your inspiration: Take your colour reader and simply measure the desired custom color in terms of three numbers (R, G, and B). See Fig. 12.8. Log on to your internet website by entering your user name and password (see Fig. 12.9).

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12.8 Colour reader of Benevue Colour Management System. The user scans the custom colour and gets three numbers (R, G, B).

12.9 Benevue website: Enter user name and password for access to the service provider’s central server which has software and database.

• • • •

You will see various options available in the menu – book formula, closest colour and tint by name, etc. (Figs 12.10, 12.11 and 12.12). You enter the three colour values of custom colour (see Fig. 12.13), and select the desired option, say ‘Tint by Reading’. You will get the exact colour formula by selecting the option ‘Tint by Reading’ (see Fig. 12.14). The computer instantly gives the tinting formula which can be sent to an automatic dispenser or you can mix it manually.

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12.10 Book formula: Users select the colour ID of the fan deck and retrieve the tint formula.

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12.11 Closest colour: After entering the three numbers (R, G, B) of the custom colour, users find the closest match from their own fan deck colour. In this output, three close matches are found. The user can visually compare and find the best match.

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12.12 Tint by name: In this option, users select the fan deck name and sample ID. The user can store a large number of fan deck colours and obtain tint formulae by using this option.

12.13 Unit ID is the ID of the colour reader. After measuring the custom colour, the user just enters the three colour numbers – Red Value (R), Green Value (G) and Blue Value (B) of the custom colour to get a tint formula. The user can also select the quantity. To find the formula, click ‘Find Formula’.

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12.14 ‘Tint by reading’ gives an instant colour match of a measured custom colour. In this option, the user just enters the three colour numbers of the measured sample to get a tinting formula.

•

Number of fan deck colours and their formulae are stored on central server along with matching software and one can get the formula by using the name of colour in fan deck or any other options.

The author has worked with the Benevue Colour Management System and introduced it to a leading paint company in India and established the technology at a number of paint dealer shops. Results are extremely good. Illustrations mentioned here are the actual data obtained by the author who has matched hundreds of pastel and saturated colours using the Benevue Colour Management System. It is a unique system offering low cost, internet based colour matching solutions. 1 2 3 4 5 6 7 8 9 40 1 2 43X

12.9.2 Conventional CCM vs internet based point of sale •

• •

•

In a conventional CCM system, the daughter system at the dealer’s shop needs an experienced operator and the database is updated as instructed by the mother system. An internet based shop system can be updated automatically as it is on a central server. In an internet based colour management system, the centralized database of the fan deck, Formula Book, etc., is on the main server which is instantly available to any paint dealer shop on the internet. The dealer need not worry about updates. An internet based system is equipped for instant use of the Formula Book, closet formula, formula for any product line, formula for any base/any tint base.

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The central server has an ‘eye’ on the paint dealer shop and one can get all the information about the usage of the dealer, number of new custom colours matched, new formulae found, competitor’s colours popularly used, regional sales, dealers sales, etc. It offers a unique data mining facility which is extremely useful for sales/marketing analysis.

12.10 Colour matching of automotive paints Computer colour matching programs for automotive paints are now addressing the needs of opaque, translucent or gonioapparent paints. Point of sale colour matching started with consumer paints but is now common also in automotive refinish and industrial finishes. The following three factors are required to be successful with instrumental colour matching. 1 2 3

The right measurement geometry for the specific paint. Appearance matching: commonly including factors such as gloss, haze, etc., gonioapparent colours must also match in apparent texture and sparkle. A reproducible application procedure, representative of the end-use: Colour may not be the same as from brushing or roller-coating, but colour match is not very critical. In automotive OEM, robotic spraying in a controlled environment, representative of assembly line conditions is necessary (X-Rite 2009; Hunterlab 2009).

12.10.1 Measurement of metallic and pearlescent colours A portable multi-angle spectrophotometer can measure metallic, pearlescent, and special effect colours on curved surfaces with five angles of measurement: 15°, 25°, 45°, 75°, and 110°. Measurements are taken remotely and can be uploaded to a PC. The full range of angular viewing allows accurate evaluation of the changes exhibited in various colour finishes. Sample preparation, procedure for creation of database and fixing tolerance limits for pass/fail for metallic colours are important points to be noted for success of this technique. Computer colour matching programs for automotive paints are now addressing the needs of opaque, translucent or gonioapparent paints. Point of sale colour matching started with consumer paints but is now common also in automotive refinish and industrial finishes (X-Rite 2009).

12.10.2 Portable multi-angle spectrophotometer The portable multi-angle spectrophotometer is a next generation measurement tool designed for consistent, precise colour measurement of metallic, pearlescent,

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and other complex special effect finishes. The unit provides 10 measurement angles and two illumination angles to create a unique master profile of each colour that serves as a benchmark for optimizing colour communication from initial design, through formulation, processing, and quality assurance. These instruments are used in automotive paint industry with partial success. The MA98 multi-angle spectrophotometer (Fig. 12.15) features a rugged, compact, ergonomically efficient design. Colour values are obtained for the following colorimetric systems: L*a*b*, ΔL*, Δa*, Δb*, C*, h°, ΔL*, ΔC*, ΔH* Flop Index, ΔE ab, ΔECMC, ΔE 94, ΔE 2000. It meets DIN and ASTM standards. Minolta instrument The CM-512m3 is a multi-angle spectrophotometer (Fig 12.16) especially designed for metallic paints. It uses an exclusive geometry with 0° viewing and illumination at 25°, 45° and 75° angles. This special geometry is truly symmetric and therefore free of orientation errors often found in traditional multi-angle instruments. In addition, this illumination gives very accurate results even when curved surfaces are measured. Therefore difficult samples such as vehicle mirror bodies or door handles can be measured easily. The CM-512m3 is extremely rugged because it is free of any moving parts (Konikam Minolta 2009). The temperature data of the sample is taken at each measurement. This data is memorized with each sample reading, so that thermochromatic effects can be analysed. Another practical feature

CM-512m3

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Spectrophotometer (spectral type, multi-angle)

Multi-angle spectrophotometer for metallic colors.

XDNA

TM

The New Technology X-Rite MA98TM Portable Multi-Angle spectrophotometer

12.15 Multi-angle instruments from Minolta and X-Rite.

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is the infrared data transfer to an external PC, which avoids troublesome cable connections. Measurement is not much of a problem as suitable software is required for the desired application. There are a few research groups working on these aspects but no more details are available due to confidentiality issues.

12.11 Future trends At present, colour matching software is based on the Kubelka-Munk theory or a modification of the K-M theory. Now researchers are looking beyond this and new software will be based on databases and internet technology. Low cost and cheaper spectrophotometers will be available in the market in coming years and custom colour matching will be driving the market. Colour matching problems with metallic, special effect pigments and fluorescent colours will be solved with new measurement techniques and mathematical solutions will be obtained for developing new colour software for increasing the accuracy of match prediction. Wet-paint colour matching is still a problem and new low cost instruments are to be developed. New pigments, resins and processing equipments for paints and coating will generate new colour matching problems and a colour measurement tool will be very useful for new developments. ‘Colour on demand’ will be the challenge and the paint formulator will have to use colour measurement tools intelligently. ‘Do it yourself’ is also a concept used in the market. As POS systems equipped with a mobile computer colour matching system will help to expand the coating business, colour measurement will continue to be an important tool which will have to be used effectively.

12.12 Conclusions We have discussed in detail how to fix three-dimensional tolerance limits (DL, Da, Db) or a single number tolerance limit (DE) for paint quality control. We have seen how to quantify various colour related properties such as whiteness index, yellowness index, colour strength of pigment, opacity, contrast ratio and hiding power. We have reviewed various theoretical and practical aspects such as quality control of incoming pigment, pass/fail decisions for outgoing paint products, colour formulation, production batch correction, quantifying colour related properties such as pigment load and opacity. We covered procedures for preparation of colour samples for quality control of paint and new techniques of wet-paint colour matching. We have looked at new colour matching systems for a paint shop and new developments based on internet technology for the POS system. We looked into measurement of metallic colours and special effect pigments, and measurement techniques for automotive paints. A new digital colour management system for paint dealer shops was illustrated indicating innovative trends in ‘computer tinting’.

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12.13 Sources of further information and advice Proceedings of conferences of CIE, AIC, SDC, AATCC, and ISCC. Publications of CIE: Central Bureau of CIE, Kegelgasse 27, and A-1030 Vienna, Austria (Email: [email protected]). See on internet: NEWSGOUP: Sci.eng.color – a number of issues related to colour measurement were raised and colour experts have answered them. The author extracted and compiled information and presented it in ‘Color Clinic’, J. Colour Technology and Management, Vol 1, No 9, September 2004, pp 4–28. Web sites of colour instrument manufacturers (Datacolor, X-Rite/ GretagMacbeth, Hunterlab, Orintex). http://www.datacolor.com: Latest colour matching software, portable spectrophotometers and articles on colour measurement by colour experts – Ken Butts and others. http://www.x-rite.com: Look for recently introduced on-line, non-contact and multi-angle spectrophotometers. http://www.konicaminolta.com: Look for non-contact spectrophotometers. See Hunterlab web site: http://www.hunterlab.com Look for colour education with the topic ‘Color theory’. This includes an excellent presentation ‘The Basics of Color Perception and Measurement’ and papers related to test methods, instrument theory as well as colour theory and colour scales. Also see articles and papers and the book The Measurement of Appearance, Second Edition, edited by Richard S. Hunter and Richard W. Harold, published by John Wiley & Sons, Inc., New York, 411 pp. (ISBN 0-47183006-2) 1987.

12.14 References

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AIC (1981), Proceedings AIC Color ’81, 21–25 September, 4th Congress of AIC Berlin. Allen E. (1966), J. Opt. Soc. Amer., 56(9), 1256. Benevue (2009), http://www.benevue.com Billmeyer F. W. Jr. and Saltzman M. (1966), Principles of Color Technology, New York, John Wiley and Sons. Billmeyer F. W. Jr and Wyszecki G. (1978), Color ’77, Adam Hilger Ltd. CIE (1931) International Commission on Illumination (1931), Proceedings of the Eighth Session, Cambridge, England. CIE (1976), CIE Recommendations Uniform Color Spaces, Color Difference Equations and Metric Color Terms, Supplement No. 2 to CIE Publication No. 15, Bureau Central de la CIE, Paris. CIE (1995), CIE Technical Report: Industrial Color Difference Evaluation, CIE Publication No. 116, Vienna, Austria, Central Bureau of the CIE. Committee on Colorimetry, Optical Society of America (1953), The Science of Color, Crowell, New York. Clark F. J. J., McDonald R. and Rigg B. (1984), ‘Modification to JPC 79 Color Difference Formula’, Jour. Soc. Dyers Col., 11, 128–132 and 281–282. Datacolor (2009), http://datacolor,com Davidson H. R. and Hemmendinger H. (1966), ‘Colour Prediction Using the Two ConstantTurbid-Media Theory’, J. Opt. Soc. Amer., 56(8), 1102. Gangakhedkar N. S. (1974), Chromatic Notes, In-house Publication, Asian Paints (India) Ltd., Mumbai, October 14.

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Gangakhedkar N. S. (1991), Understanding Computer Color Matching, Mumbai, Rutu Prakashan. Gangakhedkar N. S. (1992), Two Constant Theory for Paints & Plastics, Mumbai, Rutu Prakashan. Gangakhedkar N. S. (2003), Understanding Science and Technology of Color, Mumbai, Rutu Prakashan. Ganz E. (1975), ‘Whiteness Measurement’, J. Color Res. & Appl. 4/5, 33. Hunter R. S. (1958 & 1960), ‘Description and Measurement of White Surfaces’, J. Opt. Soc. Amer., 48, 597–605 and J. Opt. Soc. Amer., 50, 44. Hunter R. S. (1975), The Measurement of Appearance, New York, John Wiley and Sons, Inc. Hunterlab (2009) http://www.hunterlab.com Judd D. B. and Wyszecki G. (1952), Colour in Business, Science and Industry, New York, John Wiley and Sons, Inc. Judd D. B. and Wyszecki G. (1975), Color in Business, Science and Industry, 2nd edn, New York, John Wiley and Sons, Inc. Kubelka P. and Munk F. (1931), ‘Ein Beitrage zur Optik der Farbanstriche’, Z. Techn. Physik, 12, 593. Kuehni R. G. (1975), Computer Colorant Formulation, Lexington, Massachusetts, Lexington Books, DC Heath and Company. Konikaminolta (2009), http://www.konicaminolta.com Luo M. R. (2001), ‘The CIE 2000 Color Difference Formula.’ Color and Imaging Institute, University of Derby. Luo M. R., Cui G. and Rigg B. (2001), ‘The Development of the CIE 2000 Color Difference Formula,’ Color Res. Appl. 26, 340–350. McDonald R. (1987), Color Physics For Industry, Society of Dyers and Colorists – Dyers Company Publication Trust. Parker B. M. (1973), ‘Opacity, Hiding Power and Tinting Strength’ in Pigment Hand Book, Volume III, 289–339, New York, John Wiley & Sons. Paton T. C., ed. (1973), Pigment Handbook, Volume III, New York, John Wiley & Sons. Rensselaer Polytechnic Institute (RPI) (1978), Advances In Color Technology, Topics, Experiments, Reprints Bibliography, Rensselaer Polytechnic Institute, Troy, New York. Rensselaer Polytechnic Institute (RPI) (1978), Color Technology for Management, Reprint, Bibliography, Department of Chemistry, Rensselaer Polytechnic Institute, Troy, New York. Wyszecki G. and Stiles W. S. (1967), Color Science, Concepts and Methods, Quantitative Data and Formulas, New York, John Wiley and Sons, Inc. X-Rite (2009), http://www.x-rite.com

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Colour measurement of food: principles and practice D. B. MACDOUGALL, Formerly of the University of Reading, UK Abstract: Foods have an infinite variety of appearance characteristics. Their surfaces may be diffuse, glossy, irregular, porous or flat. They may be transparent, hazy, translucent or opaque and their colours may be uniform, patchy or multilayered. The interactive role of pigment absorption with light scatter from food structure can have massive effects on colour and visual appearance. This is demonstrated in studies on coffee, orange juice and fresh meat. Colour was measured by CIELAB, a visually uniform colour space, and the relative effects of pigmentation to scatter determined by the Kubelka Munk analysis. The relationship of various food colours, in colour space, from their spectra is shown. Key words: appearance, vision, colour, CIELAB, absorption, scatter, Kubelka Munk, food variety.

13.1

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Introduction

We perceive the world in which we live by our five senses, vision, hearing, touch, taste and smell, of which the sense of vision is usually the first used in detecting events and objects around us in the visual world. The process of seeing comprises many co-operating activities, first detected by our eyes and then interpreted in our brain, recognition of movement and location of object, relationships of objects to their surroundings, the intensity and quality of the light and the colour appearance of objects or events in the visual scene. From the time humans first recorded events pictorially, e.g., cave paintings and then by printing, the incorporation of colour as a medium was an integral component of the procedures. This was especially so in the development of printing and in the world of art. Artists, from earliest times, attempted to portray the colour appearance of food in their pictures as realistically as possible with the limited number and type of pigments available. Most national art galleries contain examples of both classical and modern painting where food items are part of or are central to the painting. With modern photographic techniques and computercontrolled printing, accurate and attractive pictures of food items are now expected in illustrated magazine articles. Although representational portrayal of natural objects by paint and print can appear real and visually pleasing, it was only in the last two centuries or so that a scientific understanding of the processes involved in determining colour appearance has been elucidated. Scientific studies into the mechanism of vision and human colour perception began in the seventeenth century with the recognition that the eye’s lens must 312 © Woodhead Publishing Limited, 2010

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somehow project an image of the object viewed onto the back of the eye. Newton’s classic experiments on the refraction of light led him to conclude that the rainbow did not possess colour but it was the spectrum’s rays that produced the sensation (Wright 1967). The rationality of arranging colours into orderly systems, based on Newton’s seven rainbow colours, has resulted in the construction of colour atlases which attempt to arrange their colours in such a way that equal visual distances exist between adjacent colours. Two of the most used atlases are the earlier Munsell system and the newer Swedish natural colour space system. The former, developed in the USA, is based on five hues and the latter, used mainly in Europe, is based on the six unique perceptions of black, white, red, green, yellow and blue (Hard and Sivik 1981). The experiments of Maxwell, Young and Helmholtz in the nineteenth century in mixing coloured lights (MacAdam 1970) clearly demonstrated that people with normal colour vision must have at least three retinal pigments in their eyes, detecting in the short-, mid- and long-wave regions of the visible spectrum. By the late 1920s and early 1930s, the eye’s sensitivity to light relative to wavelength was established and the so-called ‘standard observer’ defined (Wright 1980). This led to the first truly functional system for measuring colour as specified by the Commission Internationale de l’Éclairage (CIE), the so-called CIE 1931 2° visual field system of colour measurement (CIE 1986). Although colours could be defined unambiguously in this space, the space is not visually uniform. Since that time, many improvements have been incorporated into the system to make it nearly visually uniform and this research continues. With the development of the computer, complex colour measurements and calculations are now routinely used for such industrial processes as paint formulation (Best 1987), colour match prediction (Nobbs 1997) and control of the appearance of dyed textiles (McLaren 1986; McDonald 1997). Improvements in instrument specification and design have led to a considerable increase in their use in industry. In the food industry, colour measuring instruments are now routinely used in research and for studies into product functionality, for product ingredient standardisation and process control.

13.2

Colour vision: trichromatic detection

Three interacting factors are required for the measurement of the colour appearance of any object in a scene. These are an understanding of the human visual process, the effect of light on objects in their environment and the nature of the materials observed. The sensation of colour is a psychophysical phenomenon, which is only part of the overall visual perception of the information detected by the eye and interpreted by the brain. The complex visual response can be thought of as the sum of the responses recognised in the brain from the signals detected by the eye of the scene viewed. The sensation, therefore, is perceived as if it were projected out into the world from which it originated. This leads to the error of imputing to

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the scene the sensations it generated. Sensations exist in the observer’s mind and not in the external world which produces them. Human eyes have a near circular field of view and are composed of three membranes (Fig. 13.1a). The outer membrane, the sclera, is continuous posteriorly with the sheath of the optic nerve and anteriorly with the cornea. The iris and the ciliary body, which suspends the lens, arise out of the middle layer, the choroid, which contains the capillary network. The light-detecting inner membrane, the retina, lines the inside of the posterior of the eye. The first step in the visual process is the automatic control of the amount of light entering the eye through the iris. The flux is then focused by the lens on to the fovea in the central region of the retina where it is detected as colour. The signal is amplified (Normann and Werblin 1974) and then transmitted through the visual pathway (Rodieck 1979) for interpretation in specific areas of the visual cortex of the brain (Hubel 1988; Zeki 1993). The retina has two types of light-detecting receptors, the cones and rods, so named because of the shape of their structures as viewed by the microscope. The ‘photoptic’ colour-detecting cones are sensitive to three wavelength ranges in people with normal colour vision and are densely packed in mosaic pattern in the centre of the fovea. This occupies <2° of the visual field and is the basis of the CIE 1931 2° standard colorimetric observer. The >100 times more sensitive ‘scotoptic’ colourless detecting rods increase in density to 20° from the fovea and then decrease towards the periphery of vision (Fig. 13.1b). In 1964 a supplementary standard observer with a 10° field of view was created to accommodate the changes that occur in colour perception as the visual angle increases beyond 2° where some rods are included in the detecting field. Light energy, focused on the retina, is converted into electrical signals by changes in the conformation of the photopigments in the multifolded disk-shaped structures in the outer segments of the rods and cones (Wald 1968; Hurvich 1981; Jacobs 1981; Stryer 1988). Although only the rod pigment rhodopsin has been characterised, determination of the spectral absorption of the cone pigments has been possible using retinal densitometry with colour-blind observers deficient in one pigment (Smith and Pokorny 1975). The sensitivity curves of human cones determined by Estévez (1982) are presented in Fig. 13.1c. Rhodopsin absorbs maximally at 505 nm and the so-called blue (B), green (G) and red (R) cone pigments, that is the short-, mid- and long-wave sensors, are maximally sensitive at approximately 440, 540 and 570 nm respectively. The sensitivity range of B absorption marginally overlaps G and R between 450 and 550 nm, whereas the G and R absorption functions overlap substantially, displaced from each other by only 20 to 30 nm. Thus monochromatic light at 580 nm which is near maximum for R appears yellow and not red because of the combined contributions of G and R. Increasing the wavelength to > 600 nm increases the contribution of R relative to G and the perceived colour becomes redder. The fact that the R and G cones overlap gives rise to the enormous number of colours and colour differences that are experienced by people with normal colour vision.

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Retina

Ciliary body

Choroid

Iris Cornea

Sclera Lens

Fovea

Optic nerve

(a)

200 000

Cones

Number (mm–2 )

Rods

(b)

100 000

0 –10

0

10

30

50

Angle from fovea (degrees)

13.1 Detection of light in the eye: (a) structure of the human eye (b) distribution of cones and rods in the temporal side of the retina (nasal side is similar except for the blind spot between 12° and 18° from the fovea).

Because cone vision is trichromatic, it means a suitable mixture of red, green and blue primary lights, as would be expected from a three-receptor system, can match any coloured light. The actual colour-matching functions depend on the wavelengths used as primaries. Although the initial detection of the stimulus is trichromatic, subsequent post-retinal processing gives rise to an achromatic lightness/darkness mechanism and coloured red/green and blue/yellow opponent mechanisms (Hurvich 1981; Hunt 2001). The lightness component consists of a

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Relative sensitivity

200

B

G 100 R

0 (c)

400

500 600 Wavelength (nm)

700

13.1 continued. (c) spectral sensitivities of blue (B), green (G) and red (R) cone pigments.

weighted summation of all three cone pigment absorptions, whereas it is the degree of differences among the B, G, R absorptions that generates the opponent colour mechanism. However, the neural linkages among the pigment cone signals are not in simple one-to-one opposition. The simplest scheme that can be constructed is that the red/green opponent response is red activated by absorption of B plus R and green activated by G; yellow is activated by G plus R in opposition to blue activated by B.

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The influence of ambient light and food structure

13.3.1 Adaptation and colour constancy Adaptation is the process whereby the visual system conditions itself to the chromatic nature of the surroundings as affected by the quality, that is the wavelength distribution, and intensity of the illumination. It compensates for changes in the spectral power distribution of the light and serves to keep the eye in balance (Boynton 1979). The magnitude of this near automatic adjustment that chromatic adaptation has on visual experience is not usually recognised because of the limitations of human memory for individual colours and the phenomenon of colour constancy (Brill and West 1986). White objects appear to be white over a vast range of light conditions, e.g., from bright sunlight to the relatively dim levels of light found in room interiors, while colours appear to have similar colour appearance under most types of white or near white illumination. Studies into the phenomena

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of adaptation which elicit this near constancy of colour appearance have been concerned mostly with predicting the changes that occur to colour recognition when lamp type and output are altered (Bartleson 1979a). Lightness and contrast among greys are affected by luminance while colourfulness increases with an increase in the level of illumination and varies with the spectral emission of the lamp and its colour temperature (Hunt 1977). The phenomena of adaptation can be subdivided into three components, chromatic adaptation, light adaptation and colour constancy. Chromatic adaptation occurs where changes in the visual system compensate for changes in the spectral quality of the illumination. Light adaptation occurs where the visual system attempts to compensate for changes in the level of illumination and colour constancy is experienced where the colour of an object tends to remain constant although the level and colour of the illumination are changed (Berns 2000). This is further discussed in section 3.9.1 on fresh meat, where the degree of red enhancement of lamp spectra is shown to affect the perception of product attractiveness. Models of cone adaptation response have been used to predict the consequences of changing lamp spectra on object appearance (Bartleson 1979b; Nayatani et al. 1986; Hunt 1987). The concept of apparent colourfulness has been used to construct grids of constant hue from which other grids can be derived for other illuminants (Pointer 1980; 1982). Such models use logarithmic and hyperbolic functions to mimic the physiological mechanisms involved. Hunt’s (1987) model can be used to predict the changes that occur in object colours at any level of illumination for a wide range of backgrounds in the realistic situation where the eye’s fixation wanders. A considerable amount of research has been done into the subject of chromatic adaptation since the late 1980s. The most recently tested chromatic adaptation transform, CMCCAT2000, for predicting the change in colour appearance on changing illuminant has been shown to be simpler to use than previous models and has superior predictive accuracy (Li et al. 2002). These effects of light quality on colour perception illustrate the difficulties in separating the concept of vision from that of appearance. The light from the scene modulates vision, whereas the characteristics of appearance are modified by the light incident upon the object. Hence, procedures devised for observing and measuring colour must take account of the nature, quality and quantity of the light as it affects the observer’s perception. The British Standards Institute and the International Standards Organisation have recently produced general guidance and test methods for the assessment of the colour of foods (BSI 1999).

13.4

Appearance

Colour is usually considered the most important attribute of any food’s appearance (Francis and Clydesdale 1975) especially if it is associated with other aspects of food quality, for example, the ripening of fruit or the visible deterioration which occurs when a food spoils. Nearly every food product has an acceptable colour range, which depends on a wide range of factors including variability among consumers,

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their age and ethnic origin, and the physical nature of the surroundings at time of judgement (Francis 1999). However, in addition to colour specification, the nature and extent of internally scattered light and the distribution of surface reflectance are required for a more complete description of appearance. The food’s structure and pigmentation interact to affect both colour and translucency/opacity, for example, small changes in scatter may produce larger changes in colour appearance than are attributable to change in pigment concentration (MacDougall 1982). The characterisation of an object’s appearance is accomplished in two stages. The first is physical and the second is psychological. The physical characteristics are the size, shape and uniformity of the object along with the type, degree and variability of pigmentation throughout the object and the nature of the object’s structure that attenuates light. The physical information is converted to the psychological by translating the object’s reflectance or transmittance spectrum into its tristimulus values and then to a defined colour space. The concept of ‘total appearance’ (Hutchings 1999) can be applied to foods where it comprises more than just the food’s physical appearance characteristics and takes account of such social factors as the observer’s culture, memory, preferences and appreciation of the product. Foods have an infinite variety of appearance characteristics. Their surfaces may be diffuse, glossy, irregular, porous, or flat. They may be transparent, hazy, translucent or opaque and their colour may be uniform, patchy or multilayered. Hence, colour-measuring procedures for foods often have to be modified from those used in the measurement of flat opaque surfaces such as paint and paper, for which most colour-measuring instruments are designed. However this is not always recognised by those involved in food colour appearance measurement. Different instrument optical geometries will lead to difficulties in sample presentation and, coupled with the uncertainties of sample structure, are likely to give different colour values for the same material if measured on different instruments. The inclusion or exclusion of surface specular reflection in the measurement procedure depends not only on its importance as a characteristic of the food but also on the design of the detector/sensor unit in the instrument. The spreading of the light transmitted within the food depends largely on its structure as well as the level of pigmentation. Hence, lateral transmittance of light through translucent materials will affect both their reflectance and visual appearance (Atkins and Billmeyer 1966; Hunter and Harold 1988; MacDougall 1988; Hutchings 1999). The translucence effect must be allowed for in the assessment of such products as tomato paste (Brimelow 1987) because the ratio of absorption to scatter varies with aperture area and the concentration of components in the product (Best 1987; MacDougall 1987).

13.5 Absorption and scatter The reflection of light from opaque and translucent materials depends on the ratio of absorption to scatter as affected by pigmentation, refractive index and the

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light-scattering properties of the material. The Kubelka-Munk (KM) method for separating subsurface absorption and scatter (Kubelka 1948) is illustrated by Judd and Wyszecki (1975). Its use in the determination of pigment absorption in opaque materials is given in the latest edition of the book by Billmeyer and Saltzman (Berns 2000) and its use in opaque, translucent and layered materials is fully discussed by Nobbs (1997). The KM procedure relates reflectivity R∞, i.e., reflectance at infinite thickness, to the coefficients of absorption K and scatter S by K/S = (1 – R∞)2/2R∞ Hence K/S can be calculated directly from measurement of infinite thickness but to calculate K and S separately it is necessary to measure the reflectance of thin layers mounted on white and black backgrounds. If K and S are required for prediction purposes, the accuracy of their measurement can be improved by appropriate correction factors for surface reflection (Saunderson 1942). Colour calculated from R∞, with separate estimation of the specular component as gloss is usually sufficient information to describe opaque objects, but for translucent or layered materials K and S are also necessary.

13.6

Colour description: the CIE system

The CIE system of colour measurement (ASTM 2000; CIE 1986) transforms the reflection or transmission spectrum of the object into three-dimensional colour space using the spectral power distribution of the illuminant and the colourmatching functions of the standard observers (CIE 1986). The mathematical procedures are given in any standard text on colour, for example Wright (1980), Judd and Wyszecki (1975), Hunt (2001) and Berns (2000). The system is based on the trichromatic principle but, instead of using ‘real’ red, green and blue primaries with their necessity for negative matching, it uses ‘imaginary’ positive primaries X, Y, and Z. Primary Y, known as luminous reflectance or transmittance, contains the entire lightness stimulus. Every colour can be located uniquely in the 1931 CIE colour space by Y and its chromaticity coordinates x = X/(X + Y + Z) and y = Y/(X + Y + Z), provided the illuminant and the observer are defined. The original illuminant representative of daylight was defined by the CIE as source C, but is now superseded by D65, i.e., an illuminant which includes an ultraviolet component and has a colour temperature of 6500°K. The colour temperatures of lamps and daylight range from approximately 3000°K for tungsten filament lamps and 4000°K for warm white fluorescent to 5500°K for sunlight and 6500°K for average overcast daylight to approximately 20000°K for totally sunless blue sky. Because the original 2° colour-matching functions apply strictly only to small objects, i.e., equivalent to a 15 mm diameter circle viewed at a distance of 45 cm, the CIE has added a 10° observer (Fig. 13.2) where the object diameter is increased to 75 mm. Currently, the trend in colour measurement is to use D65 and the 10° observer except for very small objects. The

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Colour matching functions

2.0

1.5 x10 (λ) y10 (λ)

1.0

0.5

0.0 400

x10 (λ)

500 600 Wavelength (nm)

700

13.2 Colour matching functions of the CIE 10° standard observer.

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1986 CIE recommended procedures for colorimetry are included in the ASTM Standards (2000) and also in Hunt (2001) along with the weighting factors for several practical illuminants (Rigg 1987). These include representative fluorescent lamps, of which F2 is a typical lamp at 4230° K but with a low colour-rendering index (Ra) of 64 (Fig. 13.3). The colour-rendering index Ra is a measure of the efficiency of a lamp at a given colour temperature to render the true appearance of Munsell colours. The broadband lamp F7 has the same colour temperature (6500°K) and chromaticity co-ordinates as D65 and, because of its flatter spectrum, it has a high Ra of 90. The triband lamp F11 (4000° K) also has a moderately high Ra of 80, but its main advantage is its much improved efficiency in energy utilisation.

13.7

Colour description: uniform colour space

The original 1931 CIE Y, x, y system of colour measurement is not visually uniform (Fig. 13.4a). Constant hue and chroma are distorted and equal visual distances increase several-fold from purple-red to green. Improved spacing has been accomplished by both linear and non-linear transformations of Y, x, y (Berns 2000). Near uniform colour spaces of practical importance are the Hunter and the CIELUV and CIELAB spaces. In the Hunter (1958) L, a, b colour space the lightness co-ordinate L is the square root of the tristimulus value Y, and a, and b are the red/green and yellow/blue opponent co-ordinates. The 1976 CIELUV and CIELAB spaces (Robertson 1977) attempted to reduce the many scales then

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Relative spectral power distributions

50 40

F7

30 20 10 0

80 F11 60

40

20

0 300

400

500 600 700 Wavelength (nm)

800

13.3 Relative spectral power distributions of preferred CIE representative fluorescent lamps.

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0.8 Green 0.6 500

y

580 Yellow

0.4 630 Red

770 nm

Blue 480 380

0.2

0.0 0.0 (a)

0.2

0.4

0.6

0.8

1.0

x L* = 100 = white

+b* (yellow)

(green) –a*

C* h* +a* (red) (blue) –b*

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0 = black

13.4 Colour diagrams: (a) CIE 1931 chromaticity diagram showing non-uniformity of spacing of red, yellow and blue unique hues; (b) CIELAB uniform diagram showing relationship of red/green (a*+/–) and yellow/blue (b*+/–) opponent co-ordinates to lightness L*, chroma C* and hue angle h*.

in use to two. The lightness co-ordinate L* is the same for both but the spaces use different concepts in their construction. The CIE L*, a*, b* space (Fig. 13.4b), known as CIELAB, has generally replaced the Hunter space for industrial applications although this has been somewhat slower in parts of the food industry where methods established on the Hunter system have economic reasons for its continued use. The improvements in CIELAB are due to the

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nonlinear cube root transformation of the 1931 tristimulus values, which more approximate the visual spacing of the coloured samples in the Munsell system. The formulae are L* = 116(Y/Yn)1/3 – 16

for Y/Yn > 0.008856

)1/3

L* = 903.3 (Y/Yn

for Y/Yn < 0.008856

)1/3

– (Y/Yn

)1/3

– (Z/Zn)1/3]

a* = 500[(X/Xn b* = 200[(X/Xn

)1/3]

where Xn, Yn, Zn refer to the nominally white object colour stimulus. The co-ordinates of L*, a* and b* in CIELAB serve to define the location of any colour in the uniform colour space. However, in most industrial applications the object of measuring products is usually to determine how far they may be divergent from a set standard, both in colorimetric terms and in acceptability of visual match. The determination of uniform colour differences by CIELAB is not the same as the recognition of acceptability. CIELAB is based on the perception of just noticeable colour differences in the cylindrical co-ordinates of the system. However, acceptability differences are based on the perception of colour tolerance differences of real materials of industrial interest, e.g., textiles. Colour terms can be divided into the subjective and the objective (Hunt 1978). The subjective, i.e., the psychosensorial, are brightness, lightness, hue, saturation, chroma and colourfulness. Colourfulness, a more recently introduced term, is that aspect of visual sensation according to which an area appears to exhibit more or less chromatic colour. Although hue is easily understood as that attribute described by colour names red, green, purple, etc., the difference between saturation and chroma is less easily comprehended. Saturation is colourfulness judged in proportion to its brightness, whereas chroma is colourfulness relative to the brightness of its surroundings. A similar difference exists between lightness and brightness. Lightness is relative brightness. Lightness is unaffected by illumination level because it is the proportion of the light reflected, whereas the sensation of brightness increases with an increase in the level of illumination. The objective, i.e., the psychophysical are related to the stimulus and are evaluated from spectral power distributions, the reflectance or transmittance of the object and observer response. They provide the basis for the psychometric qualities which correspond more nearly to those perceived. For CIELAB space the terms are lightness L*, hue h* = tan–1 (b*/a*) and chroma C* = (a*2 + b*2)1/2. CIELAB total colour differences ΔE* can be expressed either as the co-ordinates of colour space or as the correlates of lightness, chroma and hue. Hence ΔE* = [(ΔL*)2 + (Δa*)2 + (Δb*)2]1/2 or ΔE* = [(ΔL*)2 + (ΔC*)2 + (ΔH*)2]1/2

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where ΔH* is used rather than Δh* because the latter is angular. For small colour differences away from the L* axis, if h* is expressed in degrees, then ΔH* = C* Δh* (π/180) The major colour scales with their associated terminology are given in Table 13.1.

Table 13.1 Overview of colour description systems and notation CIE system (1931) This is based on the imaginary positive primaries X, Y, Z (transformed from real red, green and blue trichromatic primaries which may contain negative values). In CIE space, colour is located by (Y, x, y), where Y x, y

luminous reflectance or transmittance (containing the entire lightness stimulus) chromaticity co-ordinates X = x/(X + Y + Z ) y = Y/(X + Y + Z )

CIE space is not visually uniform. Hunter Lab System (1958) In Lab space, colour space is more uniform than CIE and is defined by (L, a, b), where L a, b

correlate of lightness red/green and yellow/blue opponent co-ordinate correlates L = 10 Y1/2 a = [17.5(1.02Y – Y)]/Y 1/2 b = [7.0(Y – 0.847Z)]/Y 1/2

CIELAB system (1976) In CIELAB space, colour space is defined by (L*, a*, b*), where L* visually uniform lightness a*, b* visually uniform chromaticness co-ordinates

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L* = 116(Y/Yn)1/3 – 16 for (Y/Yn)1/3 > 0.008856 L* = 903.3(Y/Yn)1/3 for (Y/Yn)1/3 < 0.008856 1/3 a* = 500[(X/Xn) – (Y/Yn)1/3] b* = 200[–(Y/Yn)1/3 – (Z/Zn)1/3] Where Xn, Yn, Zn are the values of X, Y, Z for the reference white. Further terms used are h* = tan–1 (b*/a*) hue C* = (a*2 + b*2)1/2 chroma Note: Recent colour difference formulae CMC(1;c) and CIE94 are derivations of CIELAB with weighting functions applied to make vectors of ΔL, ΔC and H more visually acceptable. The most recent and superior formula is CIE2000. Worked example of CIE2000 is given in Luo et al. (2001) The most recent chromatic adaptation formula for describing colour appearance under different viewing conditions is given in Li et al. (2002).

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It has been the task of the CIE for several years to create a single number pass/ fail equation that would weight the three components that make up the total CIELAB colour difference ΔE*, that is, ΔL*, ΔC* and ΔH* the lightness, chroma and hue vector differences. Equations that are a distinct improvement in this regard have been devised by the CIELAB on a very large body of experimental evidence (Berns 2000). The Colour Measuring Committee (CMC) of the Society of Dyers and Colourists formulated a significant improvement in uniform colour difference formulae from the earlier JPC79 (J and P Coates) colour difference formula. In this CMC(1:c) formula, adjusting constants are incorporated by the user to weight the importance of lightness and chroma relative to hue (BSI 1988). Subsequently, on the basis of the work of Luo and Rigg (1986), Alman et al. (1989) and Berns et al. (1991), the CIE recommended the use of a new colour difference equation for use in industry, known as CIE94 where total colour difference is designated as ΔE94. It includes a term for the visually perceived magnitude of the colour difference. For those industries that require accurate colour difference measurement that is related to perception and acceptability, e.g., the textile industry E94 is used preferentially. A further improvement has now been recommended by the CIE on the basis of work described by Luo and his associates and is designated as the CIEDE2000 Colour-Difference Formula (Luo et al. 2001). It has now been officially adopted by the CIE (CIE 2001) and a worked example of its use is given in the Luo et al. (2001) publication. The food industry’s demand for such a level of precision of colour difference as recommended by CIEDE2000 remains to be assessed.

13.8

Instrumentation

Since colour is a psychological phenomenon, its measurement must be based on human colour perception. Hence, photoelectric instruments are corrected for both lighting and human visual response, while visual techniques must use observers with ‘normal’ colour vision under defined lighting. Examples of direct visual assessment are colour atlases for broad definition of the location of colours in colour space, collections or sets of printed or painted coloured papers specific to products or processes and visual matching instruments which use coloured filters. Typical of the former are the Munsell and Swedish NCS atlases which are structured on uniform colour space, and the Pantone collections of printer’s colours with defined ink mixtures printed from 10 to 100 per cent tinting strength. Probably the best known of the visual matching instruments is the Lovibond Tintometer in which the object, under specified illumination, is viewed and matched against a series of coloured filters interposed over a white background by the observer. Photoelectric colour measuring instruments can be divided into two classes, trichromatic colorimeters and spectrophotometers. The most successful of the early trichromatic colorimeters was developed in the 1940s by Hunter (1958). It comprised a light source and three wideband red, green and blue filters to

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approximate CIE standard illuminant C and the 2° observer. The tristimulus values obtained were transformed into Hunter L, a, b colour space. Until the advent of the computer and the photodiode such instruments were much less expensive than spectrophotometers and, although absolute accuracy may have been poor, they were extremely good at measuring the small colour differences demanded for industrial process control (Patterson 1987). The more modern tristimulus instruments are linked to computers with automatic calibration and the provision of a number of colour spaces. Such instruments may be supplied with a selection of sensor heads of different illuminating geometries to allow measurement of a wide range of product types depending on the nature and dimensions of their surfaces. Several companies now manufacture a range of hand-held lightweight colorimeters and miniature diode array spectrophotometers, with optical geometries comparable in function with the larger bench instruments. Their compactness is a direct result of the use of high-energy pulsed xenon arc lamps combined with filtered silicon detectors and microchip circuitry. Because such instruments, with their built-in memories and choice of colour scales, are comparatively inexpensive it has resulted in an increase in their use for in-line colour measurement in all branches of industry where colour control is necessary or desirable, e.g., in the printing and automotive industries. The most accurate instrument for measuring colour is the spectrophotometer. Reflectance instruments are usually fitted with an integrating sphere with the choice of including or excluding the specular component of reflectance. Care must be exercised in deciding which geometry is appropriate for particular applications. The diffuse component of reflectance from subsurface absorption and scatter is wavelength dependent, whereas the specular component is not. For materials with glossy surfaces the inclusion of the specular will increase measured reflectance which, when translated into colour space, can lead to large discrepancies in the interpretation of visual lightness, as usually viewed, and to a lesser extent of the chromaticness of the colour. For example, highly glossy black tiles used for instrument calibration have tristimulus Y values of approximately 0.3 when the specular is excluded but 4.5 when included. The consequence of this difference in Y of 4 per cent produces a specular excluded uniform lightness L* of 3 and an included L* of > 25. For medium grey and white tiles the excluded to included Y values are approximately 25 to 29 and 78 to 82 respectively, which give L* values of approximately 57 to 61 and 91 to 92 respectively. Hence the near constant effect of 4 per cent in Y from the specular reflectance produces a decreasing effect from black to white from > 20 to about 1 per cent in L*. The CIE recommends that colorimetric specifications of opaque materials should be obtained with one of the following conditions of illumination and viewing geometries which should be specified in any report: • •

45°/0° or 0°/45°, specular excluded diffuse/0° or 0°/diffuse, specular included or excluded.

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However, spectrophotometers most commonly used for measuring colour do not have identical geometries. Three typical instruments were compared by Patterson (1987), who points out that probably the biggest source of differences among the instruments can be traced to the specular component. Hunt (1987) suggests that if measurements are to be compared it is better to include the specular because of the considerable variation in the area of gloss traps used in different spheres. However, the more nearly correct measurements in relation to practical visual observation are with the specular excluded (Best 1987). For computer match prediction of pigmented materials, e.g., paint formulation, the total reflection (i.e., specular included) is preferred. This restriction does not usually apply to tristimulus colorimeters which normally exclude the specular component of reflectance where the illumination viewing geometry is 45°/0°, as is the case in the classic Hunter bench colorimeter. Another important source of variation among tristimulus colorimeters and spectrophotometers is the area of the viewing aperture relative to the area of the illuminating light spot, which affects both the direction and the amount of light returned from translucent materials. MacDougall (1987) demonstrated that translucent suspensions of milk exhibit a tenfold decrease in K/S for an increase in aperture area from 5 to 20mm. Best (1987) states that accurate determination of K and S by measuring thin layers on black and white backgrounds requires that the ratio of the aperture area to the thickness of the sample must be considerably greater than 10, a criterion unlikely to be met for many foods. One further source of potential error, in addition to those associated with instrument geometry and sample structure, is the wavelength interval used to calculate the tristimulus values. Although the CIE (1986) specifies the standard observer at 5 nm intervals from 380 to 780 nm, such accuracy is not required for most practical purposes. For 10 nm accuracy the intermediate 10 nm values from the 5 nm tables should be used. However, the CIE has not yet officially recommended the use of 20 nm intervals, although many modern colour spectrophotometers detect at 20 nm intervals. Tables of weighting functions at 20 nm intervals for the CIE illuminants and several fluorescent lights are published in the up-to-date colour textbooks cited in this chapter. Errors attributable to wavelength interval are likely to be less important than those from instrument geometry, except when estimating the effects of narrow-band emission lamps on materials with several absorption bands. Here the 20 nm interval may prove to be less efficient.

13.9

Food colour appearance measurement in practice

Colour fading from pigment oxidation in fresh meat, the effect of illumination on the appearance of orange juice, the effects of varying coffee and milk concentration on coffee appearance and measuring breakfast cereals by grinding to a defined particle size are given as examples of the types of problems encountered in food colour appearance measurement. The effect of the illuminant

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on the calculated CIELAB colour values for a variety of food spectra is also presented.

13.9.1 Fresh meat The surface of freshly cut meat oxygenates to bright red on exposure to air from the purple ferrous haem pigment myoglobin to the covalent complex oxymyoglobin. The red oxymyoglobin then oxidises to brownish green metmyoglobin (MacDougall 1982; MacDougall and Powell 1997) during refrigerated display and is affected by both the intensity of illumination and the temperature. Twenty per cent dilution of the surface oxymyoglobin with metmyoglobin causes the product to be rejected at retail because of its faded colour (Hood and Riordan 1973). The changes in the mean reflectance spectra of over 100 packages of beef overwrapped with oxygen permeable film and held in the light at < 5°C over a period of one week are shown in Fig. 13.5. As the pigment oxidises there is an increase in reflectance in the green region of the spectrum as the alpha and beta absorption bands decrease. This is accompanied with a distinct loss in reflectance in the red region with development of the metmyoglobin absorption band at 50

Hours

40

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Reflectance (%)

2 30 48

168

20

168 10 2

0 400

500 600 Wavelength (nm)

700

13.5 Reflectance spectra of fresh beef during oxidation of oxymyoglobin to metmyoglobin obtained on a diode array spectrophotometer at 20 nm intervals: means of over 100 samples wrapped in oxygen-permeable film and stored at <5°C under 1000 lux fluorescent illumination for one week.

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630 nm. The changes in colour, calculated for CIELAB for D65, are shown in Fig. 13.6. As meat fades there is a small loss in lightness L*, accompanied by much greater changes in a* and b*. The loss in a* and gain in b* can be interpreted as an increase in the hue angle h* in the direction of yellow with a concomitant loss in chroma C*, which is indicative of the colour becoming more grey or dull. The change in direction towards yellow with the reduction in C* is perceived as being more brown. The appearance of meat is greatly affected by the colour-rendering properties of the lamps used for display (Halstead 1978). Some fluorescent lamps recommended by the lamp industry for displaying meat have enhanced red emission which tends to maintain the preferred colour of oxymyoglobin and visually shifts the early stages of metmyoglobin development from brown towards red. This effect of red enhancement on meat colours has been shown to elicit a greater visual colour change in making brown appear red than in making red

43

L* 42

41 45

25 h*

h* C* 20

40

C*

35

15 0

1

2 3 Time (days)

4

5

13.6 Progressive changes in lightness L*, hue angle h* and chroma C* calculated from spectra of wrapped fresh beef stored at <5°C under 1000 lux fluorescent illumination during oxidation of surface oxymyoglobin to metmyoglobin.

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Table 13.2 Calculated changes in L*; C* and h* from D65 to other lamps for the fresh beef spectra shown in Figure 13.5 Storage time

2 hours

4 days

Difference in colour from D65

ΔL* ΔC* Δh* ΔL* ΔC* Δh*

ΔA

ΔWWF

ΔNFL

ΔCWF

Δ83

Δ84

2.9 7.3 −2.3 2.3 5.7 −5.9

2.0 7.1 −1.2 1.6 5.5 −1.9

0.6 2.2 3.9 0.6 2.1 4.5

2.0 7.1 6.5 −0.2 −0.7 9.1

2.2 7.6 3.3 1.7 6.3 2.8

1.2 4.9 −2.3 0.9 3.8 −3.5

Tungsten lamp: A, fluorescent lamps: WWF, warm white; NFL, natural; CWF, cool white; 83, triband at 3000° K; 84, triband at 4000° K.

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appear more red (MacDougall and Moncrieff 1988). Some find the flattering of red-enhanced lamps makes meat appear too red. The ICS Micro Match spectrophotometer used to measure these samples is equipped with the option of using alternative illuminants to calculate CIELAB. The estimated changes in meat colour attributable to different illuminants after one and four days’ exposure (Table 13.2) illustrate the effect that light quality has on lightness, hue and chroma. The changes in colour produced by the differences in colour rendering among some of the lamps are equivalent to that which occurs after four days’ fading, that is ΔL* ≤ 1, ΔC* ≤ –4 and Δh* ≤ 7. There was little change in L* on changing illuminant, but the large changes in C* and h* illustrate the effects of decreasing lamp colour temperature, altering flattery and improving fidelity. A decrease in colour temperature from D65, as red emission increases, generally increases C*, that is the colour is perceived as brighter or more intense with the observer adapted to white. However h* may become more brown (more positive) or more red (more negative) as influenced by both colour temperature and the lamp’s spectral bandwidth which affects fidelity. This illustrates the potential for mistaken misinterpretation of the data when measuring by the normal procedure of D65 and 10° when, in reality, judgement at retail or in the home will be under warmer illumination.

13.9.2 Orange juice Translucent suspensions are difficult to measure, and direct unobserved interpretation of instrumental data can lead to confusion because of the way the incident light is dispersed in the sample. Most consistent results are obtained if the instrument aperture is large relative to the incident beam (Kent 1987; MacDougall 1987). The effects of optical geometry on colour and the Kubelka-Munk absorption K (mm–1) and scatter S (mm–1) coefficients for orange juice were studied by

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MacDougall (1983) who found Y∞, the luminous reflectance at calculated infinite thickness, increased by 50 per cent if the aperture diameter was increased from 2 cm to 5 cm while the incident beam was maintained at 1 cm. The effect of dilution of fourfold orange juice concentrate on the reflectance spectra obtained on 4 cm thick samples in thin walled polystyrene bottles is shown in Fig. 13.7. As can be seen, 4 cm is practically equivalent to infinite thickness. Kubelka-Munk absorption and scatter coefficients were calculated from 2 mm thick samples on black and white backgrounds. On dilution, S decreased for X, Y and Z as the suspension became more translucent. K for Z decreased to approach the much lower near constant values of K for X and Y (Fig. 13.8). This decrease in K for Z is as anticipated for a blue absorbing pigment. The effect of loss of scattering power on dilution was to reduce Y∞, and hence lower L*. The most dilute juice, therefore, is instrumentally the darkest, and the most concentrated is the lightest (Fig. 13.9). However, this is not what is perceived. Glasses of orange juice viewed with overhead illumination range from pale yellow for concentrations less than 1 to deep orange at a concentration of 4, which is opposite to that determined instrumentally. For strongly scattering coloured materials in dilute suspension, measured colour, even supplemented by information on scatter, is inadequate to fully describe appearance. The instrument does not measure what the observer sees because light is reflected from a limited solid angle, whereas the observer’s perception is influenced by the multidirectionality of illumination, which makes coloured translucent materials appear to glow. 50 Concentration 4.0

Reflectance (%)

40

1.8

30

0.8

20

0.4 0.2

10

0 400

500 600 Wavelength (nm)

700

13.7 Reflectance spectra of concentration and diluted orange juice at a path length of 4 cm, equal to infinite thickness: reconstituted juice at normal concentration = 1:0.

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Z

0.8 K

0.4 Y X 0 0.3 Z

0.2 S

Y 0.1

X

0 0

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1

2

3

4

Concentration

13.8 Kubelka-Munk absorption K and scatter S coefficients for tristimulus values X, Y and Z for concentrated and diluted orange juice: values (mm−1) calculated from reflectance spectra obtained from 2 mm path length cells with black and white backgrounds.

13.9.3 Coffee Coffee in the cup is usually poured black for the drinker to adjust its colour by adding milk or cream. The drinker uses the combination of milk and coffee as an indication of its anticipated taste although he or she may not know the actual concentration of coffee in the drink. The most obvious appearance attribute of milk in coffee is the intensity of its colour, but translucency has also been shown

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Colour measurement of food: principles and practice 80

333

50 b*

70

40 L*

60

30

50

20 a*, b*

L*

a* 40

10

30

0

20

–10

50

120 C*

40

110

30

100

C*

h* 90

20

10

80

h*

0

70 0

1

2 Concentration

3

4

13.9 Changes in lightness L*, opponent co-ordinates a* and b*, hue angle h* and chroma C* calculated from reflectance spectra of concentrated and diluted orange juice at 4 cm path length.

to affect visual estimation of coffee strength (Hutchings 1999; Mackinney and Little 1962). The Kubelka and Munk theory of mixing pigments in a lightscattering suspension (Judd and Wyszecki 1975; Kubelka 1948) is used as the basis for colour formulation in the paint, plastics and textile industries (Nobbs 1997; McDonald 1997). By manipulating the relationship of the concentration of pigment (K) to degree of light scatter (S) it was demonstrated that it is possible to produce approximately equivalent levels of visual lightness in up to fivefold dilutions of brightly coloured milk (MacDougall 1988). For coffee, since the colour is dull brown, then neither the lightness, chroma nor hue, from different

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concentrations of the components would be expected to change markedly for the drink viewed in the cup, provided the K/S ratio remains constant and the drink does not become obviously translucent. Hutchings (1999) previously had observed that the appearance of creaminess and coffee concentration was related to the K/S ratio but his studies did not include assessment of the actual drinking quality of the beverage. MacDougall and Lima (2001) recently studied the relation of the appearance of coffee at a range of constant K/S values to apparent coffee concentration and milk concentration coupled with a sensory panel’s detection of the strength of the components in the drink. The three ‘K/S constant ratios’ of coffee to milk were made from three levels of Nescafé Gold Blend instant coffee and pasteurised semi-skimmed milk, i.e., nine mixtures in all. Unit quantity of coffee was defined as 2.5 g/l and unit quantity of milk as 100 ml/l. The constant ratios were constructed by doubling the quantity of both coffee and milk at each level and the differences between the ratios by doubling the concentration of coffee from the previous ratio. This required three levels of milk, 100, 200, and 400 ml/l, designated 1, 2 and 3, and five levels of coffee, 2.5, 5, 10, 20 and 40 g/l, designated A, B, C, D and E. Not all combinations were required for the three constant ratios of (A1, B2, C3), (B1, C2, D3) and (C1, D2, E3), i.e., coffee to milk ratios of 1:1, 2:1 and 4:1. It is to be noted that 2.5 g/l of freeze-dried coffee is equal to one heaped teaspoonful, a quantity typically used in making a cup of coffee. Samples of the cold coffee mixtures were presented in 1.5 cm deep tissue culture bottles, which have a 5 cm2 clear face, illuminated by Artificial Daylight Fluorescent Lamps (D65) at 900 lux. Twenty assessors, with normal colour vision, spaced the nine bottles on three 150 cm unmarked straight edges on a pale grey bench for degree of lightness to darkness, for coffee strength and of milk strength relative to the most extreme samples located at the respective ends of the scales. The mean spacing of the samples is given in Fig. 13.10 where they were judged to cluster according to the three constant K/S ratios even though the concentration of milk and coffee in the most concentrated sample within each ratio was fourfold higher than the lowest. The differences within the constant ratios were much less than between the ratios showing that the effect of adding milk produced considerable confusion in the observers’ ability to identify the concentrations of the components. That is, up to a fourfold increase in pigment absorption is apparently nullified by a similar increase in light scatter. When the sensory panel observed and tasted the same samples, this time prepared hot, they similarly separated their appearance when presented in white cups. The taste attributes of coffee and milk were more complexly related to their appearance. Quantitative descriptive analysis of the panel data separated the attributes primarily on the constant K/S ratios but interactively on the absolute values of their components. This demonstrates that the visual appearance of coffee and milk is not a reliable indicator of the taste.

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100 C3 B2

80

A1

Milk

Coffee

60 Value

B1 C2

D3

40

20

C1

D2 E3

0 0

20

40 60 Light to dark

80

100

13.10 Relationship of visual lightness to darkness of coffee and milk K/S mixtures to the perception of the strength of the coffee and milk.

13.9.4 Breakfast cereals Discontinuity in breakfast cereals may be defined as that condition where the components of the product and the inter-component spaces are arranged randomly, which does not lend itself to easy and reproducible measurement. The problem would appear to be intractable unless samples are manipulated to replace the discontinuity with a uniform distribution of the particles. One approach is to grind the sample to constant particle size (Kent 1987). This author studied the colour of four UK breakfast cereals (Shredded Wheat, Weetabix biscuits, Kellogg’s Corn Flakes and Bran Flakes) ground to >2 mm, 1–2 mm, 0.5–1 mm and <0.5 mm through BSI standard sieves. The increased surface area as the particles reduced in volume scattered more light but removed randomness. The 1–2 mm and 0.5–1 mm fractions were closest in appearance to the original flakes, and to each other, whereas the <0.5 mm fraction in each case was considerably lighter and more yellow. In addition to measuring the cereals’ colours, reference colours were used to visually define the degree of discrepancy between measurement and visual observation of the material (MacDougall 2001). This was accomplished with NCS colour patches under both Artificial Daylight (D65) and tungsten illumination. The greatest differences in L*, C* and h* between 1–2 mm samples for all cereals and

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their nearest colour patch were in L* which ranged from 7 to 21 units and always in the same direction of the NCS colours being the lighter. Differences in C* were <3 units and h* were <7 degrees except for bran flakes which was 15 degrees because of difficulty in deciding which atlas page most resembled the product. The greatest contribution to ΔE* therefore, was ΔL*, with chroma and hue virtually unchanged. Typical differences in L* between adjacent colours in the atlas are approximately 10 units. This demonstrates that the magnitude of visual discrepancy in colour assessment, i.e., the error, of products prepared in this fashion will be greater than differences between adjacent NCS patches.

13.10 Illuminant spectra and uniform colour Although D65 is the reference illuminant spectrum most used for calculating CIELAB, of the other lamps listed in Table 13.2 the more important for relating food colour measurement to visual colour in practice are A and 83. A is the emission spectrum of tungsten illumination and 83 is that of ‘tri-band’ fluorescent illumination, similar to F11 in Fig. 13.3. However in 83 the red phosphor is increased by >25 per cent over that of F11 and the green phosphor reduced by about 12 per cent giving the lamp a warm appearance (3000°K) similar to that of tungsten but with a considerable reduction in energy utilisation. 83 is now commonly used for food display in supermarkets because of its attractiveness, pleasant and realistic colour rendering and low heat output. To examine the size of the effect of these lamps on CIELAB colour space, a variety of foods, selected as representative of the major hues, were measured on the ICS Micro Match spectrophotometer. The red hue examples were fresh tomatoes at supermarket readiness, i.e., not excessively ripe but bright attractive redpink and Wiltshire bacon, typically translucent in appearance. The yellow spectrum was the mean of several bananas, ripe but without any indication of brown spotting. The difference between the two orange samples was that the colour of the peel of entire navel oranges is near opaque and brilliant, whereas the colour of freshly squeezed orange juice is translucent. This results in a large reduction in the juice’s reflectance at the red end of the spectrum compared to that of the peel. The green spectrum was that of the external leaves of a mid- to light-green cabbage and the brown was that of the surface of a ‘digestive’ biscuit. The near-white spectrum was that of semi-skimmed milk. All samples were measured at infinite thickness. Figure 13.11 shows the average reflectance spectra of the foods and Fig. 13.12 the location of the samples in CIELAB calculated for the three illuminants. The triplets of points for each sample show that D65 has lower b* values, that is more blue as would be expected, and A and 83 are more yellow with A more red than 83 with higher a* values. These results parallel visual observation of the products viewed under tungsten, artificial daylight (D65) and Philips 83 lamps. This study clearly demonstrates that if interpretation of CIELAB is to be related to illumination conditions in practice, then D65 should not be the only illuminant used to calculate the colour of the food.

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Colour measurement of food: principles and practice Tomato

Wiltshire bacon

80

80

60

60

% 40

% 40

20

20

0 400

500

600

0 400

700

500

nm

80

60

60

% 40

% 40

20

20

500

600

0 400

700

500

nm

600

700

nm

Banana

Digestive biscuit

80

80

60

60

% 40

% 40

20

20

500

600

0 400

700

500

600

700

nm

nm

Cabbage

Semi-skimmed milk

80

80

60

60

% 40

% 40

20

20

0 400

700

Orange juice

Orange peel

0 400

600 nm

80

0 400

337

500

600

700

0 400

nm

500

600

700

nm

13.11 Reflectance (%) spectra of a selection of foods of differing hue.

13.11 Conclusions and future trends This chapter has attempted to present the basics of colour measurement as applied to foods. It is important to realise that the wide variability in the nature of foods and food products, from both their structure and pigmentation, may limit any

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Colour measurement 80 Orange peel

70

71.2

71.4

L*

60 64.9

Banana 78.8

50

78.0 74.8 Biscuit 61.6 61.8

b* 40 Cabbage 77.1

30

58.7

76.1 75.2

20

53.2

Orange juice 59.4 54.5

S.S.Milk

10

0 –10

Tomato 47.3 47.7

51.8

87.9

43.8

Bacon 53.2 53.7

87.8

87.4

0

10

20

30

40

a*

13.12 CIELAB a*b* spacing of the food spectra from Fig. 13.11 calculated for the illuminants D65 (■), A (●) and 83 (▲). The L* values for each food are given adjacent to the symbols of the illuminants.

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colour measurement technique applicable only to that particular food. This is because the numeracy of the data is unlikely to match the visual experience of equivalent reference atlas colours. In some cases this discrepancy may be large and has to be recognised as an intrinsic property of the food. These differences may arise from the difference of the visual experience of the product when viewed under normal lighting conditions as opposed to the limitations of its optical properties when presented to the particular colour-measuring instrument. This is particularly so in the case of translucent foods. The values of measured lightness, hue and chroma are likely to be quite different from similarly coloured opaque materials. A consequence of this is that, if required for quality decisions, the variability of measured colour for any food over the range of acceptability and unacceptability needs to be established relative to real samples judged under appropriate controlled visual judgement conditions. In the light of recent studies on the increased accuracy of colour difference and perceptual acceptability formulae the industry may have to reassess its procedures. Whether or not the level of accuracy demanded by other colour industries is required by the food sector remains to be proved. If the foregoing are taken account of the use of

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colorimetry is likely to expand in the food industry in the future, especially so as portable instruments and in-line measurement techniques become accepted as reliable indicators of product quality. Care needs to be exercised in using such instruments because their detector geometries are different from that of larger reference spectrophotometers. On-line use of colour measurement is likely to increase especially where the colour values obtained by the instrument can be incorporated automatically into process control. The use of video image analysis (VIA) to couple colour measurements with variation in appearance is likely to increase. Provided the RGB signals from VIA systems and digital camera output can be translated into meaningful colour data then a wider and more adaptable approach to food colour appearance control might be possible. VIA systems could become a more direct way of relating measurement to visual assessment and effectual quality assurance. Such systems in conjunction with standard colour measurement could possibly provide direct quality links by computer from the supplier to the processor to the supermarket

13.12 References Alman, D. H., Berns, R. S., Synder, G. D. and Larsen. W. A. (1989) Performance testing of color-difference metrics using a color tolerance data set. Color Research and Application, 14, 139–151. ASTM (2000) Standards on color and appearance measurement, 6th edn. American Society for Testing and Materials, Philadelphia. Atkins, J. T. and Billmeyer, F. W. (1966) Edge-loss errors in reflectance and transmittance measurement of translucent materials. Material Research Standards, 6, 564–569. Bartleson, C. J. (1979a) Changes in color appearance with variations in chromatic adaptation. Color Research and Application, 4, 119–138. Bartleson, C. J. (1979b) Predicting corresponding colors with changes in adaptation. Color Research and Application, 4, 143–155. Berns, R. S. (2000) Billmeyer and Saltzman’s Principles of Colour Technology, 3rd edn. Wiley, New York. Berns, R. S., Alman, D. H., Reniff, L., Snyder, G. D. and Balonon-Rossen, M. R. (1991) Visual determination of suprathreshold color-differences tolerances using probit analysis. Color Research and Application, 16, 297–316. Best, R. P. (1987) Computer match prediction – pigments. In Colour Physics for Industry, ed. R. McDonald, 186–210, Society of Dyers and Colourists, Bradford. Boynton, R. M. (1979) Human Color Vision. Halt, Rinehart and Winston, New York. Brill, M. H. and West, G. (1986) Chromatic adaptation and colour constancy; a possible dichotomy. Color Research and Application, 11, 196–204. Brimelow, C. J. B. (1987) Measurement of tomato paste color: investigation of some method variables. In Physical properties of foods, vol. 2, eds. R. Jowitt, F. Escher, M. Kent, B. McKenna and M. Roques, 295–317, Elsevier, London. BSI (1988) British Standard method for calculation of small colour differences (BS6923). British Standards Institution, London. BSI (1999) Methods for sensory analysis of food – Part 10: General guidance and test method for assessment of the colour of foods (BS5929–10:1999/ISO11037:1999). British Standards Institution, London.

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CIE (1986) Colorimetry, 2nd edn. CIE publication 15.2. Commission Internationale de l’Éclairage, Vienna. CIE (2001) CIE technical report: Improvement in industrial colour-difference evaluation. CIE Pub No 142–2001. Commission Internationale de l’Éclairage, Vienna. Estévez, O. (1982) A better colorimetric standard observer for color-vision studies. The Stiles and Burch 2° color-matching functions. Color Research and Application, 7, 131–134. Francis, F. J. (1999) Colorants. Eagen Press, St Paul, Minnesota. Francis, F. J. and Clydesdale, F. M. (1975) Food Colorimetry: Theory and Applications. AVI, Westport, CT. Halstead, M. B. (1978) Colour rendering: past, present and future. In AIC 77, 97–127, Adam Hilger, London. Hard, A. and Sivik, L. (1981) NCS Natural Color System: A Swedish standard for color notation. Color Research and Application, 6, 129–138. Hood, D. E. and Riordan, E. B. (1973) Discoloration in pre-packaged beef: measurement by reflectance spectrophotometry and shopper discrimination. Journal of Food Technology, 8, 333–343. Hubel, D. H. (1988) Eye, Brain and Vision. Freeman, New York. Hunt, R. W. G. (1977) Specification of color appearance. Effects of changes in viewing conditions. Color Research and Application, 2, 109–120. Hunt, R. W. G. (1978) Color terminology. Color Research and Application, 3, 79–87. Hunt, R. W. G. (1987) A model of colour vision for predicting colour appearance in various viewing conditions. Color Research and Application, 6, 297–314. Hunt, R. W. G. (2001) Measuring Colour, 3rd edn. Ellis Horwood, Chichester. Hunter, R. S. (1958) Photoelectric color difference meter. Journal of the Optical Society of America, 48, 985–995. Hunter, R. S. and Harold, R. W. (1988) The Measurement of Appearance, 2nd edn. Wiley, New York. Hurvich, L. M. (1981) Color Vision. Sinaver, Sunderland, MA. Hutchings, J. B. (1999) Food Colour and Appearance, 2nd edn. Kluwer Academic/Plenum Publishers, Dordrecht. Jacobs, G. H. (1981) Comparative Color Vision. Academic, New York. Judd, D. B. and Wyszecki, G. (1975) Color in Business, Science and Industry, 3rd edn. Wiley, New York. Kent, M. (1987) Collaborative measurements on the colour of light scattering foods. In Physical Properties of Foods, vol. 2, eds R. Jowitt, F. Escher, M. Kent, B. McKenna and M. Roques, 277–294, Elsevier, London. Kubelka, P. (1948) New contributions to the optics of intensely light scattering materials. Journal of the Optical Society of America, 38, 448–457. Li, C., Luo, M. R. Rigg, B. and Hunt, R. W. G. (2002) CMC2000 Chromatic adaptation transform: CMCCAT2000. Color Research and Application, 27, 49–58. Luo, M. R. and Rigg, B. (1986) Chromaticity-discrimination ellipses for surface colours. Color Research and Application, 11, 25–42. Luo, M. R., Cui, G. and Rigg, B. (2001) the development of the CIE 2000 colourdifference formulae: CIEDE2000. Color Research and Application, 26, 340–350. MacAdam, D. L. (1970) Sources of Color Science. MIT, Cambridge, MA. MacDougall, D. B. (1982) Changes in colour and opacity of meat. Food Chemistry, 9, 75–88.

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MacDougall, D. B. (1983) Instrumental assessment of the appearance of foods. In Sensory Quality in Foods and Beverages: its Definition, Measurement and Control, eds A. A. Williams and R. K. Atkin, 121–139, Ellis Horwood, Chichester. MacDougall, D. B. (1987) Optical measurements and visual assessment of translucent foods. In Physical Properties of Foods, vol. 2, eds R. Jowitt, F. Escher, M. Kent, B. McKenna and M. Roques, 319–330, Elsevier, London. MacDougall, D. B. (1988) Colour vision and appearance measurement. In Sensory Analysis of Foods, ed. J. R. Piggott, 103–130, Elsevier, London. MacDougall, D. B. (2001) Discontinuity, bubbles and translucence: major error factors in food colour measurement. 9th Congress of the International Colour Association, 268–269, Rochester Institute of Science and Technology, NY. MacDougall, D. B. and Moncrieff, C. B. (1988) Influence of flattering and triband illumination on preferred redness-pinkness of bacon. In Food Acceptability, ed. D. M. H. Thomson, 443–458, Elsevier, London. MacDougall, D. B. and Powell, V. H. (1997) Relative importance of temperature, wavelength and intensity of light on the colour display of fresh and aged beef cuts. Proceedings of the 43rd International Conference of Meat Science and Technology, New Zealand, 668–669. MacDougall, D. B. and Lima, R. C. (2001) Coffee and Milk with Kubelka and Munk. In Colour Science, Volume 3: Colour Physics, eds A. Gilchrist and J. H. Nobbs, Department of Colour Chemistry, University of Leeds. Mackinney, G. and Little, A. C. (1962). Color of Foods, Avi Publishing Co., Westport. McDonald, R. (1997) Computer match prediction – dyes. In Colour Physics for Industry, 2nd edn, ed. R. McDonald, 209–291, Society of Dyers and Colourists, Bradford. McLaren, K. (1986) The Colour Science of Dyes and Pigments, 2nd edn. Adam Hilger, Bristol. Nayatani, Y., Takahama, K. and Sobagaki, H. (1986) Prediction of color appearance under various adapting conditions. Color Research and Application, 11, 62–71. Nobbs, J. H. (1997) Colour-match prediction for pigmented materials. In Colour Physics for Industry, 2nd edn, ed. R. McDonald, 292–372, Society of Dyers and Colourists, Bradford. Normann, R. A. and Werblin, F. S. (1974) Control of retinal sensitivity. 1: Light and dark adaptation of vertebrate rods and cones. Journal of General Physiology, 63, 37–61. Patterson, D. (1987) Instruments for the measurement of the colour of transparent and opaque objects. In Colour Physics for Industry, ed. R. McDonald, 35–62, Society of Dyers and Colourists, Bradford. Pointer, M. R. (1980) The concept of colourfulness and its use for deriving grids for assessing colour appearance. Colour Research and Application, 2, 99–107. Pointer, M. R. (1982) Analysis of colour-appearance grids and chromatic-adaptation transforms. Color Research and Application, 7, 113–118. Rigg, B. (1987) Colorimetry and the CIE system. In Colour Physics for Industry, ed. R. McDonald, 63–96, Society of Dyers and Colourists, Bradford. Robertson, A. R. (1977) The CIE 1987 color-difference formulae. Color Research and Application, 2, 7–11. Rodieck, R. W. (1979) Visual pathways. Annual Reviews in Neuroscience, 2, 193–225. Saunderson, J. L. (1942) Calculation of the color of pigmented plastics. Journal of the Optical Society of America, 32, 727–736. Smith, C. V. and Pokorny, J. (1975) Spectral sensitivity of the foveal cone photopigments between 400 and 500nm. Vision Research, 15, 161–171.

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Stryer, L. (1988) Molecular basis of visual excitation. Cold Spring Harbour Symposia on Quantitative Biology, 53, 283–294. Wald, G. (1968) The molecular basis of visual excitation. Nature, 219, 800–807. Wright, W. D. (1967) The Rays Are Not Coloured. Adam Hilger, London. Wright, W. D. (1980) The Measurement of Colour, 5th edn. Adam Hilger, London. Zeki, S. (1993) A Vision of the Brain, Blackwell Scientific Publications, London.

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14 Colorimetric evaluation of tooth colour A. JOINER, Unilever Oral Care, UK

Abstract: This chapter begins with an introduction to the structure of the dentition and its environment followed by a review of the optical properties of teeth. Tooth colour will be reviewed with particular emphasis on colour distribution and variation. Important factors that influence tooth colour and its perception will be discussed. With the current interest in tooth whitening, this chapter will then describe approaches to quantifying tooth whiteness and the measurement of tooth colour, in particular, visual assessment approaches and instrumental techniques. In addition, methods to improve tooth colour will be reviewed including whitening toothpastes, tooth bleaching and microabrasion. Key words: tooth colour; tooth colour measurement; tooth colour perception; tooth whitening; tooth bleaching.

14.1

Introduction

Overall appearance is a key element in social interaction and success (Baldwin, 1980), including the appearance of the teeth (Kershaw et al., 2008). The ultimate aim of aesthetics in dentistry is to create a beautiful smile, with teeth of pleasing inherent proportions to one another, and pleasing tooth arrangement in harmony with the gingival, lips and face of the patient (Mayekar, 2001). One of the most important considerations in dental aesthetics is the optical properties of the teeth, which includes their colour and translucency, for example. This chapter will begin with a basic introduction to the structure of the dentition and its environment followed by a review of the optical properties of teeth. Tooth colour will be reviewed with particular emphasis on colour distribution and variation. Factors that influence tooth colour will be reviewed, including tooth discolourations found within the teeth and on their surfaces and other important factors such as subject age and hydration state together with lighting conditions, observer and context which can impact the perception of tooth colour. In recent years tooth whitening has become one of the most rapidly growing oral care sectors, since patients now demand not only healthy smiles but also cosmetically attractive and whiter smiles. This chapter will therefore describe approaches to quantifying tooth whiteness and the measurement of tooth colour, in particular, visual assessment approaches and instrumental techniques. In addition, methods to improve tooth colour will be reviewed including whitening toothpastes, tooth bleaching and microabrasion. Finally, likely future trends in the tooth colour field will be outlined together with sources of further useful information. 343 © Woodhead Publishing Limited, 2010

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14.2

The human dentition and its environment

14.2.1 Teeth and gums The anatomy of a tooth essentially consists of enamel, dentine and the pulp (Ten Cate, 1998) (Fig. 14.1). Tooth enamel is highly mineralised and consists of about 95% by weight mineral (hydroxyapatite, Ca10(PO4)6(OH)2 and some carbonate), 4% water and less than 1% organic material, mainly protein. It can be up to 2.5 mm thick over the working surfaces (cusps) of the tooth, tapering to only a few hundred microns thick at the cervical margins. At an ultrastructural level, enamel is composed of long rod-shaped prisms as the fundamental unit. Each prism extends from its site of origin at the enamel–dentine junction to the outer enamel surface. These have an average width of 5 microns in diameter and are made up of highly organised crystallites of hydroxyapatite (Ten Cate, 1998; Addy et al., 2000). The bulk of the root and body of the tooth is composed mainly of dentine, which has been described as a porous biological composite material composed of hydroxyapatite crystal filler particles in a collagen matrix (Pashley, 1996). Dentine contains many micron-sized diameter dentinal tubules surrounded by highly mineralised (ca. 95% volume mineral phase) intertubular dentine (Addy et al., 2000). The central pulp chamber is filled with soft connective tissue, nerves and blood vessels (Ten Cate, 1998). The soft tissues that support the teeth are collectively termed the periodontium. The most superficial periodontal tissue is the gingiva, commonly known as the gums, and this is the tissue that surrounds

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Bone

14.1 The anatomy of a tooth.

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the root of an erupted tooth. It is involved in the attachment of the tooth forming a junction between the gum and tooth (Ten Cate, 1998).

14.2.2 Saliva and pellicle The hard and soft oral tissues are constantly bathed in saliva which is secreted by the various major and minor salivary glands found in the mouth. Saliva is a complex mixture of many types of proteins, glycoproteins, enzymes, lipids and several ions including sodium, potassium, calcium, phosphate and bicarbonate. It can also contain other components derived from cellular and bacterial origin. Saliva is a very important fluid since it starts the initial process of food digestion, provides lubrication which makes mastication and swallowing easier, and aids in the maintenance of oral and overall general health (Jenkins, 1978). When a tooth surface has been thoroughly cleaned, within minutes the enamel surface becomes coated by a sub-micron, colourless organic film known as the pellicle. The pellicle is formed by the adsorption of high affinity salivary proteins followed by further adsorption of other salivary proteins. The pellicle layer is of great physiological importance since it participates in all the tooth–saliva interfacial events taking place in the oral cavity, such as, de- and remineralisation, lubrication of the tooth surface and modulation of bacterial attachment (Hannig and Joiner, 2006). It is also involved in the formation of extrinsic stain which can impact on the aesthetics and colour of the teeth (see section 14.5.2).

14.3

Optical properties of teeth

A combination of the optical properties of a tooth will determine its colour and appearance. Four phenomena associated with the interactions of light with the tooth have been described (Ragain and Johnston, 2001): (1) transmission of the light through the tooth, (2) specular reflection at the surface, (3) diffuse light reflection at the surface, and (4) absorption and scattering of light within the dental tissues. Tooth colour has been shown to result from the volume scattering of light, i.e. illuminating light follows highly irregular light paths through the tooth before it emerges at the surface of incidence and reaches the eye of the observer (O’Brien et al., 1990; Van der Burgt et al., 1990). Both enamel prisms and dentinal tubules can act as optical fibres (Odor et al., 1996). The hydroxyapatite crystallites contribute significantly to light scattering in enamel (Vaarkamp et al., 1995) whereas within dentine the tubules are the predominant cause of scattering (Ten Bosch and Zijp, 1987; Zijp and Ten Bosch, 1993). Exposure of enamel to acid can give partial demineralisation of the enamel crystallites which will cause an increase in light scattering and thus the enamel can appear visibly whiter (Ko et al., 2000). The translucency of teeth is an important factor for consideration in restorative dentistry since it contributes significantly to the total aesthetics of the tooth

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and is an indication of the quality and quantity of light reflection giving vitality to the tooth (Terry et al., 2002; Chu et al., 2004). The cervical regions of the teeth have the lowest translucency and the incisal edges the highest translucency (Hasegawa et al., 2000b). The translucency of enamel is an important factor when considering tooth colour, since in vitro studies (Ten Bosch and Coops, 1995) have shown that tooth colour is determined mainly by the colour of the dentine with enamel contributing only a minor part through scattering at wavelengths in the blue range. Opalescence effects in tooth enamel occur in enamel when visible light is scattered, causing the reflection of shorter wavelengths of light and transmission of longer wavelengths. Indeed, when enamel slabs are observed in daylight, they appear to be pale blue in reflection and pale yellow in transmission (Zijp et al., 1995). This effect is most noticeable toward the incisal edges of anterior teeth (Priest and Lindke, 2000). Approaches to the measurement of opalescence of tooth enamel in vitro have been described using a spectrophotometer and opalescence was shown to be significantly different in human and bovine enamel specimens (Lee and Yu, 2007). The surface morphology of a tooth influences the amount and type of light reflection. A rough or coarse surface gives more diffuse reflection whereas a more smooth and uniform surface allows more specular reflection. Surface gloss affects the appearance and vitality of the teeth, where light reflected from the tertiary anatomy adds to vitality whereas less vitality is evident when this anatomy is worn with age (Terry et al., 2002). One goal of aesthetic dentistry is to maximise specular reflection and highlights from teeth by maintaining smooth and highly polished enamel and restorative surfaces (Serra and Otis, 2004). The amount of light reflected from tooth surfaces in vivo has been demonstrated to significantly increase following toothbrushing (Sturzenberger et al., 1975; Redmalm et al., 1985) and significantly decrease in vitro following enamel demineralisation (Serra and Otis, 2004). The fluorescence of dental enamel and dentine has been studied by a number of workers (Perry et al., 1969; Spitzer and Ten Bosch, 1975; Fujimoto et al., 1977). Enamel fluorescence excitation peaks have been found at 285 and 330 nm, with corresponding emission maxima at 360 and 410 nm (Spitzer and Ten Bosch, 1975). Dentine has fluorescence excitation peaks at <300, 325, 380 and 410 nm, and emission maxima at ca. 350, ca. 400, 450 and 520 nm (Ten Bosch and Zijp, 1987). The compounds responsible for the fluorescent properties of teeth are not fully characterised, although some organic compounds resembling dityrosine analogues have been isolated (Booij and Ten Bosch, 1982). The combination of fluorescence from enamel and dentine is reported to enhance the whiteness or value of teeth (Terry et al., 2002). However, in contrast, it is reported from in vitro experiments with extracted teeth that fluorescence does not contribute to visually observed tooth colour (Ten Bosch and Coops, 1995).

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The colour of teeth

The colour of a tooth is determined by the light scattering and absorption properties of the enamel and underlying dentine. Hall (1991) described tooth colour as containing 100 000 different colours. Despite this huge magnitude of possible different tooth colours, a number of studies have been conducted to evaluate the variations of colour in a single tooth, tooth colour distribution within an individual and across study populations with known demographics.

14.4.1 Tooth colour variation The colour of a tooth is often not uniform across the whole surface. The middle region of the tooth has been described as the site that best represents the colour of the tooth (Goodkind et al., 1987) since the incisal region is often translucent and can be affected by the background, whereas the cervical region can be affected by light scattering from the pink coloured gingival tissues. Hasegawa et al. (2000b) measured the colour of the labial surface of central incisors in five different locations along the tooth axis from the incisal edge to the cervical region and found significant variations in CIELAB values. For L*, this was highest in the central portion of the tooth (ca. 73) and was lower towards the cervical area (ca. 69) and incisal edge (ca. 69). For a*, the highest values were found towards the cervical area (ca. 8.5) and significantly lower towards the incisal edge (ca. 2.0). For b*, the cervical region had the highest value (ca. 20) which was reduced significantly towards the incisal edge (ca. 13). Similar trends were observed by O’Brien et al. (1997) when measuring the colour of 95 extracted anterior teeth from 35 patients. The mean L*, a* and b* values, respectively, were 71.4, 0.9 and 12.8 for incisal, 72.4, 1.2, and 16.2 for middle, and 72.6, 1.5 and 18.4 for cervical regions.

14.4.2 Tooth colour distribution The range and distribution of tooth colour has been described by a number of investigators. In general, the maxillary anterior teeth are slightly more yellow than mandibular anterior teeth (Goodkind et al., 1987), and the maxillary central incisors are higher in value than the lateral incisors and canines (Goodkind et al., 1987; Hasegawa et al., 2000a). Gender differences in tooth colour have been reported (Odioso et al., 2000) in a study group of 180 people from the USA, where women had statistically higher value and less yellow teeth than males. The L* values for the maxillary central incisors were approximately 3.7 units lower on average in males compared to females. In contrast, other studies have observed no significant gender differences in tooth colour (Van der Burgt et al., 1990; Hasegawa et al., 2000a). Early studies presented tooth colour data in terms of chromaticity co-ordinates and tristimulus values and have been summarised by Miller (1987) and O’Brien

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et al. (1997). However, these colour scales do not lend themselves to ease of interpretation or comparison (O’Brien et al., 1997) and therefore the majority of contemporary studies use the CIELAB colour space to describe tooth colour. In a recent review of published CIELAB tooth colours (Joiner et al., 2008a), it is evident that there is a broad range of reported L*, a* and b* values for anterior teeth. For examples, a USA (Gozalo-Diaz et al., 2007) and a Chinese (Zhu et al., 2001) population had mean L* values of 73.3 and 54.91 respectively, with a range of 38.0 to 89.5 and 21.89 to 83.75 respectively. In addition, the reported mean L* values for study populations from Spain (Rubino et al., 1994) is 67.6, from Canada (Douglas, 1997) is 54.76 and from Japan (Hasegawa et al., 2000a) is 73.1. This broad variation in L* probably reflects differences in the measurement techniques used in the various studies rather than real differences between study populations. This is confirmed in a single study population from Korea which had a mean L* value of 57.8 when measured by one type of colorimeter and a mean L* value of 74.0 when measured with a different instrument (Cho et al., 2007). In terms of mean a* values, these are generally fairly similar across different study populations and are reported as being from -1.69 to 5.4, with the overall maximum range of a* values reported as -8.07 to 9.21 (Joiner et al., 2008a). In terms of mean b* values, these range from -0.2 to 19.4 depending on study population (Joiner et al., 2008a), with the largest overall range in b* values of 6.53 to 59.89 reported for a Chinese population (Zhu et al., 2001).

14.5

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Factors that impact tooth colour and its perception

Tooth colour can be affected by what are termed intrinsic and extrinsic colourations of the tooth, and both types have associated discolourations or stains (Nathoo, 1997; Watts and Addy, 2001; Joiner, 2004). In general, intrinsic discolouration is found within the enamel and dentine whereas extrinsic discolouration is found on the surface of the tooth. Other important factors are known to impact tooth colour including subject age and hydration state, and tooth colour perception can be impacted by the variables of lighting conditions, the observer and the context that the teeth are viewed.

14.5.1 Intrinsic tooth discolouration Intrinsic tooth discolouration occurs following a change to the structural composition of the dental hard tissues and is attributable to the incorporation of chromogenic materials into the enamel or dentine (Watts and Addy, 2001; Brook et al., 2007). Intrinsic discolouration can occur during tooth development or after tooth eruption. During tooth development, a number of inherited and metabolic disorders can affect intrinsic tooth colour, such as amelogenesis imperfecta, which can give a range of colourations from cream and yellow to brown and black. Other

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pre-eruptive discolourations include dental fluorosis and the incorporation of tetracycline during tooth development. Dental fluorosis intrinsic tooth discolouration is endemic in areas where the fluoride content is above optimal values (Hattab et al., 1999). It is a subsurface hypomineralisation that occurs during tooth formation and the resulting defects can give a range of white opaque spots and streaks, through to yellow brown bands (Dayan et al., 1983). The tetracycline family of antibiotics have a broad anti-microbial spectrum and are used to treat a variety of infections. However, it is known that tetracycline can cause extensive discolouration of children’s teeth due to the deposition of tetracycline into the developing enamel and dentine (Mello, 1967; Watts and Addy, 2001; Brook et al., 2007). Upon eruption, the affected teeth are initially yellow and exhibit a bright yellow fluorescence. Over time, this initial brightyellow colour appearance becomes dark-yellow to brown, particularly on the labial surfaces of the anterior teeth. This is due to the formation of degradation products of the incorporated tetracycline under the effects of daylight (Mello, 1967; Lambrou et al., 1977). The discolouration of teeth following severe trauma is a common cause of post-eruptive intrinsic tooth stain (Brook et al., 2007). This is due to haemorrhaging within the tooth pulp chamber driving blood into the dentine where the red blood cells and haemoglobin degrade to strongly coloured chromogens to give an overall darkening of the tooth colour (Hattab et al., 1999; Watts and Addy, 2001).

14.5.2 Extrinsic tooth stain The pellicle has a tendency to develop stain, termed extrinsic stain, particularly in those areas of the dentition which are inaccessible to toothbrushing and the abrasive cleaning action of toothpaste (Forward, 1991; Dawson et al., 1998). Extrinsic stain is typically yellow-brown or dark brown-black depending on its origin, and can appear as distinct patches or lines, or across the majority of the tooth surface in severe cases. This type of stain can clearly impact the aesthetics and colour of the teeth. Extrinsic stain is reported to be promoted by smoking, dietary intake of certain foods and beverages (e.g. red wine, tea, coffee) and the use of certain cationic agents such as chlorhexidine, tin and iron (Watts and Addy, 2001). The mechanisms of extrinsic stain formation have been reviewed by a number of authors (Addy and Moran, 1995; Nathoo, 1997; Watts and Addy, 2001; Hannig and Joiner, 2006; Brook et al., 2007). The most likely mechanism involves the initial absorption and binding of materials onto the pellicle. These materials can be coloured in themselves to give direct staining or subsequently have chemical interactions with other materials to generate the discolouration. An example of direct staining was demonstrated by Macpherson et al. (2000) who showed that the levels of extrinsic

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stain formed over six weeks in vivo was correlated with the amount of smoking and tea consumption. An example of indirect staining is exhibited by the anti-plaque agent chlorhexidine. This is colourless when first bound to the pellicle, but its subsequent chemical interaction with iron salts or tea polyphenols in the mouth cause the formation of dark brown-black discolourations on the tooth surface (Addy and Moran, 1995; Watts and Addy, 2001).

14.5.3 Other factors Natural tooth colour has a significant tendency to change with the age of the subject, becoming darker and more yellow with increasing age (Goodkind et al., 1987; Zhao and Zhu, 1998; Hasegawa et al., 2000a; Odioso et al., 2000; Jahangiri et al., 2002). In the vast array of genetically determined tooth colourations, all teeth appear to darken over the course of time (Morley, 1997). Odioso et al (2000) reported that for a USA population the average tooth colour shifted by an increase of 0.10 for b* and a decrease of 0.22 for L*, for each year of life. The impact of subject age on tooth colour is due to a number of factors. As the dental pulp ages it shrinks, leaving secondary dentine in its wake (Morley, 1997), and the surrounding dentine will become harder and less vascularised. It is hypothesised that pigments and ions of an amorphous organic and inorganic nature permeate and deposit at the enamel–dentine junction, causing the dentine chroma to become more saturated (Morley, 1997). Coupled with decreasing enamel thickness due to normal tooth wear processes, the dentine colour begins to have a greater influence on the overall tooth colour. The net result is a progressive darkening and yellowing of the teeth with age (Joiner, 2004). Another important factor to impact tooth colour is the hydration state of the teeth, since dry teeth will appear whiter (Russell et al., 2000; Mayekar, 2001). For example, allowing the teeth to dry out for 15 minutes caused a significant increase in L* value as compared to the fully hydrated state, and it took at least 30 minutes to recover to baseline values (Russell et al., 2000). Therefore care must be taken to avoid excessive drying of the teeth when attempting to measure their colour.

14.5.4 Tooth colour perception The perception of tooth colour is a complex phenomenon and there are many variables that can affect how it is perceived. For example, different light sources, intensities and metamerism effects can significantly alter the accuracy of visual tooth colour measurement via shade matching (Dagg et al., 2004; Curd et al., 2006). In terms of the human observer, factors such as colour blindness, age, fatigue, nutrition, emotions, medications and binocular difference are important variables (Chu et al., 2004). The context that the tooth is viewed in is also important, since gum and lip colour can influence tooth colour perception (Chu,

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2003). For example, tooth whiteness perception can be increased by the addition of a magenta hue to the gums (Reno et al., 2000). Tooth whiteness perception can also be enhanced by reducing the yellowness (b*) of the teeth (Joiner et al., 2008a). Spatial contrast between teeth is another factor, since a recessed tooth will appear darker than the adjacent teeth whereas a protrusive tooth will look lighter (Chu et al., 2004).

14.6

Tooth whiteness

Whiteness is recognised as a special colour attribute and much research has been conducted in order to define whiteness colourimetrically (Luo et al., 2005). The quantification of whiteness has long been of importance to a number of industries including paper, paint, plastic and laundry industries, and a number of whiteness indices have been developed to address their needs. One of the whiteness indices is defined within the CIE nomenclature (WIC) along with a tint measure (T) (Hunt, 1998). WIC = Y + 800(xp – x) + 1700(yp – y)

[14.1]

T = 900(xp – x) – 650(yp – y)

[14.2]

where the subscript ‘p’ refers to the chromaticity of the white point for the colour space, and x and y are the chromaticity coordinates defined from the tristimulus values: x = X/(X + Y + Z) and y = Y/(X + Y + Z)

[14.3]

In general, the use of whiteness indices in the dental field is limited to only a few examples in the literature. Garcia et al. (1993) reported that the WIC was the best index for describing the whiteness of a group of 20 samples of porcelain teeth in vitro. More recently, Guan et al. (2005) used the WIC to determine the increase of whiteness of extracted teeth following a bleaching procedure for one hour or brushing with toothpaste for two minutes. They demonstrated using image analysis of digital images and spectrophotometer measurement techniques that the bleaching procedure gave a significant increase in tooth whiteness whereas the brushing procedure did not. The WIC has been used to value rank the shade tabs of the Vita Lumin Shade Guide (Guan et al., 2005) and the Vitapan Master 3D Shade Guide (Lath et al., 2007), and the WIC has also been applied within forensic dentistry to help determine the age of dental skeletal remains (Martin de las Heras et al., 2003). An alternative whiteness index (W*) based on CIELAB measurements and colour space has been described in the dental literature (Gerlach et al., 2002). This uses a nominal white point, defined as L* = 100, a* = 0 and b* = 0, and calculates the distance the colour of a tooth is from this point, as follows: W* = [(a*)2 + (b*)2 + (L* – 100)2]1/2

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Changes in tooth whiteness (ΔW*), for example, following a tooth whitening procedure can be calculated from: ΔW* = W*(treatment) – W*(baseline)

[14.5]

The increase in tooth whiteness (ΔW*) following bleaching procedures was demonstrated in a number of clinical studies, for example after following seven days use of a 6% hydrogen peroxide product (Xu et al., 2007) and after three weeks use of a 6.5% hydrogen peroxide product (Guerrerohd et al., 2007). In general, for a whiteness index to be valid it must be used on the type of material for which it is intended. Therefore, Luo et al. (2005) conducted a series of psychophysical experiments on a tooth shade guide to optimise the coefficients of the WIC formula according to visual results and a new optimised formula was developed for the evaluation of the whiteness of teeth (WIO), as follows: WIO = Y + 1075.012(xp – x) + 145.516(yp – y)

[14.6]

In terms of agreement with the visual assessment of tooth whiteness, the WIO formula was shown to outperform the WIC formula (Luo et al., 2005, 2009) and the most appropriate for assessing changes in tooth whiteness (Luo et al., 2007). The use of the WIO whiteness index has been utilised in a clinical study comparing 2 weeks use of either a non-whitening toothpaste or a 6% hydrogen peroxide tooth bleaching product where it was found that the bleaching product gave a significantly greater increase in tooth whiteness than the toothpaste (Luo et al., 2007). The WIO whiteness index has also been used in a series of in vitro and clinical evaluations of a silica whitening toothpaste containing blue covarine where a significant increase in tooth whiteness was measured following tooth brushing (Collins et al., 2008; Joiner et al., 2008b).

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The measurement and communication of tooth colour is important in modern aesthetic dentistry. For example, in the preparation and fitting of a tooth crown the dentist must be able to measure the tooth colour of the existing dentition accurately, then communicate the tooth colour to the laboratory technician who will select the appropriate coloured dental materials to fabricate the crown, and together they will produce a crown that is an acceptable colour match to the existing dentition. Indeed, the gold standard for tooth colour measurement is the ability to match a restoration on a single tooth to the adjacent teeth, with sufficient accuracy to be undetectable by the critical eye (Browning, 2003). With the increase in the interest and demand in tooth whitening, the measurement of change in tooth colour is important for the communication between dentist and patient. In addition, for manufacturers of oral care products the measurement of tooth colour is important to them in order to demonstrate efficacy of tooth

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whitening products in laboratory and clinical studies and to support related whitening claims. There are a number of approaches to measuring tooth colour, ranging from visual subjective assessments by comparison of the tooth with a tooth coloured shade guide, to instrumental objective methods such as using spectrophotometers, colorimeters and digital image analysis techniques.

14.7.1 Visual assessment of tooth colour The visual assessment of tooth colour by the comparison and matching of the tooth with a tooth colour shade guide under the same lighting conditions is the most frequently applied method in dentistry (Van der Burgt et al., 1990). The use of shade guides is a relatively quick and cost-effective method for measuring tooth colour. Tooth shade guides come in many types and forms, but essentially the basic design involves a series of standard tooth colours, often tooth shaped, and can be arranged according to chroma and/or value. An example is the Vita Shade Guide as shown in colour Plate XI between pages 42 and 43. As colour perception is a subjective process, it is not surprising that inconsistencies among dentists attempting to match natural tooth colours and the inability of individual dentists to reliably duplicate their own selections have been reported (Culpepper, 1970). Tooth colour perception is affected by, for example, the general variables such as lighting conditions, experience and age of assessor, fatigue, length of exposure of the specimen to the eye, previous exposure of the eye, and colour blindness (Douglas, 1997; Joiner, 2004). A number of methods and techniques for aiding the visual assessment of tooth shades have been described (Greenwall, 2001; Mayekar, 2001; Browning, 2003). One approach is to arrange the colour guide in terms of value and select the tooth value first followed by hue selection. The hue can be viewed best in the middle third of the tooth because the range of colour changes from incisal to the cervical regions (Greenwall, 2001; Mayekar, 2001). The control of the light source is important for consistent visual colour measurements with shade guides in the clinical situation and in laboratory experiments. In the latter, typically viewing cabinets or booths with controlled and specified light sources and grey coloured surfaces have been used to improve tooth shade measurement (Ragain and Johnston, 2000; Joiner et al., 2008b). The tooth colour matching ability of individuals can be improved with training and experience (Ragain and Johnston, 2000; Watts and Addy, 2001). Indeed, training and calibration exercises for clinical assessors using shade guides to measure tooth colour are often conducted when evaluating longitudinal tooth colour changes during tooth whitening clinical studies (Godder et al., 1994; Leonard et al., 1998; Kihn et al., 2000). With these precautions, shade guides have been used successfully to measure tooth colour changes in a number of tooth whitening

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clinical studies (Haywood and Heymann, 1989; Kihn et al., 2000; Nathoo et al., 2002b; Browning, 2003; Sulieman, 2004). Although shade guides are commonly used in dentistry as the colour standard to which tooth colour is matched, several disadvantages have been described. For example, the range of available shades is inadequate since it does not cover the complete natural tooth colour space (Goodkind et al., 1987; Miller, 1993); colour differences between successive shade tabs are not uniform and systematic (O’Brien et al., 1985; Li, 2003), with colour differences of up to 4.88 ΔE units between tabs of like adjacent shade (Li, 2003); none of the commercially available shade guides are identical (Yap, 1998); the results can not be transformed into CIELAB colour space (Van der Burgt et al., 1985); and the material used to fabricate the guide is different to the tooth material and the restoration material (Preston, 1985). To overcome some of these difficulties, the Vitapan 3D-Master Shade Guide (VITA Zahnfabrik, Germany) was designed to have a broader and more uniform colour range, better colour distribution, and improve repeatability of measuring tooth shade as compared to other shade guides (Paravina et al., 2002; Hammad, 2003; Analoui et al., 2004). Despite these improvements, to date this shade guide has not been widely used for evaluating tooth whitening (Paravina, 2008) and has been criticised that the difference between shade groups is too course to make the tooth whitening benefits easily measured (Browning, 2003). Indeed, no commercially available shade guide is primarily designed for evaluating tooth whitening. However, recently a new VITA Bleachedguide 3D-Master was designed and developed to include additional shade tabs and value ordered to increase its applicability for monitoring tooth whitening (Paravina et al., 2007; Paravina, 2008).

14.7.2 Instrumental tooth colour measurement

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Instrumental approaches for objectively measuring the colour of teeth have received much attention in the scientific literature. Spectrophotometers have been used to measure the visible spectrum of teeth in vitro and in vivo (Zhao and Zhu, 1998; Paul et al., 2002; Xiao et al., 2007). However, the widespread use of spectrophotometers in dental research and clinical settings has been hindered by the fact that the equipment is relatively complex and expensive (Tung et al., 2002; Chu et al., 2004). On the other hand, colorimeters have been widely used in dental research for measuring tooth colour both in vitro and in vivo (Li, 2003; Joiner, 2004) and in general shown to be reliable, have good repeatability, and are accurate for colour difference measurements (Van der Burgt et al., 1990; Goldstein and Schmitt, 1993; Douglas, 1997; Tung et al., 2002). For in vivo longitudinal tooth colour measurements with colorimeters, the use of custom made alignment devices (i.e. gum shield with apertures aligned to the anterior teeth and fabricated for each subject) are required to ensure accurate realignment of the colorimeter measuring head onto the tooth surfaces before and after treatment (Li, 2003).

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This approach has been successfully used to measure tooth colour changes following bleaching procedures in vivo (Rustogi and Curtis, 1994; Mokhlis et al., 2000; Nathoo et al., 2001). Methods to reposition a colorimeter onto extracted teeth in the laboratory have also been described and successfully utilised to determine tooth colour changes following a peroxide bleaching protocol (Rosenstiel et al., 1991). Investigations into the relationship between the colour perceived by human observers and the colour measured by colorimeters appear to be inconclusive (Tung et al., 2002), where some investigators (Johnston and Kao, 1989; Van der Burgt et al., 1990) have reported a correlation while others have reported no significant agreement (Goldstein and Schmitt, 1993; Okubo et al., 1998). The reported disadvantages of using colorimeters for tooth colour measurement include: the instruments are designed to measure flat surfaces, teeth are often not flat and can have surface anomalies; teeth are translucent which can lead to edge losses and systematic errors; only small areas of the teeth can be measured at one time, leading to results that can be unrepresentative of the whole tooth; interinstrument agreement can be poor; requires contact with the teeth which raises the need for measures to prevent cross infection, and can be intrusive to the patient (Joiner, 2004; Brook et al., 2007). Advancements in technology in this area have given rise to the availability of a number of different types of commercial computer-aided tooth shade measurement systems, based on spectrophotometers and colorimeters (Chu et al., 2004; Hugo et al., 2005). Systems vary but essentially a measurement is taken from the patient’s tooth that is then converted directly by the system’s software into a tooth shade guide value. This can be a single shade or map subtle differences in shade across the surface of the tooth (Chu et al., 2004). A number of these systems were compared with visual assessment in a clinical study measuring the colour of teeth of 57 subjects. It was found that there was a significant disagreement of the devices with human colour measurement results (Hugo et al., 2005). This indicates a deficit in the conversion of colorimetric data into colour shades adequate to the test of human perception and suggests the need for further refinements in methods. However, some types of shade taking devices have been successfully used in laboratory studies to measure quantitative changes in tooth colour following staining and bleaching protocols (Amaechi and Higham, 2002; Sulieman et al., 2003, 2004).

14.7.3 Digital image analysis techniques Recent advances in photography and computing have resulted in the widespread use of the digital camera for colour imaging (Jarad et al., 2005). The camera records digital data from an object which can be subsequently used to view an image on a computer. These images can be analysed with appropriate software to generate colour values from the whole or selected parts of the image.

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A number of digital imaging systems have been described in the literature for measuring tooth colour in vitro and in vivo (Cal et al., 2004; Guan et al., 2005; Jarad et al., 2005; Lath et al., 2007; Luo et al., 2007; Sagel and Gerlach, 2007; Mohan et al., 2008; Smith et al., 2008). In general, the systems consist of a digital camera and a controlled lighting source. There is no consensus on which camera or lighting source is best to use as different research groups utilise different makes and models. However, a number of different makes and models of digital cameras have been evaluated and shown to have potential for use in the colour replication process of clinical dentistry (Bengel, 2003; Elter et al., 2005; Wee et al., 2006). In general, the subject uses a lip retractor to expose the anterior teeth and their head positioned in front of the camera via for example, a chin rest, forehead bar or a cephalometric head apparatus. The image of the teeth is captured and transferred to a computer where the software converts the RGB values of the required tooth image pixels to, for example, CIELAB values using an appropriate algorithm. The whole system is colour calibrated via imaging a standard white tile, coloured tile or grey tile before and/or during each clinical session. The measurement of tooth colour via image analysis of digital images has been shown to be highly reproducible in both in vitro and in vivo studies (Gerlach et al., 2000; Cal et al., 2004; Smith et al., 2008), and has been reported to be more reliable in tooth colour quantification than a spectrophotometer (Guan et al., 2005). Image analysis of digital images has been applied successfully in a number of clinical trials evaluating tooth whitening procedures and has measured the increase in tooth whiteness following bleaching procedures (Gerlach et al., 2000; Ferrari et al., 2004; Mohan et al., 2008) and the use of whitening toothpastes (Collins et al., 2008). It has also been used to demonstrate uniform tooth whitening effects in different regions of the teeth by measuring colour changes in the body and interproximal tooth areas (Gerlach and Barker, 2003). Its use in colour shade matching has demonstrated the potential of the technique for improved shade matching performance than visual methods (Jarad et al., 2005). Advantages of this technique in clinical research include: increase in objectivity of measurement and improved reliability; ability to measure small differences in tooth colour; reduction of bias in studies; no contact with the tooth surface thus avoiding cross contamination issues, and the images can be archived. This latter advantage can help in clinical case presentation, planning secondary analyses or for follow-up in data quality assessment (Brook et al., 2007; Sagel and Gerlach, 2007).

14.8

Measurement of extrinsic stain

14.8.1 Clinical indices An important part of the evaluation process for a whitening toothpaste is its clinical efficacy, particularly in terms of its extrinsic stain removal and prevention.

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Extrinsic stain is usually determined by a trained clinical examiner who views the anterior teeth under controlled lighting conditions and scores the stain by using a clinical index (Macpherson et al., 2000), although photography has sometimes been used (Eriksen et al., 1979; Addy and Moran, 1985). The Lobene stain index (Lobene, 1968) divides the tooth surface into gingival and body regions. The level of stain is then scored as 0 – no stain, 1 – light stain, 2 – moderate stain, and 3 – heavy stain. In addition, the extent which the stain covers the gingival and body areas is scored as 0 – no stain detected, 1 – stain over a third of region, 2 – stain over two thirds of the region, and 3 – stain over more than two thirds of the region. This index has frequently been used to successfully evaluate whitening toothpastes (Yankell et al., 1994; Nathoo et al., 2002a), although alternative indices or modifications of the Lobene index have been reported (Davis and Rees, 1975; Shaw and Murray, 1977; Macpherson et al., 2000).

14.8.2 Instrumental methods The use of spectrophotometers and colorimeters has routinely been used to measure and quantify artificially formed extrinsic stain on teeth, tooth specimens, hydroxyapatite and perspex substrates in vitro and to evaluate the efficacy of tooth whitening products (Prayitno and Addy, 1979; Dawson et al., 1998; Suliemann et al., 2003; Joiner and Thakker, 2004; Joiner, 2006a). Image analysis techniques for the evaluation of stain removal efficacy of whitening toothpastes in vitro have also been described. Tantbirojn and Douglas (1998) used bovine enamel specimens with naturally occurring stain and showed that the image analysis technique provided an objective and quantitative measurement to distinguish stain removal efficacy of a range of toothpastes. Lath et al (2006) used acrylic blocks stained with saliva, chlorhexidine and tea and demonstrated that an imaging system was a reliable alternative to spectrophotometry for measuring stain removal by toothpastes. Quantitative light-induced fluorescence (QLF) has been used to assess artificial stain build-up and removal on extracted teeth (Amaechi and Higham, 2002). This technique involves the capturing of fluorescent images of the tooth, where stained areas show reduced fluorescence compared to non-stained areas. QLF has demonstrated the ability to detect and quantify staining longitudinally in vitro and has shown a high correlation with digital imaging (Adeyemi et al., 2006). However, its use in clinical applications has yet to be reported.

14.9

Methods to improve tooth colour

Many individuals are dissatisfied with their dental appearance and in particular with their current tooth colour. In a UK survey of 3215 subjects, 50% perceived they had some kind of tooth discolouration (Alkhatib et al., 2004). Other recent studies have shown that personal dissatisfaction with tooth colour can range from

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17.9–52.6% depending on the study population (Odioso et al., 2000; Alkhatib et al., 2005; Xiao et al., 2007). With this relatively high level of tooth colour dissatisfaction, it is perhaps not surprising that the demand from patients and consumers for tooth whitening procedures and products has increased. Indeed, the market for tooth whitening products has been growing dramatically over the last few years, particularly in the United States and Western Europe (Gerlach, 2002; Pickles et al., 2005). Tooth colour can be improved by a number of methods and approaches including whitening toothpastes, professional cleaning by scaling and polishing to remove stain and tartar, internal bleaching of non-vital teeth, external bleaching of vital teeth, microabrasion of enamel with abrasives and acid, and placement of crowns and veneers (Berman, 1982; Joiner et al., 2002; Sarrett, 2002).

14.9.1 Whitening toothpastes

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In response to the consumer dissatisfaction with their perceived tooth colour, oral care product manufacturers have developed a vast array of contemporary whitening toothpastes. Most contain the same basic functional ingredients, all of which have a specific role to play within the formulation. These typically include: solid abrasive materials; humectant to prevent the formulation from drying out; thickening agent to control the rheological properties; surfactant to generate foam and impart desirable sensorial properties during use; and active agents such as fluoride to provide health benefits, and flavour. Whitening toothpastes provide this benefit by removing and preventing the formation of extrinsic stain. It is well documented that if a very low abrasive toothpaste is used, stained pellicle usually accumulates on the surfaces of teeth (Kitchin and Robinson, 1948; Stookey et al., 1982) and it is now widely accepted that toothpastes require a certain amount of abrasivity to remove or prevent extrinsic stains from forming (Nordbo, 1977; Stookey et al., 1982; Joiner et al., 2004). The evidence to date suggests that the primary stain removal ingredient in whitening toothpastes is the abrasive, although other ingredients such as surfactants, calcium chelators, enzymes and polymers have been described in the literature (Joiner, 2007) which are claimed to aid removal and/or prevent extrinsic stain. Types of abrasives used in contemporary toothpastes include hydrated silica, calcium carbonate, dicalcium phosphate dihydrate, calcium pyrophosphate, alumina, perlite and sodium bicarbonate (Hefferren, 1998). These function by a process termed abrasion, which can be defined as the removal of material from the bulk of the substrate during relative movement of the abrasive and substrate (Davis, 1978) and the term can be applied to the removal of tooth surface films, such as stained pellicle, by toothpaste abrasives. There are a number of key parameters that have been demonstrated to affect the abrasion process, including particle hardness, shape, size, size distribution, concentration and applied load (Joiner, 2007).

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The understanding of the abrasive action and cleaning of extrinsic tooth stain has enabled oral care product manufacturers to continually develop optimised levels of abrasives within toothpaste formulations, carefully tailored to meet specific consumer needs in terms of the level and speed of the tooth whitening achieved, while moderating the abrasivity of a toothpaste towards the dental hard tissues. In this way, products have been designed to maximise cleaning while minimising hard tissue wear. Indeed, in a recent review of experimental and clinical evidence of tooth wear by toothpastes, Addy and Hunter (2003) state that, ‘brushing with a toothbrush and toothpaste produces limited dentine wear in a life time of use, and virtually no wear to enamel’. Combinations of abrasives in toothpastes can be particularly successful. For example, a whitening toothpaste containing a combination of perlite and calcium carbonate has been shown in a clinical study to significantly remove extrinsic stains in two weeks versus control toothpaste (Collins et al., 2005). This product was also shown not to give a clinically relevant level of wear to enamel or a significant increase in dentine wear compared to a marketed non-whitening toothpaste (Joiner, 2006a). An alternative optical approach to abrasive tooth whitening has recently been described (Collins et al., 2008). This involves the deposition and retention of a blue pigment from toothpaste onto the tooth surface where it can alter the optical properties of the tooth by primarily giving a yellow–blue shift in tooth colour. This yellow–blue colour shift is known for its importance in aiding the overall perception of tooth whiteness (Joiner et al., 2008a). Thus, immediately after brushing with the toothpaste the teeth appear less yellow and whiter. This optical whitening effect has been demonstrated through in vitro experiments where extracted teeth were brushed with the blue pigment containing toothpaste and measurements taken using a colorimeter and by visual assessment with a shade guide (Joiner et al., 2008b). Whitening benefits for this toothpaste was also demonstrated in a randomised clinical study (Collins et al., 2008).

14.9.2 Vital tooth bleaching Intrinsic tooth colour can generally be improved by bleaching and typically responds favourably to oxidation with peroxide (Gerlach, 2002). This is usually in the form of hydrogen peroxide or carbamide peroxide, formulated into a gel formulation which is applied to the enamel surfaces of the teeth via trays, strips or simply painted on. The mechanism of peroxide action is not fully known but involves diffusion of peroxide into the tooth where it oxidises coloured materials found within the dental hard tissues giving a reduction in tooth chroma and making the teeth whiter (Joiner, 2006b). The final tooth whitening outcome of bleaching is dependent on a number of factors, but is strongly correlated with peroxide concentration and contact time, activation by heat and light may be possible, the type of intrinsic stain and the starting tooth colour (Joiner, 2006b).

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Vital tooth bleaching products and procedures generally fall into three categories, namely, dentist supervised nightguard bleaching, in-office or power bleaching and mass market bleaching products (Heymann, 2005). Nightguard bleaching involves placing a relatively low level peroxide (i.e. 3–6% w/w) containing gel into a custom fabricated mouth guard and this is worn at night for at least two weeks. In contrast, in-office or power bleaching uses much higher concentrations of peroxide (25–35% w/w) which are applied for typically 30 minutes per treatment. Although significant tooth whitening can be achieved in one treatment, multiple treatments may be required to achieve optimum whitening (Sulieman, 2005b). In general, mass market consumer products also contain relatively low levels of peroxide (3–6% w/w) that are self applied twice per day for up to two weeks and come in a range of product formats such as trays, strips or paint-on. Recent developments in mass market bleaching products has seen the launch of higher levels of peroxide being used and products that are based on alternative bleaching agents such as activated sodium chlorite (Joiner, 2006b). Tooth shade guides have been used by dental clinicians to measure longitudinal tooth colour changes in clinical product testing (Haywood and Heymann, 1989; Kihn et al., 2000; Nathoo et al., 2002b; Sulieman, 2004). For example, a mean 2.71 shade improvement from baseline was measured following the two weeks twice per day use of an 18% w/w carbamide peroxide paint-on gel (Nathoo et al., 2002b). Colorimeters, spectrophotometers and camera-based digital imaging and analysis have all been used to measure tooth bleaching, both in vitro and in clinical trials (Joiner, 2006b). For example, the mean change from baseline in L* and b* was 2.07 and -1.67, respectively, following 14 days use of a tray-based 10% carbamide peroxide product (Gerlach et al., 2000).

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A non-vital tooth is one that has a dead pulp or has had the pulp removed as part of an endodontic treatment. A non-vital tooth can appear much darker and discoloured compared to the adjacent teeth. This type of discolouration has been successfully treated by the ‘Walking Bleach Technique’ whereby a bleaching composition is sealed inside the pulp cavity for a number of days (Sulieman, 2005a). The types of bleach compositions that have been used typically include sodium perborate alone or in combination with low levels of peroxide (Greenwall, 2001; Attin et al., 2003; Plotino et al., 2008). Much shorter exposure times of the pulp cavity with relatively high levels of peroxide (30–35% w/w) are also used, which may be activated with heat or light, in a technique termed ‘Internal Non-Vital power Bleaching’ (Suliemann, 2005a). Bleach compositions can also be applied simultaneously to the pulp and the external enamel surfaces in a technique called ‘Inside/outside Bleaching’ (Sulieman, 2005a; Kelleher, 2008).

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14.9.4 Microabrasion Microabrasion is a dental professional treatment for improving the colour of some types of intrinsically stained teeth (Croll, 1991). It involves applying an abrasive slurry (e.g. pumice, silicon carbide) in combination with a strong acid (e.g. hydrochloric acid) to the surface of teeth using a dental hand-piece. This removes the surface layers of enamel which can contain the stain (McEvoy, 1989). Clinical application needs to be used with caution to avoid excessive enamel removal since it has been shown that a commercial microabrasion product removed 134.8 microns of enamel after 20 seconds of application in vitro (Schmidlin et al., 2003). Following microabrasion, subsequent polishing has been shown to be crucial in maintaining optimal tooth aesthetics (Paic et al., 2008). The technique has been successfully applied to fluorosis discoloured teeth (Croll, 1991; Limeback et al., 2006). However the depth of discolouration cannot be known until attempts are made to remove it (McEvoy, 1998), and if it is too deep, it cannot be removed using microabrasion, and thus a restorative solution should be considered (Sarrett, 2002). Indeed, the combination of surface microabrasion and the placement of resin-based composite veneers was shown to improve the aesthetics of discoloured anterior teeth (De Araujo et al., 2000). The combination of microabrasion followed by tooth bleaching has also been shown to improve anterior tooth colour (Greenwall, 2001; Kelleher, 2008).

14.10 Future trends The colour and appearance of teeth is a complex phenomenon and this topic will progress in the future through research at the interface of many different scientific disciplines. Significant advances in the future are likely to be seen in the area of tooth colour measurement. Despite the limitations of visual assessment methods of measuring the colour of the teeth using a shade guide by the dentist, it is still a quick and simple method for recording basic tooth colour and therefore further developments of new improved shade guides and their application is likely to continue (Paravina, 2008). The major future development in tooth colour measurement will come in the area of instrumental measurement, particularly in the standardised capture of tooth images, computer software manipulation and colour calibrated computer screens. This will enable the improved computer-aided colour shade determination of natural teeth in order to reflect human visual perception better. In addition, this will enable variations in tooth colour to be viewed and improve communication between clinicians and dental materials technicians in order to produce an acceptable restoration. The application of image analysis techniques for the clinical assessment of extrinsic stain has not been fully evaluated and validated and in the future, this approach may find further clinical application. When coupled with other measurement techniques, such as QLF, this may give further advantages in clinical stain measurement (Brook et al., 2007). Progress in

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digital image analysis measurement techniques are also likely to be further applied to the colour measurement of other oral tissues such as the gums in order to give an objective assessment of gingival health (Denissen et al., 2007). Computer simulation of teeth is in its infancy (Lindsey and Wee, 2007) and there exists the means today to create far more realistic computer simulations of the dentition. With further developments, these simulations could be used in the future to investigate a number of hypotheses based on the perception of tooth colour, translucency, surface texture, gloss, patient satisfaction, etc. Further, the combination of the ability to realistically simulate human teeth with the ability of computers to automate data collection in a highly repeatable and efficient way, will allow the opportunity to give rapid progress in establishing reliable standards for the fabrication of prosthetic teeth. With the continued interest in tooth whitening by patients and consumers, the growth in understanding of tooth colour, stain mechanisms and the factors that affect its perception will concomitantly continue. Oral care product manufacturers are likely to translate this understanding into step change technology for tooth restorations and tooth whitening products in the future. What these new products and procedures will look like only time will tell. However, ultimately, patients, consumers and the field of aesthetic dentistry will all greatly benefit from these future developments.

14.11 Sources of further information and advice

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Tooth colour and its application to clinical dentistry are covered by a number of books. Fundamentals of Color: Shade Matching and Communication in Esthetic Dentistry (Chu et al., 2004) covers the basics of the science and art of colour to enable the reader to better understand the mechanics involved in the tooth shadematching process, whether visual or instrumental, together with clinical case studies. Further in depth clinical experience together with handy hints and tips on the whole process of measuring and fitting aesthetic dental restorations can be found in the book by Ahmad (2006). The practical and clinical application of dental bleaching to effect tooth whitening is described in great detail in a number of books (Greenwall, 2001; Haywood, 2007; Kelleher, 2008). These books also go into more detail regarding the chemistry of bleaching, tooth discolourations and clinical case studies. The International Association of Dental Research (www.IADR.com) has a mission to advance research and increase knowledge for the improvement of oral health worldwide. This association organises global and regional scientific conferences where topics on tooth colour and whitening are often presented and their website allows the electronic searching of recent past conference abstracts. The Society for Color and Appearance in Dentistry (www.scadent.org) is a consortium of dental professionals and other experts interested in the application of colour science and appearance in dentistry. Their website has many useful links to other relevant organisations, journals and meetings of interest.

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14.12 References Addy M, Hunter M L (2003), ‘Can tooth brushing damage your health? Effects on oral and dental tissues Int Dent J, 53, 177–186. Addy M, Moran J (1985), ‘Extrinsic tooth discoloration by metals and chlorhexidine. 2. Clinical staining produced by chlorhexidine, iron and tea’ Brit Dent J, 159, 331–334. Addy M, Moran J (1995), ‘Mechanisms of stain formation on the teeth, in particular associated with metal ions and antiseptics’ Adv Dent Res, 9, 450–456. Addy M, Embery G, Edgar W M, Orchardson R (2000), Tooth Wear and Sensitivity, Martin Dunitz Ltd., London. Adeyemi A A, Jarad F D, Pender N, Higham S M (2006), ‘Comparison of quantitative lightinduced fluorescence (QLF) and digital imaging applied for the detection and quantification of staining and stain removal on teeth’ J Dent, 34, 460–466. doi:10.106/j.jdent2005.10.006 Ahmad I (2006), Protocols for Predictable Aesthetic Dental Restorations, Oxford, Blackwell Munksgaard. Alkhatib M N, Holt R, Bedi R (2004), ‘Prevalence of self-assessed tooth discolouration in the United Kingdom’ J Dent, 32, 561–566. doi:10.1016/j.jdent.2004.06.002 Alkhatib M N, Holt R, Bedi R (2005), ‘Age and perception of dental appearance and tooth colour’ Gerodontol, 22, 32–36. Amaechi B T, Higham S M (2002), ‘Development of a quantitative method to monitor the effect of a tooth whitening agent’ J Clin Dent, 13, 100–103. Analoui M, Papkosta E, Cochran M, Matis B (2004), ‘Designing visually optimal shade guides’ J Prosthet Dent, 92, 371–376. Attin T, Paque F, Ajam F, Lennon A M (2003), ‘Review of the current status of tooth whitening with the walking bleach technique’ Int Endodont J, 36, 313–329. Baldwin D C (1980), ‘Appearance and aesthetics in oral health’ Commun Dent Oral Epidemiol, 8, 244–256. Bengel W M (2003), ‘Digital photography and the assessment of therapeutic results after bleaching procedures’ J Esthet Restor Dent, 15, S21–S32. Berman L H (1982), ‘Intrinsic staining and hypoplastic enamel: etiology and treatment alternatives’ Gen Dent, 484–488. Booij M, Ten Bosch J J (1982), ‘A fluorescent compound in bovine dental enamel matrix compared with synthetic dityrosine’ Archs Oral Biol, 27, 417–421. Brook A H, Smith R N, Lath D J (2007), ‘The clinical measurement of tooth colour and stain’ Int Dent J, 57, 324–350. Browning W D (2003), ‘Use of shade guides for color measurement in tooth bleaching studies’ J Esthet Restor Dent, 15, S13–S20. Cal E, Sonugelen M, Guneri P, Kesercioglu A, Kose T (2004), ‘Application of a digital technique in evaluating the reliability of shade guides’ J Oral Rehab, 31, 483–491. Cho B H, Lim Y K, Lee Y K (2007), ‘Comparison of the color of natural teeth measured by a colorimeter and Shade Vision System’ Dent Mater, 23, 1307–1312. doi:10.1016/j. dental.2006.11.008 Chu S J (2003), ‘Use of a reflectance spectrophotometer in evaluating shade changes resulting from tooth whitening products’ J Esthet Dent, 15, S42–S48. Chu S J, Devigus A, Mieleszko A (2004), ‘Fundamentals of color – shade matching and communication in esthetic dentistry’, Chicago, Quintessence Publishing Co Inc. Collins L Z, Naeeni M, Schafer F, Brignoli C, Schiavi A, Roberts J, Colgan P (2005), ‘The effect of a calcium carbonate/perlite toothpaste on the removal of extrinsic tooth stain in two weeks’ Int Dent J, 55, 179–182.

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Reno E A, Sunberg R J, Block R P, Bush R D (2000), ‘The influence of lip/gum color on subject perception of tooth color’ J Dent Res, 79, 381. Rosenstiel S F, Gegauff A G, McCafferty R J, Johnston W M (1991), ‘In vitro tooth color change with repeated bleaching’ Quintessence Int, 22, 7–12. Rubino M, Barcia J A, Limenez del Barco L, Romero J (1994), ‘Colour measurement of human teeth and evaluation of a colour guide’ Col Res Appl 19, 19–22. Russell M D, Gulfraz M, Moss B W (2000), ‘In vivo measurement of colour changes in natural teeth’ J Oral Rehab 27, 786–792. Rustogi K N, Curtis J (1994), ‘Development of a quantitative measurement to assess the whitening effects of two different oxygenating agents on teeth in vivo’ Compend Contin Edu Dent, 15 (Suppl. 17), S631–S634. Sagel P A, Gerlach R W (2007), ‘Application of digital imaging in tooth whitening randomized controlled trials’ Amer J Dent, 20, 7A–14A. Sarrett D C (2002), ‘Tooth whitening today’ J Amer Dent Assoc, 133, 1535–1538. Schmidlin P R, Gohring T N, Schug J, Lutz F (2003), ‘Histological, morphological, profilometric and optical changes of human tooth enamel after microabrasion’ Amer J Dent, 16 (Spec. Iss.), 4A–8A. Serra R, Otis L (2004), ‘Quantifying enamel luster’ J Clin Dent, 15, 83–87. Shaw L, Murray J J (1977), ‘New index for measuring extrinsic stain in clinical trials’ Commun Dent Oral Epidemiol, 5, 116–120. Smith R N, Collins L Z, Naeeni M, Joiner A, Philpotts C J, Hopkinson I, Jones C, Lath D L, Coxon T, Hibbard J, Brook A H (2008), ‘The in vitro and in vivo validation of a mobile non-contact camera-based digital imaging system for tooth colour measurement’ J Dent, 36, S15–S20. doi:10.1016/j.jdent.2008.02.002 Spitzer D, Ten Bosch J J (1975), ‘The absorption and scattering of light in bovine and human enamel’ Calc Tissue Res, 17, 129–137. Stookey G K, Burkhard T A, Schemehorn B R (1982), ‘In vitro removal of stain with dentifrices’ J Dent Res 61, 1236–1239. Sturzenberger O P, Beiswanger B B, King J D (1975), ‘Method for the clinical evaluation of enamel polish’ J Dent Res, 54, 931–937. Sulieman M (2004), ‘An overview of bleaching techniques. 1. History, chemistry, safety and legal aspects’ Dent Update, 31, 608–616. Sulieman M (2005a), ‘An overview of bleaching techniques. 2. Night Guard Vital Bleaching and Non-Vital Bleaching’ Dent Update, 32, 39–46. Sulieman M (2005b), ‘An overview of bleaching techniques. 3. In-surgery or power bleaching’ Dent Update, 32, 101–108. Sulieman M, Addy M, Rees J S (2003), ‘Development and evaluation of a method in vitro to study the effectiveness of tooth bleaching’ J Dent, 31, 415–422. doi:10.1016/ S0300-5712(03)00069-1 Sulieman M, Addy M, MacDonald E, Rees J S (2004), ‘The effect of hydrogen peroxide concentration on the outcome of tooth whitening: an in vitro study’ J Dent, 32, 295–299. doi:10.1016/j.jdent.2004.01.003 Tantbirojn D, Douglas W H (1998), ‘Stain removal efficacy: an in vitro evaluation using quantitative image analysis’ Quintessence Int, 29, 28–37. Ten Bosch J J, Coops J C (1995), ‘Tooth color and reflectance as related to light scattering and enamel hardness’ J Dent Res, 74, 374–380. Ten Bosch J J, Zijp J R (1987), ‘Optical properties of dentin’ In: Thylstrup A, Leach S A, Qvist V (eds): Dentine and Dentine Reactions in the Oral Cavity, Oxford, IRL Press, 59–65.

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Ten Cate A R (1998), Oral Histology – Development, Structure and Function, Mosby, St Louis. Terry D A, Geller W, Tric O, Anderson M J, Tourville M, Kobashigawa A (2002), ‘Anatomical form defines color: function, form and aesthetics’ Pract Proc Aesthet Dent, 14, 59–67. Tung F F, Goldstein G R, Jang S, Hittelman E (2002), ‘The repeatability of an intraoral dental colorimeter’ J Prosthet Dent, 88, 585–590. Vaarkamp J, Ten Bosch J J, Verdonschot E H (1995), ‘Propagation of light through human dental enamel and dentine’ Caries Res, 29, 8–13. Van der Burgt T P, Ten Bosch J J, Borsboom P C F, Flasschaert A J M (1985), ‘A new method for matching tooth color standards’ J Dent Res, 64, 837–841. Van der Burgt T P, Ten Bosch J J, Borsboom P C F, Kortsmit W J P M (1990), ‘A comparison of new and conventional methods for quantification of tooth color’ J Prosthet Dent, 63, 155–162. Watts A, Addy M (2001), ‘Tooth discolouration and staining: a review of the literature’ Brit Dent J, 190, 309–316. Wee A G, Lindsey D T, Kuo S, Johnston W M (2006), ‘Color accuracy of commercial digital cameras for use in dentistry’ Dent Mater, 22, 553–559. doi:10.1016/j. dental.2005.05.011 Xiao J, Zhou X D, Zhu W C, Zhang B, Li J Y, Xu X (2007), ‘The prevalence of tooth discolouration and the self-satisfaction with tooth colour in a Chinese urban population’ J Oral Rehab, 34, 351–360. doi:10.1111/j.1365–2842.2007.01729.x Xu X, Zhy L, Tang Y, Wang Y, Zhang K, Li S, Bohman L C, Gerlach R W (2007), ‘Randomized clinical trial comparing whitening strips, paint-on gel and negative control’ Amer J Dent, 20, 28A–31A. Yankell S L, Emling R C, Prencipe M, Rustogi K, Volpe A R (1994), ‘Clinical study to assess the stain removal efficacy of two tartar control dentifrices and a low abrasive dentifrice’, J Clin Dent, 5, 125–128. Yap A U J (1998), ‘Color attibutes and accuracy of Vita-based manufacturers’ shade guides’ Oper Dent, 23, 266–271. Zhao Y, Zhu J (1998), ‘In vivo color measurement of 410 maxillary anterior teeth’ Chin J Dent Res, 3, 49–51. Zhu H, Lei Y, Liao N (2001), ‘Color measurements of 1944 anterior teeth of people in southwest of China’ Chin J Stomat, 36, 285–288. Zijp J R, Ten Bosch J J (1993), ‘Theoretical model for scattering of light by dentin and comparison with measurements’ Appl Optics, 32, 411–415. Zijp J R, Ten Bosch J J, Groenhuis R A J (1995), ‘HeNe-laser scattering by human dental enamel’ J Dent Res, 74, 1891–1898.

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15 Hair color measurement D. J. TOBIN, University of Bradford, UK

Abstract: Skin and hair color contribute disproportionately to human communication due to their prominent role as signals for health and cultural identity. Hair pigmentation is of interest to a broad range of professionals including biomedical scientists, anthropologists, forensic science researchers, as well as the cosmetic science and toxicology industry. The chapter begins with a discussion of how natural hair color is formed in the hair follicle, and how this changes with age and in hostile environments. From there strategies for artificial hair coloring are described as well as how researchers, especially in industry, measure hair fiber color produced either naturally or via artificial colorants. The chapter concludes with comment on future trends in the field. Key words: hair follicle, hair fiber, pigmentation, melanin, melanocyte, melanogenesis.

15.1

Introduction

Skin and hair color have since time immemorial contributed disproportionately in human communication due to its prominent role in informing on health and cultural identity. Study of pigmentation and its variation is currently pursued by a broad range of interested professionals and this chapter will approach the subject from a number of angles to hopefully provide something of interest for all readers. The chapter begins with a description of how natural hair color is formed at the level of hair follicle pigmentary unit during embryogenesis in our mother’s womb. Much is set in train at this stage, most particularly the inheritance of our so-called phenotype, which includes both normal and abnormal pigmentation variants some of which can have associated pathology. Once formed the activity of the hair follicle pigmentary unit is thereafter tightly coupled to the hair growth cycle, and is regulated by a battery of extrinsic and intrinsic factors. In time the functionality of the hair follicle pigmentation system begins to fail, with the loss of hair color (through graying) being one of our most striking bellwethers of lost youth. The chapter will then address how environmental factors may impact on the functionality of the hair pigmentary system, and together with aging will comment on how the use of artificial colorants can mask some of these aging effects. The hair colorant industry and others have adopted numerous and sophisticated approaches to color measurement. The chapter concludes with a review of future trends in this subject area.

15.2

Background

If beauty is in the eye of the beholder, it is not surprising then that as social beings much of our communication is via our physical appearance. In 371 © Woodhead Publishing Limited, 2010

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particular, pigmentation has been a focus of attention and study for over 4000 years (Westerhof, 2006) due to its role in informing on health and cultural identity. Study of pigmentation variation in humans is currently pursued by a broad range of professionals including biomedical scientists, anthropologists, forensic science researchers, as well as the cosmetic science and toxicology industry. For example, a recent and unexpected piece of research to whet not only the anthropologist’s appetite has been a report that Neanderthals exhibited variants of the MC1R gene, a gene associated with the red hair phenotype in humans (Lalueza-Fox et al., 2007). Of the three pigments present in skin, hemoglobin and carotenoids contribute little to overall skin or hair color. By contrast, the main determinant to our phenotypic palette derives from a class of mixed indole-rich compounds – the melanins, which are formed within unique organelles called melanosomes. These lysosome-related organelles are produced within the cytoplasm of the melanocyte via a complex and phylogenetically ancient biochemical pathway called melanogenesis. Red hair color has dominated recent attention not least for its much rarer and more dramatic visual cue value than for other colors, but also due to its association with an increased risk of melanoma. The purported positive selection pressure, for maintaining dark skin and hair pigmentation seen in much of Africa and Asia for its associated protection from solar ultraviolet light (Rees, 2006; Parra, 2007), appears to have been lifted when humans migrated to more northern climes where requirement for Vit D synthesis competed with lower UVR levels. Hair growth and pigmentation have clearly facilitated evolutionary success in non-human mammals. However, it is not at all clear how (or if) these traits have contributed to survival in humans. More enigmatic still is why humans should be unique among the primates in growing scalp hair that is very thick, long, and highly pigmented. Teleologically, this phenotype is likely to reflect particular evolutionary selective pressures that were present during the early stages of human evolution. Some authors, for example, have suggested that this may have aided human development along sea coasts and river banks where fish were a dominant part of the diet (Morgan, 1985). In this context it would have been important to develop strategies to prevent the build-up of toxic metals from fish species which concentrate heavy metals. The selective and avid binding of toxins and metals to melanin within the rapidly growing and highly melanized hair fiber would have been an important adaptation. The highly proliferative hair follicle bulb tissue (second only to the bone marrow) would, via its melanin-accepting keratinocyte population, rid the body of this harmful substances via an extruded pigmented hair shaft. There may have been further evolutionary pressures for pigmented hair growth including those which pertained to mate selection strategies, and to the fact that our relative nakedness draws much attention to our facial and scalp hair. Thus, sexual dimorphism based on traits like beard growth may have been critical for the development of optimal communication strategies.

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The African origin of humans reflects well the current deep brown-black predominance of human hair color – true for over 90% of the world’s human population. However, that black scalp hair predominates in all primates living in tropical climes may appear somewhat paradoxical given its associated thermal insulating properties via the trapping of radiant heat. A probable explanation may be the protection against sunstroke afforded by deep skin and hair pigmentation, as well as the contribution of melanin’s very efficient and fast exchange of ion transport and efflux to adequate salt balance (Wood et al., 1999). If over 90% of the human population have ‘environmentally-friendly’ brown-black hair and tanning skin, why then did the remaining 5–10% (mostly ‘originating’ in northern Europe) emerge with a bewildering array of hair colors that range from white blonde, yellow blonde, auburn to red and all shades in between? Recent advances in molecular genetics offer intriguing clues that may explain the basis of this dramatic diversity of human cutaneous pigmentation and phenotype. In particular, the discovery of the role played by the melanocortin-1 receptor (MC1R) gene has been hugely significant, and more particularly its tendency to mutate to yield functional variability (i.e. polymorphism) (Rees, 2000). Regardless of our MC1R genetic inheritance, the follicular melanin unit appears to have an intrinsic ‘biologic clock’, and canities is an inevitable harbinger of disappearing youth.

15.3

Natural hair color

15.3.1 Development of the hair follicle pigmentary unit Melanocytes of both the epidermis and the hair follicle derive from immature cells that migrate from the neural crest (a transient component of the ectoderm located between the neural tube and the epidermis) into the skin during embryogenesis. How some of these neural crest cells commit to the melanocyte lineage remains a subject of intense research (Dupin & Le Douarin, 2003). The most obvious evidence of melanocyte maturation is the initiation of melanogenesis (melanin synthesis), and in human skin this occurs very early at around seven weeks of gestation (Holbrook et al., 1989). Melanoblasts have the potential to proliferate and differentiate further when within the epidermis. Some progeny leave the epidermis to distribute in the now developing hair follicles. Importantly, these hair follicle-homing melanocytes represent a mixed population of 3,4-dihydroxy phenylalanine (dopa) oxidase-positive (i.e. express active tyrosinase) and dopa oxidase-negative cells (i.e. either fail to express tyrosinase or express an inactive tyrosinase) cells (Peters et al., 2002). Of the more than 100 genetic loci shown to affect pigmentation (Nakamura et al., 2002; Steingrimsson et al., 2006), mutations in receptor tyrosine kinase KIT and its cognate ligand SCF, and endothelin 3 and its receptor Ednrb have been most informative. Mutations in these loci can cause almost complete lack of hair pigmentation.

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Casual observation of our fellow humans highlights a relative independence of the epidermal- and follicular-melanin units. For example, one can readily appreciate the co-expression of white hair and black skin in aging people of African descent and conversely the raven hair of some white-skinned Europeans. Furthermore, melanocytes of the follicular-melanin unit are larger, more dendritic, have more extensive cytoplasmic organelles and produce larger melanosomes compared to melanocytes in the epidermal-melanin unit (Tobin & Bystryn, 1996; Tobin & Paus, 2001). While melanin produced by the latter degrades almost completely in the differentiating layers of the epidermis, brown/black eumelanin granules transferred into hair cortical keratinocytes remain minimally digested; hence, the similarly pigmented proximal and distal ends of a typical hair shaft. Melanocytes in the mature adult anagen (i.e. growing) scalp hair follicle are distributed in several distinct anatomic compartments where they are associated with a region-specific differentiation status (Fig. 15.1). Melanogenically-active melanocytes positive for 3,4-dihydroxy phenylalanine (dopa)-oxidase activity are readily detectable in the basal layer of the infundibulum and in the hair bulb matrix around the upper dermal papilla. Moderately differentiated melanocytes may also be detected in the basal layer of the sebaceous gland, although it is not

Epi

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15.1 Cartoon of pigmented and graying human anagen scalp hair follicle, showing loss of melanization in the hair bulb and hair shaft with graying. Some amelanotic melanocytes can be seen in the outer root sheath (ORS) and in the most proximal and peripheral hair bulb (HB). SB, sebaceous gland; Epi, epidermis.

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immediately clear what their function is there (anti-microbial?). The hair bulb is the only site of pigment production for the hair shaft (Fig. 15.1), and contains melanogenically-active melanocytes. Melanogenically-active melanocytes are located just below the pre-cortical keratinocytes, from where melanin can be transferred to the hair shaft cortex (Fig. 15.2), less so to the medulla, and very rarely the hair cuticle. The presence of immature melanocytes in fully-developed adult anagen hair follicles has been confirmed both in vivo and in vitro and some of these may indeed have bona fide melanocyte stem cell potential (Horikawa et al., 1996; Tobin, 2003). Dopa oxidase-negative amelanotic melanocytes can be readily detected in the mid-to-lower outer root sheath, but also in the periphery of the bulb and the most proximal matrix (Figs 15.1, 15.2). Although amelanotic hair follicle melanocytes may lack dopa-oxidase activity, low levels of the tyrosinase protein itself may still be detected in some of these cells, as well as KIT and Bcl-2. However, these melanocytes are still immature as they do not express the melanogenic enzymes tyrosinase-related protein-1 (TRP1) and TRP2 (dopachrome tautomerase, DCT) (Horikawa et al., 1996). These pigment cells could represent a pool of ‘transient’ melanocytes that migrate from the hair follicle bulge to other areas of the outer root sheath (Slominski et al., 1994; Tobin et al., 1999; Van Neste & Tobin, 2004). By far the most striking difference between the epidermal- and follicularmelanin units, and one with significant implications for the regulation of hair pigmentation, is the observation that the activity of the hair bulb melanocyte is under tight cyclical control (Slominski et al., 1994). Epidermal melanocytes, by contrast, appear to be continuously active as far as melanogenesis is concerned (Nordlund & Ortonne, 2006), though this constitutive activity can be stimulated further (e.g. after exposure to UV radiation).

15.3.2 Hair follicle melanocytes during the hair cycle Active hair pigmentation only occurs during the period of active hair growth or anagen (Fig. 15.2). In the human scalp, hair follicle active melanogenesis can last up to 10 years, though more usually for approximately three years (Tobin et al., 1999; Stenn & Paus, 2001). By contrast, melanogenesis continues for one month or less in other body sites (e.g. eyebrow). Telogen To follow the fate of melanocytes during the hair follicle growth cycle it is perhaps useful to look first at their status in the ‘resting’ or telogen hair follicle, during which no melanin is actively produced. Some immature melanocytic (stem?) cells are distributed in the so-called secondary germ of telogen follicles and can be detected immunohistochemically for DCT.

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15.2 Longitudinal section of a fully-pigmented human anagen scalp hair follicle showing intense melanization of the hair bulb and hair shaft.

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Early anagen After the first one or two days of new anagen growth (so-called anagen I) some immature melanocytic cells begin to express the tyrosinase gene, and then some tyrosinase protein. Melanocytes distributed close to the anagen follicular papilla are more melanogenically-active than melanocytes located elsewhere in skin. We have shown that the follicular papilla of early anagen hair follicles pool high concentrations of L-phenylalanine, a potential requirement for the supply of L-tyrosine for melanogenesis (Schallreuter et al., 1998). Melanocytes start to divide as early as anagen II, with a burst of melanocyte mitosis a little later in anagen III (Sugiyama, 1979). Hair bulb melanocytes continue to increase in number until full anagen VI and also to produce more of the cellular machinery needed for melanogenesis. Maturing melanocytes also become much more dendritic at this stage, in preparation for the active transfer of mature melanosomes to pre-cortical keratinocytes. Full anagen The hair pigmentary unit becomes fully-functional with respect to melanin synthesis during full anagen VI, and the heterogeneous melanocyte subpopulations are now distributed in discrete locations throughout the hair follicle (Fig. 15.1). It is the nature of this variable of follicle melanocyte subpopulation status which remains so enigmatic, as they exhibit characteristic protein expression profiles. Only melanocytes distributing to the so-called melanogenic zone of the hair follicle (i.e. the hair bulb matrix above the follicular papilla) express the crucial TRP1, DCT, tyrosinase and KIT proteins in the majority of melanocytes. Although anagen VI or full anagen lasts for three or more years in normal human scalp, it is not clear whether melanocyte activity/status changes significantly during full anagen VI. The physiologic decrease in follicular melanogenesis during the end of anagen VI may reflect either an exhaustion of an active signaling system that stimulates melanogenesis, and/or may be caused by the production of inhibitors of melanocyte activity (Sugiyama, 1979). Catagen Some of the earliest signs of imminent hair follicle regression include the retraction of melanocyte dendrites and the attenuation of melanogenesis during late anagen VI (Keogh & Walsh, 1965). Perhaps surprisingly, a limited amount of keratinocyte proliferation even continues for a short while thereafter, such that the most proximal telogen hair shaft is usually unpigmented. The need for melanocyte replacement is clear, because at least a proportion of the mostly highly melanotic (and possibly terminally-differentiated) hair bulb melanocytes do not survive

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catagen (Tobin et al., 1998). Some melanogenically-active melanocytes may derive however from a subpopulation of catagen-surviving melanocytes (Commo & Bernard 2000). Our current view suggests that newly-recruited immature melanocytes, derived from the melanocyte reservoir in the upper hair follicle, rebuild the pigmentary unit (Tobin et al., 1999; Nishimura et al., 2002). This is supported by the existence of a subpopulation of immature melanocytes in the hair bulge (Botchkareva et al., 2001; Nishimura et al., 2002), progeny of which migrate into the reforming anagen hair bulb.

15.3.3 Regulation of hair follicle pigmentation Melanogenically-active follicular melanocytes appear to be located beyond the reach of direct stimulation by UV radiation – the principle regulator of melanocytes in the epidermis. Despite this, follicular pigmentation is still responsive to numerous intrinsic factors including: hair-cycle-dependent changes, body distribution, racial and gender differences, variable hormone responsiveness, genetic defects, and age-associated change (Tobin et al., 2008; Tobin, 2005; Slominski et al., 2004). As with any multi-step process, several positive and negative regulators/factors regulate hair pigmentation (Boissy et al., 1998; Commo et al., 2004; Slominski et al., 2004; Nordlund & Ortonne, 2006). Melanogenesis can be mostly conveniently divided into: (1) melanosome/ melanin granule biogenesis and (2) the biochemical pathway that converts phenylalanine/L-tyrosine into the melanins. Melanosome structure correlates with the main type of melanin produced. For example, melanocytes in darkest brown/black hair follicles contain the largest number of melanosomes (and most electron-dense), with a characteristic and highly ordered internal fibrilar matrix. These melanosomes are termed true or eu-melanosomes, and their melanin termed true or eu-melanin. Brown hair bulb melanocytes also contain eumelanosomes, but these are somewhat smaller than in black hair. Melanocytes in blonde hair follicle bulbs however, produce only weakly melanized melanosomes, with little eumelanin, such that their melanosomal matrix is visible and not obscured by melanin. By contrast, red hair bulb melanocytes produce the pheomelanosome – with a characteristic disordered internal structure. Unlike the filbrillar core of the eumelanosome the pheomelanosome contains a vesicular matrix with red/yellow melanin deposited irregularly as blotches, apparently in a random fashion not guided by a substructure. Perhaps surprisingly, eumelanogenic and pheomelanogenic melanosomes often co-exist in the same melanocyte. The constitutive color of an individual’s hair is due to absolute tyrosinase activity, rather than levels of tyrosinase protein expression. Thus, the regulation of tyrosinase and its activity is critical. Activities of phenylalanine hydroxylase increase linearly with inherited skin color, i.e. activities up to eight-fold greater in black skin compared to white skin (Schallreuter et al., 2004).

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Melanogenesis is also regulated by hormones, neurotransmitters, cytokines, growth factors, eicosanoids, cyclic nucleotides, nutrients, and the physicochemical milieu (Slominski et al., 2004). These factors act sequentially and redundantly (i.e. in parallel). The cognate receptor of α-MSH is MC1R, and despite its very low level of receptor density on human melanocytes (Jackson et al., 2007) appears to be an important positive regulator of hair pigmentation (Rees, 2000). This receptor is activated upon binding with POMC-derived ACTH, α-MSH, and β-MSH peptides. The resultant signal transduction cascade activates adenylate cyclase, leading to subsequent cAMP production, and gene transcription for increased melanocyte proliferation, melanogenesis, and dendrite formation. However, while the injection of α-MSH into human skin increases epidermal melanogenesis (particularly of sun-exposed skin), no effect has been reported in hair follicles (Levine et al., 1991). However, not all pigmentation researchers agree with the totality of the current MC1R-centric view of the control of pigmentation. This author would encourage caution as it is likely that such an important functionality as pigmentation (protection against harmful UVR effects) will have back-up systems or redundancy. In this regard, we have examined a POMC/MC1R complementary system – the β-endorphin/µ-opiate receptor system, and have shown that this pathway participates in the regulation of both human epidermal and follicular melanocyte biology at least in vitro (Tobin, 2005; Kauser et al., 2003; Kauser et al., 2004). We have also examined the role of the most proximal element of the hypothalamic– pituitary–adrenal axis (our body’s central response mode to stress), i.e. corticotropin-releasing factor (CRF), in the regulation of follicular melanogenesis. Like POMC, CRF is produced locally in the human skin (Slominski et al., 1995) and can modify the phenotype of human hair follicle melanocytes in vitro by upregulating cell dendricity and pigmentation levels (Kauser et al., 2006).

15.4

Gray hair and age

We are born with what appears to be a full complement of approximately five million hair follicles, about 100 000 of which are found on the scalp. Still there is significant plasticity in hair follicles we do have, such that a single human hair follicle is capable of producing several different types of hair fiber during its lifetime. These include fine pigmented lanugo hair during fetal life, short (mostly unpigmented) vellus hair or fine pigmented intermediate hair in the prepubescent, and long thick terminal hair shafts in the adult. Moreover, hair growth rates also vary during human aging. When averaged for various sites and for post 40-year-old non-balding individuals, remarkably hair grows most rapidly and with greater individual fiber thickness in individuals during the 50–70 decades (Pelfini et al., 1969) Hair color also shows striking age-related changes, particularly in those individuals of Eurasian origin. There is often a switch from fair-colored

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‘intermediate hair’ to more deeply-pigmented coarser ‘terminal hair’ during puberty. Indeed, hair color in children usually darkens with advancing age (Allende, 1972) and it is not unusual for a blonde child to be dark-haired after puberty. Similarly, the phenomenon of heterochromia also becomes more apparent with age and is often strikingly apparent for scalp and beard, but may also affect the scalp alone (Lee et al., 1996). However, the continuing extension of human longevity focuses increasing interest in elucidating the mechanisms of skin and hair aging, whether these are contributed from intrinsic (e.g. genetics, evolutionary selective pressures) or extrinsic (e.g. sun exposure, environmental insults and stress) factors. Chronologic skin aging in humans is associated with a 10–20% reduction in pigment-producing epidermal melanocytes (whether in exposed or unexposed skin), for every decade after 30 years of age (Quevedo et al., 1969; Whiteman et al., 1999). Nevertheless, it appears that epidermal melanocytes are relatively long-living cells, protected in part from reactive oxygen species (ROS), including those generated during melanogenesis. The accumulation of oxidative damage is an important determinant in the rate of aging, though it is unclear whether it is the primary cause of aging (Harman, 1956; Gutteridge & Halliwell, 2000). Reactive oxygen species (ROS) damage DNA (both nuclear and mitochondrial) that can lead to an accumulation of mutations, can induce oxidative stress, and thereby trigger antioxidant mechanisms. It is likely that the antioxidant systems within the hair follicle melanocyte become impaired with age, leading to uncontrolled damage to the melanocyte itself including from its own melanogenesis-related oxidative stress (Kauser et al., 2007). Recent work suggests that the follicular–melanin unit of graying hair is indeed associated with increased melanocyte apoptosis and oxidative stress (Arck et al., 2006). This study also reported that the ‘common’ deletion in mitochondrial DNA (associated with oxidative stress) occurred more prominently in graying compared to normally pigmented hair follicles. Graying hair follicles in this study were also less well equipped to handle an exogenous oxidative stress, which is likely to be the result of impaired antioxidantmechanisms. Melanin synthesis, by its very nature, is a rather toxic business. Melanogenesis also produces mutagenic intermediates, and so the induction of replicative senescence in melanogenic hair bulb melanocytes may be an important selfprotective mechanism against cell transformation. Even the casual observer of cutaneous melanocyte phenotypes will be impressed by the very high melanin load and phenomenal synthetic capacity for melanin production of bulbar melanocytes, compared to that of their epidermal cousins. Indeed, a relatively small number of melanocytes (<100 cells per scalp anagen hair follicle) can, in a single hair growth cycle, produce sufficient melanin to intensely pigment up to 1.5 m of hair shaft. This prolonged melanogenesis is likely to generate large amounts of ROS via the oxidation of tyrosine and dopa to melanin (Hegedus, 2000). If not adequately removed, their accumulation may generate significant

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oxidative stress in both the melanocyte itself and perhaps also in the highly proliferative anagen hair bulb epithelium. Given the potential risks for mutation under these circumstances it is perhaps best for melanogenic bulbar melanocytes to assume a post-mitotic, terminally differentiated ‘(pre)senescence’ status to prevent cell transformation. Scalp hair bulb melanocytes are at their most active during our youth, when the follicular melanin unit is just a few hair growth cycles old, and also when they are most responsive to the full post-puberty hormonal stimulus. On average, an individual scalp hair follicle will experience fewer than 15 melanocyte seedings from the presumptive reservoir in the average fully ‘gray-free’ life span of 40 years for Caucasians (Keogh & Walsh, 1965). There is experimental evidence to suggest that the melanocyte stem cell reservoir in adult hair follicles may be rather limited. The onset and progression of hair graying correlates closely with chronological aging and occurs to varying degrees in all individuals, regardless of gender or race. The age of onset is genetically controlled and inheritable. Thus, the average age for Caucasians is mid-30s; for Asians, late-30s; and for Africans, mid-40s. Indeed, hair is said to gray prematurely only if it occurs before the age of 20 years in whites, before 25 years in Asians, and before 30 years in Africans. Although not formally tested, a good rule of thumb is that by 50 years, 50% of people have 50% gray hair (Tobin & Paus, 2001). However, what our eyes see when we look at the hair fiber is a complex interplay of many physical characteristics, not just color. Also important here are the hair fiber’s geometry, consisting of curvature, and its shine and luster. The darker the hair the more noticeable early graying will be. Conversely, graying can be more extensive in dark hair before total whitening is apparent; the reverse is true for blonde hair. Scalp hair graying first appears usually at the temples, and spreads to the vertex and then the remainder of the scalp, affecting the occiput last. Beard and body hair is usually affected later. It is likely that hair bulb melanocytes of the follicular melanin unit also influence their pre-cortical keratinocyte neighbors in several ways (and vice versa) (Tobin, 2003, 2008, 2009). For example, melanin transfer to keratinocytes appears to reduce the latter’s proliferative potential and rather may stimulate their terminal differentiation. This melanin-associated ‘brake’ on keratinocyte proliferation or its modulation of keratinocyte differentiation appears to be lifted somewhat in graying or white hair follicles. Indeed, white beard hair appears to grow faster than do adjacent pigmented hair in vivo (Nagl, 1995). Moreover, unpigmented hair follicles exhibit a higher rate of hair fiber elongation in vitro than do matched pigmented hair follicles (Arck et al., 2006). If this interpretation is correct, then melanin granules can be viewed as ‘regulatory packages’ (Slominski et al., 1993), e.g. by providing a buffer for calcium. Furthermore, the saturation binding of transition metals (e.g. iron, copper) to melanin is also likely to influence the antioxidant defense for the melanosome-receiving keratinocyte (Kauser et al., 2007).

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There is evidence of melanocyte–keratinocyte interactivity in the hair fiber from a clinical perspective also. Graying hair in its early stages is often coarser, wirier, and more unmanageable compared to pigmented hair, which would again reflect a change in the chemical and physical properties of the post-pigmented hair fiber (Van Neste & Tobin, 2004). Additionally, gray hair is often unable to hold a set and is more resistant to incorporating artificial color. These changes have significant implications for the cosmetics industry. On the basis of the above findings it appears that graying hair follicles may reprogram their matrix keratinocytes to increase the production of medullary, rather than pre-cortical, keratinocytes.

15.5

Effect of environment

The hair fiber is constructed as a highly integrated system of several components including in order of decreasing amount; keratins, water, lipids, pigment, and trace elements. Despite the significant variability in hair form between humans of different ethnicities, e.g. with regard to environment, diet, and hair texture, the chemical composition of hair protein across the ethnic groups is remarkably uniform (see Tobin, 2005). Thus, there are no significant differences in amino acid composition of hair of different ethnicities. Extreme trauma, protracted mechanical stress, and acute/chronic ultraviolet light exposure can result in damage to the normal hair shaft and these need to be distinguished from hair shaft abnormalities due to intrinsic defects, either genetic or nutritional. The location of the damage along, say, a one meter-long length of hair is also highly relevant, as the ‘oldest’ fractions (i.e. distal sections towards and at the tip) will have been ‘weathered’ by repeated washings, cosmetic treatments, brushings, and other manipulations that cause splitting and cuticular damage. Conversely, observing these features in short hair lengths suggests either the presence of more traumatic damage, e.g. aggressive grooming or cosmetic treatments or an underlying nutritional deficiency or intrinsic defect. Another source of structural damage to normal hair can be due to extreme heat stress. Cosmetic practices, for example those associated with straightening of African hair, may induce so-called ‘bubble’ hair where extreme heat induces bubble formation under the cuticle within the hair cortex. Hair pigments, hair proteins and hair lipids can also be degraded by visible and ultraviolet radiation (UVR). Indeed, lipids making up the membrane complexes holding the hair cortex together may be even more sensitive to light radiation than UVR (Hoting & Zimmermann, 1997). This may be particularly severe if the hair has already been pre-exposed to chemical bleaching (see below). Sunlight affects the amino acids of the hair fiber’s cuticular covering more so than its inner bulk cortex, given their greater exposure. Hair proteins appear to absorb light principally between 254 and 350 nm and so most light degradation may occur between these wavelengths (Arnaud, 1984). Interestingly, amino acids in darker hair may be more resistant to photodamage than paler shades. Chronic exposure to sunlight is

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likely to cause fiber brittleness that if further subjected to even relatively mild chemical bleach conditions, i.e. aqueous alkalinity and peroxides, may result in marked loss of structural integrity. Ultrastructural assessment of sun-exposed hair fibers revealed that damage occurs in the cuticle as well as damage to melanin pigments within the hair cortex (Braida et al., 1994). Recent research suggests that gray hair undergoes more severe UV damage and so may need more UV protection than naturally pigmented hair (Gao & Bedell, 2001).

15.6

Artificial hair coloring shades

Our increasing longevity ensures a continual upward trajectory in sales of all types of products used for hair care. Unsurprisingly, the growth in hair colorant use has been particularly marked, as the principal application of these dyes is to cover gray hair. Additionally, recent societal trends are seeing whole populations of young adult males, especially those in western societies, being brought into this mass ‘cosmetication’ process, alongside the traditional constituency of young women. These target groups seek to either add color to their hair (original or different) or to lighten/bleach their hair to generate a preferred, lighter, color or to permit subsequent addition of color(s) shades lighter than their natural color. Despite this increasing use of chemical hair colorant products, technological developments in this area have unfortunately failed to keep pace. Indeed, most of the technological advances have been with improvements in the safety profiles of the current formulations. However, there remains a strong consumer preference and drive for all things ‘natural’ and this, together with tightening regulation of chemical dyes, has begun to stimulate interest in pursuing the development of more natural hair coloring agents. In the subsequent paragraphs, I restrict my discussions to the former category of colorants/hair dyes including: bleaching agents, permanent dyes, semi-permanent dyes, temporary dyes, and metallic dyes as these have the widest application. Others including vegetable dyes will only be alluded to here.

15.6.1 Chemical bleaching Hair can be bleached by both chemical and photochemical (i.e. sunlight, see above) oxidative mechanisms. Typically, hair is bleached by hydrogen peroxidecontaining systems that also include an alkaline hair-lightener base, with additional persulfate salts to accelerate or boost the reaction. To adequately destroy the melanin, the hydrogen peroxide bleach may also degrade hair proteins due to the numerous oxidizable cysteine groups in the hair cortex and cuticle (Wolfram, 1970). Indeed, normal bleaching (across a few shades) may break as much as 25% of the fibers disulfide bonds, while bleaching from black to blonde may break as many as 50% of these bonds (Robbins & Kelly, 1969). The high pH values of currently used hair aqueous alkaline bleaches (pH 9–11) are also likely to increase

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the rates of hydrolysis of resultant oxides to compete with that of oxidation, such that cleavage of cysteine occurs via a S-S fission route to yield principally sulfonic acid. Moreover, hydrolysis of peptide bonds within hair protein (cystine, methionine, tyrosine, histidine and lysine) may also occur during severe bleaching episodes using higher concentrations of bleach and for longer exposure times (Robbins, 2002). Oxidation of hair that lacks melanin occurs much more slowly than does melanized hair fibers (Wolfram & Hall, 1975; Wolfram & Albrecht, 1987), suggesting that peroxide reacts preferentially with melanin pigment compared to hair proteins, but only after peroxide gains access to the core of the fiber, i.e. after the pigment-free cuticle is breached. While isolated pigment granules are resistant to a large range of reagents, they are readily dissolved by alkaline hydrogen peroxide at pH 11.75 (Wolfram & Hall, 1975; Wolfram & Albrecht, 1987).

15.6.2 Permanent hair colorants

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The permanent hair colorants or oxidative dyes represent approximately 80% of the hair color market (Anderson, 2000) and consist principally of uncolored precursors of p-amines and p-aminophenols that diffuse into the hair fiber where they condense with dye couplers (e.g. resorcinol) and are then oxidized into active intermediates (e.g. diiminium or quinonimunium species) by (most commonly) hydrogen peroxide. Other secondary compounds are involved including surfactants, preservatives, pH-adjusting additives (to pH 8–10), etc. This chemistry is designed to provide for shampoo-fast hair color and a depth of color that can be controlled by adjusting relative amounts of the three principal agents. Most dyes are diffusion-controlled, so that ‘ring-dyeing’ is achieved, i.e. whereby the dye is restricted to the surface of the hair fiber. However, this is variably achieved, as dye will penetrate more deeply into some hair fibers than others. Somewhat surprisingly, this variability does not always correlate with fiber caliber. Permanent dyes have a very complex chemistry. Briefly, the dye precursor (e.g. p-phenylenediamine) is oxidized to its diiminium ion and this active intermediate condenses with an electron donating coupler (e.g. resorcinol) to form a dinuclear product. This is then oxidized to an indo dye. For a more detailed discussion of associated chemistry the interested reader is directed to an excellent and comprehensive treatment of this subject by Robbins (2002). Permanent dyes are formulated for use as a two-part modality, consisting of the precursor-coupler base (with secondary agents for texture, etc., as described above) and the oxidizing agent (with associated secondary products for stabilization, etc.). Strand testing allows for inter-individual variability to be minimized. A significant potential concern with use of permanent dyes is induction of an allergic reactive in sensitive individuals, and therefore testing is recommended prior to full application of these

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products. Brown to black shaded compounds are formed when dye precursors are oxidized in the absence of couplers, while couplers can modify the color formed by the precursor. Highly colored indo dyes can fade, possibly due to the addition of aromatic moieties to the dinuclear indo dyes.

15.6.3 Semi-permanent hair colorants If consumers prefer not to have a permanent color change to their hair they can choose to change their hair color in a non-permanent way, i.e. color that can be removed after about 4–6 shampoos (Corbett, 1973). These dyes do not involve the use of hydrogen peroxide, and usually contain multiple dyes in combination (Brown, 1982). As with permanent dye, several secondary compounds are also included in these dyes, e.g. surfactants, pH adjusters, etc. These hair dyes consist of highly polar agents including neutral aromatic amines, nitro aromatic amines, or antraquinone derivatives (Brown, 1982). Rinse-out rates of these dyes correlate with size of the dye molecules used, but also depends on the region of the hair fiber. For example, dye is removed most readily from weathered hair fiber tips than the scalp end and so accommodation needs to be built into the formulations to ensure evenness of tones. Furthermore, some dyes, especially small mononuclear dye molecules, may be washed out more readily from previously bleached hair. As with permanent dyes, ring-dyeing is also observed with this group of hair colorants, consistent with diffusion-controlled reactions.

15.6.4 Temporary hair colorants/rinses Another option is to use hair colorants that can be washed out of the hair with a single shampoo. These contain several (as many as five) color ingredients (e.g. D&C brown No.1, Acid violet 43, etc.) to achieve the desired shade, and can be applied directly to the hair or sprayed onto the hair. More dye can be applied if a more intense color is sought and less dye if only tint addition to gray hair is desired. The dye molecules used here tend to be larger than those used for semipermanent colorants and are chosen for their maximum water solubility and minimum penetration, thereby facilitating easy rinse out (Wall, 1957).

15.6.5 Metallic hair dyes Historically, salts of metals like silver, bismuth, cobalt, copper, iron, mercury and lead have been used to dye hair. However, only lead salts (e.g. lead acetate with sulfur) are still in use in some jurisdictions. These are often chosen by men with graying hair as the darkening of the hair shaft occurs very gradually, mostly likely via the formation of lead-sulfur complexes in the periphery of the hair shaft. A caveat here is that these complexes may be unstable, especially when exposed to other chemicals and dyes.

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15.6.6 Vegetable hair dyes While the classical literature contains several ‘colorful’ references to the use of vegetable hair colorants, only henna and chamomile are still used to any significant commercial extent, although several patents based on this technology have been filed by major hair colorant companies. Henna (lawsone) is found in the leaves of the Egyptian privet plant and contains 2-hydroxy-1,4-naphthoquinone. This compound can add red (ionized form) and yellow (non-ionized form) shades to protein in hair and nails when applied in acidic media. Deeper henna shades are possible at high concentrations in alkaline media, while acidic pHs are more suitable for hair dyeing. The pH of these may control the chemical bonding of the lawsone to the hair fiber, but levels of color fastness are generally relatively low. This can be improved if lawsone is mixed with p-phenylenediamine, though this does not come without some adverse reactions (Le Coz et al., 2000). Chamomile flowers contain an active coloring polyhydroxy flavone called 4´,5,7-trihydroxy flavone that has been used for dyeing of cloth as well as hair.

15.6.7 ‘Natural’ hair colorants The hair colorant industry is currently under significant pressure to develop economical, natural hair dyes. Although not yet commercialized, there may be some scope for using DOPA (3,4 dihydroxyphenylalanine), which after oxidation provides a natural brown dye. In the presence of cysteine, natural red pigments (pheomelanins) can be formed from DOPA, while the presence of sulfurcontaining nucelophile (rather than cysteine) can increase still further the range of hair color shades possible. Here, hydrogen peroxide is a superior oxidizer, though the reaction can proceed even with basic atmospheric oxygen. Brown colors can be deepened to intense blacks if potassium ferricyanide is added.

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15.7

Color measurement methods and instruments

Hair ‘color’ needs some definition in order to be a useful parameter for study and for accurate comparison of studies. This is especially true at the margins of color categories, e.g. is ‘dirty’ blonde rather a brown? Moreover, not all hair strands in a head of hair are identical in color; indeed different regions of the scalp can have hair of quite different shades. For example, hair at the back of the head is commonly of deeper pigmentation than the crown of the vertex. Thus, a representative assessment of the entire scalp is needed to generate a truly accurate determination. In this context hair color can be perceived as varying along a continuum, rather than occupying discrete color spaces. The rods and cones of the human eye are sensitive to changes in both lightness (mostly rods) and color (cones) (Hunt, 1998). The technique that has been most widely used, including for biomedical and cosmetic sciences, is reflective

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spectrophotometry. This methodology is widely viewed as truly an objective measure for assessing skin and hair skin pigmentation (Shriver and Parra, 2000; Parra et al., 2007; Naysmith et al., 2004). An objective description of color requires a model of the so-called color space. Among the several models existing to measure color is one developed by the Commission Internationale de l’Eclairage, CIELAB or CIE L*a*b*, which measures color on three axes broadly linear with human perception (Ford and Roberts, 1998). Another benefit of this model is that it provides a grid point for each specific color (TASI, 2004) and in addition, has been used successfully in human pigmentation studies (Shriver and Parra, 2000; Takahashi and Nakamura, 2004; Parra, 2007). Here color intensity is measured on the ‘L*’ axis from a value of 0 (black) to 100 (white), while color itself is measured on the ‘a*’ axis from a value of -100 (green) to +100 (red) and on the ‘b*’ axis from –100 (blue) to +100 (yellow). The smallest difference the human eye can detect is equated to one unit on the L*, a*, or b*axes (TASI, 2004). Together, this color grid allows for a quantitative comparison of color, though is limited ultimately by the operational limits of the instruments used. The CIE L*a*b* system has been used together with reflective spectrophotometry to measure hair color where color intensity (‘L*’) was found to correlate well with the Melanin Index, especially in those of non-European ancestry (Shriver & Parra, 2000). Others have found a good correlation of hair color as measured with b* and the MC1R gene variant expression (Naysmith et al., 2004). A study by Vaughn and colleagues (2008) reported that, when given a limited choice of colors, self-reported and observer-reported hair color determination was accurate to 85.7%, with one shade (lighter and darker) discrepancies found in approximately 14% of cases where the observer mostly graded the hair color as darker than the self-reporter. However, this is often not useful for a fully quantitative assessment of hair color. Indeed, there can be poor separation of hair color categorization by observers and the clustering analysis via reflective spectrophotometry using the CIE L*a*b* system – further emphasizing the importance of using an objective measurement. Vaughn and co-workers also found that the b* component (yellow) of the CIE L*a*b* color space provided the most discriminating information for hair color grouping (which concurred with the observed strongest correlation between b* and the MC1R genotype (Naysmith et al., 2004), suggesting a multi-component approach needs to be adopted. So-called trichromatic vision (though in reality more than three types of color receptor cones) leads to significant similarities in the reporting of color by observers and by the spectrophotometry with clustering method. However, the latter is more sensitive in one or all three of the color axes than biologic discriminations (limited to one unit) and so can provide more discriminatory power. Recently Vaughn and co-workers (2009) conducted a comparison of the more cumbersome reflective spectrophotometry and digital image analysis for measurement of hair color. While reflective spectrophotometry has been used mostly at the macroscopic scale, forensic scientists have been using digital image

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analysis to measure hair color of single hair fibers at the microscope scale (Bednarek, 2004). Digital images, measured and displayed on the computer screen in the Red-Green-Blue (RGB) colour space need to be converted into the colour space required (CIE L*a*b* in this case) using standard mathematical algorithms (Herbin et al., 1990). The results of this study showed however that digital image analysis, while more convenient than reflective spectrophotometry, is inferior to it and so has very limited potential for high resolution studies of color measurement. In summary, objectively defined and measured colors, rather than observer or self-reported colors, are necessary to classify individuals for further study and at this time, this is best achieved through reflective spectrophotometric measurement.

15.8

Future trends

The future growth in hair color measurement will be led principally by the hair colorant industry and the forensic sector of both archeological and police investigations. There will be increasing interest in the characterization of melanin variability (i.e. eumelanins, pheomelanins and mixed melanins), which is a current source of significant challenge. Furthermore, there will be parallel and mindboggling advances in imaging technology and a greater appreciation of the unique growth dynamics of hair. These aspects are currently leading to a resurgence of interest in hair analysis. Within this hair color analysis and pigment pattern recognition will be hugely important.

15.9

Sources of further information and advice

In addition to the bibliography for specific reading, perhaps the most impressive single source for all things pigmentation in terms of biology/physiology is the current edition of The Pigmentary System by Nordlund JJ, Boissy R, Hearing VJ, King R, Oetting W, Ortonne J-P (eds), Blackwell Publishing, 2006. 1 2 3 4 5 6 7 8 9 40 1 2 43X

15.10 References Allende MF (1972), ‘The enigmas of pigmentation’, JAMA 220(11), 1443–1447. Anderson JS (2000), ‘The chemistry of hair colorants’, J Soc Dyers Col 116, 193–196. Arck PC, Overall R, Spatz K, Liezman C, Handjiski B, Klapp BF, Birch-Machin MA, Peters EM (2006), ‘Towards a “free radical theory of graying”: melanocyte apoptosis in the aging human hair follicle is an indicator of oxidative stress induced tissue damage’, FASEB J. 20, 1567–1569. Arnaud J (1984), ‘ESR study of hair and melanin-keratin mixtures – The effects of temperature and light’, Int J Cosmet Sci 6, 71. Bednarek J (2004), ‘An attempt to establish objective criteria for morphological examinations of hairs using the image analysis system’, Probl Forensic Sci LVI, 65–77. Boissy RE, Sakai C, Zhao H, Kobayashi T, Hearing VJ (1998), ‘Human tyrosinase related protein-1 (TRP-1) does not function as a DHICA oxidase activity in contrast to murine TRP-1’, Exp Dermatol 4, 198–204.

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Botchkareva NV, Khlgatian M, Longley BJ, Botchkarev VA, Gilchrest BA (2001), ‘SCF/cKIT signaling is required for cyclic regeneration of the hair pigmentation unit’, FASEB J 15, 645–658. Braida D, Dubief C, Lang G (1994), ‘Photoageing of hair fiber and photoprotection’, Skin Pharmacol 7(1–2), 73–77. Brown K (1982), ‘Hair colorants’, J Soc Cosmet Chem 33, 375–383. Commo S, Bernard BA (2000), ‘Melanocyte subpopulation turnover during the human hair cycle: an immunohistochemical study’, Pigment Cell Res 13, 253–259. Commo S, Gaillard O, Thibaut S, Bernard BA (2004), ‘Absence of TRP-2 in melanogenic melanocytes of human hair’, Pigment Cell Res 17, 488–497. Corbett J (1973), ‘The role of meta difunctional benzene derivatives in oxidative hair dyeing. I. Reactions with p-diamines’, Cosmet Toiletries 24, 103–134. Dupin E, Le Douarin NM (2003), ‘Development of melanocyte precursors from the vertebrate neural crest’, Oncogene 22, 3016–3023. Ford A, Roberts A (1998). Colour space conversions. www.poynton.com/PDFs/coloureq. pdf. Gao T, Bedell A (2001), ‘Ultraviolet damage on natural gray hair and its photoprotection’, J Cosmet Sci 52(2), 103–118. Gutteridge JM, Halliwell B (2000), ‘Free radicals and antioxidants in the year 2000. A historical look to the future’, Ann NY Acad Sci 899, 136–147. Harman D (1956), ‘A theory based on free radical and radiation chemistry’, J Gerontol, 11, 298–300. Hegedus ZL (2000), ‘The probable involvement of soluble and deposited melanins, their intermediates and the reactive oxygen side-products in human diseases and aging’, Toxicology 145, 85–101. Herbin M, Venot A, Devaux JY, Piette C (1990), ‘Color quantitation through image processing in dermatology’, IEEE Trans Med Imag, 9, 262–269. Holbrook KA, Underwood RA, Vogel AM, Gown AM, Kimball H (1989), ‘The appearance, density and distribution of melanocytes in human embryonic and fetal skin revealed by the anti-melanoma monoclonal antibody, HMB-45’, Anal Embryol (Berl), 180(5), 443– 455. Horikawa T, Norris DA, Johnson TW, Zekman T, Dunscomb N, Bennion SD, Jackson RL, Morelli JG (1996), ‘DOPA-negative melanocytes in the outer root sheath of human hair follicles express premelanosomal antigens but not a melanosomal antigen or the melanosome-associated glycoproteins tyrosinase, TRP-1, and TRP-2’, J Invest Dermatol 106, 28–35. Hoting E, Zimmermann M (1997), ‘Sunlight-induced modifications in bleached, permed, or dyed human hair’, J Soc Cosmet Chem 48 (2), 79–91. Hunt RWG (1998), Measuring Colour, Kingston-upon-Thames, Fountain Press. Jackson IJ, Budd PS, Keighren M, McKie L (2007), ‘Humanized MC1R transgenic mice reveal human specific receptor function’, Hum Mol Genet 16(19), 2341–2349. Kauser S, Schallreuter KU, Thody AJ, Gummer C, Tobin DJ (2003), ‘Regulation of human epidermal melanocyte biology by beta-endorphin’, J Invest Dermatol 120, 1073–1080. Kauser S, Schallreuter KU, Thody AJ, Gummer CL, Tobin DJ (2004), ‘Beta-endorphin as a regulator of human hair follicle melanocyte biology’, J Invest Dermatol 123, 184–195. Kauser S, Slominski A, Wei ET, Tobin DJ (2006), ‘Modulation of the human hair follicle pigmentary unit by corticotropin-releasing hormone and urocortin peptides’, FASEB J 20(7), 882–895.

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Kauser S, Westgate G, Green M, Tobin DJ (2007), ‘Age-associated downregulation of catalase in human scalp hair follicle melanocytes’, Pigment Cell Res 20(5), 432. Keogh EV, Walsh RJ (1965), ‘Rate of graying of human hair’, Nature 207, 877–878. Lalueza-Fox C, Rompler H, Caramelli D, Staubert C, Catalano G, Hughes D, Rohland N, Pilli E, Longo L, Condemi S, de la Rasilla M, Fortea J, Rosas A, Stoneking M, Schoneber T, Bertranpetit J, Hofreiter M (2007), ‘A melanocortin 1 receptor allele suggests varying pigmentation among Neanderthals’, Science 318, 1453–1455. Le Coz CJ, Lefebvre C, Keller F, Grosshans E (2000), ‘Allergic contact dermatitis caused by skin painting (pseudo-tattooing) with black henna, a mixture of henna and p-phenylenediamine and its derivatives’, Arch Dermatol. 136(12), 1515–1517. Lee WS, Lee IW, Ahn SK (1996), ‘Diffuse heterochromia of scalp hair’, J Am Acad Dermatol 35, 823–825. Levine N, Sheftel SN, Eytan T, Dorr RT, Hadley ME, Weinrach JC, Ertl GA, Toth K, McGee DL, Hruby VJ (1991), ‘Induction of skin tanning by subcutaneous administration of a potent synthetic melanotropin’, JAMA 266, 2730–2736. Morgan E (1985), The Ascent of Woman, London, Souvenir Press. Nagl W (1995), ‘Different growth rates of pigmented and white hair in the beard: differentiation vs. proliferation?’, Br J Dermatol, 132, 94–97. Nakamura M, Tobin DJ, Richards-Smith B, Sundberg JP, Paus R (2002), ‘Mutant laboratory mice with abnormalities in pigmentation: annotated tables’, J Dermatol Sci 28, 1–33. Naysmith L, Waterson K, Ha T, Flanagan N, Bisset Y, Ray A, Wakamatsu K, Ito S, Rees JL (2004), ‘Quantitative measures of the effect of the melanocortin 1 receptor on human pigmentary status’, J Invest Dermatol 122, 423–428. Nishimura EK, Jordan SA, Oshima H, Yoshida H, Osawa M, Moriyama M, Jackson IJ, Barrandon Y, Miyachi Y, Nishikawa S (2002), ‘Dominant role of the niche in melanocyte stem-cell fate determination’, Nature 416, 854–860. Nordlund JJ, Ortonne J-P (2006), ‘The normal color of human skin’, in Nordlund JJ, Boissy RE, Hearing VJ, King RA, Oetting WS, Ortonne J-P, The Pigmentary System: Physiology and Pathophysiology, New York, Blackwell, 504–520. Parra EJ (2007), ‘Human pigmentation variation: evolution, genetic basis, and implications for public health’, Yearbk Phys Anthropol 50, 85–105. Pelfini C, Cerimele D, Pisanu G (1969), ‘Aging of the skin and hair growth in man’, in Montagna W, Dobson RL, Advances in Biology of the Skin – Hair Growth, New York, Pergamon Press, 153–160. Peters EM, Tobin DJ, Botchkareva N, Maurer M, Paus R (2002), ‘Migration of melanoblasts into the developing murine hair follicle is accompanied by transient c-Kit expression’, J Histochem Cytochem 50(6), 751–766. Quevedo WC, Szabo G, Virks J (1969), ‘Influence of age and UV on the population of dopa-positive melanocytes in human skin’, J Invest Dermatol 52, 287–290. Rees JL (2000), ‘The melanocortin 1 receptor (MC1R): more than just red hair’, Pigment Cell Res 13, 135–140. Rees JL (2006), ‘Plenty new under the sun’, J Invest Dermatol 126, 1691–1692. Robbins CR (2002), Chemical and Physical Behavior of Human Hair, New York, SpringerVerlag, 153–192. Robbins C, Kelly C (1969), ‘Amino acid analysis of cosmetically altered hair’, J Soc Cosmet Chem 20, 555–564.

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Schallreuter KU, Beazley WD, Hibberts NA, Tobin DJ, Paus R, Wood JM (1998), ‘Pterins in human hair follicle cells and in the synchronized murine hair cycle’, J Invest Dermatol 111, 545–550. Schallreuter KU, Wazir U, Kothari S, Gibbons NC, Moore J, Wood JM (2004), ‘Human phenylalanine hydroxylase is activated by H2O2: a novel mechanism for increasing the L-tyrosine supply for melanogenesis in melanocytes’, Biochem Biophys Res Commun 322(1), 88–92. Shriver MD, Parra EJ (2000), ‘Comparison of narrow-band reflectance spectroscopy and tristimulus colorimetry for measurements of skin and hair color in persons of different biological ancestry’, Am J Phys Anthropol 112, 17–27. Slominski A, Paus R, Schadendorf D (1993), ‘Melanocytes as “sensory” and regulatory cells in the epidermis’, J Theor Biol 164, 103–120. Slominski A, Paus R, Plonka P, Schallreuter KU, Paus R, Tobin DJ (1994), ‘Melanogenesis during the anagen-catagen-telogen transformation of the murine hair cycle’, J Invest Dermatol 102, 862–869. Slominski A, Ermak G, Hwang J, Chakraborty A, Mazurkiewicz JE, Mihm M (1995), ‘Proopiomelanocortin, corticotropin releasing hormone and corticotropin releasing hormone receptor genes are expressed in human skin’, FEBS Lett 374, 113–116. Slominski A, Tobin DJ, Shibahara S, Wortsman J (2004), ‘Melanin pigmentation in mammalian skin and its hormonal regulation’, Physiol Rev 84, 1155–1228. Steingrimsson E, Copeland NG, Jenkins NA (2006), ‘Mouse coat color mutations: from fancy mice to functional genomics’, Dev Dyn 235(9), 2401–2411. Stenn KS, Paus R (2001), ‘Controls of hair follicle cycling’, Physiol Rev 81, 449. Sugiyama S (1979), ‘Mode of re-differentiation and melanogenesis of melanocytes in murine hair follicle’, J Ultrastructural Res 67, 40–54. Takahashi T, Nakamura K (2004), ‘A study of the photolightening mechanism of blond hair with visible and ultraviolet light’, J Cosmet Sci 55, 291–305. TASI (2004), ‘Colour theory: understanding and modeling colour’, Technical Advisory Service for Images, Bristol, UK, University of Bristol. Tobin DJ (2003) ‘The ageing hair follicle pigmentary unit’, in Van Neste D, Hair Science and Technology, Tournai, Belgium, Skinterface srl, 155–168. Tobin DJ (2005) ‘Pigmentation of human hair’, in Tobin DJ, Hair in Toxicology – An Important Biomonitor, Cambridge, The Royal Society of Chemistry, 57–88. Tobin DJ (2005), ‘The human hair fiber’, in Tobin DJ, Hair in Toxicology – An Important Biomonitor, Cambridge, The Royal Society of Chemistry, 57–88. Tobin DJ (2008), ‘Human hair pigmentation – biological aspects’, Int J Cosmet Sci, 30(4), 233–257. Tobin DJ (2009), ‘Biology of hair follicle pigmentation’, in Blume-Peytavi U, Tosti A, Whiting D, Trüeb R, Hair Growth and Disorders, Berlin, Springer-Verlag, 51–71. Tobin DJ, Bystryn J-C (1996), ‘Different populations of melanocytes are present in hair follicles and epidermis’, Pigment Cell Res, 9, 304–310. Tobin DJ, Hagen E, Botchkarev VA, Paus R (1998), ‘Do hair bulb melanocytes undergo apoptosis during hair follicle regression (catagen)?’, J Invest Dermatol 111, 941–947. Tobin DJ, Paus R (2001), ‘Graying: gerontobiology of the hair follicle pigmentary unit’, Exp Gerontol, 36(1), 29–54. Tobin DJ, Slominski A, Botchkarev V, Paus R (1999), ‘The fate of hair follicle melanocytes during the hair growth cycle’, J Invest Dermatol Symp Proc 4, 323–332. Van Neste D, Tobin DJ (2004), ‘Hair cycle and dynamic interactions and changes associated with aging’, Micron 35, 193–200.

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Vaughn MR, van Oorschot RAH, Baindur-Hudson S (2008), ‘Hair color measurement and variation’, Amer J Phys Anthropol 137, 91–96. Vaughn MR, van Oorschot RAH, Baindur-Hudson S (2009), ‘A comparison of hair colour measurement by digital image analysis with reflective spectrophotometry’, Forensic Science International 183, 97–101. Wall FE (1957), ‘Bleaches, hair colorings and dye removers’ in Sagarin E, Cosmetics Science and Technology, New York, Interscience, 486–488. Westerhof W (2006), ‘The discovery of the human melanocyte’, Pigment Cell Res 19, 183–193. Whiteman DC, Parsons PG, Green AC (1999), ‘Determinants of melanocyte density in adult human skin’, Arch Dermatol Res 291, 511–516. Wolfram LJ, Albrecht L (1987), ‘Chemical-bleaching and photobleaching of brown and red hair’, J Soc Cosmet Chem, 38, 179–191. Wolfram LJ, Hall K (1975), ‘Photodegradation of human hair’, J Soc Cosmet Chem 26, 247. Wolfram LJ (1970), ‘Chemical bleaching’, J Soc Cosmet Chem 19, 675. Wood JM, Jimbow K, Boissy RE, Slominski A, Plonka PM, Slawinski J, Wortsman J, Tosk J (1999), ‘What’s the use of generating melanin?’, Exp Dermatol 8, 153–164.

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Index

AATCC, 108, 116 AATCC 6, 204 AATCC 8, 204 AATCC 15, 204 AATCC 16, 205 AATCC 23, 204 AATCC 61, 206 AATCC 101, 203 AATCC 104, 204 AATCC 106, 204 AATCC 107, 204 AATCC 109, 204 AATCC 115, 204 AATCC 116, 204 AATCC 125, 206 AATCC 129, 204 AATCC 132, 206 AATCC 133, 207 AATCC 157, 204 AATCC 162, 204 AATCC 164, 204 AATCC 172, 206 AATCC 173, 212 AATCC 181, 205 AATCC 188, 206 AATCC 190, 206 AATCC EP3, 210 AATCC EP8, 210 AATCC EP 1, 208 AATCC EP 2, 208 AATCC EP 8, 204 AATCC Fading Units, 205 AATCC Test Method 110, 289 AATCC Test Method 173–1991, 171 abrasion, 358 absolute accuracy, 238 achromaticity, 53–4 Acoat system, 51 active pixel sensor see CMOS Adams Nickerson space, 281 adaptation, 316–17 Adaptive Neuro Fuzzy Inference System, 137 additive colour mixing, 196 amelogenesis imperfecta, 348 American Upland cotton, 254 analogical photosensor see optoelectronic sensor ANLAB, 82

antecedent, 273 anterior chamber, 4 aqueous, 4 Artificial Daylight D65 Lamp, 334, 336 artificial intelligence, 135 artificial neural networks, 34, 125–44 application to colour measurement, 132–5 architecture, 127–8 artificial neuron, 128 network layers, 128 network types, 128 schematic diagram, 127 case studies, 140–1 actual and predicted L*, a* and b* values, 143 spectral reflectance curves, 142 evaluation method, 140 actual and predicted dyeing time, 140 actual and predicted L* a* and b* values, 141 feed-forward neural networks, 130 future trends, 141–4 history, 126–7 learning process, 129–30 illustration, 130 reinforcement, 129 supervised, 129, 130 unsupervised, 129 recipe prediction, 135–9 dyeing time prediction, 139 theory, 135 training using back propagation algorithm, 130–2 sigmoid activation function, 133 training phases, 131–2 transfer function, 132 artificial neurons, 128 ASTM 1925, 289 ASTM (2003), 93 ASTM (2005), 103, 108, 110 ASTM D 1729, 223 ASTM D 1925, 289 ASTM D E313–73, 289 ASTM Designation E313–73, 288 ASTM E308:08, 78 ASTM E313, 289 ATD models, 7–10 atmospheric contaminants, 204

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Index

back propagation algorithm, 129, 131 Bayesian regularisation technique, 134 BCRA tile data, 299 BCRA tile test, 237 Beer’s law, 136 Benevue Colour Management System, 301, 306 Berger formulae, 102, 104 Boynton model, 8, 9, 13 brightness, 323 BS 6923, 234 bubble hair, 382 BYK–Gardner Colour View, 193 calibration, 237 camera-based colour measurement, 147–63 case studies, 158–62 multi-spectral capture system, 160–2 spatial-chromatic dithering, 158–60 CCD and CMOS application fields, 148 future trends, 162–3 principles, 149–51 imaging capture process, 150 procedures, 151–4 monochromatic based experimental set-up, 152 strengths and weaknesses, 154–8 characterisation models, 154 luminance adaptation model, 155 multi-spectral capture systems, 155–7 spectral vs colorimetric imaging, 157–8 cameras, 159 CCC see Clemson colour clustering CCD, 147–8 centroid calculation, 275 chemical bleaching, 383–4 Chevreul colour order system, 49–50 choroid coat, 4 chroma, 53, 71, 84, 323 Chroma Cosmos 5000, 30, 37 Chromagen 707, 37 chromatic adaptation, 317 chromatic attributes, 223 chromatic opponency theory, 7 chromatic perception, 10–12 appearance of colour, 11–12 apparent mix, 12 Aubert-Abney effect, 12 Bezold-Brücke effect, 11–12 crispening effect, 12 Helmholtz-Kohlrausch effect, 12 physical and perceptual colour descriptors, 11 simultaneous contrast, 12 discrimination, 10–11 chromatic stimulus, 6 chromatic transference scale, 210 chromatic vision, 6–10 models, 6–10 ATD models, 7–10 chromatic opponency theory, 7 Smith and Pokorny fundamentals, 9 trichromatic theory, 6–7 stimulus and perceived colour, 6 chromaticity coordinates, 80 chromaticity diagram, 80–1 illustration, 81 chromaticness, 38

Cibacron blue TR-E, 136 Cibacron brilliant red 4G-E, 136 Cibacron yellow G-E, 136 CIE 1931 colour space, 313, 314, 320 CIE 1976 colour space, 225 CIE 94 formula, 285, 325 CIE 1964 supplementary standard observer, 76 CIE 1976 uniform colour space, 281 CIE 1931 XYZ, 149 CIE colorimetric system, 23, 26 CIE DE 2000 formula, 283, 285 CIE illuminant D65, 288 CIE Lab colour space, 224–5, 387 CIE LCh colour space, 225 CIE nomenclature, 349 CIE Publication 15.2, 288 CIE standard illuminant D65, 92 CIE standard observer, 74–6 illustration, 74 unreal primaries, 75–6 graphical representation of real and unreal primaries, 77 illustration of real and unreal primaries, 75 CIE standardised light sources, 72–3 CIE system, 72, 222 limitations, 82 transformation and improvement, 82–6 chroma C calculation, 85 CIEL*a*b* 1976 Colour Space, 84 hue circle, 85 usefulness, 81–2 CIE two degree standard observer, 76 CIE whiteness formulae, 106–8, 117, 289 old and new tint limits in the CIELAB a* – b* diagram, 106–8 CIE-XYZ values, 154, 157, 160 CIECAM97s, 134 CIEDE2000, 168, 215, 232, 325 CIELAB, 187, 191, 192, 347, 351, 356 CIELAB 1976 formula, 283, 285 CIELAB colour difference, 225 CIELAB colour space, 320, 322–3 CIELAB colour tolerancing, 282 CIELAB equation, 171 CIELUV colour space, 320 classic Colour Checker, 151 Clemson colour clustering, 174–7 modified method, 180 CM-512m3, 308 CMC see Colour Measuring Committee CMC colour difference formula, 226, 232, 234, 283, 285 CMCCAT2000, 317 CMOS, 147–8 colorant formulation, 295 Colorant Mixture Computer, 296 Colorcurve colour order system, 50 colorimeters, 357 colorimetric characterisation, 153 colorimetric measurements, 155 Colorimetry Committee of the Optical Society of America, 89 Coloroid system, 47–9 colour attribute, 222 Colour Checker charts, 151 colour constancy, 15–17, 151, 316–17 adaptive mechanism, 15–16

© Woodhead Publishing Limited, 2010

Index chromatic adaptation and gain control mechanisms, 16–17 light deduction, 17 colour control process, 160 colour difference, 207, 224 Colour-Difference Formula, 325 colour difference unit, 293 colour digital cameras, 160 colour imaging technology, 277 Colour iMatch Industrial software, 182 colour matching, 295 colour measurement artificial neural networks, 125–44 application, 132–5 architecture of ANNs, 127–8 basic principles, 126–7 case studies, 140–1 evaluation method, 140 feed-forward neural network, 130 future trends, 141–4 learning process, 129–30 recipe prediction, 135–40 training using back propagation algorithm, 130–2 cotton grading, 253–77 determining and improving accuracy, 184–94 absolute correction of instrumentally generated spectrometer values, 190 accuracy improvement, 190–1 applications, 193–4 definitions, 186–7 determining uncertainty, 184 uncertainty, 185–6 experimental modelling, 191–3 distribution of specimens in CIELAB L* vs a* plane, 192 original uncorrected and corrected values vs bin size, 194 specimen location in CIE a*–b* colour space, 192 and fastness assessment, 196–216 hair colour, 371–88 methods for textiles, 221–51 paint films and coatings, 279–309 principles and practice for food, 312–39 uncertainty values in units of the value itself single baby blue smooth glossy plastic plaque for Instrument 100, 189 single baby blue smooth glossy plastic plaque for Instrument 500, 189 single yellow smooth glossy plastic plaque for Instrument 100, 188 single yellow smooth glossy plastic plaque for Instrument 500, 189 Colour Measuring Committee, 325 colour merge value, 175 colour mixing, 196 colour notation systems aim points database, 19 device dependent, 19 mathematical systems, 19 colour order systems, 19–65 accuracy, 54 application, 24–5 classification, 25–9 colorant mixture systems, 25–6 colour appearance systems, 28–9

395

colour mixture systems, 26–8 comparison and interrelation, 51–4 achromaticity representation, 53–4 chroma/saturation representation, 53 hue representation, 52–3 computer-based systems, 54–61 digital colour atlases, 59–61 HSV and HSL colour spaces, 58 future trends, 63–5 merits-demerits, 29–30 systematic arrangements, 22–3 universal colour language, 61–2 universal colour names, centroid colours and hue angle boundaries, 63 various colour order systems, 31–51 Coloroid system, 47–9 DIN system, 43–4 Munsell colour order system, 31–7 natural colour system, 38–41 OSA-UCS system, 45–7 Ostwald system, 41–3 other colour system, 49–51 practical colour coordinate system, 37 Colour Science Association of Japan, 115 colour shade sorting, 167–82 555 fixed-grid shade sorting system, 168–74 apparel colour specification, 170 boxes arrangement, 169 packing of spheres, 173 shade sorting effectiveness indices, 174 tolerance ellipsoid and position of samples, 169 truncated octahedron, 173 Clemson colour clustering, 174–7 comparing two populations sorted by CCC and 555 methods, 176 demonstration for adoption by industry, 177 groups produced by various sorting methods, 177 process, 175 defined, 167 K-means clustering, 178–80 clusters graphic presentation, 179 numbers of clusters vs mean distance, 179 modified CCC shade sorting method, 180 shade sequencing and clustering, 180–2 linear tapering of blue dyed sample, 181 colour space, 387 colour specifications, 70–1, 224–6 Colour Standards and Colour Nomenclature, 26–7 colour strength, 286–7 calculations using different methods, 287 computer output, 287 colour tolerance, 232 colour vision theories and principles, 3–17 advantages, 3 chromatic perception, 10–12 colour constancy, 15–17 defective colour vision, 12–15 human colour vision, 6–10 human visual system, 3–6

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Index

colourfastness assessment, 196–216 colour and colourfastness, 196–7 standard test format, 201–2 test method development, 199–200 test standard setting organisations, 200–1 use and usefulness of testing, 197–9 results assessment, 207–16 AATCC chromatic transference scale, 211 AATCC grey scale for evaluating change in colour change, 209 AATCC grey scale for evaluating staining, 210 grey scale for staining step values, 214 grey scales, 208–12 instrumental colour measurement of test data, 212–14 use of digital camera-based systems, 214–16 specific tests, 203–7 consumer use, 203–6 production processes, 203 refurbishment, 206–7 colourfulness, 323 colours, 222 numerical expression, 70–86, 222–4 chromaticity coordinates and diagram, 80–1 CIE standard light sources/illuminants, 72–3 CIE Standard Observer and unreal primaries, 74–6 CIE system, 72 CIE XYZ system, 81–2 future trends, 87 limitations of CIE system, 82 reflectance measurement, 79–80 specification, 70–1 transformation and improvement of CIE system, 82–6 tristimulus values computation, 77–9 computer colour matching, 295–7 computer tinting, 297, 309 confusion point, 14 consequent, 273 contrast ratio, 290 conventional colorimeters, 160 cosmetication process, 383 cotton colour grading affecting factors, 255–9 redness, 255–6 redness contribution to cotton chroma, 257 redness range for each colour category, 257 reflectance, yellowness and redness of USDA physical standards, 256 spots, 257–8 trash particles, 258–9 American Upland cotton colour grades, 254 colour image analysis, 259–63 colour measurement, 261–3 identified irregular regions, 261 imaging colorimeter set-up, 259 irregular regions identification, 260–1 L*C*h distributions, 261 multi-dimension thresholding, 262 reflectance distribution, 263

reflectance vs yellowness, 262 colour measurement, 253–77 CR-210 colorimeter measurement influence of leaves, 259 influence of spot, 258 disagreements between different grading methods, 276 fuzzy logic, 268–76 cotton colour grades boundaries, 268–9 defuzzification, 274–5 fuzzification, 271–4 fuzzy inference system, 269–70 fuzzy rules, 274 fuzzy sets and membership functions, 270–1 input fuzzy sets membership functions, 272 output fuzzy sets triangular membership functions, 272 parameters for fuzzy sets of input variables, 270 weighted membership functions of output fuzzy sets, 275 weighted output functions aggregation, 275 white and light spotted colours, 269 history, 253 HVI colorimeter, 254–5 irregular regions in cotton image, 260 neural network, 263–8 colour grade disagreement distributions, 267 cotton colour grading results by NN classifier, 266–8 disagreement in major categories, 263 disagreement in subcategories, 264 HVI–classer and NN–classer, 266 MLP topology, 265 multilayer perception, 264 NN classifier for cotton colour grading, 265–6 USDA cotton colour grades, 253–77 cotton fabric, 138 Cotton Incorporated, 254 CR-241, 299 CR-210 colorimeter, 258 D illuminants, 73, 82 De Valois-De Valois model, 9 defective colour vision, 12–15 anomalies and deficiencies, 12–14 colour vision based on cones, 13 confusion lines and colour discrimination tests, 14–15 deuteranopic confusion lines, 15 tritanopic subject, 14 defuzzification, 274–5 dental fluorosis, 349 dentine, 344 desert island experiment, 22–3 deuteranomaly, 14 deuteranopia, 13, 15 Dictionary of Colour, 27–8 DigiEye, 158, 215 digital Colour Checker Semi Gloss, 151 digital colour imaging, 147 digital image analysis, 387–8 techniques, 355–6 digital photography, 151

© Woodhead Publishing Limited, 2010

Index digital trichromatic cameras, 158 DIN 53160, 201 DIN system, 43–4 DOPA, 386 drift test, 237 dry fabrics, 247–8 dye, 197 Dynamic Linked Library, 190 error back-propagation algorithm, 266 eumelanin, 378 eumelanosomes, 378 Eurocolour system, 51 European Standards, 200 European Standards Organisation, 200 extrinsic tooth stain, 349–50 fastness colour difference, 213 feed-forward neural networks, 127, 128, 130, 131 feed-forward network, 127 mathematical representation, 131 feedback architectures see interactive feedback networks, 128 fibre, 238 FIS see fuzzy inference system 555 fixed-grid shade sorting, 168–74 flat-field correction, 152 fluorescence, 346 fluorescent lamps, 73 fluorescent whitening agents, 88, 289 fluoride, 358 FMC-II formula, 285 food colour measurement absorption and scatter, 318–19 appearance, 317–18 CIE system, 319–20 colour matching functions, 320 relative spectral power distributions, 321 concentrated and diluted orange juice changes in lightness, opponent co-ordinates and hue angle and chroma, 333 K-M absorption and scatter coefficients for tristimulus values, 332 reflectance spectra, 331 future trends, 337–9 illuminant spectra and uniform colour, 336 CIELAB a*b* spacing, 338 foods reflectance spectra of differing hue, 337 influence of ambient light and food structure, 316–17 instrumentation, 325–7 measurement in practice, 327–36 breakfast cereals, 335–6 calculated changes in fresh beef spectra, 330 coffee, 332–4 fresh beef during oxymyoglobin oxidation, 328 fresh meat, 328–30 orange juice, 330–1 visual lightness to darkness relationship of coffee and milk, 335 wrapped fresh beef progressive changes in colour, 329

397

principles and practice, 312–39 trichromatic detection, 313–16 light detection in the eye, 315–16 uniform colour space, 320–5 colour description systems and notation, 324 colour diagrams, 322 four-dimensional Riemannian colour space, 21 fuzzification, 271–4 fuzzy clustering, 134 fuzzy inference system, 269–70 classified white and light spotted colours distributions, 277 cotton colour grading results, 275–6 schematic diagram, 270 fuzzy logic, 134, 268–76 fuzzy logic technique, 133 FWAs see fluorescent whitening agents Gaertner-Griesser device, 96–7, 112 Ganz-Griesser method, 108–10 Ganz linear whiteness and tint formulae, 104–6 with Berger formulae, 106 with Stensby formulae, 106 Gardner Scale, 110 Gaussian distribution curve, 271 geometric attributes, 223 German Standard DIN 6164, 43 gingiva, 344–5 Global Organic Textile Standard, 199 GretagMacbeth spectrophotometer, 243 grey hair, 379–82 grey scale difference, 214 grey scales, 208–12 staining step values, 214 Guide to Measurement Uncertainty, 185 gums see gingiva Guth model, 10 hair colour artificial hair colouring shades, 383–6 chemical bleaching, 383–4 metallic hair dyes, 385 natural hair colorants, 386 permanent hair colorants, 384–5 semi-permanent hair colorants, 385 temporary hair colorants/rinses, 385 vegetable hair dyes, 386 background, 371–3 effect of environment, 382–3 grey hair and age, 379–82 hair follicle melanocytes during hair cycle, 375–8 catagen, 377–8 early anagen, 377 full anagen, 377 telogen, 375 human anagen scalp hair follicle fully-pigmented, 376 pigmented and greying, 374 measurement, 371–88 future trends, 388 methods and instruments, 386–8 natural, 373–9 hair follicle pigmentary unit development, 373–5

© Woodhead Publishing Limited, 2010

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Index

regulation of hair follicle pigmentation, 378–9 henna, 386 heterochromia, 380 hidden, 128 hiding power, 290–1 calculation, 291 measurement for white vs coloured paints, 290–1 non-coloured paints, 290 hierarchical agglomerative clustering technique, 175 High Volume Instrument system, 253 Hopfield network, 134 hue, 52–3, 224, 323 hue difference, 225 human visual system, 3–6, 314 eye structure, 4 Hunter bench colorimeter, 327 Hunter Colorimeter (1950), 253, 275 Hunter formulae, 102–3 Hunter Lab colour space, 82, 239, 243, 320 Hunt’s model, 317 HVI see High Volume Instrument system HVI colorimeter, 254–5, 276 ICI Colour Atlas, 25–6 ICS Micro Match spectrophotometer, 330, 336 imaging colorimeters, 162 imaging spectrographs, 162 indo dye, 384 input units, 128 Inside/outside Bleaching, 360 insolubility, 197 Instrument 100, 188 Instrument 500, 188, 189 instrument calibration, 237 instrument metamerism, 226 instrument uncertainty, 186 instrument uncertainty conditions, 186 instrumental measurement visual evaluation of whiteness and yellowness, 88–117 application in industry, cosmetics and dentistry, 111–15 assessment of whiteness, 90–4 future trends, 115–17 introduction, 88–90 measuring techniques and instruments, 95–100 whiteness and yellowness indices, 100–11 inter-instrument agreement, 238 Inter-Society Colour Council Subcommittee, 91 interactive, 128 Internal Non-Vital power Bleaching, 360 International Organisation for Standardisation, 200 intrinsic tooth discoloration, 348–9 IR drying technique, 292 ISO 105, 203 ISO 12232:2008, 154 ISO 14524:2009, 154 ISO 15739:2003, 154 ISO 17025, 185 ISO 17321:2006, 154 ISO 22028:2006, 154

ISO 17321–2, 154 ISO 105-A02, 208 ISO 105-A03, 208 ISO 105-B01, 205 ISO 105-B02, 205 ISO 105-B06, 205 ISO 105-B08, 205 ISO 105-C06, 206 ISO 105-C08, 206 ISO 105-D01, 206 ISO 105-E01, 204 ISO 105-E02, 204 ISO 105-E03, 204 ISO 105-E04, 204 ISO 105-E05, 204 ISO 105-E07, 204 ISO 105-E08, 204 ISO 105-E09, 203, 204 ISO 105-E10, 203 ISO 105-E11, 203 ISO 105-E12, 203 ISO 105-E13, 203 ISO 105-G02, 204 ISO 105-G03, 204 ISO 105-G04, 204 ISO 105-J05, 212 ISO 105-N05, 203 ISO 105 N01–4, 203 ISO 105 P01, 203 ISO 105-P01, 203, 207 ISO 105-P02, 203 ISO 105-X01, 203 ISO 105-X02, 203 ISO 105-X04, 203 ISO 105-X06, 203 ISO 105-X08, 203 ISO 105-X11, 203 ISO 105-X12, 204 ISO 105-X16, 204 ISO Blue Wool standards, 205 ISO/CD 105-A11, 216 ISO/FDIS 105-B07, 206 ISO/TR 12116:2008, 207 Jade Green Standard, 287 JPC79 formula, 283, 325 JVCKY-F55B, 259 K-means clustering, 178–80 K/S constant ratios, 334 knitted sleeves, 239 Konica, 246 Kubelka-Munk equation, 191, 247, 296 Kubelka-Munk theory, 126, 136, 141, 286, 309, 330–1 lateral geniculate nuclei, 5–6 learning process, 129 LEDs, 163 lens, 4 levelness, 186 levelness uncertainty, 186 levelness uncertainty conditions, 186 light adaptation, 317 light fastness, 205–6 light spotter, 257 lightness, 224, 323

© Woodhead Publishing Limited, 2010

Index Lobene stain index, 357 loops, 128 Lovibond Tintometer, 325 luminance adaptation model, 155 luminance factor Y, 100 MA98 multi-angle spectrophotometer, 308 MacAdam limits, 43 Macbeth 7000, 193 Macbeth colour checker chart, 29 Macbeth D65 fluorescent lamps, 92 machine learning, 129 macula lutea, 5 Marks & Spencer, 235 Maxwell Colour Triangle, 76 MDC1R gene see melanocortin-1 receptor mean distance, 178 Mean Plus algorithm, 194 measurement system, 186 Melanin Index, 387 melanins, 372 melanocortin-1 receptor, 373 melanocyte, 372 melanogenesis, 372, 378–9 melanogenic zone, 377 melanosomes, 372 metamerism, 155, 158, 226–31, 293 degree of illuminant metamerism, 227 instrumental test, 229–31 metameric match, 227–8 matched in daylight but not in artificial light, 227 not a reflectance match, 228 non-metameric match, 227–8 dark grey fabric, 229 fawn colour fabric, 228 with two crossings, 229 reducing effects, 231 Metamerism Index, 293 microabrasion, 361 milling acid dyes, 203 Minimum Path, 181 Minolta, 246 Minolta CF 1440, 246, 299 Minolta colorimeter CR-210, 257, 262 monochrome camera, 156 Motion Control HVI, 262 M&S 89, 235 M&S colour difference equation, 235 multi-dimension thresholding algorithm, 261 multi-spectral capture systems, 155, 160 covering VIS and NIR radiation for textile applications, 160–2 reflectance spectra, 161 reconstruction of spectral reflectances from multi-channel colour values, 155–7 image capturing systems classification, 156 transmittances with acquisition channels of multi-spectral system, 157 multilayer perceptrons, 130, 133, 264 Munsell Book of Colours, 71 Munsell colours order systems, 31–7, 70–1, 222, 313, 323, 325 limitations, 34–5 SCOTDIC colour atlas, 35–7 Munsell neutral scale, 71

399

National Cotton Council Quality Task Force Committee, 255 natural colour system, 38–41 Nescafé Gold Blend, 334 neural network, 263–8 neurodes, 128 neuron function, 130 neurons, 128 Nickerson-Hunter colour diagram, 254, 255, 268, 276 nodes, 130 non-contact spectrophotometers, 246, 299 non-metamerism, 293 non-vital tooth bleaching, 360 object metamerism, 226 observer metamerism, 30, 226 Oeko-tex 100 certification, 199 on-line colour measurement, 243–6 opacity, 290 opalescence effects, 346 operator uncertainty, 186 operator uncertainty conditions, 186 opponent colour theory, 39 optimal colour stimuli theory, 43 optoelectronic sensor, 149 Orintex, 243 Orintex software, 243 OSA-UCS system, 45–7 cubo-octahedron, 46 Ostwald system, 41–3 paint films and coatings colour control system, 297–9 colour measuring instruments, 297–8 instrument metamerism problems, 299 instrument performance, 298 today’s colour measuring instruments, 298 colour difference in FMC and CIELAB unit, 294 colour matching of automotive paints, 307–9 metallic and pearlescent colours, 307 portable multi-angle spectrophotometer, 307–9 colour measurement, 279–309 applications, 295 future trends, 309 sample preparation, 291–2 computer colour matching for paints, 295–7 instant colour matching at the paint shop, 300–7 conventional CCM vs Internet based POS, 306–7 Internet based at Point of Sales, 302–6 paint quality control, 280–91 CIE 1976 colour space, 281 CIE L, C, h colour space, 281–2 CIE L C h tolerance limits, 283 CIE Lab colour space and CIE L, C, h colour space, 281 CIELAB colour co-ordinates, 282 CIELAB vs CIE LCh, 284 colour as numbers, 280 colour strength, 286–7 colour tolerance, 282–5 FMC-II acceptance tolerance limits, 285

© Woodhead Publishing Limited, 2010

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Index

opacity, contrast ratio and hiding power, 290–1 pigment load, 291 tristimulus values computation, 280 white pigments tinting strength, 287–8 whiteness and yellowness indices, 288–90 pigment quality control, 292–5 application techniques, 292–3 pigment evaluation, 294 pigment testing and making colorant primaries, 293–4 uncertainties in sample preparation, 295 wet paints colour properties measurement, 299–300 non-contact spectrophotometer, 299 wet-paint colour matching, 300 Pantone colour system, 26 pellicle, 345 periodontium, 344 permanent hair colorants, 384–5 pheomelanins, 386 pheomelanosome, 378 Philips 83 lamps, 336 pigment, 197 pigment load, 291 PocketSpec Spectrophotometer, 301 Point of Sales system, 300 instant colour matching, 300–7 Internet-based colour matching, 302–6 access credentials, 303 Benevue Colour Management System colour reader, 303 book formula, 304 closest colour, 304 tint by name, 305 tint by reading, 306 unit ID, 305 low cost spectrophotometer, 301 polyester fabrics, 247–8 polyester yarns, 133 Pope colour system, 50 portable multi-angle spectrophotometer, 307–9 Minolta and X-Rite multi-angle instruments, 308 Minolta instrument, 308–9 POS see Point of Sales system practical colour coordinate system, 37 principal component analysis, 156 Procion Brillian Red HE 7B, 140 protanomaly, 13 protanopia, 13, 15 pseudo-colour visualisation system, 161 psychometric scales, 28–9 interval, 28–9 nominal, 28 ordinal, 28 quantitative light-induced fluorescence, 357 RAL system, 49 ratio scales, 29 raw error, 131 recurrent neural networks, 128 Red-Green-Blue colour space, 356, 388 reflectance, 79–80 reflectance values, 133, 142 reflective spectrophotometry, 386–7

refurbishment, 206–7 retina, 4 saliva, 345 saturation, 224, 323 scaling, 273 sclerotic coat, 4 SCOTDIC colour atlas, 35–7 constant hue loci in CIELAB space, 36 Scotsort method, 177 semi-permanent hair colorants, 385 sensitivity, 238 sensor, 149 severe trauma, 349 shade cards, 21–2 shade number see sort code shade sequencing, 180–2 shade sorting see colour shade sorting Shade Vision, 114 ShadePilot, 114 SheLyn Inc., 182 sigmoid function, 132 sigmoid units, 132 single number shade passing, 283, 285 Smith and Pokorny fundamentals, 7 sort code, 171 source metamerism, 226 spatial characterisation, 152 spatial-chromatic dithering, 158 effects on visual appearance and measurement, 158–60 spatio-chromatic dithering in imaging capture process, 159 spatial-CIELAB, 160 SPD see spectral power distribution spectral characterisation, 152 spectral match, 230 spectral power distribution, 73, 90, 113 spectral radiance factor, 137 SpectraProbe XE, 245–6 spectrophotometers, 133, 153, 158, 297, 326, 357 spectroradiometers, 158 SpectroShade, 114 specular excluded mode, 293 Spinlab HVI, 262 Stearn’s method, 79 Stensby formulae, 103 strand testing, 384 subtractive colour mixing, 196–7 Super CCD, 162 supervised learning, 129 Swedish natural colour space system, 313, 325 SYSTAT, 178 Taube formulae, 103 TC38, 200, 212 TCI/81, 215 tele-colorimeters, 150, 159 tele-spectrocolorimeter, 159 TeleFlash 130, 246, 299 TeleFlash 445, 299 Tethered spectrophotometer, 301 tetracycline, 349 textiles, 197 colour as numbers, 222–4

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Index chromatic attributes, 223 colour and appearance, 223 colour vs appearance, 223 geometric attributes, 223 colour inspection of finished fabrics, 248–50 enzyme wash details, 249 finishing effect on textile colour data, 250 colour measurement methods, 221–51 future trends, 250–1 metamerism, 226–31 why colours do not match, 231–2 colour measurement techniques, 236–43 fibre, yarn, fabric and knitted sleeves, 238–9 instrumental considerations, 237–8 Minolta non-contact spectrophotometer, 241 parameters for instruments, 236–7 sample holders, 240 sample preparation method, 239–41 yarn orientation effect on values, 240 colour specification, 224–6 CIE Lab colour space, 224–5 CIE LCh colour space, 225 CMC colour difference and tolerance, 226 dry and wet fabrics, 247–8 reflectance spectra, 248 on-line colour measurement, 243–6 centre-selvage, 244 Hunterlab SpectraProbe XE, 245–6 map control, 245 map control system, 244–5 non-contact spectrophotometers, 246 production equipment, 243 sample presentation method, 242–3 developing a repeatable technique, 242–3 measurement technique, 242 sample positioning, 242 sample thickness, 242 visual vs numerical pass/fail, 232–6 CIELAB colour differences, 233–4 colour difference equation, 234–5 DE acceptable limits, 234 M&S equation, 235 setting the pass/fail value, 233 tips for instrumental pass/fail, 235–6 thermochromism, 238 tinting strength, 287–8 tooth colorimetric evaluation, 343–62 future trends, 361–2 optical properties of teeth, 345–6 tooth whiteness, 351–2 colour, 347–8 distribution, 347–8 variation, 347 colour measurement, 352–6 digital image analysis techniques, 355–6 instrumental measurement, 354–5 visual assessment, 353–4 factors that impact tooth colour and its perception, 348–52 extrinsic tooth stain, 349–50 intrinsic tooth discoloration, 348–9 other factors, 350 tooth colour perception, 350–1

401

human dentition and its environment, 344–5 saliva and pellicle, 345 teeth and gums, 344–5 tooth anatomy, 344 measurement of extrinsic stain, 356–7 clinical indices, 356–7 instrumental methods, 357 methods to improve tooth colour, 357–61 microabrasion, 361 non-vital tooth bleaching, 360 vital tooth bleaching, 359–60 whitening toothpastes, 358–9 tooth enamel, 344 tooth whiteness, 351–2 total radiance factor, 95 traditional whiteness formulae, 102–4 coefficient in the Berger whiteness formulae, 103 hue preference comparison, 104 training set, 134 translucency blurring, 194 TRF see total radiance factor trichromatic cameras, 163 trichromatic colorimeters, 325–6 trichromatic theory, 6–7, 319 trichromatic vision, 387 tristimulus, 280 tristimulus match, 297 tristimulus system, 285 tristimulus values, 77–80 tritanomaly, 14 tritanopia, 13, 15 truncation, 273 Turbid-Medium theory, 296 Two Constant Theory, 297 ultraviolet light, 382–3 uncertainty analysis, 185–6 determination, 184 relationship of parameters, 187 uncertainty values in units of the value itself single baby blue smooth glossy plastic plaque for Instrument 100, 189 single baby blue smooth glossy plastic plaque for Instrument 500, 189 single yellow smooth glossy plastic plaque for Instrument 100, 188 single yellow smooth glossy plastic plaque for Instrument 500, 189 universal colour language, 61–2 universal colour names, centroid colours and hue angle boundaries, 63 Universal Cotton Standards, 255 US cotton classing system, 276 USDA cotton colour grades, 253–77 USDA Cotton Program, 255, 268 USDA universal standards, 253 VeriColour Solo, 246 VeriColour Spectro, 246 VeriColour spectrophotometers, 299 VeriColour System, 246 video image analysis, 339 vinyl sulphone dyes, 139, 140 visual evaluation

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1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 20 1 2 3 4 5 6 7 8 9 30 1 2 3 4 5 6 7 8 9 40 1 2 43X

402 1 2 3 4 5 6 7 8 9 10 1 2

1 2 3 4 5 6 7 8 9 40 1 2 43X

Index

instrumental measurement for whiteness and yellowness, 88–117 application in industry, cosmetics and dentistry, 111–15 assessment of whiteness, 90–4 future trends, 115–17 introduction, 88–90 limits of white objects and optimal colours, 89 measuring techniques and instruments, 95–100 spectral reflectance factor measurements, 89 whiteness and yellowness indices, 100–11 visual perception, 4–5 visual sequencing, 180 visual tapering method, 180 Vita Bleachedguide 3D-Master, 354 Vita Lumin Shade Guide, 351, 353 vital tooth bleaching, 359–60 Vitapan 3D-Master Shade Guide, 351, 354 Vivitar Electronic Flash 1900, 259–60 Von Kries coefficient law, 16 Walking Bleach Technique, 360 water base paints, 293 wavelength calibration, 237 Weber and Fechner law, 43 wet fabrics, 247–8 whiteness application in industry, cosmetics and dentistry, 111–15 cosmetics, 114 dentistry, 114–15 food, 113 leather, 113 paper and pulp, 112–13 textiles, 111–12 future trends, 115–17 new whiteness formulae, 115–16 open question in instrumental evaluation, 116–17 measuring techniques and instruments, 95–100 CIE WI of textile samples, 99 Gaertner-Griesser UV calibrating device, 97

practical daylight simulators, 96–8 reflectance curve, 99 total radiance factor, 95 TRF of non-fluorescent vs fluorescent white textile, 96 UV radiation levels, 98 white vs near-white samples, 98–100 visual and instrumental evaluation, 88–117 illumination effect, 91–3 practical daylight simulators, 93–4 sorting and ranking methods, 94 whiteness and yellowness indices, 100–11 brightness function, 101 CIE whiteness and tint formulae, 106–8 colour difference, 104 Ganz-Griesser method, 108–10 Ganz linear whiteness and tint formulae, 104–6 luminance factor Y, 100 paper brightness, 100–2 SPD for light source between CIE illuminants A and C, 101 traditional whiteness formulae, 102–4 whiteness index, 100–11, 288–9 whitening toothpastes, 358–9 WI E313, 288 X-Rite, 243, 246, 299 XYZ values see tristimulus values yarn, 238–9 yellowness application in industry, cosmetics and dentistry, 111–15 cosmetics, 114 dentistry, 114–15 food, 113 leather, 113 paper and pulp, 112–13 textiles, 111–12 visual and instrumental evaluation, 88–117 whiteness and yellowness indices, 100–11 coefficients of the ASTM E 313–05, 111 data points of the 18 Gardner filters, 110 Gardner scale value, 111 yellowness indices, 110–11 yellowness index, 100–11, 289–90

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