IJCST 21,5. Received 2 December 2008 Revised 18 February 2009 Accepted 18 February 2009

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1 The current issue and full text archive of this journal is available at wwwemeraldinsightcom/ htm IJCST 21,5 256 Received 2 December 2008 Revised 18 February 2009 Accepted 18 February 2009 International Journal of Clothing Science and Technology Vol 21 No 5, 2009 pp q Emerald Group Publishing Limited DOI / Key variables in the control of color in the textile supply chain Lina Maria Cárdenas, Renzo Shamey and David Hinks Department of Textile Engineering, Chemistry, and Science, North Carolina State University, Raleigh, North Carolina, USA Abstract Purpose The purpose of this paper is to address the key variables that determine the level of control of color in a typical textile supply chain, including lighting variability, color perception, and color measurement Design/methodology/approach A fishbone diagram is used to demonstrate the wide range of variables that affect the control and communication of color within the textile supply chain Findings It is important to identify the important parameters and variables that influence the control of color within various stages of the textile supply chain In regard to visual assessment variability, the results obtained in an ongoing study at North Carolina State University based on the psychophysical testing of 50 observers demonstrate a statistical difference for visual judgments of small color differences between naïve and expert observers Results of a paired t-test between the second and the third trial conducted by naïve observers indicate that the repetition of the visual observations significantly affects the assessment of small color differences Research limitations/implications Assessment of lighting measurements of several stores in the USA demonstrate variability in lighting, with many stores having at least two different light sources This variability, in combination with uncontrolled lighting from external windows and entrance/exit areas, can lead to significant variability in the color perception of textile garments displayed in such areas, and may lead to consumer experience being significantly different from that intended by the designer Practical implications The optimization of variables that influence the assessment and communication of color is vital to achieving effective communication between all parties involved This can significantly reduce costs and lead times resulting in improved competitiveness and cost efficiency associated with increased consumer satisfaction and confidence in the industry Originality/value The repetition of visual observations significantly affects the assessment of small color differences Keywords Colours technology, Textiles, Supply chain management Paper type Research paper 1 Introduction In the textile industry, efficient color control and communication between designer, dyer, and retailer are critical to obtaining high quality and cost efficiency For instance, The authors acknowledge the National Textile Center (Grant No , C04-NS11) for financial support of this work The authors also thank Mr John Darsey and colleagues at DyStar for generation of the dyed fabric samples, Kashif Noor for his spectroradiometric analysis of area lighting in a retail store, Professors Nancy Cassill and Richard Aspland for their valuable comments, and Professors Roger Woodard, Rolf Kuehni, and Warren Jasper for helpful discussions The authors would also like to thank those US retail stores that facilitated store lighting measurements and to all observers who participated in this paper

2 variability in lighting may mean that the color of a product in a store is perceived significantly differently from that originally intended by the designer, despite a high level of color and lighting control during the design, and production of the product Color communication throughout the supply chain is a dynamic process, and attempts at optimizing color control must focus on the variability that arises due to the complex interaction between supplier and consumer Digital color communication is emerging as an effective path to process optimization, especially in reducing time and costs However, this approach requires optimization of numerical models and a clear understanding of the scope and limitations of the assessment methodology employed The primary objective of this communication is to illustrate to all participants in the textile supply chain the most important considerations when attempting to improve efficiency of color control in textile product development Key variables in the control of color Key variables in color control In color communication, a significant array of variables exist that need to be clearly identified understood and ultimately minimized A practical approach to understanding the complexity of the number of variables in color control is the use of a fishbone diagram or cause-and-effect diagram The fishbone diagram is a tool to identify the potential or real causes that contribute to a single outcome (Graystone, 2000; Eckes, 2003) The causes are organized in order of significance creating a hierarchical structure in relation to the outcome The variability in the control of color through the supply chain can be broken down into the following broad categories: concept; human factor; manufacturing; color quality control; and point-of-sale Figure 1, developed as a part of a large color control study at North Carolina State University, shows some of the most important causes of variability in the control and communication of color within the textile supply chain The figure incorporates various components (Parrot, 2001; Butts, 2007; Sanger, 2007) that affect the communication and control of color The illustration of all or even the majority of variables would make such diagrams very complex, as exemplified by a comprehensive study of all the variables of one batch dyeing process (which may form one small part of the supply chain fishbone diagram shown in Figure 1) (Koksal et al, 1992) However, a less complex design can be used as a framework to evaluate the significance of procedures and to indicate stages within the supply chain where critical problems may have been overlooked Where necessary, a complete fishbone diagram can be developed for each sub-factor 21 Concept Arguably, a successful design that considers not only consumer needs, but also production requirements and constraints, can lead to significant gains in the production, handling, and retail merchandising of a product Color plays a vital role in almost all industries, but especially the retail textile industry Thus, the choice of color

3 IJCST 21,5 258 Figure 1 Variables in the control of color in the textile supply chain Calibration & specification Type of Sample preparation instrument Geometry Instrumental Assessment Accuracy & repeatability Software & equations Non Store Paper Viewing conditions Color quality control Environmental Accuracy & conditions repeatability Material type Catalog Television Resolution Color production system Design Monitor Browser Print quality Point of sale Tolerances Internet Human factor Tolerances Color definition & color difference Calibration & settings Color gamut Design Display & surrounds Handling & storage Manufacturing Visual assessment Geometry Booth & specifications Bulk production (batch & continuous) See lab Floor plan Store lighting Rotation of samples Concept Machinery Substrate Control parameters Dyeing Finishing See lab trials See lab trials trials Printing Preparation Lab trials Recipes Dyes and pigments Digital Atlases data Age Gender Inherited color deficiency Human factor Standards Substrate Specifications Color management system CAD Materials Technical specifications Software Emotions Training Psychological factors Past experience Culture Hardware Equipment Color production system Original design Trends Human Factor Market Market place & trends Customer QC personnel Designer Observers Surround Adaptation mechanisms Operator & manufacturer Viewing conditions Acquired color deficiency Fatigue Appearance Lighting Physiological phenomena Cost Key variables in the control of color in the supply chain at the point of concept must correspond with production capabilities, as well as consumer needs and trends (Sanger, 2007) The selection of colors in a design initially involves careful planning and assessment of the components of the collection by the design team Every piece in the collection must complement the others, and most importantly, the components need to represent the brand Designers gain inspiration for their ideas, including color, from different sources such as forecast reports, seasonal trends, cultural background and of course, personal creativity and experience A theme for inspiration can be an exotic place, a range of culturally based colors such as colors of Africa, a period of time, art, etc The approach taken by each designer for the generation of the collection is often unique, but the main goal is often to ensure the timely generation of a new perspective and experience for the customer (Sanger, 2007) Another important factor in the selection and generation of a design is the end-use application and the overall cost of the product In many cases, designers need to develop products that require matching colors on several substrates thus adding further variability, complexity and cost to the communication and reproduction of color (Koksal, 1992; Sanger, 2007) The use of technology for the development of patterns and garments has had a profound impact on the reproduction of color in recent years and statements such as what you see is what you get have often been used (Mahale and Townsend, 2007) However, communication of color ideas between designers and manufacturers can be frustrating for both parties and matching the desired color attributes is often a challenge The representation of the original design color in a digital format is bounded within the limits of production color gamut for the medium of choice Textile and fashion design software, whether off the shelf or proprietary, come with a variety of color options Computer-aided design (CAD) tools, monitors, scanners, and desktop printers also have a range of colors each with their own device dependant color gamut,

4 thus limiting the range of overall production colors In addition, despite significant research to overcome variations there are inherent differences in the technology used to reproduce a color on-screen (based on red, green, and blue emission signals) compared to that used for the generation of a physical sample (based on cyan, yellow, magenta, and black primaries) Furthermore, the on-screen color produced with CAD systems can vary from monitor to monitor and printed samples can also vary according to the type of device/printer or substrate used (eg paper, cotton, nylon, or polyester) (Mahale and Townsend, 2007) Recent developments in color communication include the introduction of calibration devices (Tippet, 2005) that allow the use of more accurately reproduced digital palettes potentially resulting in better communication of color between two or more parties in the supply chain However, to date, there is no standard methodology to ensure the communication is consistent and optimized This troublesome problem in the control of color in a textile supply chain stems from differences in perspective, understanding, and communicating the language of color between a designer, dyer, retailer, and consumer Therefore, all those involved in the decision-making process within the supply chain should receive appropriate training on a continuous basis and should develop and utilize an agreed upon (internally standardized) communication protocol One example of communication breakdown is the use of color difference terms used by the designer and dyer when a modification to a dyed sample is required Recently, Wardman et al (2006) reported a promising approach to define dyers terms of depth, brightness, and hue This approach is currently a new work item within an International Organization for Standardization (ISO) (ISO TC 38/SCI, 2007) color measurement committee, and may lead to a useful standard communication protocol that relates descriptive color terms to measured attributes of color Key variables in the control of color Manufacturing Reproduction of a color according to specific criteria that matches a target (standard) color is one of the most challenging aspects in the control of colored products within the textile supply chain Despite technological advances, interpreting a color which may be based on the conceptual inspirations of a designer, who may or may not be familiar with technical limitations in the production of textiles, is not an easy task (Sanger, 2007; Strickland, 2007) The communication of digital color data between a designer and a dyer and the reproduction of a target color based on such communications is dependant on both parties using the same color language, as well as the same set of standard methods, including calibrated instruments for measurement and standardized viewing and lighting conditions The optimized control of the myriad variables often requires complex control models In addition, matching the color of two or more substrates according to the specifications of a textile designer can be difficult or in some cases impossible, depending on the limitations of the available colorants Often, the primary goal of the dyer or printer is to obtain a match to a specified color (the standard) that is as close and as inexpensive as possible The match must be within a specified tolerance, be cost effective, and the finished product must meet technical requirements such as wash, rub, and light fastness (Koksal, 1992; McDonald, 1997) A further requirement that is now becoming of greater significance to many in the supply chain is a reduction in color inconstancy in which the color of an object appears to change due to a change in the lighting used (Luo et al, 2003) Of particular

5 IJCST 21,5 260 importance is, of course, the color inconstancy of the object under the lighting conditions intended for use and the lighting conditions under which the product is to be displayed to the consumer Color inconstancy can be predicted using a color inconstancy index that was recently standardized by ISO 105-J05 (2007) In addition, the production of color standards in the laboratory under specified conditions is also usually significantly different from the bulk color production of substrates, even under identical temperature, liquor flow, ph, and other conditions This is one of the main sources of variation in the reproducibility of colors in a production setting, and a satisfactory comprehensive solution to resolving these issues in a consistent manner has yet to be put forward in many cases (Koksal, 1992) In regard to color standards, further complication often arises as the dyer often selects the first production batch that is accepted by the customer as the standard used to match future dyeings for that particular style Hence, in a supply chain several pseudo standards may be used for a given target color, unbeknownst to the retailer 23 Color quality control Accurate color quality control is key to reducing lead times in delivering the final colored product As previously stated, control in the textile industry can be carried out both visually and instrumentally Visual color assessment requires the preparation and standardized presentation and viewing of physical samples, which inevitably involves subjective judgments by trained observers (American Association of Textile Chemists and Colorists (AATCC), 1986) Moreover, physical standard samples or production samples often have to be sent between supplier (manufacturer) and customer (eg retailer) for approval and feedback, which introduces significant time delay and increases production costs Also, the storage and handling of standard samples over prolonged periods of time can result in a significant change of color, which in turn can lead to potentially erroneous judgments Hence, considerable reproduction issues can result, particularly considering that many retailers today involve multiple suppliers for the production of the same components of a particular product Therefore, digital color communication has attracted a considerable amount of attention by all parties involved in the textile supply chain, with a view to minimizing or eliminating some of these variables It should be noted, however, that instrumental assessment of samples also requires specification of all variables including measurement conditions using calibrated instruments such as spectrophotometers Moreover, the models used to predict color differences in use today are not optimized Again, standardization of methodologies used by various sectors is necessary to ensure that all parties use the same color language when communicating within the supply chain (Connelly, 1997; Laidlaw, 1997; Butts, 2007) It is evident that in order to create an optimized digital color communication system, all variables that contribute to the change in color of substrates should be identified and minimized 231 Lighting variability In a retail store when consumers inspect the color of a commercial product, the spectral power distribution (SPD) of the light source where the product is located may differ significantly from the standard illuminant(s) that was used in the original color specification Variability in lighting may mean that the color of a product in a store is perceived significantly differently from that originally intended by the designer, despite a high level of color control during the design and production of the product Also, the background viewing environment may have

6 a significant effect on the perceived color Possible ways in which variability in lighting may occur include: incorrect lamp installed; mixing different lamps together in the same region of space (eg fluorescent and incandescent); variability in lamp emission; light pollution, eg from entrance/exit areas, as well as exterior windows; and use of strongly colored surfaces surrounding the product Key variables in the control of color 261 No standardized method exists for the spectroradiometric measurement of lighting in a retail store such that the spectral data can be used as custom illuminant data for the calculation of color differences of products displayed in the store The SPD of key areas in more than 12 stores that sell the products of leading US retail companies were measured in a previous study to ascertain the level of variability within typical stores in the USA, to obtain inter-store variability, and also to compare the area lighting to standard illuminant data used in colorimetric calculations (Hinks et al, 2000, 2001; Noor, 2003) In addition, the lighting variability in standard viewing booths currently in use in various sectors of the textile supply chain, including design, color standard development, dyeing and finishing quality control, and retail color management has also been measured to evaluate the potential need and feasibility of the following developments (Hinks et al, 2000): new standard illuminants that more accurately reflect real lighting conditions; new standard light sources used in standard viewing booths that more accurately reflect real lighting conditions; and a set of recommendations to retailers on improving lighting to optimize the color appearance of textile materials A standard method of area lighting measurement has been proposed (Hinks et al, 2001) The control of lighting in retail stores must be an integral part of the color quality control within the textile supply chain In fact, without an integrated and well-controlled color management process, the variability in communication and production of color would likely be high, leading to long, lead times, and higher than necessary production costs 24 Point-of-sale Figure 1 shows some of the variables that influence color control at the point-of-sale The colors of commercial products are never seen in isolation by the consumer The viewing conditions of colored samples have a tremendous impact on the perception of colored products and phenomena such as simultaneous contrast affect each individual s color experience The effect of simultaneous contrast on perceived color of textiles is well documented (Fairchild, 2005; Kuehni, 2005) The lighting used to display products in a retail store also critically affects the perceived color of multi-component products, which typically involve varying levels of color inconstancy and metamerism Importantly, as already pointed out research indicates that many US retail chain stores employ varied and uncontrolled lighting conditions for the display of

7 IJCST 21,5 262 their merchandise Also, store designers may change the lighting design of the floor due to seasonal or other design/managerial specifications (Noor, 2003) Such variations, which may result in a significant change of perceived product color compared to the original design, are likely to be seen in many retail stores However, several large retail stores have been found to control their lighting precisely, both within different areas of a store and between stores in a retail chain By way of example of poor lighting control, the variability in SPD of light sources used and measured at various locations of store A that contained merchandize of the retailer along with many other brands is shown in Figure 2 From the SPD data for this store, it is seen that two types of lighting are used, namely fluorescent sources in combination with incandescent lamps Figure 3 shows the variability in illuminance (lx) at the measured locations in the store Clearly, since the brightness in the store can vary between approximately 250 lx and over 1,000 lx, considerable variability exists that could impact significantly the viewing experience of a consumer For instance, the details of very dark samples viewed under low-illuminance conditions (eg 300 lx) will not be easily discerned In addition, an analysis of color inconstancy for a pair of blue metameric textile samples in different parts of the store based on the CIE (2004) color inconstancy index, D 65 as the reference illuminant, and the SPD of the lighting in a particular location of the store as the test illuminant is shown in Figure 4 The blue standard used was considerably color inconstant under standard illuminants, and although Figure 4 shows a high-color inconstancy index value for all locations in the store, the value is relatively consistent, ranging from 32 to 57 It would be valuable to further determine the significance of the color inconstancy index values 2,000 1,800 1,600 1,400 1,200 SPD 1, Figure 2 Normalized SPD data for measurements taken at store A Wavelength

8 Changing rooms Check out counter Illumination levels Indoor mall lighting , Key variables in the control of color Indoor store lighting Figure 3 Illuminance values for the various locations of store A Color inconstancy Changing rooms Indoor mall lighting Check out counter Indoor store lighting Figure 4 Color inconstancy index for standard blue metamer for store A on variability in visual color difference assessment; and establish a color inconstancy acceptability tolerance for a typical commercial product In addition to the above factors, increasing use of non-store shopping including catalogue as well as the internet and television shopping has introduced different color control and communication issues Among some of the important issues include accurate color reproduction via paper printing, resolution and size of color images,

9 IJCST 21,5 264 monitor type, monitor color gamut and calibration, potential to control background viewing conditions, and metamerism (Kuehni, 2005; Butts, 2007) While the variables in the control of color for these media may be entirely different compared to those in a store, they have to be identified and controlled to ensure that the consumer experiences closely what the product designer intended 25 Human factor The human factor is arguably the most fundamental aspect in the fishbone diagram shown in Figure 1 Within the textile supply chain, there are many stages where decisions and processes rely on individual subjective skills and performances Even with standardized procedures, humans often perform and make subjective assessments differently Such high levels of variability lead to poor or inconsistent decisions and interpretations within the supply chain For instance, although the light reflecting properties of objects are physically quantifiable, color is a human experience that can never be defined in absolute terms (Berns, 2000; Kuehni, 2005) Moreover, practically all factors shown in Figure 1 are affected, to certain extent, by the human factor, which in turn leads to variations in the assessment of colored materials Some of the factors that influence color decisions made by observers include psychological and environmental factors, age, gender, past experience in handling color-related decisions, as well as viewing conditions A good example of the impact of the human factor in color control and communication is the visual assessment of color Evidently, all individuals responsible for color assessment must first be tested for normal color vision (Ishihara, 1917; Farnsworth, 1943; Neitz, 2001) Yet, not all color-related companies test employees for color vision, and there have been cases of color assessors in industry that had defective color vision The evaluation of color is also subject to individual s perception of color as well as various cognitive aspects Perceptual aspects of the evaluation of color include the immediate recording in the brain of the stimuli in terms of lightness, hue, and chroma, while the cognitive aspects include later processing of the information which include color memory, color meaning, etc (Gao and Xin, 2006) In that sense, a color normal observer s experience is a response of the sensory system that is impossible to define Moreover, certain aspects of color psychology, such as color preference, can influence the assessment of color and bias individual as well as group differences Personality, gender, race, and age have been widely investigated as a group response in relation to the evaluation of color Whitfield and Whiltshire (1990) evaluated studies of color preference as a function of individual and group differences and state that color preference and aesthetic values are strongly influenced by cultural differences Diagnostic tests such as Lüsher and Rorschach tests have also been developed to assess the relationship between color responses and personality variables In terms of age, studies carried out by Gale (1933), Granger (1955) and Staples and Walton (1933) suggested that young children have different color preferences than adults, but the influence of age on color is somewhat ambiguous (Whitfield and Whiltshire, 1990) Moreover, the optimization of digital color communication involves high levels of technical capability and expert technical knowledge at each stage of the supply chain At the heart of color control are mathematical models that correlate visual assessment of color to measured values In order to achieve efficacy in digital communication,

10 the scope and limitations of these models, as well as the myriad variables that influence this relationship, should be elucidated 251 Issues surrounding visual assessments Arguably, the most important factor within the textile supply chain is the accurate assessment of color differences between two textile materials Optimization of the correlation between the visual assessment of color and mathematical models that predict color differences is fundamental to any digital system of color management A number of visual methods of assessing color differences between two textile samples have been developed (CIE, 2001; Luo et al, 2001; Aspland and Shanbhag, 2004; Gay and Hirschler, 2003) In an ongoing study at North Carolina State University, the sources and extent of variability in visual assessment of color differences of textile samples is being studied (Hinks et al, 2006) The goal is to optimize the experimental methodology and establish the minimum repeatable variability possible among a statistically significant set of visual observers under highly controlled conditions of observation In this study, color difference assessments are being carried out using a Macbeth Spectralight III viewing booth equipped with a filtered tungsten artificial daylight simulator About 50 color normal observers were used in the first stage of the study and a total of 3,100 assessments were obtained using the AATCC (1986) gray scale for change in shade based on 31 textile samples Sample pairs were produced on 100 percent polyester knitted fabric and contained small color differences in lightness, chroma, and hue The gray scale consists of nine pairs ranging from 1 to 5 in half steps A ranking of five represents no perceived difference between the trial and the standard (AATCC, 1986) Of the observers selected for the study 25 were naïve observers tested for normal color vision using the Neitz (2001) test that had no prior knowledge of commercial pass/fail color difference assessments, while 25 were expert observers whose employment involved, or has involved, commercial shade matching in the textile industry Naïve observers were mostly students of North Carolina State University and included 11 females and 14 males ranging from 18 to 25 years of age The 25 expert observers who were mostly industrial colorists from the US textile industry included 10 females and 15 males and ranged from 25 to 70 years of age The observers were adapted to the illumination conditions by observing the illuminated viewing booth for two minutes and were then presented with textile samples with small color differences compared to a standard and asked to determine the perceived color difference of samples using the gray scale Naïve observers repeated the assessment three times However, due to constraints in availability and geographic location of the test expert observers assessed samples one time Figure 5 shows the conditions employed during visual assessments The average grey scale rating for each pair was compared for naïve and expert assessors Tables I and II show the preliminary analysis of results using t-tests (Figure 6) Results of a paired t-test between the second and the third trials conducted by naïve observers indicate that the repetition of the visual observations significantly affects the assessment of small color differences In addition, the comparison of average naïve vs experts assessments shows a statistical difference at the 95 percent confidence interval, with expert observers generally perceiving a larger color difference than naïve observers Since experts did not repeat the assessment the inter-observer variability between naïve and experts cannot be established at this point and requires further study (Cardenas et al, 2006) Key variables in the control of color 265

11 IJCST 21,5 266 Figure 5 Visual assessment experimental setup Group t p Significance Table I Summary statistics for repeat assessments carried out by naïve observers Trial 1 vs trial No significant difference at 95 percent confidence interval Trial 2 vs trial Significant difference at 95 percent confidence interval Trial 1 vs trial No significant difference at 95 percent confidence interval Group t p Significance Table II Summary statistics for assessments carried out by naïve and experts observers Trial 1 vs experts 1088, Significant difference at 95 percent confidence interval Trial 2 vs experts 914, Significant difference at 95 percent confidence interval Trial 3 vs experts 1146, Significant difference at 95 percent confidence interval 3 Conclusions It is important to identify the important parameters and variables that influence the control of color within various stages of the textile supply chain For effective color control, the goal must be to build confidence in the data that quantifies the sources of

12 Gray scale grade Key variables in the control of color Experts Average naive # Pair Figure 6 Average results in grade units for the visual assessments variation within the process and to minimize variations Initially, variations in visual assessment of small color differences in textile materials as well as those due to lighting employed during design, production and display of textiles should be examined to establish the optimum conditions and the level of observer variability A fishbone diagram was used to demonstrate the wide range of variables that affect the control and communication of color within the textile supply chain The optimization of variables that influence the assessment and communication of color is vital to achieving effective communication between all parties involved This can significantly reduce costs and lead times resulting in improved competitiveness and cost efficiency associated with increased consumer satisfaction and confidence in the industry Assessment of lighting measurements of several stores in the USA demonstrated variability in lighting, with many stores having at least two different light sources This variability, in combination with uncontrolled lighting from external windows and entrance/exit areas, can lead to significant variability in the color perception of textile garments displayed in such areas, and may lead to consumer experience being significantly different from that intended by the designer In regard to visual assessment variability, the results obtained in an ongoing study at North Carolina State University based on the psychophysical testing of 50 observers demonstrate a statistical difference for visual judgments of small color differences between naïve and expert observers (Cardenas et al, 2006) Results of a paired t-test between the second and the third trial conducted by naïve observers indicate that the repetition of the visual observations significantly affects the assessment of small color differences

13 IJCST 21,5 268 References AATCC (1986), Evaluation procedure 9 Visual assessment of the change in color, AATCC Technical Manual, Vol 16, American Association of Textile Chemists and Colorists, Research Triangle, NC Aspland, JR and Shanbhag, P (2004), Comparison of color difference equations for textiles: CMC(2:1) and CIEDE2000, AATCC Review, Vol 4 No 6, pp Berns, RS (2000), Billmeyer and Saltzman s Principles of Color Technology, 3rd ed, Wiley, New York, NY Butts, K (2007), Why don t my numbers match yours?, paper presented at the Color Management Workshop, Raleigh, NC Cardenas, L, Hinks, D, Shamey, R, Kuehni, R, Jasper, W and Gunay, M (2006), Comparison of naïve and expert observers in the assessment of small color differences between textile samples, paper presented at the CGIV Conference, Leeds CIE (2001), CIE Technical Report: Improvement to Industrial Colour-difference Evaluation, CIE, Vienna CIE (2004), Colorimetry, 3rd ed, CIE Publication 15:2004, CIE, Vienna Connelly, R (1997), Good sample presentation: how to get color measurement results that make sense, Color Technology in the Textile Industry, 2nd ed, American Association of Textile Chemists and Colorists, Research Triangle, NC Eckes, G (2003), Six Sigma for Everyone, Wiley, Hoboken, NJ Fairchild, MD (2005), Color Appearance Models, 2nd ed, Wiley, Hoboken, NJ Farnsworth, D (1943), The Farnsworth-Munsell 100 hue dichotomous tests for color vision, Journal of the Optical Society of America, Vol 33, pp Gale, AV (1933), Children s Preferences for Colors, Color Combinations, and Color Arrangements, University of Chicago Press, Chicago, IL Gao, XP and Xin, JH (2006), Investigation of human s emotional responses on colors, Color Research and Application, Vol 31 No 5, pp Gay, J and Hirschler, R (2003), Field trials for CIEDE2000 correlation of visual and instrumental pass/fail decisions in industry, Proceedings of the 25th Session of the CIE, San Diego, CA, p 26 Granger, GW (1955), An experimental study of color preferences, Journal of General Psychology, Vol 52, pp 3-20 Graystone, J (2000), Integrating colour delivery skills, paper presented at PRA Conference on The Colour Delivery Challenge, Leeds University, Leeds Hinks, D, Draper, S, Che, Q, Nakpathom, M, El-Shafei, A and Conelly, R (2000), Effect of lighting variability on color difference, AATCC Review, Vol 1 No 11, pp Hinks, D, El-Shafei, A, Draper, S, Che, Q, Nakpathom, M and Conelly, R (2001), Radiometric measurement of area lighting critical to color assessment in the textile industry, AATCC Review, Vol 11 No 1, pp 35-9 Hinks, D, Shamey, R, Aspland, JR, Cassill, N, Jasper, W and Kuehni, RG (2006), Optimizing color control throughout the textile supply chain NTC, available at: wwwntcresearch org/projectapp/indexcfm?project¼c04-ns11 (accessed June 2007) Ishihara, S (1917), Series of Plates Designed as Tests for Colour-blindness, Handaya Company, Tokyo

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