Advanced Methods and Tools for Color Measuring and Matching: For Quality Check of Colored Products of Textiles and Apparel Industry

Abstract

This introductory chapter for advances in colorimetry mainly addresses the basic principles of color quantification and the measurement of different known color parameters, color differences, dye uniformity, and tolerances for different parameters of color differences for assessing color matching efficacy. Few advances in the analysis of color-related issues for the textile and apparel industry have made this task easier with the aid of computer-aided color measuring and matching systems as indispensable tools. In addition, identification of colorant molecules on colored products by using UV–VIS spectrophotometry has become essential. Analysis of the quantifiable grade/rating of compatibility between two dyes for a pair of binary mixtures of colorants (dyes and pigments) to obtain a desirable shade has become a daily need in the production of dyed textiles and apparel products. Hence, in the present chapter, all such issues of advanced colorimetric analysis for color measurement and matching and quality checks of dyed/colored products of textiles and the apparel industry are discussed here along with the principles of working and design features of reflectance spectrophotometers. A few case studies with experimental data as examples are also elaborated here to understand these issues for better dissemination of knowledge in this field for industry colorists and researchers.

1. Introduction

Color is primarily a visual sensation in the human brain, producing relevant sensation in the retina of the human eye in the combined presence of a light source, a colored object and a perfect eye. The perception of color by the human brain is a color response related to the stimulation of the retina (comprising rod cells and cone cells) by the light spectrum reflected from the object after it absorbs a part of the light from the incident light spectrum of the light source. The retina of the human eye has two types of photosensitive cells: rod cells and cone cells. Rods are sensitive to dullness or brightness of light and are situated at the periphery of the eyeball. Cones are sensitive to color hues and are mainly situated in the forea region of the eye, and these cells are sensitive to color in three different wavelength bands—long, medium and short bands—which braineventually yield an output sensation interpreted as red, green and blue and/or proportional combinations of these three primary colors. The spectral sensitivity of the human eye corresponding to three primary/basic colors (red, green and blue). In the visible region of the human eye (400–700 mm), the color of the object is reflected by the complementary color wavelength or is sensed by the human eye after absorption of a certain wavelength by the coloring material (dye or pigment, etc.) from the source light spectrum. Hence, in a completely dark environment, we cannot see any object colored. The chemical constitution of the colorants (dye, pigment, etc.) is responsible for the absorption of part of the incident light energy. The human eye can observe and sense a color subjectively and can compare, but it cannot be compared quantitatively in terms of any numeric value for which human color perception differs from person to person for many reasons, such as (a) differences or changes in the light source spectrum or (b) differences in the observer’s/sensitivity power, defective vision and eye fatigue. (c) Differences in direction and angle of vision. There are many other variable factors, such as adjacent color, background color, border color, sample size, surface texture and scattering, variation in observation angle and instrument setting.

According to Trotman [1], dyes are specific organic molecules that can absorb a certain part of the light selectively and can reflect the other part of the light in the visible zone. Since then, many investigations have reported on which part of the dye molecules of natural/synthetic dyes/pigments are responsible for the visible color of that object, with a predominant hue at a specific wavelength.

According to Shah and Gandhi [2], color is defined as a psycho-chemical phenomenon that involves a psychological stimulus to the human eye. Therefore, to a chemist, it is a chemical compound with conjugations; to a physicist, it is a multiple physical phenomenon occurring simultaneously, such as the reflectance, absorption and scattering of light waves; to a physio-logist, it is a sensory measurable response of electrical signals produced to nerves of human eyes; and to a psycho-logist, it is a sensation of mind in response to reflected color waves. To artists and other common people, it is a sensory stimulant in the human brain that is used as an observer.

Ordinarily, for many industrial processes and products, it is difficult to use the accuracy of the appearance of color to the human eye because it is difficult to differentiate one color from another by only the red/blue/green responses of the human eye. According to an earlier report by Samanta [3] on the use of computer-aided color measurement and matching for textiles, apparels, paint, leather, pharmaceuticals, polymers/plastics, paper and food, etc., it is necessary to work hard to prepare a color database for checking and maintaining color matching quality, as the color parameters of these products, along with other physical or chemical properties, are important product specifications. Earlier, color specifications were assessed by visual methods of human eye perception, but currently, assessments of color strength produced for particular hues and color differences for color matching from given or desired standard shades have started to be performed instrumentally by computer-aided color measuring systems. Hence, computer-aided color match prediction has also gradually started in all these industries for precision matching.

There are many disputes and misconceptions [2, 4] regarding match/nonmatch decisions in color matching, and such decisions are assessed via visual human eye procedures. Therefore, instrumental precision measurement techniques for practical color measurement and for checking color matching results in terms of quantifying color differences on red, blue, green, yellow and dark or light scales should be considered better options than human-perceived color (assessed by visual methods).

In any industry [2, 3, 4], the desired matched color is obtained by the application of a mixture of two, three or four dyes/pigments. In a few industries, an experienced senior dyer, called a ‘shade bank’, maintains a record of hard copy shades produced with a small piece of attached sample for color matching of different substrates from his day-to-day experience. Therefore, for the next sample for color matching, Sr.

An experienced dye master first selects one of the color recipes from his or her shade bank that is very nearer or very closer to the color of the standard sample given for matching. Then, color is added or subtracted, and nearer or nearest matching is performed via trial and error of actual dyeing. However, the present computer-aided color match instrument can be obtained from the saved color database of that class of dyes. This instrumental system can predict much closer matches with expected color parameters and color strength to produce within limits of set tolerances within a few minutes. Even if exact color matching is not obtained in the first predicted formulations, it is possible to add or subtract a certain amount of color in the predicted formulation either automatically via menu or via a computer-aided color matching system to obtain the exact match or manual addition.

Due to the increase in the variety of natural and manmade fibers and their use in multifiber blended textiles and the increased availability of synthetic dyes and pigments available on the market, an increasing number of companies have made them available. Therefore, special care of the nature and type of fibers and the type of dyes used are also important for determining color recipe formulations using computer-aided color measuring and matching systems. The color matching formulation should also be the most economical and least metameric, reproducible, most uniformly dyed, and have moderate to good washing and lightfastness properties in the matched product as a better quality colored textile product, which is not easy to achieve, without the use of computer-aided color measuring and matching tools [3, 5].

This new technology of using computer-aided color measuring and matching tools enables expert computer-aided dye masters to easily predict the number of color matching formulations/recipes with multiple options to select the most appropriate least expensive and least metameric recipe [2, 3, 4, 5] for color matching depending on the current price of the dyes and degree of precision in matching by measuring color differences (in terms of the DE, DL, Da and Db values and the Labd metamerism index value).

The computer-assisted color-measuring and color-matching system, which is applicable for any textile industry, apparel industry or other industry, is therefore considered a powerful tool for improving the quality of precision color matching of products and provides a great opportunity to maintain less dye inventory, reduce the cost of production and reduce the time required for predicting and producing a good color matching recipe.

The colored molecules (dyes and pigments) can also sometimes be identified from colored textiles and other products to assess the nature of the color molecules used and their properties, eco-friendliness, etc. Colored molecules can be identified from colored textiles by subjecting them to the extraction of those colored ingredients (dyes or pigments) by water or any other solvent extraction process to extract colorants from colored textiles and apparel products using suitable solvents under a known method of such extraction; then, the extract can be subjected to UV–VIS spectroscopic analysis to obtain wavelengths vs. optical density/absorbance curves as fingerprints of particular color ingredients/molecules present there. This may also be confirmed by chromatography, HPLC, NMR, LC–MS and FTIR analysis. However, after dyeing textiles with a mixture of colors to obtain a particular compound shade, it is more important in the industry to measure color variation for variations in dyeing conditions, dye and mordant (in the case of natural dyeing) concentrations and % application/proportions of different dyes to obtain a perfect match of color/shade within certain prescribed and acceptable limits of tolerances of different color parameters. For compound shades, another parameter known as colorant/dye compatibility has also become a very important factor for producing a shades of precision match.

Davidson et al. [6] first introduced an analogy for using a computing device for predicting a color recipe from a preprepared stock of color database used for color matching of wall paint initially and gradually, which was successfully applied to textiles and apparel products.

At present [3, 4], almost every dye house in the textile industry uses computer-aided color measuring and matching systems. To generate/predict the color matching recipe from the preprepared color database, we checked the instrumental color differences to check the quality of the precision matching results and to carry out batch correction to achieve a better match if the predicted recipe did not work in one shot. A computer-aided color measuring and matching system therefore involves three basic assemblies, viz.:

1. Color measuring unit/instrument: A reflectance spectrophotometer, which measures the reflectance values of any object initially and then expresses the color values in numerical form quantitatively for any desirable color parameters, which are easy to process/display on a computer to obtain a meaningful color parameter.

2. Color matching software: Dedicated software is used for precision color measurement and prediction of color matching formulation with the aid of a suitable reflectance spectrophotometer as an instrumental aid for subsequent color measurement and match prediction sequences by expressing the color-related measured/stock data converted to a desirable color parameter that is relevant for obtaining direct results/matching formulations, as per the requirements of the colorist.

3. Computer hardware: A PC with high resolution and high storage capacity and a quick data processing system to operate, detect and display the resulting data and curves.

2. Basic principles of the use of computerized color measuring and matching tools

Precision color matching [2, 3, 4] for textile substrates is mainly based on the Kubelka munk function [2, 3, 4, 5, 6], Park and Stearn’s [7] algorithms and Allen’s [8] matrix for generating closer recipes via computer-aided color matching formulation [9] programs using a reflectance spectrophotometer, a suitable color database for specific substrates and specific dye classes, and dedicated software for calculations of required color parameters and match prediction formulation programs processed on a can computer attached to a spectrophotometer.

Multiplication of the variability of the dyeing process to achieve accurate color measurements via computer-aided color matching systems requires preoptimization of the dyeing process variables and minimization of all other processing and measurement errors in the preparation of color databases and in the generation of color matching formulations [3, 9] by assumptions, approximations, simplifications and iterations, etc., considering that color matching of any product is an iterative trial and error process. Manual matching of color by expert/skilled colorists/divers can never achieve that level of precision in the color matching of any product as a computer-aided system can. Therefore, manual color matching is not easily accepted by customers, as manual color matching shows wide variations or differences in color variation, as human eye perception varies from person to person. For instrumental matching, variation from illuminate to illuminate or instrument to instrument is also present, but this variation may be minimized by choosing the least metameric match. Thus, for instrumental color matching vs. manual color matching, the former (instrumental color matching) has many advantages over manual processing.

The basic process of computer-aided color matching [2, 3, 4, 5, 9] for predicting color matching formulations for the textile and apparel industry involves the following five steps:

1. Preparation of the color database and its storage (color type or dye classwise, companywise and substratewise)

2. Determination of the optical constants or K/S values of all database samples and standard samples

3. Prefixing allowable color difference tolerances for the prediction of acceptable formulations under standard illuminants

4. Comparison of the least-cost and least-metalistic color matching formulations after an actual dyeing trial

5. Batch correction for any deviation between the predicted formulation and actual dyeing trial results.

The optical constants of the Kubelka Munk function [2, 3, 4], i.e., K/S values (surface color strength), are initially measured after a textile substrate is dyed with each dye at a multilevel concentration to obtain a linear relationship between dye concentrations and K/S plots for each dye after dyeing a specific textile substrate with different concentrations of colorants of a specific class of dyes from the same company at 8–10 different levels and with 1 sample as a control undyed sample as the substrate. All calibration of the color database was performed by using optimized dyeing process parameters for uniform dyeing results. Accurate color database preparation is the basic need for generating precision color matching formulations within specific tolerances of multilevel RBGY scales of color differences. After checking the accuracy of the prepared color database, these data were stored in a computer-aided color matching library system in a separate file for future use. The accuracy of the color database for each colorant should be checked by linearity [3, 5] in each case by plotting the dye concentrations vs. K/S values, and if it is not linear, either redyeing or approximation/deletion of some particular points or linear curve fitting should be performed appropriately. Once the color database is ready, the generation of color match formulations can be performed by following the iterative process of the color match prediction algorithm following the flow chart given below (as shown in Figure 1), where these process steps remain in built-in dedicated software presented in the computer-aided color matching system):

Figure 1.

Flowchart of the computer-aided color matching algorithm for the prediction of color match formulations.

For further correction of shades, the reformulation of batch correction mathematics for computing the incremental value of the incremental concentration of one or two colors for different dyes may require further iteration at that stage, which may be represented mathematically as follows:

$${\mathrm{DC}}_1=-\frac{\partial {\mathrm{C}}_1}{\partial \mathrm{X}}\mathrm{DX}+\frac{\partial {\mathrm{C}}_1}{\partial \mathrm{Y}}\mathrm{DY}+\frac{\partial {\mathrm{C}}_1}{\partial \mathrm{Z}}\mathrm{DZ}$$E1

$${\mathrm{DC}}_2=-\frac{\partial {\mathrm{C}}_2}{\partial \mathrm{X}}\mathrm{DX}+\frac{\partial {\mathrm{C}}_2}{\partial \mathrm{Y}}\mathrm{DY}+\frac{\partial {\mathrm{C}}_2}{\partial \mathrm{Z}}\mathrm{DZ}$$E2

$${\mathrm{DC}}_3=-\frac{\partial {\mathrm{C}}_3}{\partial \mathrm{X}}\mathrm{DX}+\frac{\partial {\mathrm{C}}_3}{\partial \mathrm{Y}}\mathrm{DY}+\frac{\partial {\mathrm{C}}_3}{\partial \mathrm{Z}}\mathrm{DZ}$$E3

The dye concentrations of the colorants (C_1, C_2, C₃ or Cn) used for obtaining a match recipe and X, Y and Z are the tristimulus values of the colored/dyed samples, and the DX, DY and DZ values are the differences in the Tristimulaus values between the produced samples and standard samples or differences between new sample batches and produced samples dyed with the predicted color matching formulation; further batch correction is needed for precision matching.

To generate a recipe formulation for the color match prediction process, measurements of the color of standard shade must first be performed to match a specific substrate, and then, dyes can be selected according to the dye inventory/color database type available for use by assigning or fixing the required tolerance limits [2, 3, 5, 6] for color difference values. Then, the computer color matching system starts determining/predicting possible match formulations from specific dye class database sources available/stored in the system after actual dyeing. The color values, in terms of the tristimulus values of the standard sample to match, are computed under a specific light D65 source with the predicted match formulation. By comparing them with their color data and optical constants, for example, for a predicted formulation from a specific class of dyes and its stored color data bank, the unknown concentration of a specific dye mixture required for matching with a particular color standard can be determined with the help of software.

If the predicted recipe falls practically after actual dyeing, it needs to be corrected by the batch correction program. Matching of shades for the blended textile fabric also essentially needs to be carried out similarly, but the data banks with different classes or types of dyes (according to the fiber type in the blends) applied to both the components of the blended textiles are stored as specific color databases for blended components separately for use in match prediction on blended textiles. Dan and Randall [10] clarified the need for uniform dyeing (with less than 5% CV for K/S values at 10 different points for colored textiles) and explained that the preparation of reliable calibration dyeing (with checking of linearity of dye concentrations vs. K/S values in calibrated dyeing series) for the preparation of a color matching database and its successful storage (type of dye classwise, manufacturer/companywise, etc.) is the first and foremost very important step for the generation/development of a computer-aided color match prediction for any substrate within a range of acceptable color differences [11].

The solutions of the above color matching equations for computer-aided color match prediction (CCMP) usually follow those of the Kubelka munk function [2, 3, 4, 5, 6], Park and Stearn’s [7] algorithmic equation and Allen’s [8] color matrix for computer-aided color matching formulation [9] program. An isomeric match is a match under all illuminations, while a two-color sample that appears as a match under one illuminate but does not match under any other illuminate is termed illuminate metamerism, and that type of match is called a metameric match. Our objective is to find the minimum metameric or least metameric match prediction, and formulation sorting is based on the determination of the general metamerism index or the illuminance of the metamerism index to maintain the least metamerism. Some improvements in computer-aided color matching formulations based on minimum color difference tolerances, fewer or least metameric effects, etc., are needed [12, 13].

However, due to practical variations in the dyeing conditions, machinery settings and human interventions of dyeing operators, there may still be some differences in the actual color yield of the products dyed by the predicted formulations; however, some corrections, called batch corrections, are needed to achieve/improve the desired precision of the color matching quality of the produced sample against the color of the given standard dyed product [14]. Recently, a new test method for analyzing dye compatibility via relative quantitative rating/grades for the use of a mixture of colors to obtain uncommon compound shades on textiles has been established that is applicable for both natural dyes [15, 16, 17] and synthetic dyes [18], the details of which are available from the current relevant literature.

The effects of dyeing process variables on the color yield and color fastness properties of cotton khadi fabric dyed with deoiled Redsandal wood waste [19] were analyzed via colorimetric analysis after cotton fabric was dyed with red-sandal wood waste as a natural dye.

Improvements in the color fastness of naturally dyed textiles and their antimicrobial properties have been reported in recent literature via detailed colorimetric analysis [20]. The application of computer-aided color measuring and matching instruments for the textile and apparel dyeing industry has been comprehensively described by P Samanta [21] due to their advantages and disadvantages.

Android phone-based color measurement and color data analysis, i.e., digital imaging techniques for color parameter tests and color fastness assessment with DigiEye software to achieve maximum accuracy, are of interest to color scientists and industry dyeing managers. The Clarion color fastness app has already been developed in this direction by Kuraray, Japan.

IS standards for the identification and determination of the purity of natural indogo have recently been standardized by BIS [22], which is also discussed here as a case study, as an important application of colorimetric analysis in natural dyed textiles.

The basics of the quantitative analysis of colorimetric parameters for colored textile and apparel products are therefore depicted below to clarify the subject clearly for proper analysis.

2.1 Principles of color quantification and measurements

Color can be measured quantifiably and absolutely by its three coordinates known as tristimulus values, i.e., X, Y and Z, or by total reflectance (Rλ_max) values or its derivative formula function, such as K/S values. The CIE tristimulus value [2, 3, 4, 5] of a colored substrate may be defined as

$$\mathrm{X}=\mathrm{\Sigma }{\mathrm{P}}_{\mathrm{\lambda }}{\mathrm{X}}_{\mathrm{\lambda }}{\mathrm{R}}_{\mathrm{\lambda }}$$E4

$$\mathrm{Y}=\mathrm{\Sigma }{\mathrm{P}}_{\mathrm{\lambda }}{\mathrm{Y}}_{\mathrm{\lambda }}{\mathrm{R}}_{\mathrm{\lambda }}$$E5

$$\mathrm{Z}=\mathrm{\Sigma }{\mathrm{P}}_{\mathrm{\lambda }}{\mathrm{Z}}_{\mathrm{\lambda }}{\mathrm{R}}_{\mathrm{\lambda }}$$E6

where P_λ = the spectral power distribution of the standard source.

R_λ = Spectral reflectance of the substrate.

X_λ. Y_λ. Zλ = color factor of the standard observer for the red, blue and green sensations of the human eye.

To describe color in a two-dimensional plot called the CIE color space diagram, the CIE is defined by the following chromaticity coordinates (x, y, z) to measure color and color differences:

where

$$\mathrm{x}=\frac{\mathrm{X}}{\mathrm{X}+\mathrm{Y}+\mathrm{Z}}$$E7

$$\mathrm{y}=\frac{\mathrm{Y}}{\mathrm{X}+\mathrm{Y}+\mathrm{Z}}$$E8

and

$$\mathrm{z}=\frac{\mathrm{Z}}{\mathrm{X}+\mathrm{Y}+\mathrm{Z}}$$E9

and x + y + z = 1 from which the saturation can be determined, and from the two third can be determined.

A color match [2, 3, 4, 5] means that the color of the produced sample (SL) is the color of the standard (SD), i.e., (X_SL,Y_SL, Z_SL) = (X_SD, Y_SD, Z_SD), while X, Y & Z are the tristimulus values of the sample (SL) and standard (SD) or (reflectance)_SL (400 to700 nm) = (reflectance)_SD (400 to 700 nm) or (K/S) _SL = (K/S) _SD, while K/S = α C_D, where K/S is the Kubelka Munk function called color strength, which is the coefficient of absorption divided by the coefficient of scattering, and C_D is the concentration of dye/colorants used.

The CIE theory of color–Tristimulus values (X, Y and Z) and CIE color communication parameters updated in 1976, i.e., DE*, DC*, DH*, L*, a* and b* values, etc., are well described by Samanta [3] with descriptions and figures in a book chapter of the Intech open published book on colorimetry.

For a mixture of colorants used to match a compound shade, the following three equations are solved as a function of dye concentrations of the colorants (C_1, C_2, C₃ or Cn), and tristimulus values can be measured through reflectance measurements [2, 3, 4, 5].

$$\mathrm{f}\left({\mathrm{C}}_{1,}{\mathrm{C}}_{2,}{\mathrm{C}}_{3,}\text{or}\mathrm{Cn}\right)=\mathrm{X}, \mathrm{f}\left({\mathrm{C}}_{1,}{\mathrm{C}}_{2,}{\mathrm{C}}_{3,}\text{or}\mathrm{Cn}\right)=\mathrm{Y} \text{and} \mathrm{f}\left({\mathrm{C}}_{1,}{\mathrm{C}}_{2,}{\mathrm{C}}_{3,}\text{or}\mathrm{Cn}\right)=\mathrm{Z}$$E10

where X, Y, and Z are the tristimulus values of the samples to be matched and C_1, C_2, and C₃ or C_n are the concentrations of dyes or color pigments needed. In practice, the reflectance values at 400 to 700 nm are measured from solid-colored textile surfaces, and those reflectance data are processed for K/S value generation for ultimate matching. The reflectance vs. concentration of dye is nonlinear and nonadditive, so the reflectance vs. dye concentration data cannot be directly used as basic data for color match prediction; rather, plots of dye concentration vs. K/S values are linear, and the K/S values are additive. The K/S data can be used for computer-aided color match prediction. The empirical relationship of the Kubelka Munk function [2, 3, 4, 5] (i.e., K/S values) varies linearly with dye concentration.

$$\begin{array}{c}\mathrm{K}/\mathrm{S}=\frac{\mathrm{Co}‐\text{efficient of absorption}}{\mathrm{Co}-\text{efficient of scattering}}\\ =\frac{{\left(1-{\mathrm{R}}_{\text{λmax}}\right)}^2}{2{\mathrm{R}}_{\text{λmax}}}=\mathrm{\alpha }{\mathrm{C}}_{\mathrm{D}}\end{array}$$E11

where R is the reflectance value at a chosen user wavelength or at λ_max (at the maximum absorbance wavelength).

Thus, a higher K/S indicates a higher absorption for dyes or a mixture of dyes, indicating a higher absorption value or a higher dye uptake.

Thus, K/S α C_D = α C_D, where α is a constant.

As K/S is also additive in nature, for mixtures of colorants used to dye textile fabrics for compound shades, the total K/S will be or can be determined by the following additive relationship [2, 3, 4, 5]:

$${\left(\mathrm{K}/\mathrm{S}\right)}_{\text{total after dyed with Mixture of Dyes}}={\left(\mathrm{K}/\mathrm{S}\right)}_{\text{undyed subs}}+{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}1}+{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}2}+{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}3}\dots \dots +{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{Dn}}$$E12

$$\text{Or}{\left(\mathrm{K}/\mathrm{S}\right)}_{\text{dyed substrate}}={\left(\mathrm{K}/\mathrm{S}\right)}_{\text{undyed\hspace{0.17em}subs}}+{\mathrm{\alpha }}_1\mathrm{C}1{\mathrm{d}}_1+{\mathrm{\alpha }}_2{\mathrm{C}}_2{\mathrm{d}}_2+{\mathrm{\alpha }}_3{\mathrm{C}}_3{\mathrm{d}}_{3\dots \dots \dots \dots \dots }+{\mathrm{\alpha }}_{\mathrm{n}}{\mathrm{Cnd}}_{\mathrm{n}}$$E13

For dyes on textiles, it is assumed that dyes do not contribute to scattering, and if the substrate is not changed, the scattering of the substrate also remains constant, while K is the coefficient of absorption, i.e., the sum of dye stuff absorption and substrate absorption (as the substrate is fixed, there are no changes in scattering due to the substrate). Therefore, K/S directly varies with the concentration of dyes, and scattering is independent of dye concentration (which is not the case for pigments in paint).

For textiles, for a particular sample (for which the material, yarn and fabric construction and surface finish remain unaltered), the scattering remains constant. Therefore, in textiles, this theory is called the single constant theory. K/S = α C_D.

Finally, the reflectance vs. dye concentration is not linear, and hence, it is difficult to interpolate or curve fit to predict the achievable color strength from any of the reflectance or tristimulus values of dyes.

The K/S vs. dye concentration is linear; hence, it can be interpolated to any desired dye concentration and thus can be safely used [2, 3, 4, 5] in computer-aided match prediction software to predict color matching formulations for mixtures of two or three dyes or more.

While

$${\mathrm{L}}^{\ast }=116{\left(\mathrm{Y}/{\mathrm{Y}}_{\mathrm{o}}\right)}^{1/3}-16 {\mathrm{\Delta L}}^{\ast }=\mathrm{L}{\ast }_1-\mathrm{L}{\ast }_2$$E14

$${\mathrm{a}}^{\ast }=500\left[{\left(\mathrm{X}/{\mathrm{X}}_{\mathrm{o}}\right)}^{1/3}-{\left(\mathrm{Y}/{\mathrm{Y}}_{\mathrm{o}}\right)}^{1/3}\right] {\mathrm{\Delta a}}^{\ast }=\mathrm{a}{\ast }_{\mathrm{i}}-\mathrm{a}{\ast }_2$$E15

$${\mathrm{b}}^{\ast }=200{\left[\mathrm{Y}/{\mathrm{Y}}_{\mathrm{o}}\right)}^{1/3}\left]-{\left(\mathrm{Z}/\mathrm{Zo}\right)}^{1/3}\right] {\mathrm{\Delta b}}^{\ast }=\mathrm{b}{\ast }_1-\mathrm{b}{\ast }_2$$E16

The chroma (psychometric chroma) values in the Cl ₁₀₄ color space were calculated as follows:

$${{\mathrm{C}}_{\mathrm{ab}}}^{\ast }=\left(\mathrm{a}{\ast }^2+\mathrm{b}{\ast }^2\right), {\mathrm{\Delta C}}^{\ast }=\mathrm{C}{\ast }_{1\left(\mathrm{ab}\right)}-\mathrm{C}{\ast }_{2\left(\mathrm{ab}\right)}$$E17

where C*_1(ab) and C*_2(ab) are the chroma values for the standard sample and produced sample, respectively.

CIE 1976 metric Hue-Difference (ΔH_ab = [(ΔE_ab*)² – (ΔL*)² – (C_ab*)²)]^1/2.

An isomeric match, i.e., a match under all illuminants and when two colored samples match under one illuminant but do not match under any other illuminant, is termed a metameric match [2, 3, 4, 5]. Least metameric match prediction and sorting are based on the calculation of the general metamerism index [2, 3], as given below:

$$\text{General metamerism Index}=\frac{\sum {\left(\mathrm{DR}\overline{x}\right)}^2}{{\mathrm{X}}^2}+\frac{\sum {\left(\mathrm{DR}\overline{y}\right)}^2}{{\mathrm{Y}}^2}+\frac{\sum {\left(\mathrm{DR}\overline{z}\right)}^2}{{\mathrm{Z}}^2}$$E18

where DR = Difference in reflectance between a pair of metamers;

x, y, z = CIE standard observer color functions;

X, Y, Z = the CIE tristimulus value normally taken for illuminant C, which is the average of two specimens.

Additionally, the CIE LAB, i.e., the LABD metamerism index [2, 3, 4, 5], is calculated from the following CIE Lab equations [2, 3]:

$${\mathrm{MI}}_{\left(\text{LABD}\right)}={\left[{\left(\mathrm{D}{L_1}^{\ast }-\mathrm{D}{L_2}^{\ast }\right)}^2+\left(\mathrm{D}{a_1}^{\ast }-{\left(\mathrm{D}{a_2}^{\ast }\right)}^2\right.+\left(\mathrm{D}{b_1}^{\ast }-{\left(\mathrm{D}{b_2}^{\ast }\right)}^2\right.\right]}^{\mathrm{1/2}}$$E19

where DL₁*, Da₁*, and Db₁* are the Delta CIE Lab* -1976 color coordinates [2, 3] between Standard and Sample for the first illuminate and DL₂*, Da₂*, and Db₂* are the Delta CIE Lab* color coordinates between Standard and Sample for the second illuminate.

2.2 Methods for predicting color matching formulations

The prediction of color matching formulations is based on the use of binary or ternary mixtures of different colorants to obtain the resulting compound shade. Effective coloration with a mixture of dyes depends on the compatibility between dye and dye, i.e., the rate of color build-up when dyed under a specific optimized set of dyeing conditions. Therefore, before predicting color matching formulations, determining dye compatibility between any two dyes/pigments is another important parameter. Along with visual assessment, the rate of dyeing was assessed, and the coefficient of the dye diffusion test was assessed to determine the rate of progressive color build-up of both individual dyes in a binary mixture of two dyes when dyed under optimized dyeing conditions. The latter is a very popular conventional method to test dye compatibility, i.e., to determine the rate of color build-up of two individual dyes when the substrate is dyed with a binary mixture of dyes together, and the textile substrate under reference is to be dyed under two different sets of dyeing conditions for testing dye compatibility (in one set by varying the profile of dye concentration while keeping other dyeing conditions fixed and in the second set of dyeing conditions is to be done with variations in the dyeing time and dyeing temperature, keeping the dye concentration and other dyeing parameters fixed). After these two sets of dyeing under different conditions, the rate of increasing chroma/hue for gradual color build-up with increasing dyeing time/temperature and with increasing dye concentration were checked by comparing the pattern of color built-up by plots of K/S Vs DL and DC vs. DL in this conventional method. However, this conventional method of dye compatibility testing has many limitations, such as being time consuming, requiring high analytical skill and being cumbersome to follow and not quantifiable in terms of the degree of compatibility. Hence, a newer and simple advanced method of dye compatibility analysis between two dyes/pigments, which is simpler, quicker and easier to apply, has been established by Samanta and Agarwal [15, 16].

The second important issue before computer-aided color match prediction for any textile substrate is the preparation and storage of an accurate color database to store the dyeing results of calibrated dyeing samples on the same textile substrate with a specific class of 9–10 dyes of different colors after checking the accuracy of the dyeing results by checking the linearity of the dye concentration vs. K/S values for each colorant and after checking the dye uniformity (less than 5% CV of the K/S values).

Therefore, after measuring the color of the dyed samples under commonly used illuminants such as D65, the reflectance, K/S values and L*, a*, and b* values resulting from the calibrated dye were recorded and stored in a color matching computer system.

The reflectance spectrophotometer measures reflectance values at different wavelengths from 400 to 700 nm at 10 nm intervals, and these data are used by computer-aided color matching software to calculate the DL*, Da*, Db* and DE* scale of color difference values for the newly dyed/produced sample compared to the standard colored fabric to match. Currently, computer-aided data for each class/type of dye and each color of that specific class of dye stored in the system are used by computer-aided color match prediction software based on the linearity and additive nature of the relationship between dye concentrations and the K/S value for the prediction of a particular color match using different binary or ternary mixtures of dyes, i.e., using the basis of the following CIE equations to calculate the total K/S value of the dyed sample to match the K/S value of the standard sample, as already shown above and described below.

$${\left(\mathrm{K}/\mathrm{S}\right)}_{\text{dyed with mixture of dyes}}={\mathrm{C}}_0{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{un}\text{dyed substrate}}+{\mathrm{C}}_1{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}1}+{\mathrm{C}}_2{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}2}+{\mathrm{C}}_3{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}3}$$E20

$$\text{Or}{\left(\mathrm{K}/\mathrm{S}\right)}_{\text{dyed substrate}}={\mathrm{C}}_0{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{un}\text{dyed substrate}}+{\mathrm{C}}_1{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}1}+{\mathrm{C}}_2({\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}2}+{\mathrm{C}}_3{\left(\mathrm{K}/\mathrm{S}\right)}_{\mathrm{D}3}$$E21

Thus, this method is also based on the gradual increase in the color yield from the mixture of different dyes used in the mixture, the matching of the total K/S values between the standard sample to match and the total color strength (K/S) value of the newly produced sample, which are the determining points of match predicting formulations showing the amount required for C1 concentrations Dye-D1 and C2 concentrations of Dye-D2 and C3 concentrations of Dye-D3. Finally, this method provides the required information about the computer-aided match prediction formulation (S) in more than one or as many as nos of different formulations are possible with similar dyes/colorants having closely similar hues, thereby proving/making a computer-aided color match prediction system, as a more advanced tool, for better quality and precision color, match prediction for more practically useful purposes of industrial dyers/colorizts.

Color differences in terms of DL (lighter or darker), Da (redder or greener) and Db (bluer and yellower) values showing less or more darker or lighter, redder or greener and bluer and yellower indicate the need for batch corrections to eliminate minor differences in any color difference notation to meet the desirable or preset tolerances of DL, Da, Db and DE values. However, in practice, dyers feel that it is difficult to control any of the color differences among DL, Da and Db without affecting other tolerances.

Hence, another newer index of color differences, known as the color difference index (CDI) [15, 16], was recently established to obtain an easy understanding of overall color differences and the dispersion of color on the fabric surface after dyeing for different dyeing variables/conditions via the following equation (considering the magnitudes of the relevant ΔE, ΔH, ΔC, and MI values (ignoring their sign and direction):

$$\text{Colour Difference Index}\left(\mathrm{CDI}\right)=\frac{\mathrm{\Delta }E\times \mathrm{\Delta H}}{\mathrm{\Delta C}\times \mathrm{MI}}$$E22

A color difference index (CDI) less than 5 is considered acceptable for preparing a color matching database because this difference is within the tolerance limits of color differentiation. However, the higher the color difference index (CDI) value is, the greater the nonuniformity in dyeing, which has a wide variation in color strength, according to a comparison of one dyed textile sample with the other, and that match is not acceptable.

This CDI index is also currently used in the relative compatibility rating (RCR) method for determining compatibility between any two dyes for use in binary mixtures of two natural or synthetic dyes, where the differences in the maximum and minimum CDI values are used as a reference for determining the rate of color build-up in the relative compatibility method after dyeing with different proportions of binary mixtures of those two dyes to determine the numeric rating of compatibility between those two dyes under test, from 0 to 5 ratings of compatibility by the said RCR method [15, 16].

Additionally, another newer index, the color matching index”, is defined by the following empirical relationship, depending on the compatibility of the dyes used in a mixture for compound shades for checking the appropriateness of predicted color matching formulations using an appropriate color database after maintaining a linear relationship between the K/S and dye concentrations in computer-aided color match prediction predictions of color data. The compatibility between any two dyes used in a binary mixture (by dyeing under 2 different sets for checking their color build-up rate), (i) by varying the overall dye concentration (taking 50:50 of each dye) in one set while keeping the dyeing conditions fixed and (ii) in another set by varying the dyeing temperature and time profile gradually keeping the dye concentration fixed and then plotting K/S Vs DL and DC vs. DL to check their color build-up rate, which may mathematically be expressed as follows (for the above two sets of dyeing results for the progressive build-up of color in compound shade by a binary pair of any two dyes):

$$\mathrm{Dye}\text{Compatibility Index}\left(\text{between}\mathrm{any}\mathrm{two}\text{dyes used in binary mixture}\right)=\frac{K/S/\mathrm{DL}}{\mathrm{DC}/\mathrm{DL}}\mathrm{i}.\mathrm{e}\frac{K/S}{\mathrm{DC}}$$E23

When the DCI is higher, the dye pair is less compatible, and when the DCI is lower, the dye pair is more compatible. However, this newer index has not yet been widely tested and needs to be tested in different cases to understand/prove its efficacy compared to other methods of determining dye compatibility.

Another newer index, the color matching index (CMI), can also be expressed by the following empirical relationship (irrespective of their sign and direction, ignoring any negative or positive sign) by comparing the color data of the standard sample and produced matching sample, as given below:

$$\text{Color matching Index}\left(\mathrm{CMI}\right)=\frac{K/S\times \mathrm{DC}}{\mathrm{DL}\times \mathrm{DL}\times 100}$$E24

Additionally, CMI values above 5, in any case, are not considered acceptable for compatible dyes to match any standard shade and can be stored in computer-aided color matching databases. However, this CMI value must be widely used by textile industry colorists to understand its true significance in textile color matching.

2.3 Color measuring instruments

The human eye cannot quantify color quantitatively in terms of some useful colorimetric functional terms/data. The comparison perceived by the human eye is subjective only. Moreover, assessments of color vary from person to person in terms of the viewing angle, eye sensitivity, vision defectiveness, eye fatigue, etc. Additionally, color comparisons by human eyes differ with respect to the light source, which is affected by the background/boarder and adjustment, and cannot be used to determine exact reproducibility in repeated assessments in addition to the above eye limitations.

Therefore, there is a need for an instrumental repeatable measurement technique for a color surface or solution in quantitative terms that can provide reproducible results at the saved preset condition of measurement, where the conditions of measurement and detection can be controlled to generate quantitative colorimetric data suitable for comparison in quantitative terms, in addition to facilitating color communication in specified quantitative terms after instrumental measurements.

Fundamentally, there are two types of color measuring instruments—(a) a tristimulus colorimeter and (b) two types of UV–VIS color spectrophotometer—one for measuring the color of a colored solution and the other for measuring the color of colored solid samples.

2.3.1 Tristimulus colorimeter for color measurement

A Tristimulus colorimeter is an instrument used to obtain color response functions that are directly proportional to those of the CIE standard colorimetric values of red, green and blue primaries in terms of tristimulus values (X, Y, and Z). In this instrument, the radiant power from the light source is incident to the colored objects. This reflected radiant power is measured through three tristimulus filters, and the filtered responses fall on the photodetector to determine the spectral response proportion to the corresponding tristimulus values (X, Y, and Z) of the object under a standard light source. These raw data can be transferred to a microprocessor for the comparison of the absolute CIE trust values (X, Y and Z). Colorimetric measurements are thus very easy and quicker to perform with this low-cost colorimeter. However, these instruments can measure only tristimum values and are less accurate; hence, they cannot provide an overall assessment of all types of colorimetric parameters.

2.3.2 A UV–VIS spectrophotometer was used to measure the color of the solution/extracts or solid samples

Hence, for more accurate color measurements, two types of UV–VIS colorimetric spectrophotometers have been designed. There are two types of UV VIS spectrophotometers used for color measurements: UV–VIS absorption spectrophotometers and UV–VIS reflectance spectrophotometers [2, 3]. Thus, (i) an absorbance spectrophotometer can measure the color in liquid or extracted solutions (a) in terms of the absorbance (Abs) of the liquid color sample, and (ii) a reflectance spectrophotometer can measure the color at the surface of the colored solid sample in terms of (b) the surface reflectance (R) of the surface of the colored solid sample at the chosen/preset wavelength or the absorbance maxima wavelength in both the visible region and the UV region for the measurement of the color values of any liquid or solid sample, respectively.

For computer-aided color measurements and matching systems, color measurements, quality control checks of the color produced, match predictions and other arrangements of analysis of color data in different modes are required to judge the efficacy of computer-aided color measuring and matching systems using a reflectance spectrophotometer with a computer and the associated software, which is briefly explained here.

2.3.2.1 Principles of using a reflectance spectrophotometer as a color measuring instrument

The use of reflectance spectrophotometer color measurement techniques has become very popular and widely accepted in the textile, leather and paint industries because of the advantages of linking computer-based operation technology with color measuring spectrophotometric instruments for storing, analyzing and comparing old and new color data at finger tips, particularly with the advent and development of friendly color measuring and match prediction software.

Reflectance spectrophotometers [3] are used to measure the reflectance value or reflected radiant power in terms of red, blue and green responses, which are the same as those of a standard sample at any desired prefixed wavelength or at a predetermined maximum absorbance point. The reflectance spectrometer for color measurement consists of a light source. The emitted light falls incident on the surface of the object at a definite angle or as diffused light on to the object, while the resultant reflected light passes through a mono-chro-meter (of chosen wavelength) or different monochrometers for a measurement in order to arrange a wavelength at a predetermined wavelength value. The mono-chro-meter disperses the incident radiant energy of source light spectrally and transmits it via a narrow band of wavelength through the exit slit. The detector carries a detection system that receives the spectral radiant power reflected from the object and from the standard sample to generate a ratio of spectral signal that is transmitted to the computer for analysis and displayed in different colorimetric terms according to the color measurement software design. With the fundamental reflectance data, it is possible to compute colorimetric data of various types as needed for different applications.

2.3.2.2 Design features of a good color-measuring reflectance spectrophotometer

A good color-measuring reflectance spectrophotometer should have the following design features:

1. The instrument should be robust, shock- and vibration-proof and very simple to operate.

2. The measured colorimetric results obtained from the instrument should be bereproducible, i.e., the repeatability of the results should be within a tolerance limit. For this purpose, an instrument with a symmetrical double-beam optical arrangement with a 0/45 or 45/0 degree angle of incidence and reflectance is preferred.

3. The instrument should be able to measure colorimetric data with a desirable precision level of accuracy for both polychromatic and monochromatic measurements.

4. The sample holding attachment should have small areas of view and large areas of view in the instrument with different types of sample holders for fiber, yarn, fabric and other forms, as needed.

5. The instrument should have autocalibrated and autodiagnostic facilities with Calibrated white tiles and black tiles of known values of X, Y and Z.

6. The desired dataset in usable format for practical use should be easily obtainable from a computer without much modifications.

7. The instrument should be compatible with the latest version of computer operating systems.

8. The color measuring and match prediction software should be very user friendly.

9. The instrument should have the lowest cost and least maintenance requirement.

10. The instrument should not generate heat in the simple holding zone to avoid thermochromatic problems.

11. The transmittance mode should also be available optionally for optional measurements.

Color of a liquid or colored solution:

2.3.2.3 Important technical specifications of the reflectance spectrophotometer

The important technical specifications of a reflectance spectrophotometer are as follows.

1. Spectral Range:

   The instrument’s optics are designed so that they cover the spectral range from 400 to 700 mm for the measurement of color metric data at 10 mm intervals for general practice purposes. However, it is preferable to have a spectral range from 300 to 1100mm, with colorimetric data collected at 5 nm intervals.

2. Illumination and Viewing Geometry:

   CIE has been recommended for geometries defining the direction of the incident light (incident angle of source light) and the direction of detecting the reflected light (the angle viewed by the detector placed appropriately), generally at 0°/45°, 45°/0°, near 0⁰ or 8°/diffuse and diffuse/80°.

   However, these four modes of viewing angles can be graphed into two major types: (i) bidirection types, i.e., having either 40/0 or 0/45 geometries (such geometries are most suitable for measuring samples with smooth surfaces, where the specular mirror reflection is usually exchanged), and (ii) integrated sphere types, where the sample is placed at one of the parts of an integrated sphere coated internally with Near 0⁰ or 8°/diffuse and diffuse/0⁰.

   Geometries (In such geometry, the sample is illuminated either by diffuse light at all angles from the internal sphere, where the reflected light is viewed by the detector at 0° or near Zero 0° or above or near the normal to the sample surface (D/0 geometry); alternatively, the sample can be illuminated at or near Zero degree or at the normal to sample surface, and the reflected light is viewed diffusely (by 0/D geometry). D/o or near 0°, i.e., 8° or 0°/D geometries, can be used to measure samples with relatively nonsmooth sample surfaces, such as textiles or any textured samples. For smooth sample surfaces, an 8°/D integrated sphere-type geometry provides an optional measurement mode for including or excluding specular/mirror reflection by providing an adjustment facility for closing/opening a white specular port/component, with a black trap/hole along the 8° direction of the viewing/detection angle.

3. Light source, optics and optical alignment

   Light Source: A light source/capable of producing light, equivalent to natural daylight, artificial daylight and fluorescent daylight is required for the study of extreme meteorism, etc. A continuous source of pulsed-xenon flash lamp is currently used for this purpose in color-measuring reflectance spectrophotometers.

   Optics: Symmetrical double-beam optics are preferred as suitable for overcoming fluctuations in the intensity of the light source and the response error of photomultipliers by scanning the sample and comparing it with the standard simultaneously. Additionally, both polychrometic and monochromatic illumination/optical arrangements should be available, particularly for fluorescent samples, for which the detection of polychrometic reflectance is essential. For monochromatic measurements, appropriate dispersion elements, such as gratings, filters and continuous wedge interface filters, are used.

   Optical Alignment: The autooptical alignment feature must be a modern color-remaining instrument.

4. Detector: Reflectance from the sample is measured in photodetectors using phototubes/photocells or photomultipliers, which also convert light output into electrical signals. An array of silicon photodetectors acts as an efficient defector and is preferred in modern color-measuring spectrophotometric instruments.

5. Other equipment modes: One of the other important equipment modes is the ability of small-area-of-view and large-area-view modes to measure color for small samples and larger samples, respectively.

   Another mode may be the use of special sample holders (powder, fiber, yarn, etc.) or the provision of UV and l.R. cutoff filters.

   The inclusion of the specular reflection component (SCI) and exclusion of the specular reflectance component (SCE) are other important modes for instrument use for specific purposes. An important feature of modern color measuring instruments is that they have options for autocalibration and autodiagnostic check facilities.

6. Special portable mode of the reflectance spectrophotometer: A specific

   The development of on-line measurements of color in running production samples by a compact small spectrophotometer operated both by a battery and a line current, for more meaningful use of these spectrophotometers for on-site measurement of color in the production center for measuring color from distance, is possible for on-line control of color matching.

   The microprocessor is based on a small screen and is a facilitator for obtaining minimum standard colorimetric data such as color strength, reflectance, witness, and yellowness indices. These data can be transferred to a computer that is suitably interfaced and has appropriate software support for further analysis and comparison for any other specific purpose.

   Such a special reflectance spectrophotometer for on-line remote distance color measurement: Remote/distance measurement of color was not possible earlier with convention. Spectrophotometry, as an ordinary quartz and tungsten lamp light source, produces a light source of considerable/relatively greater duration, causing interference with ancient light, and the results are presented. However, the tenant of the pulsed xenon flash tube, which produces illumination of very short duration (20–30 μs) with very high radiant intensity (10 watts), has a higher signal/noise ratio, as does the use of modern diffraction grating with a detector array of parallel wavelengths (instead of a conventional holographic grating), which can produce a complete spectrum in parallel, where the use of a silicone photodiode detector array enables simultaneous measurement of all wavelengths parrlally and thar from a single pulse of illumination from moving objects or color data from distances from running colored textile fabrics. Online color measurements are now possible under normal light conditions without all other interference, such as air scattering.

Thus, with all the above discussed design features of color measuring spectrophotometers, computer color measuring and matching systems consist of the following:

1. A suitable reflectance spectrophotometer (preferably a double beam integrating sphere-type reflectance spectrophotometer).

2. A current generation computer with graphics design & printer integrated with the above-described reflectance spectrophotometer.

3. A suitable software version compatible with the current operating system for processing databases and color data for computer-aided color measuring and match prediction was developed.

Computer-aided color measurement and match prediction software is primarily based on the matchmetical conversion of basic reflectance data measured by a spectrophotometer to K/s values (as per Kubelka-Mark’s equation), X, Y & Z tristimulus values or L, a, b values (as per C I E’s formula) and the calculation of total color values on textiles by combinations of dyes (using the additive nature of K/s, value using Park & Steorn’s algorithm Allen’s metrix, etc.) for color matching recipe formulation, in addition to calculations of whiteness, yelloness and brightness indices as needed using relevant standard formulas.

Some of the eminent computer-aided color measuring and matching system (CCM System) manufacturing companies, which are widely used in India, are as follows:

1. An Applied Color System, USA.

2. Macbeth, a division of Kollmorgan Co. USA & UK. (Presently Gretag-Mcbeth)

3. Premier Color Scan Instrument Ltd., Navi -Mumbai, India,

4. Instrument color system (ICS), UK.

5. Jay-Kay Pack CCM System by Jay Sinth Color Computer Co., India. Data Colur AG, Switzerland.

6. Pretema, Switzerland.

7. Hunter-Lab, USA.

8. Milton Roy & Co. (Diano Color Products), USA and,

9. Many others, including the BYK gardener in Germany.

2.3.2.4 Preparatory steps for executing color measurement and computer-aided color match prediction

The following are the sequential preparatory steps during its use for color measuring and color matching problem solutions with specific built-in software embedded in the textile industry and other industries:

2.3.2.4.1 Calibration of the instrument (reflectance spectrophotometry)

The calibration of the reflectance spectrophotometer before any new measurement is necessary, so the instrument provides accurate measurements of color values to predict and measure color with accuracy and a perfect scale of measurement.

Color is measured for dyed textiles, papers or leather goods, etc., at 400–700 nm in the visible zone and at 200–390nm in the UV zone (with the arrangement of UV on and off); hence, the instrument is calibrated against standard white tiles (giving a reading of 100% white) and then against standard black tiles (representing maximum absorption, i.e., 100% black). Therefore, with this white tile and black tile calibration, to accurately measure the color parameters on a perfect scale, the instrument scans a standard white tile and a standard black tile, and a reflectance spectrophotometer is used to adjust the reflectance values to the proper scale to obtain accurate measurements.

2.3.2.4.2 Quality control checks for dyed textiles and apparels

In the quality control option, the color data of any sample can be measured and stored for comparison of different color parameters between standard samples and a newer incoming batch produced and thus can be compared against color parameters of any set of existing standards to measure color difference values, variations in color tone with color difference space diagrams, etc., in addition to reflectance and K/S values and metamerism indices, etc., to determine whether those color values are within a tolerance limit, i.e., whether the sample is passed or failed in quality checks for acceptance and rejection decisions.

Thus, a colorimetric analysis of any two dyed/colored samples (produced and standard sample) can be performed in terms of their difference in predominant hue and their L, a, b color differences in terms of lightness/darkness, redness/greenness, yellowness/blueness to understand and correct the tonal variation with determination of color differences by saturation/chroma value and finally by evaluation of the overall position of the L, a and b color coordinates or position of the coordinates of the X, Y and Z tristimulus values in the color space diagram.

The quality control option in the computer-aided color measuring and matching software menu provides graphical plots showing the representation of dye concentration vs. reflectance, dye concentration vs. K/S values, and all graphical representations of any color parameters against 400–700 nm wavelengths in the visible region to determine the predominant wavelengths where the absorption maxima of the color parameters/values are obtained for the measured samples in Ref./compared with any standard sample.

There are options for determining the whiteness index, yellowness index, brightness index, and metamerism index between any two colored samples (produced and standard) when these data are important for quality checks of any colored sample.

2.3.2.4.3 Color measurement parameters

The following are the important color parameters, which can be evaluated using a calibrated reflectance spectrophotometer attached to a CPU processor and dedicated software to process and store the color data useful for color matching of any textile product:

1. The reflectance values (R)/X, Y and Z are the Tristimulus values of any colored textile. The basic measured values from the UV–VIS reflectance spectrophotometer are either the Reflectance values or the Tristimulus values of the colored substrate, which can later be converted to any other color parameter using proper/relevant colorimetric relationships with the respective CIE lab-1976 relevant equations/formulas [2, 3].

2. Color Differences: Color differences between any two samples (standard and produced) were evaluated by analysis of the respective color data observed and graphical plots of color difference analysis.

Color Strength: To evaluate the surface color strength, i.e., the K/S values of a colored sample.

Indices: To evaluate different color indices, such as the whiteness, yellowness, brightness indices and metamerism indices.

Combined Output: To evaluate all color parameters by measuring the reflectance of colored samples in terms of surface color strength and color difference, all graphical plots were combined for analysis of the output results at a glance on a single screen.

Color database: A standard color database is prepared and stored for each dye class for each separate textile substrate for use in predicting color matching recipes.

2.4 Major application areas for computer-aided color measuring and matching systems

The following are the major application areas for computer-aided color measuring and matching systems.

1. The following color parameters were measured: the reflectance (R) and the resistivity (X, Y and Z) at λ_{max (}at the maximum absorbance wavelength) were measured, the K/S values were calculated, and all the color data were stored for further analysis.

2. The color differences were calculated by the CIELab*-1976 formula for determining the total color difference values, i.e., DE* = √[DL*)² + (Da*)² + (Db*)²], for comparison of the color differences in terms of the L*, a* and b* scales and plotting the data in a color space diagram in terms of the L, a and b coordinates, i.e., the DL*, Da* and Db* values in addition to the DC*_h, MI, etc. A quality check of the newer shades of the produced dyed textiles for shade sorting revealed the deviations of the three-coordinate color difference from the central point in terms of the lightness/darkness (DL*), redness/greenness (Da*) and blueness/dieslowness (Db*) color variations with the least possible metameric values (MI-Labd scale).

3. The color database for particular classes of dyes (classwise and companywise) applicable for particular substrates was prepared and stored, and the degree of linearity/nonlinearity was checked by checking plots of dye concentrations vs. K/S values and linearization of those color databases before storing those data as standardized color database files by curve fitting with least square or moving average methods for use in the prediction of computerized color matching formulations to obtain the least expensive and least metameric color matching formulations using previously stored specific types of color databases of different classes of dyes for different companies for specific substrates.

4. To carry out color matching, recipe correction by the manual batch correction method or by the computerized autocrection method is used to obtain precision matching of shades with the least metameric deviations.

5. The calculations were related to the use of dye waste liquor with the remaining color in the waste solution by further decreasing the quantity of dye added to the waste liquor.

6. Prediction of % purity and checking of the quality of the incoming new batch of dyes with the help of transmittance mode.

7. Calculations of indices such as the whiteness index, yellowness index, and brightness index of scoured and bleached textiles at corresponding standard scales following the relevant CIE, Hunter Lab, ASTM-E-313, Stansby/Burger and ISO-2470/Tappi scales were performed.

8. To determine the efficacy of action of optical brighteners or optical bluing agents (OBA) utilizing a computer-aided color measuring and matching system, a particular color dataset was measured with and without UV light in on and off mode, and the results of the peak value in the UV zone and visible zone in UV-in mode and UV-off mode were compared.

9. The quantitative prediction of color fading behavior after washing/crocking/perspiration and exposure to UV light, rather than the conventional visual assessment of loss of depth and staining scale, was performed according to the precision rating of color fastness grades instead of the use of the grayscale rating option.

10. To determine the efficacy of a detergent/surfactant for soil removal, reflectance values were measured before and after washing the soiled clothes under preset washing conditions.

In addition to the above applications, several advanced applications of the UV VIS absorbance colorimetric/photometric spectrophotometer (by using both an absorbance spectrophotometer and a reflectance spectrophotometer) include the following:

1. Identification of specific color constituents of any natural or synthetic color/dye/pigment by UV–VIS spectral scan and its peaks for wavelength vs. absorbance as fingerprints for particular color constituents in a dye or pigment.

2. To study dye compatibility by analyzing the dyeing rate or progressive color buildup under various dyeing conditions—either by (a) varying the dyeing temperature and time profile or by (b) varying dye concentrations while keeping all other dyeing parameters fixed—and then comparing plots of DC vs. DL and by plotting DL vs. K/S vs. DL and by (iii) applying varying proportions of two dyes for compatibility tests and analyzing their color difference index values to determine differences in the CDI for different proportions of dyes used.

3. To study the effects of variations in dyeing conditions (such as dye concentration, mordant concentration for natural dyeing, dye bath pH, dyeing time, dyeing temperature, MLR, and salt concentrations)/any other dyeing additive, the dyeing variables/dyeing process parameters were standardized to obtain the optimum color yield.

2.5 Some practical considerations for accurate measurement of color

The practical aspects of preparing accurate color databases and their linearization for subsequent use in generating newer color matching formulations/recipes using stored standardized color databases are too important. Equally important is setting up proper tolerance values for DE and multiple color difference tolerances in terms of DE*, DL*, Da*, and Db* values under a specified known standard illumination (e.g., D65 or artificial day light, fluorescent light). Accurate measurement of the color data set in a UV–VIS reflectance spectrophotometer depends on the following factors; hence, care during measurement of the color data set should be taken to avoid accurate measurements:

1. Background Opaqueness of the samples mounted by multiple folding of the colored samples.

2. Uniformity in actual dyeing (not exceeding 5% CV of K/S values).

3. Sample orientation (unidirectional, either warp or weftwise prefixed).

4. Changes in the surface structure or scattering of the surface of the sample are avoided.

5. The two-sided nature of the sample may have affected the results.

6. Slabbing or dyeing defects should be avoided.

7. Separate measurements were required for fluorescent colorants.

8. Separate factors to include for dull shade, bright shades, etc.

9. Accurate shades may arise due to inaccurate weights of dyes and chemicals.

3. Some case studies for quality checks of colored textiles and dyed apparel products

3.1 Identification of color constituents (based on the ingredient index) from dyed textiles

Natural dyes such as Indigo can be identified and distinguished from synthetic indigo from natural indigo-dyed cotton textile fabrics by extracting the color component, followed by UV–VIS spectrophotometry. The test method [22] standardized by the BIS to distinguish synthetic indigo from natural indigo is as follows:

First, 0.1 g of each of the two types of indigo dyes (natural indigo and synthetic indigo) was dissolved in 1000 ml of dichloromethane, diluted 5–10 times and scanned through a UV–visible spectrophotometer to obtain the WL vs. absorption plots. In the visible zone, identification of the natural indigo dye can be confirmed from the values of the lambda maxima (absorption/optical density) of both natural indigo and synthetic indigo and by comparison of the characteristic peaks in both plots, as shown in Figure 2. Table 1 summarizes the observed peaks in tabular form in the UV–visible spectra of natural indigo and synthetic indigo.

Figure 2.

UV–vis spectra of natural indigo and synthetic indigo.

Peak value (nm)	For Synthetic Indigo	For Natural Indigo	Remarks on observed differences; In peak value and type
220–295	228 nm (1.18 OD)	293 nm (1.425 OD)	The sharp peak at 228 nm is clear characteristic of UV–VIS peaks of synthetic indigo
605–611	608 nm (0.20 OD)	610 nm (0.201 OD)	The lambda max peak for natural indigo is shifted to 610–611 nm from 608 nm (for synthetic indigo) due to natural contaminants and association of other natural minor matters/constituents in natural indigo.

Table 1.

UV–VIS spectral plots of natural and synthetic indigo.

Thus, the abovementioned UV–vis spectroscopy-based colorimetric analysis helped with the corresponding UV–vis plots to help identify natural indigo compared to synthetic indigo from colored textiles.

3.2 Verifying the linearity of the plots of dye concentrations vs. K/S values for the stored color database

Kubelka and Munk’s functions [2, 3, 4, 5] do not always exhibit a satisfactory linear relation between dye concentration and K/S plots; therefore, a number of empirical formulas, such as color difference index (CDI) [15, 16, 17] values or modified magnetic relationships according to K–M theory [2, 3], have been proposed and subsequently used with caution to achieve accurate shade percentages during the preparation of color databases for storage.

One of the deficiencies of K–M theory is that the optical discontinuity for the intervention of air vs. sample interfaces is not taken into consideration when formulating K–M theory. This optical discontinuity results in the reflection of light from the surface of the specimen kept open in air without even interacting with dye molecules.

A number of theoretical techniques have been proposed to minimize the error introduced in recipe calculations due to the nonlinear relation between the K–M function and the concentration of dyes. If a sufficiently high exhaustion of dye can be achieved at a high level of dyeing, approximately good linearity may be obtained. The simplest way to determine the optical data is to take the average of all absorption coefficient values obtained for specimens at six to eight or eight to ten levels of dyeing during calibration dyeing to prepare/generate a color database for a particular dye class for a specific textile substrate.

For the preparation of a standard color database (Dye Casswise) for computer-aided color matching databases, the following factors are considered for least metameric color match formulations:

1. No layers of folded dyed fabric were used to maintain the uniformity of the produced dyed sample for measuring reflectance and K/S color data for all samples of dyed fabrics.

2. The orientations of the sample are either warp or weft wayall throughout the measurements for the produced dyed samples.

3. Predecided standards for level/unlevel decisions in dyeing interms of the dyeing uniformity value expressed by the % coefficient of variation (CV) for K/S values (within 5% CV for K/S values at different points of colored fabric is considered to indicate uniform dyeing).

4. Color matching specifications of tolerance values for multiple color difference scales (in terms of DE*, DL*. Da* and Db* are predetermined for comparing and predicting color matching formulations. Instrumental data vs. human eye-perceived color differences for match/nonmatch decisions with predecided tolerances, i.e., predecided tolerances of DE* (some where also shown/designated ΔE, both are the same), should be within 0.5 to 1.0, with optional limits of DL*, Da*, and Db* tolerances. DE* values above 1.0 are not generally considered acceptable matched samples.

5. The linear/nonlinear behavior of the plot of dye concentration vs. K/S can be expressed as

After the produced sample was dyed for color database preparation, the sample was checked. Therefore, for all dyed samples, plots (calibration curves of the color database for each dye) of dye concentration vs. K/S must be set to be linear by appropriate curve fitting/linearization processes. If such a calibration curve for any dye is not linear, it can be linearized by the least square linear curve fitting technique or by eliminating a few points/concentrations of dye to make it linear to be stored as a color database for color match prediction for particular fabric and dye class combinations.

With the above point (v) in mind, all color database results of the produced samples are shown in Table 2 and were checked for linearity of dye concentration vs. K/S plots before entering and storing these color databases for each dye for predicting precision color match formulations with that particular selective class of natural/synthetic dyes.

Name and ID of the Dye	Dye conc, K/S (%)		CV% of K/S Value	DE	DL	Da	Db	DC	ΔH	MI	CDI
Dye-1	0.5	5.07	3.86	6.15	4.44	0.13	4.25	4.63	0.13	2.62	0.07
	1.0	6.03	4.24	7.05	4.90	0.16	5.06	4.08	0.15	2.84	0.07
	1.5	6.49	4.14	5.44	3.81	0.46	3.86	4.04	0.42	2.48	0.24
	2.0	6.75	3.90	3.83	3.85	1.21	2.99	3.72	1.08	2.2	0.62
	2.5	7.96	356	6.23	4.19	0.62	4.57	4.32	0.54	2.57	0.28
	3.0	9.75	3.67	6.17	3.59	0.96	4.92	4.00	0.82	2.63	0.39
	3.5	10.49	4.02	5.20	2.84	1.78	2.45	4.15	1.61	1.95	1.04
	4.0	10.54	5.09	5.10	3.00	0.66	4.07	4.57	0.38	2.26	0.21
	5.0	10.87	5.13	5.07	2.98	1.91	4.26	4.51	1.63	2.34	0.81
Dye-2	0.5	4.67	3.60	5.81	3.90	1.84	3.88	12.63	1.60	2.67	0.87
	1.0	4.71	3.41	5.72	4.14	0.97	3.83	14.33	0.87	2.46	0.53
	1.5	5.33	3.40	5.82	3.86	1.89	3.92	14.64	1.65	2.69	0.88
	2.0	5.34	3.83	5.27	3.15	2.30	3.55	15.35	2.02	2.7	1.06
	2.5	5.80	3.90	5.03	3.12	3.28	3.17	15.03	2.89	2.82	1.46
	3.0	5.90	3.10	6.23	3.87	2.18	4.37	15.46	1.86	2.92	0.88
	3.5	6.01	3.37	5.05	3.50	3.07	3.14	15.57	2.71	2.81	1.41
	4.0	6.32	4.18	5.55	2.52	3.40	3.59	15.73	2.96	3.05	1.35
	5.0	6.93	4.12	5.64	2.53	3.60	3.53	15.51	3.77	3.13	1.43

Table 2.

Color database results for two selected dyes applied to the produced samples for calibration dyeing for preparing a color database for precision color matching.

For practical examples of such linearization of the color database, the following example of a simple case study for linearization of the color database produced, for example, for one selective dye, is shown in Table 2, along with the plot of dye concentration vs. K/S values in Figure 3, where linearization was performed, as shown by the other broken lines following curve fitting performed by the least squares method in Figure 3.

Figure 3.

(a) And (b) linearization of K/S vs. dye concentration (0.5%–5.0%) for achieving accuracy in color match prediction in a database for 2 different dyes.

Figure 3 shows that Dye 1 (orange line) had an almost linear relationship with the Dye concentration vs. the K/S plot, with minimum variation and lower ranges of the CV % of the K/S value and greater ranges of DC values (Table 2) compared to those of Dye-2, showing much greater linearity (blue–violet line) for the Dye concentration vs K/S plot (Figure 3). Hence, the linear curve fitting principle must be applied in the case of Dye-1, as shown by the extra fitted linear curve shown therein, and the linearized data must be stored as a color database. Similarly, more dye calibration data for preparing a total color database for particular classes of dyes will be needed.

3.3 Study of the compatibility of dyes for producing compound shades

There are many methods to test compatibility between any two dyes, and hence, compatibility can be determined in different ways. However, only two methods are popular: (i) the conventional method of comparing the color build-up rate by dyeing under two different SETs—by varying the dyeing time and temperature profile in one set and by varying the dye concentrations in another set—and then to compare plots of K/S vs. DL and DC vs. DL, where two dyes under the test reference showing the degree of similarity of the rates of color build-up in these two sets are more compatible.

Recently, another easier and simple colorimetric method called the relative compatibility rating method [15], which is based on calculations of differences in the CDI of dyes with various proportions of any two dyes and can be used to rate compatibility on a 1–5 scale, was established. Both of these methods are shown as examples for dyeing cotton with direct dyes, as given below:

3.3.1 Conventional methods for dye compatibility testing

This method is based on a comparison of the gradual color build-up rate for dyeing under two sets of variations during dyeing for gradual color build-up, i.e., (i) SET-1, by varying the dyeing time and temperature profile (keeping the dye concentration and dyeing process variables fixed), and (ii) SET-II, by varying the overall dye concentration of the 50;50 binary mixture of any two dyes (keeping the dyeing time and temperature and other dyeing parameters fixed). The data for 4 RBGY colored selected binary pairs of direct dyes taken for dyeing a preselected cotton textile substrate are shown here in Tables 3–5.

Dyeing profile	SET-I: At Varying Dyeing Time/Temperature profile (min, °C)					SET-II: At Varying dye concentration (in parts) and prefixed other conditions of dyeing
Dye-1: Dye-2; 50:50	K/S	ΔL	Δa	Δb	ΔC	Dye parts	K/S	ΔL	Δa	Δb	ΔC
Undyed	0.53					0
50 °C, 10 min	1.82	15.7	10.6	13.8	7.83	10	1.33	10.9	0.57	11.1	4.52
60°C, 20 min	1.88	14.4	7.41	13.4	2.34	20	1.73	12.1	0.75	12.8	2.75
70°C, 30 min	3.81	15.4	8.72	13.9	3.15	40	2.27	12.5	0.36	12.9	2.71
80°C, 40 min	3.76	12.0	1.2	12.0	3.51	60	3.13	13.4	0.109	13.2	2.36
90°C, 50 min	3.87	12.5	2.8	11.8	3.10	80	3.82	13.5	1.082	12.9	2.47
100°C, 60min	3.62	11.6	3.0	11.1	3.59	100	5.53	13.9	3.37	12.8	1.56

Table 3.

Color parameters under two sets of color build-up experiments for testing dye compatibility for selected binary mixtures of two dyes (50:50): M1: Direct red color-1 and direct green color-2.

Dyeing profile	SET-I: At varying dyeing times/SET-II: At varying dye concentrations (in parts) and temperature profile (min, °C) prefixed for other conditions of dyeing
Dye-2: Dye-3; 50:50	K/S	ΔL	Δa	Δb	ΔC	Dye parts	K/S	ΔL	Δa	Δb	ΔC
Undyed	0.53					0	0.53
50 °C, 10 min	1.4	8.63	13.5	9.65	5.49	10	1.22	8.55	14.02	9.51	5.91
60°C, 20 min	1.75	8.58	13.7	9.30	5.57	20	1.75	9.78	14.36	10.3	6.37
70°C, 30 min	2.06	9.58	14.8	10.4	6.84	40	1.98	9.96	15.82	10.9	7.92
80°C, 40 min	2.62	9.79	15.0	11.1	7.19	60	2.53	9.84	15.32	10.6	7.36
90°C, 50 min	2.72	9.97	15.2	10.7	7.34	80	3.48	9.89	14.79	10.3	6.78
100°C, 60min	3.38	10.1	15.2	10.5	7.19	100	3.63	10.3	14.93	10.9	7.05

Table 4.

Color parameters under two sets of color build-up experiments for test of dye compatibility for selected binary mixtures of two dyes (50:50) M2: Direct green Color-2 and direct T. Blue Color-3.

Dyeing profile	SET-I: At varying dyeing times/SET-II: At varying dye concentrations (in parts) and temperature profile (min, °C) prefixed for other conditions of dyeing
Dye-2: Dye-4; 50:50	K/S	ΔL	Δa	Δb	ΔC	Dye parts	K/S	ΔL	Δa	Δb	ΔC
Undyed	0.53					0	0.53
50 °C, 10 min	5.61	1.29	9.97	9.14	11.6	10	2.68	1.05	10.27	3.85	7.47
60 °C, 20 min	5.96	1.35	10.0	7.45	10.2	20	3.62	1.35	10.96	3.86	7.93
70°C, 30 min	6.36	1.97	11.2	4.74	8.77	40	4.52	2.08	11.46	3.43	7.95
80°C, 40 min	8.37	3.76	10.9	0.99	5.96	60	5.56	3.45	11.24	1.06	6.19
90°C, 50 min	8.53	4.06	11.2	0.39	5.33	80	6.51	3.13	11.29	1.38	6.43
100°C, 60 min	4.47	6.25	10.9	2.87	3.86	100	8.93	3.80	10.64	0.04	5.08

Table 5.

Color parameters under two sets of color build-up experiments for test of dye compatibility for selected binary mixtures of two dyes (50:50) M3: Direct green color-2 and direct yellow color-4.

The DL vs. K/S and DL vs. DC plots of the corresponding data from the above Tables 3–5 for Set-I and Set-II indicate no particular trend of the color build-up rate for M1 (direct red color 1 and direct green color-2) for both sets, and these two dyes are therefore poorly compatible. For M2: Direct Green Color-2 and Direct T. Blue Color-3.

Similarly, both Set-I and Set-II had moderately compatible colors, and M3 (direct green color-2 and direct yellow color-4) had increasing colors in the same direction in both Set-I and Set-II; however, these colors varied here, and they were judged to be fairly compatible, as per the conventional test of dye compatibility, as understood from Figure 4. Compatibility tests of K/S vs. DL and plots of DC vs. DL for each binary pair of dyes, M1: Direct Red color-1 and Direct Green color-2, M2: Direct Green color-2 and Direct T. Blue color-3 and M3: Direct Green color-2 and Direct Yellow-4 pairs of direct dyes applied to cotton, are shown below:

Figure 4.

DC vs DL and K/S vs DL plots for dyeing results under two dyeing conditions, set-I and set-II, for determining the progressive color build-up rate to test the compatibility of dyes in the conventional colorimetric analysis method for M1: Direct red color-1 and direct green color-2; M2: Direct green color-2 and direct T; and blue color-3 and M3: Direct green color-2 and direct yellow color-4.

3.3.2 Newer RCR method for dye compatibility testing

In this newer and simple method [15, 16, 17], the dyeing of selected fabrics was carried out with each mixture of binary pairs of selected dyes in varying proportions, and the colorimetric measurements are shown in Table 6. The differences in the CDI values for different proportions of dyes used are shown in Table 7, where the relative compatibility.

Dyes/combination	K/S at ·ℷmax	CV % of K/S	∆E	∆C	∆H	MI	BI	CDI
For 75:25 ratio of each color/dye in each of M1,M2 and M3 binary pairs of dyes taken
M1 (D Red color-1:D Green color-2)	5.76	5.10	21.02	−14.44	15.12	4.41	9.88	4.99
M2(D. Green color-2:DT.Blue color-3)	7.66	3.67	20.03	14.49	16.74	5.07	9.43	4.56
M3(D. Green color-1: D Yellow color-4)	10.08	5.13	21.08	13.27	10.34	4.02	6.92	4.08
For 50:50 ratio of each color/dye in each of M1,M2 and M3 binary pairs of dyes taken
M1(D Red color-1:D Green color-2)	6.71	8.37	20.16	−14.87	13.87	4.31	6.69	4.36
M2(D. Green color-2:DT.Blue color-3)	8.00	6.25	21.99	16.53	18.32	4.89	9.93	4.80
M3(D. Green color-1: D Yellow color-4)	10.54	7.13	18.53	14.10	18.47	5.23	5.43	4.64
For 25:75 ratio of each color/dye in each of M1,M2 and M3 binary pairs of dyes taken
M1(D Red color-1:D Green color-2)	7.19	2.13	19.47	−14.33	12.82	5.05	8.37	3.44
M2(D. Green color-2:DT.Blue color-3)	8.68	3.67	22.30	16.86	18.56	5.15	10.89	4.76
M3(D. Green color-1: D Yellow color-4)	11.58	8.39	21.14	14.61	15.59	5.69	4.86	3.94

Table 6.

Colorimetric analysis of data for testing dye compatibility by the RCR method by dyeing any two selected dyes in different proportions for cotton fabrics dyed with each binary mixture of direct dyes in three different proportions.

Binary pair of Dyes combination	CDI values			CDI max – CDI min difference	RCR; From chart	Compatibility grade as per RCR values from chart
Prportion of dyes	75:25	50:50	25:75	—
M1(D Red color-1:D Green color-2)	4.99	4.36	3.44	4.99–3.44 = 1.55	2	Poor
M2(D. Green color-2:DT.Blue color-3)	4.80	4.98	4.76	4.98–4.76 = 0.22	3–4	Moderate
M3(D. Green color-1: D Yellow color-4)	4.08	4.64	3.94	4.64–3.94 = 0.70	2–3	Fair

Table 7.

The color difference index (CDI) and maximum differences in the CDI values [CDI_max – CDI_min] were compared with the charted relative compatibility rating/grade (as per the RCR values charted in Table 8) to determine the compatibility rating.

Ratings/grades for each binary pair of selected dyes are assigned/graded according to the chart value of the rating (as shown in Table 8 as per earlier established chart values [15, 16, 17]) with respect to the maximum differences in the CDI values obtained, as shown in Table 7. Both conventional methods and simple newer RCR methods for compatibility testing between selected dyes showed the same or similar results, proving the equal efficacy of the newer RCR method for dye compatibility tests; hence, this method may be considered useful for industry because it is quicker and easier to use than other conventional methods.

Compatibility Grade	RCR values	Differences between maximum CDI and minimum CDI values among all dyeing results for use of all proportions of binary pair of dyes taken for dyeing *
Excellent	5	>0 but ≤0.05
Very Good	4–5	>0.05 but ≤0.10
Good	4	>0.10 but ≤0.20
Moderate	3–4	>0.20 but ≤0.30
Average	3	>0.30 but ≤0.40
Fair	2–3	>0.50 but ≤1.00
Poor	2	>1.00 but ≤5.00
Very Poor	1–2	>5.00 but ≤10.00
Worst	1	>10.00 but ≤15.00
Non-compatible	0	> > 15.00

Table 8.

Chart values [15*] for relative compatibility rating (RCR) vs compatibility grade.

*

Source: [15].

3.4 Computer-aided color match prediction data and its practical use

For the prediction of computer-aided color matching formulations for any or different known and unknown standard samples, different color matching formulations can be easily obtained after setting allowed/permissible color matching alternatives under specific illuminants and under specific instrumental setups, resulting in different levels of metamerism and cost.

Among all the predicted color matching formulations generated, the least cost and least metameric match results are identified for practical use. One such match prediction formulation is shown in Table 9, where formulation 1 is the least metameric and formulation 2 is the least expensive.

Standard Name		=	C-25	Cotton	Formulation report				Mode = R	Spectro-RFL
R	2.28	2.50			3.1287	4.09	4.74	4.27	4.41	4.71	5.08
6.19	5.48	5.08	4.82		4.512	4.31	4.07	3.88	3.79	3.61	3.33
File name: AKS-2			Database: AKS-Cotton-Direct Dye Set-3					Nos. of Dyes: 3 dyes combination
Dye ID# selected		All	=	D-1, D-2, D-3, D-4, D-5, D-6, D-7, D-8, D-9, and D-10							On cotton
DE*, D65’. 1.00		Tolerence of DE-value =				1.00 t = 100		Batch Amount = 100 kg
Dye Exhaustion factors (included)			1.0		Substrate ID#- 3, Substrate Name: Bleached cotton- 3 & Substrate factor = 1.0
Colorant ID# and Recipe/Formulation		Dye Cost	Amount for 100 kg batch and also in %.		da*	db*	dL* ClE		dE*	Rs (Dye Cost)
Formula#1		(Rs/kg)					Least metameric match
1	Dye-1	320	10.0 (1.0%)		D	−0.11	−0.10	0.04	0.05	32.00
5	Dye-5	400	18.5 (1.85%)		A	−0.61	0.20	0.0	0.21	74.00
9	Dye-9	390	07.0 (0.07%)		F	−0.42	0.31	0.11	0.52	27.30
	Total dye		35.5 (2.92%)							133.30
Formula # 2							Least cost match
1	Dye-1	320	8.0 (0.08%)		D	−0.40	−0.30	0.0	0.51′	25.60
2	Dye-2	300	12.5 (1,25%)		A	−0.52	0.31	0.0	0.61	37.50
6	Dye-6	350	13.5 (1.35%)		F	−0.50	0.31	0.32	0.72	47.25
	total dye		34.0 (1.68%)							110.35

Table 9.

Predicted color matching formulations were generated using a specific color database stored for a specific substrate (cotton) for a specific dye class from a specific company.

**Note = This color matching formulation/recipe was generated in a computer-aided reflectance spectrophotometer (Make: Premier Color Scan Pvt. Instrument Ltd., Model SC 5100A, associated with Color-Lab plus software) using a formulation menu and direct dye database prepared in the laboratory for color match prediction for cotton with a predetermined tolerance of ΔE* within 1.0.

After one formulation (e.g., Formulation 2 as the least cost recipe) from among the two or more formulations generated, actual/practical lab dyeing must be performed with the predicted percentage of the mixture of dyes/colorants to check the practical match results of the produced samples by comparing their color parameters, for a particular match of the standard sample (C-25), after practical dyeing in the laboratory and then in the bulk, and then to compare the predicted and actual practically measured color difference parameters of the produced samples against the standard sample given to match.

Table 10 contains comparative color data for further analysis of predicted formulations vs. actual dyeing results for checking the acceptability of matching formulations with newer color matching indices, as shown below.

Sample ID	Dye used/and type of fabric	K/S at 420 nm	CV % of K/S	∆E	∆L	∆a	∆b	∆C	∆H	MI	CMI
Sample C25-Std Sample	(Standard Dyed cotton)	6.98	5.07	55.17	−42.88	16.84	30.27	8.92	−22.98	5.62	—
Predicted Formulation −2 (vide Table-5)	No actual Dyeing, predicted data-	—	—	−.51	−0.41	0.30	0.0	—	—	—	—
Predicted Formulation −1 (vide Table-5)	No actual dyeing, predicted data	—	—	0.50	0.04	−0.11	0.10	—	—	—	—
Dyed-with Formulation-2 (Least cost)	Actual dyed with F-2	6.80	4.17	0.90	0.35 (darker)	−0.25 (greener)	−0.21 (bluer)	7.30	−20.47	7.61	4.05
Dyed-with Formulation-1 (least-metameric)	Actual Dyed with F-1	6.68	4.57	0.50	0.40 (Less darker)	−0.30 (less greener)	−0.0 (Still bluer)	7.25	−19.47	6.91	3.02

Table 10.

Comparative color data of formulation 2 and formulation 1 by determination of color matching index (CMI) after actual dyeing for matching against standard given colored samples for checking match acceptability.

The higher the color matching index (CMI) is, the greater the deviation of the color parameters of the predicted computer-aided color match results from the results of the actual dyed sample, i.e., the lower the efficiency of the computer-aided color match-predicted formulations. Among the two predicted formulations generated in Table 9, two predicted formulations vs. actual dyeing with formulations 1 and 2 are compared:

Table 10 indicates that a closer match is obtained by Formulation 1, which is the least metameric, with a CMI of 3.02 compared to the CMI of 4.05 for formulation 2, which is the least expensive formulation. However, as CMI values for both formulations are within 5.0, they are considered acceptable matches. However, for any predicted formulation, after practical dyeing, with a CMI greater than 5, there is a reasonable difference/deviation in color parameters after actual dyeing against the predicted formulation; hence, this difference should be corrected by batch correction tools until a more precise match with a CMI within 5 is achieved.

3.5 Effects of variations in the concentrations of minerals (for natural dyeing) and dye/pigment colored components and other dyeing process variables

To understand the effects of mordant concentrations and dye concentrations and other dyeing conditions/dyeing process variables may be studied by varying one.

The other parameters of the dyeing variables are fixed – by varying one by one (e.g., the dye concentrations are varied, while the other dyeing variables are fixed and held constant), and the observed dyeing results can be assessed/compared to determine which value of the input parameter of the dyeing process (Say– dye concentration) yields the optimum color yield or maximum K/S value, and that value of the dyeing parameter is taken as the optimum to standardize it, as per the data shown in Tables 11 and 12, for the variation of all the different parameters of a specific dyeing process for a particular combination of mordant and dye for a specific natural dyeing process. to standardize the staining conditions. For example, in a specific case study, for overall varying concentrations of Harda (H) and Potash alum (Al), which are combined dual mordants applied in sequence on cotton, and for varying dye concentrations (2–8% purified catechu extract dye), the observed dyeing results for varying dyeing parameters are shown in Table 11.

Sample	K/S	ΔE	ΔL	Δa	Δb	ΔC	ΔH	BI	MI	CDI
Control	0.055							80.12
H-10%- Cat-2%	3.28	50.45	−36.96	17.09	29.77	25.66	−22.81	9.63	1556.37	0.028
H-10%-Cat-4%	3.98	49.45	−36.06	16.19	28.97	24.76	−22.20	9.33	1520.37	0.030
H-10%-Cat-6%	4.77	48.38	−36.11	15.61	28.16	23.48	−22.03	9.15	1516.19	0.029
Al −10%-Cat-2%	4.57	49.97	−36.83	17.87	28.67	25.25	−22.44	6.74	1548.97	0.027
Al −10%- Cat-4%	4.81	50.21	−38.49	15.71	28.15	23.53	−22.03	6.02	1588.25	0.029
Al −10% - Cat-6%	4.98	56.12	−40.88	15.84	29.27	23.90	−22.08	5.98	1665.94	0.028
Al-H-5%, + Cat-2%	4.46	48.95	−38.58	15.62	25.76	21.57	−21.02	10.23	1583.57	0.030
Al-H-10% +,Cat-2%	4.57	49.97	−36.83	17.87	28.67	25.25	−22.44	10.24	1548.97	0.027
Al-H-15% +,Cat-2%	5.19	50.94	−39.49	16.66	27.51	23.59	−21.36	10.27	1604.94	0.028
Al-H-20% +,Cat-2%	5.46	51.73	−41.53	16.51	26.05	22.36	−21.24	9.16	1641.49	0.029
Al-H-5%, + Cat-4%	5.78	52.73	−41.37	17.00	27.92	24.14	−22.06	8.50	1642.16	0.029
Al-H-10%, + Cat-4%	5.81	50.21	−38.49	15.71	28.15	23.53	−22.03	8.02	1588.25	0.029
Al-H-15%, + Cat-4%	5.37	54.12	−42.59	16.86	28.82	24.75	−22.41	9.40	1667.54	0.029
Al-H-20% +, Cat-4%	5.21	51.96	−39.72	17.33	28.66	24.91	−22.38	7.18	1610.89	0.029
Al-H-5% + Cat-6%	5.99	57.38	−45.91	17.94	29.37	25.85	−22.73	8.60	1723.81	0.029
Al-H-10%, + Cat-6%	6.98	55.12	−42.88	16.84	30.27	25.90	−22.98	5.62	1675.94	0.029
Al-H-15%, + Cat-6%	6.55	54.13	−41.37	16.34	30.12	24.7	−22.28	8.34	1645.32	0.029
Al-H-20% +,Cat-6%	6. 78	52.29	−41.98	17.29	29.67	25.12	−21.29	7.56	1610.12	0.027
Al-H-10%, + Cat-8%	6.58	54.12	−41.88	15.84	29.27	24.90	−21.98	5.92	1680.94	0.030

Table 11.

Variation in single and double mordants and dye concentrations caused by the addition of various concentrations of potash alum (Al) plus Harda (H) [Al-: H: 50:50] dual mordant in sequence and 2–8% purified catechin extract as a natural dye.

Sample	K/S	ΔE	ΔL	Δa	Δb	ΔC	ΔH	BI	MI	CDI
Control	0.055							80.12
H-10%- Cat-2%	3.28	50.45	−36.96	17.09	29.77	25.66	−22.81	9.63	1556.37	0.028
H-10%-Cat-4%	3.98	49.45	−36.06	16.19	28.97	24.76	−22.20	9.33	1520.37	0.030
H-10%-Cat-6%	4.77	48.38	−36.11	15.61	28.16	23.48	−22.03	9.15	1516.19	0.029
Al −10%-Cat-2%	4.57	49.97	−36.83	17.87	28.67	25.25	−22.44	6.74	1548.97	0.027
Al −10%- Cat-4%	4.81	50.21	−38.49	15.71	28.15	23.53	−22.03	6.02	1588.25	0.029
Al −10% - Cat-6%	4.98	56.12	−40.88	15.84	29.27	23.90	−22.08	5.98	1665.94	0.028
Al-H-5%, + Cat-2%	4.46	48.95	−38.58	15.62	25.76	21.57	−21.02	10.23	1583.57	0.030
Al-H-10% +,Cat-2%	4.57	49.97	−36.83	17.87	28.67	25.25	−22.44	10.24	1548.97	0.027
Al-H-15% +,Cat-2%	5.19	50.94	−39.49	16.66	27.51	23.59	−21.36	10.27	1604.94	0.028
Al-H-20% +,Cat-2%	5.46	51.73	−41.53	16.51	26.05	22.36	−21.24	9.16	1641.49	0.029
Al-H-5%, + Cat-4%	5.78	52.73	−41.37	17.00	27.92	24.14	−22.06	8.50	1642.16	0.029
Al-H-10%, + Cat-4%	5.81	50.21	−38.49	15.71	28.15	23.53	−22.03	8.02	1588.25	0.029
Al-H-15%, + Cat-4%	5.37	54.12	−42.59	16.86	28.82	24.75	−22.41	9.40	1667.54	0.029
Al-H-20% +, Cat-4%	5.21	51.96	−39.72	17.33	28.66	24.91	−22.38	7.18	1610.89	0.029
Al-H-5% + Cat-6%	5.99	57.38	−45.91	17.94	29.37	25.85	−22.73	8.60	1723.81	0.029
Al-H-10%, + Cat-6%	6.98	55.12	−42.88	16.84	30.27	25.90	−22.98	5.62	1675.94	0.029
Al-H-15%, + Cat-6%	6.55	54.13	−41.37	16.34	30.12	24.7	−22.28	8.34	1645.32	0.029
Al-H-20% +,Cat-6%	6. 78	52.29	−41.98	17.29	29.67	25.12	−21.29	7.56	1610.12	0.027
Al-H-10%, + Cat-8%	6.58	54.12	−41.88	15.84	29.27	24.90	−21.98	5.92	1680.94	0.030

Table 12.

Effects of variation of single and double mordants and dye concentrations by varying concentration of potash alum (Al) plus Harda(H) [Al-: H: 50:50] dual mordant applied in sequence and 2–8% purified catechu extract as natural dye.

The effects of varying other dyeing process parameters and other dyeing conditions, such as dye bath pH, time of dyeing, temperature of dyeing, MLR of dye bath, and salt % added to the dye bath, on the color yield/surface color strength are shown in Table 12.

All other dyeng process variables were held constant, with the variation of the abovementioned dyeing conditions one after another for natural catechu dyeing on cotton, with 10% overall application of alum plus hardness (50:50) applied in sequence, followed by 6%.

The aqueous extract of the purified catechu dye powder gave the best/maximum K/S values and was more uniform. With the mordant and dye concentrations fixed, the effects of other dyeing conditions/dyeing process variables, such as dye bath pH, time of dyeing, temperature of dyeing, MLR of dye bath, and salt percentage, on the color yield/surface strength show maximum/optimum color values, which are considered the optimum dyeing conditions (Table 12) for the use of a dyeing time of 60min and a dyeing temperature. At 70°C and a dye bath pH of −4-5 (acidic) with an MLR of 1:30 and a salt concentration of 5 gpl, a 10% overall application of Alum plus Harda (50:50) double mordant was applied in sequence, followed by dyeing with a 6% aqueous extract of purified catechu powder under the abovementioned standardized dyeing conditions.

The above data in Table 12 indicate a greater dye yield at an acidic pH of 4–5 than at an alkaline pH of 8–12 for 10% overall Al + Harda (50:50) dual premorded cotton, which had the highest k/s value of approximately 6.95–6.98.

3.6 Analysis of color fastness to washing, light and rubbing for the use of single and double mordants

The wash fastness of the natural dyed cotton fabrics was evaluated according to the IS 3361–1979 method. The lightfastness of the naturally dyed cotton fabric was evaluated according to the IS: 2454: 1985 method. Additionally, the rubbing fastness of naturally colored cotton textiles was evaluated by the IS M766–1988 method [23].

Thus, the relevant data in Table 13 indicate that dual mordanting provides better color yield and higher wash fastness.

Mordant and Dye Used	Color Fastness to
	Washing		Light	Rubbing
	LOD	ST		Dry	Wet
H-10%-Cat-2%	3	3	3	3	2
H-10%-Cat-4%	3–4	3–4	2–3	3–4	1–2
H-10%-Cat-6%	3	3	3	3–4	1–2
Al −10%-Cat-2%	3–4	3–4	3–4	3–4	2–3
Al −10%- Cat-4%	4	3–4	3–4	3–4	2–3
Al-10% - Cat-6%	3–4	3	3	3	2
Al + H-10%(50:50)- Cat-2%	3–4	3–4	3–4	3–4	2–3
Al + H-10%(50:50)- Cat-4%	4	4	3–4	3–4	3
Al + H-10%(50:50)- Cat-6%	4	4	3–4	4	3

Table 13.

Color fastness results for 10% Harda single premordanted and 10% overall concentrations of alum and Harda (50:50) dual premordanted cotton fabric dyed with varying concentrations of catechu.

Moreover, by changing the extraction solvent or varying the extraction conditions, the color depth may be altered in the case of natural dyes applied to any textile. To eliminate such variations, separate standardization of dyeing process variables with the use of different mordant combinations (for example, the use of K-Al and Gall nut combinations instead of K-AL + Harda combinations results in different color yields). Moreover, alcoholic extraction, sonicator-assisted extraction or microwave-assisted extraction results in better color yields and other responses, such as UV resistance or antimicrobial properties, at different scales.

Thus, from the results of varying concentrations of single and double mordants and varying dyeing process variables, etc., and color fastness to wash, light and rubbing, as shown in Tables 11–13, few important points of observations arise from these concentrations, which may be explained scientifically as follows:

3.6.1 Why acidic pH shows better color yield than alkaline or neutral pH of dye bath?

Initially, upon the immersion of cotton in dye bath water, the surface of cotton contains ions that do not support the absorption of anionic natural dyes; however, cationic materials and acid protons gradually negate this negative charge on cotton and allow more dye to be absorbed on premordanted and/or acidic protonated cotton to absorb more anionic natural dyes. Moreover, the acidic pH of the dye bath facilitates the quicker and greater anionization of natural dye anions in the dye bath, allowing more dye anions to be absorbed by the cotton than does the alkaline pH of the dye bath. In addition, the anionic part of the acidic salt/acid mixture used in dye baths causes the common ion effect to make dye absorption slightly slower as a retarder; hence, more uniform dyeing results in an acidic pH bath, which is an added advantage in this case.

3.6.2 Why does the use of double mordants produce a greater color yield than the use of a single mordant?

The simple answer is to use dual or double mordants using two types of mordants that act in two different ways for fiber-mordants and natural dye complexing, where the metallic mordant, such as Al-from alum, acts to form complexes with natural dyes either by producing coordinated covalent bonds or simply by hydrogen bonding or both, while the 2nd mordant harda or gall nut, being tannates of biossources, acts to form larger dye-fiber complexes with these already aluminum mordanted fibers and dyes utilizing their tannate/tannic acid functionally; thus, a larger/larger complex of [cotton fiber mordant 1-mordant-2-natural dye] with two mordants is formed, providing superior dyeability and better color fastness to wash.

3.6.3 Is there any reason why different tannin-based biomordants show different results when dyeing cotton with the same natural dyes?

The simple explanation is that it is obvious that two types/two different biomaordants have different functionalities. For example, harda containing chebulinic acid provides functional COOH and few OH groups to form more complexes with both dye and fibers, while gall nuts containing elagic acid and gallic acid tannates or flavonoids provide much higher tannate contents than tannate content of harda; hence, such complexing of these tannate-based biomordants with fiber and natural dye is expectedly more or more for gall nut extract than harda extract used separately as biomordant.

3.6.4 Is there any differences in extraction of color components under two different solvents, if so, why?

The simple answer is that different types of solvents have different abilities to extract color components and are associated with other contents in the two different extracts obtained by the use of two different solvents or their mixtures.

For example, when eucalyptus leaves or barks are extracted with boiling water or an alcoholic solution, the results of the compositional analysis of these two extracts differ. Water extraction results in yellowish light brown liquor with less eucalyptol and other components, but 50:50 water and ethyl or methyl alcohol-based extracts at 70°C result in much deeper brown liquor with more or greater percentages of colored eucalyptol and other components extracted, as well as few UV-resistant components, which are not extractable by water boiling only.

4. Concluding remarks

Thus, the use of a UV VIS Reflectance Spectrophotometer and a UV VIS Absorbance Spectrophotometer attached to a suitable high-definition Computer-CPU/work station and dedicated software for managing instrumental color measurements, storing a color database and maintaining records of color match prediction and match corrections for textile and apparel products has become common practice in a textile dyeing finishing process house for maintaining color quality by achieving precision match results.

In addition, quality control activities for checking color quality for dyed textiles and apparels, i.e., determination of batch-to-batch color differences after dyeing each new batch, were performed to evaluate differences in color values in terms of the DE*, DL*, Da*, and Db* color difference CIE 1976 scale of color measurements.

Computer-aided UV–VIS reflectance spectrophotometers and suitable application software have been used to modernize dye houses in the textile and apparel industry to determine the quality of the dyeing results.

Thus, dye uniformity and reproducibility of color match results have become easily possible; hence, any industrial colorist can confidently reproduce any color/shade using computerized color measurements and match prediction systems within a permissible minimum total color difference (DE) of DE = 1 for cotton, silk, and woolen textiles and within a DE value = 2 for jute-based textiles/decorative colored/printed bags, home furnishings, etc.

The effects of any variation in the dyeing process parameters or dyeing conditions on the color yield and colourfastness to wash, light and rubbing fastness of any textile fabric dyed with direct, reactive, or natural acid dyes can be studied, and accordingly, e-Shade cards can be prepared. However, in some cases where the color fastness is not adequate, methods for improving the color fastness can be tested via different posttreatment methods.:

Pilot plant trials for the reproduction of computer-aided dyeing recipes/formulations using a Lab mini jigger have been completed, and the accuracy of the database for determining color matching recipes can be checked by using the color matching index as a newer index.

To produce compound shades, testing for compatibility of individual dyes in a binary pair of dye mixtures can also be performed. The quality of dyes, dyeing uniformity, etc., and other important color parameters for the production of wash-fast and lightfast dyed textile and apparel products can be separately identified for the use of precision color quality.