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 deals with basic principles of color quantification and measurement of different known color parameters, color differences, dye uniformity, setting up of tolerances for different parameters of color differences for assessing color matching efficacy. Few advance analysis of color related issues for textile and apparel industry have made this task easier with the aid of computer aided color measuring and matching system as indispensable tools. Besides that, identification of colorant molecules on colored products by using UV-VIS spectrophotometry become essential now a days. Analysis of quantifiable grade/rating of compatibility in between two dyes for a pair of binary mixture of colorants (dyes and pigments), to get a desirable shade also become 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 measuring and matching and quality check of dyed/colored products of textiles and apparel industry, have been discussed here along with principles of working and design features of reflectance spectrophotometer. 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 colorist and researchers.

1. Introduction

Color is primarily a visual sensation in the human brain, producing relevant sensation in the retina of human eye in a combined presence of light source, colored object and perfect eye. Perception of color by human brain is turned as color response and it relates to stimulation of retina (comprising Rod cells and Cone Cells) by light spectrum reflected from the object after absorbing a part of light from incident light spectrum of light source. The retina of human eye have two type photosensitive cells: Rod cells and Cone Cells. Rods are sensitive to dullness or brightness of light and is situated of the periphery of eye ball. Cones are sensitive to color hues and mainly situated in the forea region of the eye and these cells are sensitive to color in three different wave length bands – long, medium and short bands, which in brain-eventually yields output sensation interpreted as Red, Green and Blue and/or proportional combinations of these three primary colors. The spectral sensitivity of human eye corresponding to three primary/basic colors (Red, Green and Blue). In the visible region of human eye (400–700 mm), the color of the object seen is the complementary color wave length reflect, or emits sensed by human eye after absorption of certain wavelength by the coloring mater (dye or pigment etc.) from the source light spectrum fall on it. Hence, in a complete dark, we cannot see any object colored. The chemical constitution of the colorants (Dye or pigment etc.) is responsible for absorption of a part of the incident light energy. Human eye can observe and sense a color subjectively and can compare, but it cannot be compared quantitatively in term of any numeric value for which human color perception differs from person to person for many reasons like (a) for a difference or change in light source spectrum, (b) difference in observer’s/sensitivity power, defective vision and fatigue of eye. (c) difference in direction and angle of vision etc. There are many other variable factors such as adjacent color, background color, border color, size of the sample, surface texture and scattering, variation in observes angle and also on setting of the instrument.

According to Trotman [1] dye stuffs are specified organic molecules having conjugation, that possess capability of absorbing a certain part of light selectively and to reflect the other part of light in visible zone. Ever since, many investigations have been reported their work, to know which part of the dye molecules of natural/synthetic dyes/pigments, are responsible for visible color of that object visible with a predominating hue at specific wave-length.

According to Shah and Gandhi [2] Color is defined as psycho-chemical phenomenon with a psychological stimulus to human eye. So, to a chemist, it is a chemical compound with conjugations, to a physicist it is a multiple physical phenomenon occurring simultaneously like reflectance, absorption and scattering of light wave, 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 of reflected color waves. To artist & other common person, it is the sensory stimulative in the brain of the human being as observers.

Ordinarily, for many industrial processes and products, accuracy of appearance of color to human eye is difficult to differentiate one color from another by only red-blue-green responses of human eye. According to earlier report by Samanta [3] for use of computer aided color measurement and matching for textiles, apparels, paint, leather, pharmaceutical, polymer/plastic, paper and food etc., it need to work hard on preparing color data base for checking and maintaining color matching quality, as color parameters of these products is an important product specification too along with other physical or chemical properties. Earlier, color specification were assessed by visual methods of human eye perception, but now-a-days, assessment of color strength produced for particular hue and color difference for color matching from a given or desired standard shades, have been started assessing instrumentally, by a computer aided color measuring system. Hence Computer aided color match prediction has also gradually started in all these industry for precision match.

There are many disputes and misconceptions [2, 4] on match/non match decision in color matching and its assessment made by visual human eye procedures. So an instrumental precision measurement techniques of instrumental practical measuring color and checking of its color matching results in terms of quantified values of color differences in red, blue, green, yellow and dark or light scale, should be considered as a better option than human perceived color (assessed by visual method).

In any Industry [2, 3, 4] the desired matched color is obtained by application of mixture of two, three or four dyes/pigments. In few Industries, still a experienced senior dyer maintains a record of a hard copy shades produced with a small piece of attached sample for color matching of different substrate from his day to day experience, called as ‘shade bank’. So for next sample of color matching to do, that Sr. experienced dye master first select one of the color recipe from his shade bank as very nearer or very closer matching to the color of standard sample given for matching. And then add or subtract color and do more nearer or nearest matching by trial and error of actual dyeing repetitively. However, Present instrumental system of computer aided color match can be obtained from saved color data base of that class of dyes. This instrumental system can predict much closer match with expected color parameters and color strength to produce within limit of set tolerances within few minutes. Even if, exact color matching if not obtained in first predicted formulations, it is possible to add or subtract amount of certain color in the predicted formulation either by automatically menu by computer aided color matching system to obtain the exact match or manual addition is possible also.

Due to increase in variety of natural and manmade fibers and their use in multi-fibers blended textiles and availability of almost endless number of companies made synthetic dyes and pigments available in the market. So, special care of nature and type of fibers and type of dyes used are also important for the determining color recipe formulation using a computer aided color measuring and matching system. The color matching formulation should also be most economical and least metameric, reproducible, most uniformly dyed, having moderate to good wash and light fastness property in the matched product as a better quality colored textile product, which is not easy to achieve, without use of computer aided color measuring and matching tool [3, 5].

This new technology of using computer aided color measuring and matching tools, enables the expert computer savvy dye- master, easily to predict nos of color matching formulations/recipes with multi-option to select the most appropriately least cost 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 DE, DL, Da and Db values and Labd metamerism index value).

The computer assisted color-measuring and color-matching system applicable for any textile industry or apparel industry or other industry is therefore considered as a powerful tool to the colorist’s hands to improve the quality of precision color matching of their products and provides great opportunity for keeping less dye inventory, less cost of production and less time requirement for predicting and producing a good color matching recipe.

The colored molecules (dyes and pigments) can also need to be identified sometimes from colored textiles and other products, for assuring nature of color molecules used and its properties, eco friendliness etc. Colored molecules can be identified from colored textiles by subjecting it to extract 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, and then subjecting the extract to UV-VIS spectroscopic analysis to obtain wave length vs. Optical density/absorbance curves as finger print of particular color ingredient/molecules present there. This may also be done by Chromatography, HPLC, NMR, LC-MS and FTIR analysis etc. to confirm. However, after dyeing textiles with mixture of colors to get a particular compound shade, more important in the industry to measure color variation for variation of dyeing conditions, dye and mordant (in case of natural dyeing) concentrations and % application/proportions of different dyes admixed in order to obtain a perfect match of color/shade, within certain prescribed and acceptable limit of tolerances of different color parameters. For compound shades another parameter known as colorant/dye compatibility has also become very important factor too, to produce a shades of precision match.

Davidson et al. [6] first introduced an analogy for using computing device for predicting a color recipe from pre-prepared stock of color data base used for color matching of wall-paint initially and gradually that was applied to textiles and apparel products successfully.

At present [3, 4] almost every dye house in textile industry^, uses computer aided color measuring and matching system. For generating/predicting color matching recipe, from pre-prepared color data base, checking instrumental color differences for check of quality of precision matching results and to carry out batch correction for achieving a better match, if the predicted recipe do not work in one shot. A computer aided color measuring and matching system therefore involves three basic assemblies, viz.:

1. Color measuring unit/instrument: i.e. Reflectance Spectrophotometer, which measures 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 in computer to a meaningful color parameter.

2. Color matching software: A dedicated software is used for precision color measurement and prediction of color matching formulation by the aid of a suitable reflectance spectrophotometer, as instrumental aid, for following color measuring and match prediction sequences by expressing the color related measured/stock data converted to a desirable color parameter which are relevant for obtaining direct results/matching formulations, as per requirement of the colorist.

3. Computer hardware: A PC with high resolution and high storage capacity and quicker data processing system to operate, detect and display data and curves as results.

2. Basic principles of working of computerized color measuring and matching tools

Precision color matching [2, 3, 4] for textile substrate is mainly based on Kubelka munk function [2, 3, 4, 5, 6], Park and Stearn’s [7] algorithms and Allen’s [8] matrix for generating more closer recipe by computer aided color matching formulation [9] program using a reflectance spectrophotometer, suitable color data base for specific substrate and specific dye class, and a dedicated software for calculations of required color parameters and match prediction formulation program processed in a attached computer to the spectrophotometer.

Multiplication of variabilities of dyeing process to achieve accurate color measurement by computer aided color matching system, need pre-optimization of dyeing process variables and to minimize all other processing and measuring errors in the preparation of color data base and in generation of color matching formulation [3, 9] by assumptions, approximation, simplification and iteration etc. considering color matching of any product is an iterative trial and error process. Manual matching of color by expert/skilled colorist/dyers, never can achieve that level of precision in color matching of any product as computer aided system can give. So, manual color matching do not have easy acceptance by the customers, as manual color matching shows an wide variation or differences of color variation, as human eye perception varies from person to person. For instrumental matching variation from illuminate to illuminate or instrument to instrument are also be there, but that may be minimized by choosing least metameric match. Thus, for Instrumental color matching vs. manual color matching, former one (instrumental color matching) has many more advantages over manual process.

The basic process of computer aided color matching [2, 3, 4, 5, 9] for predicting color matching formulation for textiles and apparel industry, it involves following five steps to follow:

1. Preparation of color data base and its storing (Color type or dye class wise, company wise and substrate wise)

2. Determination of optical constants or K/S values of all data base samples and standard samples

3. Pre-fixing allowable color difference tolerances for prediction of acceptable formulation under standard illuminant

4. Sorting color matching formulations on least cost and least metamerism basis to compare after actual dyeing trial

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

Optical constants of 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 multilevel concentration to obtain linear relationship between dye concentrations vs. K/S plots for each dye, after dyeing specific textile substrate with different concentrations level of colorants of specific class of dyes from same company, at 8–10 different levels and 1 sample as control undyed sample as substrate. All calibration dyeing for color data base are to be done by using optimized dyeing process parameters for uniform dyeing results for color-data base. An accurate color data base preparation is the basic need for generating precision color matching formulations within a specifically given tolerances of multilevel RBGY-scale of color differences. After checking of accuracy of color data base prepared, these data are stored in the computer aided color matching library system, which are stored in a separate file for future use. Accuracy of color data base for each colorant should be checked by linearity [3, 5] in each case by plot of dye concentrations Vs K/S values and if it is not linear, either re-dyeing or approximation/deletion of some particular points or by linear curve fitting are to be done appropriately. Once the color data base is ready, generation of color match formulations can be done by following iterative process of color match prediction algorithm following the flow chart given below (as Figure 1), where this process steps remain in built in the attached dedicated software present in computer aided color matching system):

Figure 1.

Flow-chart of Computer aided color matching algorithm for Prediction of color-match formulation.

For further correction of shades, the reformulation of batch correction mathematics for computing the incremental value of incremental concentration of one or two colors for different dyes may be required to be used by 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

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

To generate recipe formulation for color match prediction process, it requires measurements of standard shade’s color value first aiming at to match on a specific substrate, and then selection of dyes as per dye inventory/color data base type available to use is to be selected with assigning or fixation of required tolerance limits [2, 3, 5, 6] for the color differences values and then the computer color matching system starts determining/predicting possible match formulations from specific dye-class data-base source available/stored in the system after actual dyeing. The color value in terms of tristimulus values of standard sample to match are to be computed under specific light D65 source with the predicted match formulation. By comparing them with their color data and optical constants etc., for predicted formulation from a specific class of dyes and its stored color data bank, the unknown concentration of specific dye mixture required for a matching with particular color standard is determined with the help of software made for this.

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

The solution of the above color matching equations for computer aided color match prediction (CCMP) usually follow those of Kubelka munk function [2, 3, 4, 5, 6], Park and Stearn’s [7] algorithmic equation and Allen’s [8] color matrix for recipe of computer aided color matching formulation [9] program,. An isomeric match, meant match under all illuminate, while two colored sample when appears as matched under one illuminate but do not match under any other illuminate, that phenomenon is termed as illuminate metamerism and that type of match is called metameric match. Our Objective is to find minimum metameric or least metameric match prediction and selection of formulation sorting is based on the determination of general metamerism index or illuminate metamerism index to keep least metamerism, Some improvement in computer aided color matching formulation based on minimum color difference tolerances, less or least metameric effects etc. are minimized [12, 13].

However, due to practical variations in dyeing conditions, machinery setting and human interventions of dyeing operator etc., there may be still some differences in actual color yield dyed by predicted formulations, which then however need some corrections, called batch correction, to achieve/improve the desired precision of color matching quality of the produced sample against color of the given standard dyed product [14]. A newer test method of analyzing dye compatability in relative quantitative rating/grades for use of mixture of colors to obtain uncommon compound shades on textiles has been established recently, applicable for both for natural dyes [15, 16, 17] and synthetic dyes [18], the details of which are available from current relevant literature on it.

Effect of dyeing process variables on color yield and color fastness properties for cotton khadi fabric dyed with de-oiled Redsandal wood waste [19] are analyzed using colorimetric analysis after dyeing cotton fabric with red-sandal wood waste as natural dyes.

Improvement of color fastness of natural dyed textiles and its antimicrobial properties are reported in recent literature with detailed colorimetric analysis [20]. Application of computer aided color measuring and matching instruments for textile and apparel dyeing industry has been comprehensively described by P Samanta [21] with its pros and cons.

Android phone based color measurement and color data analysis i.e. digital imaging technique for color parameter tests and color fastness assessment with digi-eye software with achieving maximum accuracy are present interest of color scientist and industry dyeing managers. Clarion color fastness app has already been developed in this direction by Kuraray, Japan.

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

Basics of quantitative analysis of colourmetric parameters for colored textile and apparel products are therefore depicted below: for understanding of the subject clearly for following proper analysis of it.

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 values or by total reflectance (R_λmax) values or its derivative formula function like K/S values, 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 = Spectral power distribution of standard source.

R_λ = Spectral reflectance of substrate.

X_λ. Y_λ. Z_λ = color factor of standard observer for red, blue and green sensation of human eye.

To describe color in two dimensional plot called CIE color space diagram, CIE defined 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 & from the two third can be found out.

A color match [2, 3, 4, 5] means: color of produced sample (SL) = color of given standard (SD) i.e. (X_SL,Y_SL,Z_SL) = (X_SD,Y_SD,Z_SD), while X,Y & Z are the tristimulus value of 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 Kubelka Munk function called color strength, which is Coefficient of absorption divided by Co-efficient 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 description and figures in a book chapter of Intech open published book on colorimetry.

For a mixture of colorants used to match a compound shade, the following three equations are to be 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 measurement [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 tristimulus values of samples to be matched and C_1, C_2, and C₃ or C_n are concentration of dyes or color pigments required. In practice, the reflectance values at 400 to 700 nm are measured from solid colored textile surface and those reflectance data are processed for K/S values generation for ultimate matching. Reflectance vs. concentration of dye is non-linear and non-additive, so this Reflectance vs. dye concentration data can not be directly used as basic data for handling color match prediction, rather plots of dye concentration vs. K/S values, being linear and the K/S values being additive also, the K/S data can be used for computer aided color match prediction. The empirical relationship of Kubelka Munk Function [2, 3, 4, 5] (i.e K/S Values) varies with dye concentrations linearly…

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

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

Thus higher the K/S value, meant higher absorption value for dyes or mixture of dyes, meant higher absorption value signifying or indicating higher dye uptake.

Thus K/S α C_D = α C_D; α being a constant.

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

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

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

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

For textiles, for the particular sample (Material, Yarn & fabric construction & surface finish remaining unaltered), scattering remains constant. So, in textiles, it is called single constant theory for. K/S = α C_D.

Finally reflectance Vs. dye concentration is not linear and hence is difficult to interpolate or curve fitting to predict achievable color strength from any values of reflectance or tristimulus values of dyes.

While K/S Vs. dye concentration is linear and hence it can be interpolated to any desired dye concentration and thus can be used [2, 3, 4, 5] in computer aided match prediction software safely to predict color matching formulations for two/three dyes mixture 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

Chroma, (psychometric chroma) values in Cl ₁₀₄ color space was 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 standard sample and produced sample.

CIE 1976 metric Hue-Difference (∆H_ab = [(∆E_ab*)² – (∆L*)² – (C_ab*)²)]^1/2.

An isomeric match i.e. match under all illuminant & when two colored sample show match under one illuminant but do not match under any other illuminant is termed as metameric match [2, 3, 4, 5]. Least metameric match prediction and sorting is based on the calculation of 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 pair of metamer;

$\overline{x}$, $\overline{y}$, $\overline{z}$ = CIE standard observer color functions;

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

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

$${\mathrm{MI}}_{\left(\text{LABD}\right)}={\left[{\left(\mathrm{D}{L_1}^{\ast }-\mathrm{D}{L_2}^{\ast }\right)}^2+\left(\mathrm{D}{a_1}^{\ast }-{\left(\mathrm{D}{a_2}^{\ast }\right)}^2\right.+\left(\mathrm{D}{b_1}^{\ast }-{\left(\mathrm{D}{b_2}^{\ast }\right)}^2\right.\right]}^{\mathrm{½}}$$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 of predicting color matching formulations

Prediction of color matching formulation is based on use of binary or ternary mixture of different colorants to obtain a resultant compound shade. Effective coloration with mixture of dyes, depend on compatibility between dye to dye i.e. rate of color build up when dyed under a specific optimized set of dyeing conditions. So, Before Predicting color matching formulation, determining dye-compatibility between any two dyes/pigments is another important parameter. Along with visual assessment, assessing rate of dyeing, assessing coefficient of dye diffusion test and to determine the rate of progressive color build up of both individual dye in a binary mixture of two dyes, when dyed under a optimized dyeing conditions. The last one is 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 binary mixture of dyes together, the textile substrate under reference is to be dyed under two different sets of dyeing conditions for test of dye compatibility (in one set by varying profile of dye concentration keeping other dyeing conditions fixed and in second set- dyeing is to be done with variation of dyeing time and dyeing temperature, keeping dye concentration and other dyeing parameter fixed). After this two sets of dyeing under different conditions, the rate of increasing chroma/hue for gradual color build up with increase in dyeing time/temperature and with increase in dye concentrations, are to be checked by comparing pattern of color build up by plots of K/S Vs DL and DC vs. DL in this conventional method. But this conventional method of dye compatibility test have many limitations like more time consuming, needing high analytical skill and the processes are cumbersome steps to follow and are not quantifiable in terms of degree of compatibility. Hence, a newer and simple advanced method of dye compatibility analysis between two dyes/pigments has been now established by Samanta and Agarwal [15, 16], which is very simpler, quicker and easy to apply.

The Second important issue before computer-aided color match prediction o run for any textile substrate, is preparing and storing of accurate color data base to store dyeing results of calibrated dyeing samples on same textile substrate with specific class of 9–10 dyes o different colors, after checking accuracy of dyeing results by check of linearity of dye concentration vs. K/S values for each colorant and after checking of dye uniformity (less than 5% CV of K/S values).

So after measuring the color of the dyed samples under commonly used illuminants say D65; the Reflectance, K/S values and L*, a*, b* values results of calibrated dyeing are recorded and stored in color matching computer system.

The reflectance spectrophotometer measures reflectance values at different wavelength from 400 to 700 nm at 10 nm interval and this data is used by computer aided color matching software to calculate the DL*, Da*, Db* and DE* scale of color difference values for newly dyed/produced sample as compared to the standard colored fabric to match. Now from computer aided data base earlier stored for each class/type of dye and each color of that specific class of dye with its cost, stored in the system, are used by the Computer aided color match prediction software using the calculation based on its linearity and additive nature for relationship between dye concentrations and K/S value for prediction of a particular color match using different binary or ternary mixture of dyes i.e. using the basis of following CIE-equations to calculate total K/S value of dyed sample to match the K/S value of standard sample to match, as already shown above and reiterated 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 buildup of color yield from the mixture of total amount of different dyes used in the mixture, for matching total K/S values between the standard sample to match and the total color strength (K/S) value of the newer produced sample, are the determining point of match predicting formulations showing amount required for C1 concentrations Dye- D1, C2 concentrations of Dye-D2 and C3 concentrations of Dye-D3 and finally gives 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 different dyes/colorants having closely similar hues, thereby, proving/making Computer aided color match prediction system, as a more advanced tool, for better quality and precission color, match prediction for more practically useful purposes of an industrial dyers/colorists.

Color difference 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, gives ideas on further need of batch corrections for eliminating minor differences in any of color difference notations to meet the desirable or pre-set tolerances of DL, Da, Db and DE values. But in actual practice dyers feel difficult to control any one of the said color difference values amongst DL, Da and Db, without affecting other tolerances.

Hence, another newer index on color differences is established recently, known as Color difference index (CDI) values [15, 16], to obtain easy understanding of overall color differences and also dispersion of color on fabric surface after dyeing for use of different dyeing variables/conditions, by the following given equation (considering magnitudes of the relevant ∆E, ∆H, ∆C, 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

Color difference index (CDI) value below 5, is considered as acceptable for preparing color matching data base, as this difference is within the tolerance of human eye perception of limit of color differentiation. However, higher is the color difference index (CDI) value, it meant higher is the non -uniformity in dyeing having wide variation in color strength by comparison of one dyed textile sample with the other and that match is not acceptable.

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

Also another newer index said as Color matching Index” is defined by following empirical relationship, depending on compatibility of the dyes used in a mixture for compound shades for checking the appropriateness of predicted color matching formulations using appropriate color data base after maintaining linear relationship of K/S Vs Dye Concentration in computer aided color match prediction storing 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 overall dye concentration (taking 50:50 of each dye) in one set keeping dyeing conditions fixed and (ii) in another set by varying dyeing temperature and time profile gradually keeping dye concentration fixed and then to either plot K/S Vs DL and DC vs. DL, to check their color build up rate, which mathematically may be expressed as follows (for above two sets of dyeing results for progressive buildup of color in compound shade by 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

Higher is the value of DCI, the dye pair are less compatible and lower the value of DCI, the dye pair is the more compatible. Though this newer index is not yet tested widely and need to test for different cases to understand/prove its efficacy as compared to other methods of determining dye compatibility.

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

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

Also CMI values above 5, in any case, is not considered as acceptable dyeing data for compatible dyes to match any standard shade, that can be stored in computer aided color matching data base system. However, this CMI value must go through wider use by the textile industry’s colorists, to understand its true significance in textile color matching.

2.3 Color measuring instruments

Human eye cannot quantify color quantitatively in terms of some useful colormetric functional terms/data. The comparison perceived human eye is subjective only. Moreover, assessment of color varies from person to person with the viewing angles, eye-sensitivity, defectiveness of visions, eye fatigue-ness etc. Also the color comparison by human eyes alters with light source, affected by background/boarder and adjustment and can not judge exactly reproduceness in repeat assessment besides above said eye limitations.

So, there is a need of a instrumental repeatative measurement technique of a color surface or solution in quantitative terms, measurable to give reproducible results at the saved pre-set condition of measurement, where conditions of measurement, and detection can be controlled for generating quantitative colourmetric data suitable for comparison in quantitative terms, besides facility of color communication in specified quantitative terms, after instrumental measurements.

Fundamentally, color measuring instruments are of two types – (a) Tristimulus Colorimeter and (b) UV–VIS color Spectrophotometer of two types –one for measuring color of colored solution and another type for measuring color from colored solids samples.

1. Tristimulus colorimeter for color measurement.

   Tristimulus colorimeter is an instrument to obtain color response functions that is directly proportions 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, radiant power from the light source that incidents to the colored objects. This the reflected radiant power based are measured through three tristimulus filters and that filtered responses falls on the photodetector, to give a spectral response proportion to corresponding tristimulus values (X, Y, and Z) of the object under standard light source. This raw data can be transferred to a microprocessor for the comparison of the absolute CIE trustiness values (X, Y and Z), Colorimetric measurement thus is very easy and quicker to operate under this low cost colorimeter. But these instrument only can measure tristimulaus values and are less accurate and hence, cannot give an overall assessment of all sorts colorimetric parameters.

2. UV-VIS Spectrophotometer for color measuring from solution/extracts or solid samples.

Hence, for more accurate color measurements, UV–VIS colorimetric spectrophotometers of two types have been designed. There are two types of UV VIS spectrophotometer for color measurements – One type is UV–VIS absorption spectrophotometer and another type is UV–VIS Reflectance Spectrophotometer [2, 3]. Thus, (i) Absorbance spectrophotometer can measure color in liquid or extracted solutions (a) in terms of absorbance (Abs) of liquid color sample and (ii) Reflectance spectrophotometer can measure color at surface of colored solid sample in terms of (b) Surface reflectance (R) of surface of the colored solid sample respectively, at chosen/pre-set wave length or Absorbance maxima wavelength or at both in visible region and also in UV region, for measurement of color values of any liquid or solid sample respectively.

For computer aided color measurements and matching system, color measurements, quality control check of color produced, match predicted and other arrangement of analysis of color data in different mode are required to judge the efficacy of computer aided color measuring and matching system using a reflectance spectrophotometer with a computer and required software attached to it, which is briefly explained here.

2.3.1 Principles of working of a reflectance spectrophotometer as color measuring instrument

Color measurement techniques by use of Reflectance Spectrophotometer has become very popular and widely accepted in Textile, leather and paint industry, because of its advantages of linking computer based operation technology with color measuring spectrophotometric instrument, for storing, analysis and comparing old and new color data at finger tips, particularly with advent and development of use friendly color measuring and match prediction software.

Reflectance Spectrophotometers [3] is used to measure reflectance value or reflected radiant power in terms of red, blue and green responses, comparing the same with that of a standard sample at any desired prefixed wavelength or at predetermined maximum absorbance point of wavelength. The reflectance spectrometer for color measuring consist of a light source. Whose emitted light falls incident on the surface of the object at a definite angle or as a diffused light on to the object, while the resultant reflected light passes through a mono-chro-meter (of chosen wave length) or different monchrometer for a measurement in arrange a wave-length at a predetermined wave length value. The mono-chro-meter disperses the incident radiant energy of source light spectrally and transmits it via a narrow band of wave length through the exit slit. The detector carry a detection system that receives the spectral radiant power reflected from the object and also from the standard sample, to generate a ratio of spectral signal that is transmitted to the computer for analysis and display in different colourmetric terms as per color measurement software design. With the fundamental data of reflectance value, it is possible to compute all sorts colorimetric data for various type as per need for different applications.

2.3.2 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 coloromatic results obtained from the instrument should be reproducible i.e. repeatability of the results should be within a tolerance limit, For this the instrument with symmetrical double beam optical arrangement with 0/45 or 45/0 degree angle of incidence and reflectance is preferred.

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

4. The sample holding attachment should have small areas view and large areas of viewing in the instrument with different type of samples holders for fiber, yarn, fabric and other form, as required.

5. The instrument should have auto calibration and auto - diagnostic facility with calibrated white tiles and black tiles of known values of X, Y and Z.

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

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

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

9. The instrument should be of least cost and least maintenance requirement.

10. The instrument should not generate heat to the simple holding zone, to avoid thermo-chromatic problem.

11. Transmittance mode should also be available optionally for optional measuring color of a liquid or colored solution:

2.3.3 Important technical specifications of reflectance spectrophotometer

The Important Technical Specifications of a reflectance spectrophotometer is as follows.

1. Spectral Range:

   The instrument’s optics is to be so designed that the it covers the spectral range from 400 to 700 mm for measurement of color metric data at 10 mm internal for general practiced purposes. But it is preferable to have spectral range from 300 to 1100 mm, with provision measurement of colorimetric data of 5 nm interval.

2. Illumination and Viewing Geometry:

   CIE has 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 or.

   However, these four mode of viewing angles can be graped into two major types (i) Bi-direction type, i.e. having either 40/0 or 0/45 geometries (such geometries is most suitable for measuring samples with smooth surface, where usually, the specular mirror reflection is exchanged) and (ii) Integrated sphere type; where the sample is placed at one of the part of an integrated sphere coated white 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 at near the normal to the sample surface (D/0 geometry) or 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 sample with relatively non-smooth sample surface such as textiles or any textured samples. For smooth sample surface, 8°/D integrated sphere type geometry provide 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 viewing/detection angle.

3. Light Source, optics and optical alignment.

   Light Source: Light source/capable of producing light, equivalent to natural day-light, artificial day light and Fluorescent daylight is required for study of eliminant meteorism etc. Continuous source of pulsed-xenon flash lamp is used now a days for this purpose in color measuring reflectance spectrophotometer.

   Optics: The symmetrical double beam optics is preferred as suitable optics to overcome the problem of fluctuations on the intensity of light source and response error of photomultipliers etc. by scanning the sample and comparing with the standard simultaneously. Also both poly-chrometic and mono-chromatic illumination/optical arrangements should be available, particularly to deal with fluorescent samples, for which detection of poly-chrometic reflectances is essential. For monochromatic measurement, appropriate dispersion element such as gratings, filters and continuous wedge interface filter are used.

   Optical Alignment: Auto optical alignment feature is must is modern color remaining instrument.

4. Detector: Reflectance from sample is measured in photo-detectors using phototubes/photocells or photomultipliers, which also convert light output into electrical signals. An array of silicon photo-detectors act as efficient defector to be preferred in modern color measuring spectrophotometric instruments.

5. Other Equipment mode: One of the other important equipment mode is the facility of small area of view and large area view mode for measurement of color for small sample and larger sample respectively.

   Another mode may be the use of special sample holders for different from (powder, fiber, yarn etc.), or also provision of UV and l.R. cut off filter etc.

   Inclusion of specular reflections component (SCI) and exclusion of specular reflectance component (SCE) is another important mode to be present with instrument for specific purposes. The very important feature is modern color measuring instrument to have option for auto calibration and auto diagnostic check facility.

6. Special Portable mode of Reflectance spectrophotometer: A specific development of on line measurement of color in running production sample by a compact mode of small spectrophotometer operated both by battery and line - current, for more meaningful use of these spectrophotometer for onsite measurement of color in the production center for measuring color from distance, is made possible for on line control of color matching.

   The microprocessor based small screen on it, facilitators to get minimum standard colourmetric data like, color strength, reflectance, witness, & yellowness indias etc. These date can be transferred to the computer suitably interfaced and with appropriate software support, for further analysis and comparison for any other specific purposes.

   Such Special Reflectance spectrophotometer for On-line remote distance color measurement: Remote/Distance measurement of color was not earlier possible with convention! spectrophotometer, as ordinary quart & tungsten lamp light source, produces a light source of considerable/relatively higher duration, causal interference with ancient light and gave results. But with the tenant of pulsed xenon flash tube which produces illumination of very short duration (20–30 μs) with very high radiant intensity (10 watts) having higher signal/noise ratio as well as use of modern diffraction grating with detector array of parallel wave length (instead of conventional holographic grating) which can produces a complete spectrum in parallel, where use of silicone photo-diode detector array enables simultaneous measurement of all wavelength parrlally and thar from a single pulse of illumination from a moving objects or color data from distance from a running colored textile fabrics. The on line color measurement is now possible at normal light keeping aside all other interference like air scattering.

Thus, with all the above discussed design features of color measuring spectrophotometer, a computer color measuring and matching systems consists of the followings:

1. A suitable reflectance spectrophotometer, (preferably double beam integrating sphere type reflectance spectro-photometer.

2. A current generation computer with graphics design & printer integrated with the above said reflectance spectro photometer.

3. A suitable software version compatible with current operating system for processing data base and color data for computer aided color measuring & match prediction.

Computer aid Color measuring and match prediction software is primarily based on matchmetical conversion of basic reflectance data measured by a spectrophotometer to K/s value (as per Kubelka-Mark’s equation), X, Y & Z tristimulus values or L, a, b values (as per C I E’s formula) and calculating total color value on textiles by combination of dyes, (using additive nature of K/s, value using Park & steorn’s algoritm Allen’s metrix etc) forcolourmatching recipe formulation, besides calculations of whiteness, yelloness and brightness indices as per need using relevant standard formula.

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. 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. Instrumental color system (ICS), UK.

5. Jay-Kay Pack CCM System by Jay sinth Color Computer Co, India

6. Data Colur AG, Switzerland.

7. Pretema, Switzerland.

8. Hunter-Lab, USA.

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

10. Many others including BYK gardener, Germany etc.

2.3.4 Preparatory steps for executing color measuring and computer aided color match prediction

Following are the sequential preparatory steps during its use for color measuring and color matching problems solution with specific in-built software embedded there for textile and other industry:

2.3.4.1 Calibration of the instrument (reflectance spectrophotometry)

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

Color is measured for dyed textiles, papers or leather goods etc. in 400–700 nm in visible zone and 200–390 nm in UV zone (with arrangement of UV on and Off) and hence the instrument is calibrated against a standard white tiles (giving a reading of 100% White) and then by a standard black tiles (representing maximum absorption i.e. 100% Black). So by this white tile and black tile calibration, in order to set measurement of color parameters in perfect scale with accuracy, the instrument scans a standard white tile and a standard black tile, the reflectance spectrophotometer, then adjust its reflectance values to the proper scale to give accurate measurement.

2.3.4.2 Quality control checks for dyed textiles and apparels

In Quality Control option, color data of any sample can be measured and stored for comparison of its different color parameters between standard samples and a newer incoming batch produced and thus can be compared against color parameters of any set existing standard to measure color differences values, variations in in color tone with color difference space diagram etc. besides reflectance and K/S values, metamerism index etc. to check whether those color values are within a tolerance limit or not, i.e. whether sample is passed or failed in quality check for acceptance and rejection decision.

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

Quality control option in the computer aided color measuring and matching software menu, gives a graphical plots showing representation of dye concentration Vs reflectance, dye concentration Vs K/S values, and all graphical representation of any color parameters against 400–700 nm wavelength in the visible region to determine the predominating wavelength where absorption maxima of the color parameters/values are obtained for the measured samples in reference/compared with any standard sample.

There are options for determining whiteness index, yellowness index, brightness index, metamerism index between any two colored samples (Produced and standards), when these data are important for quality check of any colored sample.

2.3.4.3 Color measuring parameters

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

1. Reflectance values (R)/X, Y and Z values as Tristimulus values of any colored textiles: Basic measured values in UV–VIS reflectance spectrophotometer are either Reflectance values or Tristimulus values of colored substrate, which later can be converted to any other color parameters using proper/relevant colorimetric relationship using respective CIE lab-1976 relevant equations/formulae [2, 3].

2. Color Differences: To evaluate Color Differences between and any two samples (Standard and Produced) by analysis of respective color data observed and graphical plots of color difference analysis.

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

Indices: To evaluate different color indices like Whiteness, Yellowness, Brightness Indices and Metamerism indices.

Combined Output: To evaluate all color parameters by measurements of reflectance of colored samples in terms of surface color strength, color difference, with all graphical plots together for analysis of output results at a glance in a single screen.

Color data Base: A standard color data base is to be prepared and to be stored for each dye class for each separate textile substrate, for use in predicting computerized prediction of color matching recipe.

2.4 Major application areas for use of computer aided color measuring and matching system

The followings are the major application areas for use of computer aided color measuring and matching system.

1. For measuring the following color parameters: Reflectance value (R) and Tristimulus (X, Y and Z) values at λ_{max (}at maximum absorbance wave length) and calculation of K/S values and storing all color data for further analysis.

2. Calculation of color differences by CIELab*-1976 formula for determining total color difference values i.e. DE* = √[DL*)² + (Da*)² + (Db*)²], for comparison of color differences in terms of L*, a* and b* scale and plotting those data in color space diagram in terms of L, a and b co-ordinates i.e. DL*, Da* and Db* values besides DC*_h, MI etc. Quality check of newer shades of produced dyed textiles for shade sorting – representing deviations of three coordinates color difference from a central point in terms of Lightness/Darkness (DL*), Redness/Greenness (Da*) and Blueness/Yellowness (Db*) color variations with least possible metameric values (MI-Labd scale).

3. Preparation and store of color data base for particular class of dyes (class wise and company wise) applicable for particular substrate, checking of degree of linearity/or non-linearity by checking of plots of dye concentrations vs. K/S values and to do linearization of those color data base before storing those data as standardized color data-base file by curve fitting with least square or moving average methods, for its use for prediction of computerized color matching formulation to obtain least cost and least metameric color matching formulations using previously stored specific types of color data-base of different classes of dyes for different companies for specific substrate.

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

5. Calculations related to utilization of dye waste liquor with remaining color in the waste solution, by further less quantity addition of required dyes in waste liquor.

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

7. Calculations of indices like whiteness index, yellowness index, and brightness index of scoured and bleached textile in corresponding standard scales of those indices following relevant CIE, Hunter Lab, ASTM-E-313, Stansby/Burger and ISO-2470/Tappi scales etc. as per requirement.

8. To determine the efficacy of action of optical brighteners or optical bluing agents (OBA) utilizing the Computer aided color measuring and matching system measuring a particular color data set with and without UV light under on and off mode and to compare the results of its peak value at UV zone and visible zone at UV-in mode and UV-off mode.

9. Quantitative prediction color fading behavior after wash/crocking/perspiration and exposure against UV -light – instead of conventional visual assessment of loss of depth and staining scale, according to precision rating of color fastness grades instead of use of gray scale rating option.

10. To determine the efficacy of a detergent/surfactant for its Soil removal criteria by measuring reflectance values before and after washing of soiled clothes at pre-set washing conditions.

Besides the above applications, some advanced applications of the UV VIS absorbance colorimetric/photometric spectrophotometer (by using both absorbance spectrophotometer and reflectance spectrophotometer) are as follows:

(i) Identification of Specific color constituents of any natural or synthetic color/dyes/pigments by UV–VIS spectral scan and its peaks for wave length vs. absorbance as finger prints for particular color constituent in a dye or pigment.

(ii) To study dye compatibility by analysis of dyeing rate or progressive color build up by varying dyeing conditions –either by (a) under varying dyeing temperate and time profile and by (b) Varying dye concentrations keeping all other dyeing parameters fixed and then comparing plots of DC vs. DL and also by plotting DL vs. K/S vs. and also by (iii) application of varying proportion of two dyes taken for compatibility test and analysis of their color difference index values to determine differences in CDI for different proportion of dyes used.

(iv) To study effects of variation in dyeing conditions (like dye concentration, mordant concentration for natural dyeing, dye bath pH, dyeing time, dyeing temperature, MLR, salt concentrations etc)/any other dyeing additives, to standardize the dyeing variables/dyeing process parameters to obtain optimum color yield.

2.5 Some practical consideration for accurate measurement of color

The practical aspects of preparing accurate color data base and its linearization for its subsequent use in generating newer color matching formulation/recipe, using those stored standardized color data bases are too important. Equally important is setting up of proper tolerances values for DE and multiple color difference tolerance in terms of DE*, DL*, Da*, and Db* values under a specified known standard illuminate (say, D-65 or Artificial Day Light, Fluorescent Light etc). Accurate measurement of color data set in UV–VIS reflectance spectrophotometer depends on the following factors and hence care during measurement of color data set are to be taken to avoid in accurate measurements:

1. Back ground Opaqueness of the samples mounted, by multi folding the colored samples.

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

3. Sample orientation (unidirectional either warp or weft wise pre-fixed).

4. Change in surface structure or scattering of surface of the sample is to be avoided.

5. Two side ness of the sample may vary results.

6. Slabbing or dyeing defects part to be avoided.

7. Separate measurement required for Fluorescent colorant.

8. Separate factors to include for Dull shade Bright shades etc.

9. In accurate shades% may arise due to inaccurate weight of dyes and chemicals.

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

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

Natural dyes like Indigo can be identified and can be distinguished from use of synthetic indigo from natural indigo dyed cotton textile fabrics by extracting the color component and followed by UV–VIS Spectro photometric analysis method. A test method [22] standardized by BIS, to distinguish synthetic indigo from natural indigo, is as follows:

0.1 g of both two types of indigo dyes (natural indigo and synthetic indigo) weighed separately were dissolved in 1000 ml dichloromethane and were diluted further 5-10 times and scanned through UV-Visible spectrophotometer to obtain WL vs. absorption plots. In the visible zone, identification of the natural indigo dye can be confirmed from value of lambda maxima(absorption/optical density) of both natural indigo and synthetic indigo and by comparison of characteristic peaks in both the plots as shown in Figure 2, while Table 1 represents the summary of observed results of peaks in a tabular form as follows in UV-Visible spectra of natural indigo and synthetic indigo

Figure 2.

UV-Visible 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.

Observations on UV-VIS spectral plots of Natural and Synthetic Indigo.

Thus, this UV VIS spectro based colorimetric analysis as above helps by corresponding UV-VIS plots helps to identify natural indigo, as compared to synthetic indigo from a colored textiles.

3.2 Check of linearity of plots of dye concentrations vs K/S values for storing color data base

Kubelka and Munk function [2, 3, 4, 5] do not exhibit always satisfactory linear relation between dye concentration Vs K/S plots and therefore number of empirical formulae like color difference index (CDI) [15, 16, 17] values or modified mperical relationship on K-M theory [2, 3] are proposed and used later with caution for achieving accurate shade % during preparation of color data base to store.

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

Number of theoretical techniques has been proposed to minimize the error introduced in recipe calculation due to non-linear relation between K-M function and concentration of dyes. If sufficiently high exhaustion of dye, at high level of dyeing exhaustion can be achieved, approximately good linearity may be obtained. In that case the simplest way to determine the optical data is to take average of all absorption co-efficient values obtained for specimens at six to eight or eight to ten level of dyeing, during calibration dyeing for preparing/generating color data base for particular dye class for specific textile substrate to dye.

For preparation of standard color data base (Dye cass wise) for computer aided color matching data base following factors are to be considered for least metameric color match formulations:

1. No of layers of fold of dyed fabric are to be decided to maintain the opaqueness of the produced dyed sample for measuring reflectance and K/S color data for all samples of dyed fabrics.

2. Orientation of the sample are to be maintained either warp way or weft way all along the measurements for produced dyed samples.

3. Pre-decided standard to be worked out for level/unlevel decision in dyeing in terms of dyeing uniformity value expressed by % co-efficient of variation (CV) for K/S values (within 5% CV for K/S values at different points of colored fabric is considered as uniform dyeing).

4. Color matching specifications of tolerance values for multiple color difference scales (in terms of DE*, DL*. Da*, Db* are to be pre-decided for comparing and predicting color matching formulation. Instrumental data vs. human eye perceived color differences for match/non-match decision with pre -decided tolerances, i.e., pre-decided tolerance of DE* (some where also shown/designated as ∆E, both are same) should be within 0.5 to 1.0, with optional limit of DL*, Da*, Db* tolerances. DE* values above 1.0 is not generally considered as acceptable matched sample.

5. Linear/non-linear behavior of the dye concentration vs. K/S plot are to be checked after dyeing of produced sample for color data base preparation. So, for all dyed samples, plots (calibration curves of color data base for each dye) of dye concentration vs. K/S must be set to be linear by appropriate curve fitting/linearization process. If such calibration curve for any dye is not linear, it is to be linearized by least square linear curve fitting technique or by eliminating few points/concentrations of dye to make it linear to store as the color data base for color match prediction for particular fabric and dye class combinations.

With the above point (v) in mind, all color data base results of produced samples are shown in Table 2, and were checked for linearity of dye concetration vs. K/S plots, before entering and storing these color database for each dye for predicting precision color match formulation 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	∆H	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 data base results for selective two dyes applied in the produced samples for calibration dyeing for preparing color data base for precision color matching.

For practical example of such linearization of color data base, the following example as a simple case study for llinearization of the color data base produced for example for one selective dye is shown in Table 2 along with the plot of dye concentration value vs. K/S values in Figure 3; where linearization was done, as shown by another broken lines following curve fitting done by least square method in this 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 data base for 2 different dyes.

From Figure 3, it is observed that Dye −1 (orange line) almost produced linear relationship for Dye concentration Vs K/S plot with minimum variation and lower ranges of CV % of K/S value and higher ranges of DC values (Table 2), as compared to Dye-2, showing much higher variation from linearity (Blue-violet line) for Dye concentration Vs K/S plot-(Figure 3). Hence Linear curve fitting principle is required to apply in case of Dye-1, as shown by an extra fitted linear curve shown therein and that linearized data were entered to store as color data base. Similarly, more dye calibration data for preparing total color data base for particular class of dyes will be needed.

3.3 Study of compatibility of dyes for producing compound shades

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

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

3.3.1 Conventional methods of dye compatibility test

This method is based on comparison of gradual color build up rate for dyeing under two sets of variations during dyeing for color build up gradually i.e. (i) SET-1: by varying dyeing time and temperature profile (keeping dye concentration and dyeing process variables fixed) and by (ii) SET-II by varying overall dye concentrations of 50;50 binary mixture of any two dyes (keeping dyeing time and temperature and other dyeing parameters fixed). Data for 4 RBGY colored selected binary pair of direct dyes taken for dyeing a pre-selected 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	∆L	∆a	∆b	∆C	Dye parts	K/S	∆L	∆a	∆b	∆C
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, 40min	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 set of color build up experiments for test of dye- compatibility for selected binary mixture of two dyes (50:50): M1: Direct red color-1 and direct green color-2.

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-2: Dye-3; 50:50	K/S	∆L	∆a	∆b	∆C	Dye parts	K/S	∆L	∆a	∆b	∆C
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, 40min	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, 60 min	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 set of color build up experiments for test of dye- compatibility for selected binary mixture of two dyes (50:50) M2: Direct green color-2 and direct T. Blue color-3.

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-2: Dye-4; 50:50	K/S	∆L	∆a	∆b	∆C	Dye parts	K/S	∆L	∆a	∆b	∆C
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 set of color build up experiments for test of dye- compatibility for selected binary mixture of two dyes (50:50) M3: Direct green color-2 and direct yellow color-4.

DL vs. K/S and DL vs. DC plots of corresponding data from above Tables 3–5 for Set-I and Set-II, indicate no particular trend of color build up rate for M1 (Direct Red color −1 and Direct Green color-2) for both the set and these two dyes are therefore poorly compatible. While for M2: (Direct Green color-2 and Direct T. Blue color-3) there is a similar trend of color build up for both Set-I and Set-II for both the plots indicate that these two dyes are moderately compatible and finally for M3: (Direct Green color-2 and Direct Yellow color-4) follow a increasing color build up in same direction in both Set-I and Set-II, but varying here and there and is judged as fairly compatible, as per conventional test of dye compatibility, as understood from Figure 4 for Compatibility test by plots of K/S vs. DL and plots of DC vs. DL for each of 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 on cotton, are as given 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 progressive color build up rate to test compatibility of dyes in conventional colorimetric analysis method for 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 color-4.

.

3.3.2 Newer RCR method of dye compatibility test

In this newer and simple method [15, 16, 17], dyeing of selected fabrics were carried out with each mixture of binary pairs of selected dyes in varying proportions and the colorimetric measurements are shown in Table 6 and differences in CDI values for different proportions of dyes used are shown in Table 7, where relative compatibility rating/grade for each binary pair of selected dyes are assigned/graded, as per chart value of rating (as shown in Table 8 as per earlier established chart value [15, 16, 17] with respect to maximum differences in CDI values obtained, vide Table 7. Both Conventional methods and simple newer RCR methods of compatibility test between selected dyes finally showed same or similar results proving equal efficacy of the newer RCR method of dye compatibility tests, hence it may be considered as useful for industry for quicker and easy determination method than other conventional methods for same.

Dyes/combination	K/S at λmax	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 test of dye compatibility by RCR method by dyeing any two selected dyes under different proportions for cotton fabrics dyed with each binary mixture of direct dyes in three different proportions.

Binary pair of Dyes combination	CDI values			CDImax – CDImin 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.

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

Compatibility Grade	RCR; values	Differences between maximum CDI and minimum CDI values amongst 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: Samanta AKP, Agarwal DS, Dutta S. Application of single and mixtures of red sandalwood and other natural dyes for dyeing of jute fabric: Studies on color parameters/color fastness and compatibility. Journal of Textile Institute, 2009, 100(7): 565–587.

3.4 Computer aided color match prediction data and its practical use

For Prediction of Computer aided color matching formulation for any/different known and unknown standard sample given, different color matching formulations can be easily obtained after setting of allowed/permissible color matching tolerences under specific illuminant chosen and under specific instrumental set up, showing different level of metamerism and cost.

Amongst 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 least metameric and formulation 2 is least cost.

Standard Name		=	C-25	Cotton	FORMULATION REPORT				Mode = R	Spectro- RFL
R	2.28	2.50	2.73		3.13	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		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 using specific color database stored for specific substrate (Cotton) for specific dye class of specific company used.

**Note = This color matching formulation/recipe is generated in computer aided reflectance spectro-photometer (Make: Premier Color Scan Pvt Instrument Ltd., Model SC 5100A, associated with Color-Lab plus software) using formulation menu and direct dye data base prepared in laboratory for color match prediction for cotton with set up of pre-decided tolerance of ∆E* within 1.0.

Now, after choosing one formulation (say Formulation 2 as a least cost recipe) out of total two or more formulations generated, actual/practical lab dyeing has to be done with the predicted percentage of mixture of dyes/colorants to check the practical match results of produced samples by comparing their color parameters, for a particular match of the standard sample (C-25), after practical dyeing in lab and then also in 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.

Below given is Table 10, containing comparative color data for further analysis of predicted formulations vs. actual dyeing results for checking acceptability of matching formulation with newer color matching index, as shown there.

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 9)	No actual Dyeing, predicted data-	—	—	−.51	−0.41	0.30	0.0	—	—	—	—
Predicted Formulation −1 (vide Table 9)	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.

Higher is the Color matching index (CMI) values, higher is the deviation of color parameters of predicted computer aided color match results from results of actual dyed sample, i.e. lesser is the efficiency of the computer aided color match predicted formulations. Amongst two predicted formulation generated in Table 9. here, is the comparison of two predicted formulations vs. actual dyeing with formulation 1 and 2, are shown in Table 10, which thus indicate that a closer match is obtained by Formulation 1 is least metameric, having CMI value 3.02 as compared to CMI value for formulation −2 being 4.05, which is least cost formulation, However, as CMI values for both the formulation are within 5.0 and are considered as acceptable match. However, if for any predicted formulation, after practical dyeing, show CMI value more than 5, it is understood that there is reasonable difference/deviation of color parameters after actual practical dyeing against the predicted formulation, hence, it should be corrected by batch correction tools until a more precision match with CMI value within 5 is achieved.

3.5 Effects of variation of concentrations of mordents (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 studied by varying one parameter keeping other parameter of dyeing variables fixed – by varying one by one (say for example, −dye concentration are varied_, keeping other dyeing variable pre-fixed and constant) and observed dyeing results can be assessed/compared to know, which value of input parameter of dyeing process (Say-- dye concentration) gives optimum color yield or maximum K/S values and that value of dyeing parameter is taken as optimum to standardize it as per data shown in Tables 11 and 12 for variation of all different parameter of specific dyeing process for particular mordant and dye combination for a specific natural dyeing process. to standardize that dyeing conditions. For an example, a specific case study, for overall varying concentrations of Harda (H) and Potash alum: (Al) as combined dual mordants applied in sequence on cotton and also for varying dye concentrations (2–8% purified catechu extract dye), the observed dyeing results for varying dyeing parameters are shown 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.

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.

Sample.	K/S	ΔE	ΔL	Δa	Δb	ΔC	ΔH	BI	MI	CDI
Control	0.055							80.12
Al-H- 10% + Cat-6% -at pH -3	5.63	44.18	−35.11	8.61	25.16	17.48	−17.03	10.15	1616.19	0.032
Al-H- 10% + Cat-6% -at pH -4	6.95	45.45	−37.46	8.87	23.76	15.65	−18.43	9.20	1615.78	0.030
Al-H- 10% + Cat-6% -at pH -5	5.85	48.24	−38.85	10.39	26.64	19.43	−20.98	9.26	1602.89	0.033
Al-H- 10% + Cat-6% -at -pH -8	5.48	46.91	−38.95	9.39	24.38	16.97	−19.87	10.42	1600.45	0.034
Al-H- 10% + Cat-6% -at pH -10	5.93	42.45	−30.46	6.87	23.76	16.05	−19.43	10.20	1510.78	0.035
Al-H- 10% + Cat-6% -at pH -12	4.92	41.45	−28.46	5.97	22.76	14.65	−19.13	10.10	1460.08	0.038
Al-H- 10% + Cat-6% -MLR-1:10	4.27	48.79	−37.78	15.09	27.77	23.46	−22.05	8.24	1611.20	0.038
Al-H- 10% + Cat-6% -MLR-1:20	5.77	50.79	−38.98	15.39	28.76	23.76	−22.25	8.85	1600.41	0.029
Al-H- 10% + Cat-6% MLR-1:30	6.92	49.10	−38.85	13.74	26.72	21.23	−21.26	9.67	1595.70	0.031
Al-H- 10% + Cat-6% -MLR-1:40	6.22	49.10	−38.05	12.74	27.72	22.23	−22.26	9.17	1534.70	0.036
Al-H-10% + Cat-6% -at 40°C	4.31	42.69	−30.63	9.78	28.08	20.45	−21.59	14.53	1420.05	0.032
Al-H-10% + Cat-6% -at 55°C	5.67	51.10	−40.53	13.75	27.92	22.23	−21.78	8.46	1632.94	0.030
Al-H-10% + Cat-6% -at 70°C	6.95	50.12	−40.12	14.24	28.45	23.12	−21.89	8.98	1680.02	0.035
Al-H-10% + Cat-6% -at 85°C	5.55	45.12	−40.12	13.24	26.45	22.12	−20.39	7.98	1630.02	0.038
Al-H-10% + Cat-6% for 30 Min	4.28	50.45	−36.96	17.09	29.77	25.66	−22.81	9.63	1556.37	0.028
Al-H-10% + Cat-6% for 60 min	6.98	49.45	−36.06	16.19	28.97	24.76	−22.20	9.33	1520.37	0.030
Al-H-10% + Cat-6% for 90 Min	6.77	48.38	−36.11	15.61	28.16	23.48	−22.03	9.15	1516.19	0.029
Al-H-10% + Cat-6% + Salt-0-gpl	4.89	47.45	−35.96	15.09	26.77	23.66	−21.81	8.63	1556.37	0.027
Al-H-10% + Cat-6% + Salt-5gpl	6.89	48.95	−38.30	14.65	26.73	21.76	−21.35	3.93	1582.10	0.030
Al-H-10% + Cat-6% + Salt -10gpl	6.55	49.05	−38.62	14.97	27.13	22.86	−21.29	9.02	1586.22	0.028
Al-H-10% + Cat-6% + Salt-15 gpl	5.83	50.91	−38.78	15.76	28.98	24.23	−22.38	8.94	1596.04	0.029

Table 12.

Effects of varying other dyeing conditions/process variables for overall 10% natural potash Alum plus Harda (50:50) as dual mordant applied in sequence and 6% purified catechu extract as natural dye.

The effects of varying other dyeing process parameters and other dyeing conditions like dye-bath pH, Time of dyeing, Temperature of dyeing, MLR of dye bath, and Salt % added in dye bath, on color yield/Surface color strength are shown in Table 12.

Keeping all other dyeng process variables constant, with the variation of above said dyeing conditions one after another for catechu natural dyeing on cotton, with 10% overall application of Alum plus Harda (50:50) applied in sequence –followed by 6% aqueous extract of purified catechu dye powder, here it gives best/maximum K/S values and more uniform dyeing. Keeping mordant and dye concentrations fixed, the effects of other dyeing conditions/dyeing process variables like dye bath pH, Time of dyeing, Temperature of dyeing, MLR of dye bath, and salt % on color yield/Surface color strength shows maximum/optimum color values, which is considered as optimum dyeing conditions (Table 12) for use of dyeing Time-60 Min, dyeing Temp. 70°C and dye bath pH -4-5 (acidic) with MLR 1:30, and salt concentration −5 gpl, when 10% overall application of Alum plus Harda (50:50) double mordant are applied in sequence -followed by dyeing with 6% aqueous extract of purified catechu powder dyeing at above said standardized dyeing conditions.

The above data in Table 12 indicate higher dye yield at acidic pH at 4–5, than alkaline pH 8–12 for 10% overall Al + Harda (50:50) dual pre-mordanted cotton having highest k/s Value 6.95–6.98 or so.

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

The wash fastness of the said natural dyed cotton fabrics were evaluated according to IS: 3361–1979 method. The light fastness of the said natural dyed cotton fabric was evaluated as per IS: 2454: 1985 method. Also the rubbing fastness of those natural colored cotton textiles was evaluated by IS M766–1988 method [23].

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

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 pre- mordanted and 10% overall concentrations of alum and Harda (50:50) dual pre-mordanted cotton fabric dyed with varying concentration of catechu.

Moreover changing solvent of extraction or varying extraction conditions, color depth may be altered in case of natural dyes application on any textiles. So to eliminate such variations, separate standardization of dyeing process variables with use of different mordants combinations (For example use of K-Al and Gall nut combinations instead of K-AL + harda combinations gives different color yield). Moreover, alcoholic extraction or sonicator assisted extraction or microwave assited extraction gives better color yield and other responses like uv resistant or antimicrobial properties at different scales.

Thus from 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 arised from there, may be explained scientifically as follows:

1. why acidic pH shows better color yield than alkaline or neutral pH of dye bath?

   The answer is Initially, on immersion of cotton in dye bath water, cotton develops −ve ions in its surface and do not support absorption of anioainc natural dyes, but in one hand cationic mordants and acid protons gradually negate this -ve charge of cotton and allows more dye to be absorbed on pre-mordanted and/or acidic protonated cotton to absorb more anionic natural dyes. Moreover, Acidic pH in dye bath helps quicker and more anionization of natural dye anions in the dye bathmaking more dye anions to be absorbed to cotton, than use of alkaline pH in dye bath. Besides this, the left anionic part of acidic salt/acids used in dye bath, gives a common ion effect to make dye absorption a bit slower as retarder and hence a more uniform dyeing results in acidic pH bath, which is an added advantage in this case.

2. Why use of double mordants produce a higher color yield than use of single mordant?

   The simple answer is Use of dual or double mordants using two types of mordants acts in two different ways for fiber -mordants and natural dye complexing, where the metallic mordant like Al-(from alum) acts to make complex with natural dyes either by producing co-ordinated co-valent bonds or simply by hydrogen bong or both, while the 2nd mordant harda or gall nut being tannates of bio-source, act as to form larger dye-fiber complex with these already aluminum mordanted fiber and dye utilizing its tannate/tannic acid functionally and thus a more bigger/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. Is there any reason for different tannin-based bio-mordants shows different results for dyeing with same natural dyes on cotton?

   The simple explanation is that it is obvious that two types/two different bio-maordants have different functionality. For example harda containing chebulinic acid, provides functional −COOH and few OH group to form more complex with both dye and fibers, while Gall nut containing elagic acid and gallic acid tannates or flavonoids provide much higher tannate contents than tannate content of harda and hence such complexing of these tannate based bio-mordants with Fiber and natural dye is expectedly more or higher for gall nut extract than harda extract used separately as bio -mordant.

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

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

   For example, when eucalyptus leaves or barks are extracted with pure water boiling or alcoholic solution separately, the results for compositional analysis of those two extracts show different results. Water extract gives yellowish light brown colored liquor with less amount of eucalyptol and other components, but 50:50 water and ethyl or methyl alcohol based extract at 70°C gives much deeper brown liquor with more or higher percentages of colored eucalyptol and other components extracted additionally extracting few UV resistant components too, which are not extractable by water boiling only.

4. Concluding remarks

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

Besides the above, Quality control activities for checking color quality for dyed textiles and apparels i.e. determination of batch to batch color differences after dyeing of each new batch to check the Differences in color values in terms of DE*, DL*, Da*, and Db* color difference CIE 1976 scale of color measurement.

Application of computer aided UV-VIS reflectance spectrophotometer along with suitable application software has modernized the dye houses of textile and apparel industry to know the quality of dyeing results.

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

Study on effects of variation of any dyeing process parameter or dyeing conditions on color yield and colorfastness to wash, light and rubbing fastness for any textile fabric dyed with direct, reactive, natural dyes acid dye classes can be experimented and accordingly e-Shade card can be prepared. However, in some cases where color fastness are not adequate, method of improvement of the same can be tested as a trial by different post-treatment methods.:

Pilot plant trials for reproduction of computer aided dyeing-recipe/formulation using Lab mini jigger is almost completed and efficacy of the accuracy of database for determination of color matching recipe can be checked by using color matching index as a newer index.

For producing compound shades, testing for compatibility individual dyes in a binary pair of dye mixture can also be checked. Quality of dyes, dyeing uniformity etc. and other important color parameters for production of wash-fast and light-fast dyed textile and apparel products can be separately identified for use of precision color quality.