Correlation of Visual and Instrumental Color Measurements to Establish Color Tolerance Using Regression Analysis

Color is one of the important parameter considered in the determination of quality for fashion materials like leather. The color variation in each piece of leather sample in a batch should be within the acceptable range. Visual assessment is currently used in leather industry for quality control and color sorting. The current method used is subjective and often leads to disagreement between buyer and seller. Color measurement using reflectance spectrophotometer evaluates color consistently and is an objective assessment system. However, there is always an apprehension that the instrumental color assortment may not agree with the human perception of color difference. Hence, in the present investigation, an attempt was made to screen four color difference formulae, viz., CIELAB76, CMC (2:1), CIE94 and CIE2000 for their suitability in obtaining pass/fail decisions, which would be in conformity with that of the average human observer.
Regression analysis was performed to find a correlation between visual and instrumental color assessments and the results indicate that CMC(2:1) formula may be the most suited for the purpose. A detailed analysis of visual and instrumental color values revealed visual non-uniformity towards sensitivity to hue, chroma and lightness. This difference in sensitivity was also taken into consideration in instrumental color sorting and pass/fail tolerance was established that led to closer conformity between visual and instrumental methods.


A commercial batch of dyed suede garment leathers (220 leathers) in grayish black shade from a leather manufacturing in Chennai, India was chosen for the study. The buyer’s sample was used as standard for assessment of color variation. The color on the suede side of the standard and each sample in the experimental batch of leathers is given in Figure 1. 221 leather samples were separately compared for visual as well a instrumental color analysis.

Visual Assessment of Color Tolerance Twenty experienced color matching experts of leather industry were selected for the study and D65 light source representing average daylight was used for viewing. The standard was compared against each leather sample and visual color matching was decided as “pass” or “fail” as per the color perception of each expert. The assessment of leather samples made by the experts was recorded as ‘0’ for fail and ‘1’for pass. Logit regression analysis25 was used to convert the recorded binary data response in terms of visual difference (DV) using Equation 8, which is a modified form of the equation reported by H. Mangine

Si is the number of experts who found the match acceptable and Ni is the total number of experts. The main difference between the current modified Equation 8 and the equation reported by Mangine et al. is the use of constant 2, which was obtained by trial and error method to get positive DV value as well as meaningful correlation with instrumental DE values.

In this study a detailed color analysis for grayish black color data with lightness value (L* ranging from 19 to 28) varying in hue and chroma was carried out. Pass/Fail decisions based on single number ΔE* ab value (ΔE >1 as fail and ΔE <1 as pass) does not correlate with visual assessment of color. Three dimensional tolerance limits based on L, C, h color space provides closer agreement to visual perception. The methodology for setting tolerance limits using two dimensional grouping based on hue and chroma along with lightness difference provides an objective solution for color sorting problem. Tolerance limits in terms of L,C, h vary for different spectral colors. Instrumental color sorting helps the buyer/seller to fix the tolerance of each color parameter (i.e. in terms of hue, chroma, lightness) providing consistent sorting which results in accurate and faster pass/fail decisions.


Mathematical models were developed to correlate visual color difference and instrumental methods based on four color difference equations. Both t-test and coefficient of determination (R2 ) showed that the equations from best to worst fit follows the order CMC(2:1) > CIE94 > CIEDE2000 > CIE76. For a substrate like leather with non-uniform and porous surface, CMC equation may be the best suited to correlate with visual color perception. A detailed analysis of the color values established that, for an average expert the order of sensitivity is arranged as hue > chroma > lightness.
An objective solution for color sorting problem has been found by setting tolerance limits based on hue-chroma (two dimensional grouping) along with lightness led to closer conformity between visual and instrumental methods.