Abstract
In the last years the need to numerically define color by its coordinates in n-dimensional space has increased strongly. Colorimetric calibration is fundamental in food processing and other biological disciplines to quantitatively compare samples' color during workflow with many devices. Several software programmes are available to perform standardized colorimetric procedures, but they are often too imprecise for scientific purposes. In this study, we applied the Thin-Plate Spline interpolation algorithm to calibrate colours in sRGB space (the corresponding Matlab code is reported in the Appendix). This was compared with other two approaches. The first is based on a commercial calibration system (ProfileMaker) and the second on a Partial Least Square analysis. Moreover, to explore device variability and resolution two different cameras were adopted and for each sensor, three consecutive pictures were acquired under four different light conditions. According to our results, the Thin-Plate Spline approach reported a very high efficiency of calibration allowing the possibility to create a revolution in the in-field applicative context of colour quantification not only in food sciences, but also in other biological disciplines. These results are of great importance for scientific color evaluation when lighting conditions are not controlled. Moreover, it allows the use of low cost instruments while still returning scientifically sound quantitative data.
Highlights
Color is defined as the visible electromagnetic spectrum reflected by an object and perceivable by a sensor within its detection range, being one of the most important attributes of objects’ appearance.Being a highly informative variable, trials for its quantification by measurement have conducted since the early 1900s
Thin-Plate Spline (TPS-3D) warping, and it will be introduced in detail below
Given two configurations of homologous landmarks, the thin-plate spline is a map from plane to plane that maps each landmark to its correspondent
Summary
Color is defined as the visible electromagnetic spectrum reflected by an object and perceivable by a sensor within its detection range, being one of the most important attributes of objects’ appearance. The color of a natural (animal/food) object can change according to: (i) lifetime [23], shelf-life [24], harvesting conditions [25], or in the case of organisms, according to gender-, age-, or other intra-specific differences [26]; (ii) the photoreceptor or processing system, since different individuals (or devices) can be sensitive to different wavelengths, as in the case of the color blindness; (iii) the processing system (i.e., the interpreter of stimuli coming from the visual receptors), which can exert a distortion reporting a different color from the physically reflected one, as in the case of the well known phenomenon of the “color constancy” in humans, (i.e., the tendency to perceive the same color of an object under different illuminations) [27]; (iv) the lighting condition source This latter factor should be deeply considered prior to any colorimetric measurement, since when not properly evaluated it could produce important biases [28]. The calibration efficiency of this method was compared with the one obtained through the use of a widely used commercial software (i.e., ProfileMaker) as well as with that obtained by multivariate linear regression (Partial Least Squares)
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