Goat milk authentication by one-class classification of digital image-based fingerprint signatures: Detection of adulteration with cow milk

[Display omitted] •A chemometrics-assisted color histogram-based system used for green food analysis.•Digital image-based fingerprint signatures used as analytical information.•Identification of goat milk adulteration by adding cow milk.•One-class classification models by OC-PLS and DD-SIMCA were compared.•RGB/DD-SIMCA model had the highest sensitivity specificity and efficiency rates. This work used a chemometrics-assisted color histogram-based analytical system (CACHAS) as a low-cost alternative analytical tool for the authentication of goat milk in terms of its adulteration with cow milk. For this fingerprint signatures using Grayscale RGB (red–greenblue) and HSI (hue-saturation-intensity) histograms were explored as analytical information. Then One-Class Partial Least Squares (OC-PLS) and Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) were employed as one-class classifiers. The best result was obtained by the RGB/DD-SIMCA model correctly classifying all pure goat milk and adulterated samples in the test set. This differentiation is favored by the presence of carotenoids in cow milk having a slightly yellowish color while goats convert β-carotene to retinol. Thus considering the complexity of the raw materials and the accessibility of the developed methodology the proposed CACHAS method stands advantageously out by finding the principles of green food analysis for screening the authenticity of goat milk.