Color Classification for Red Alcohol Vinegar to Control the Quality of the End-Product

Abstract

A feasible analytical method aimed at the quality control of red alcohol vinegar is introduced, with no need for dilutions, filtration, or expensive instrumentation to perform the analysis. The remarkable benefit is no need to add chemicals. Thirty sample solutions were prepared to aim to produce vinegar with good quality following a 2² central composite design (CCD), with a central point (625:3) and axial points $(-\sqrt{2}; \sqrt{2})$. The poor taste and quality parameters were also tested with sample contents far from the central point (1.5-fold). Data from square images converted into color histograms of red (R), green (G), and blue (B), hue (H), saturation (S), and value or intensity (V) or (I), relative colors of RGB denoted as r, b, and g, and luminosity (L) was used for the calculations of multivariate classification models, such as k-nearest neighbors (kNN), soft independent modeling of class analogy (SIMCA), and partial least squares–discriminant analysis (PLS-DA), and the figures of merit showed that the reliability of the multivariate classification models was higher than 0.9 for accuracy, sensitivity, and specificity. Afterward, a low-cost and easy-to-use analytical method can aid in identifying operational errors and end-product out-of-specifications.