An eco-friendly analytical methodology based on digital images for quality control of commercial Mikania glomerata syrups

Abstract

This paper aimed at the use of a chemometrics-assisted color histogram-based analytical system (CACHAS) to provide a fast and reliable analytical tool for the qualitative and quantitative quality control of commercial syrups containing Mikania glomerata, popularly known as guaco and widely used in Brazil in the treatment of respiratory problems. For this, Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) was initially employed to authenticate sugar-free syrups, and then Partial Least Squares (PLS) was used to quantify coumarin as a chemical marker. The best results were obtained by using the Grayscale + RGB(Red-Green-Blue) + HSV(Hue-Saturation-Value) histogram as analytical information for both the qualitative and quantitative analysis. In the first case, DD-SIMCA (α = 0.01) authenticated all samples of commercial sugar-free syrups for diabetics, achieving sensitivities of 1.00 in both the training and test sets, and specificity of 0.91 in the test set, i.e., with only 3 misclassifications from a total of 60 samples, achieving, therefore, overall efficiency of 0.97. Additionally, for the quantification of coumarin, the predictive ability of PLS achieved coefficient of correlation (R2pred) of 0.9919, root mean square error of prediction (RMSEP) of 0.8969 µg mL−1, ratio of performance to deviation (RPDpred) of 11.38, and relative error of prediction (REP) of only 2.57%. The proposed digital image-based study can be used as a rapid, non-destructive, and promising, green analytical methodology to offer a safe and reliable product to consumers and help regulatory agencies.