Multiparametric analysis and authentication of Argentinian vinegars from spectral sources

Ultraviolet-visible (UV-Vis) and near infrared (NIR) spectroscopies allied to chemometrics were investigated for quality control and authentication of Argentinean wine and balsamic vinegars. First, a multiparametric approach was conducted to acquire predictive models by using partial least squares regression (PLS) to quantify total acidity, volatile acidity, fixed acidity, pH and total polyphenols that are the main quality parameters used to control products. Individual UV-Vis and NIR sensors as well as merged data were assessed. Reliability models with correlation coefficients higher than 0.99 and prediction error lesser than 2.2 were acquired for the UV-Vis data. Furthermore, a classification approach was performed on wine vinegar samples to classify them according to their acetification process. At first, the data provided by each individual sensor (UV-Vis and NIR) were separately analyzed by PLS-discriminant analysis. Then, datasets were jointly analyzed by applying sequential and orthogonalized PLS coupled with linear discriminant analysis (SO-PLS-LDA). The overall accuracy of the fused model reached an optimal performance with a value of 0.92 in the prediction stage. Finally, according to the analysis proposed, this work reveals when it is proper to conduct a data fusion methodology.