Multiparametric analysis and authentication of Argentinian vinegars from spectral sources

WAGNER, MARCELO; ZALDARRIAGA HEREDIA, JORGELINA; MONTEMERLO, ANTONELLA; ORTIZ, DANIELA; CAMIÑA, JOSÉ MANUEL; GARRIDO MARIANO; AZCARATE, SILVANA MARIELA

JOURNAL OF FOOD COMPOSITION AND ANALYSIS

ACADEMIC PRESS INC ELSEVIER SCIENCE

Lugar: Amsterdam; Año: 2024 vol. 125

0889-1575

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