Multivariate Statistical Models for the Authentication of Traditional Balsamic Vinegar of Modena and Balsamic Vinegar of Modena on ¹H-NMR Data: Comparison of Targeted and Untargeted Approaches

Abstract

This work aimed to compare targeted and untargeted approaches based on NMR data for the construction of classification models for Traditional Balsamic Vinegar of Modena (TBVM) and Balsamic Vinegar of Modena (BVM). Their complexity in terms of composition makes the authentication of these products difficult, which requires the employment of several time-consuming analytical methods. Here, 1H-NMR spectroscopy was selected as the analytical method for the analysis of TVBM and BVM due to its rapidity and efficacy in food authentication. 1H-NMR spectra of old (>12 years) and extra-old (>25 years) TVBM and BVM (>60 days) and aged (>3 years) BVM were acquired, and targeted and untargeted approaches were used for building unsupervised and supervised multivariate statistical modes. Targeted and untargeted approaches were based on quantitative results of peculiar compounds present in vinegar obtained through qNMR, and all spectral variables, respectively. Several classification models were employed, and linear discriminant analysis (LDA) demonstrated sensitivity and specificity percentages higher than 85% for both approaches. The most important discriminating variables were glucose, fructose, and 5-hydroxymethylfurfural. The untargeted approach proved to be the most promising strategy for the construction of LDA models of authentication for TVBM and BVM due to its easier applicability, rapidity, and slightly higher predictive performance. The proposed method for authenticating TBVM and BVM could be employed by Italian producers for safeguarding their valuable products.

Keywords

• qNMR
• linear discriminant analysis (LDA)
• chemometrics
• Traditional Balsamic Vinegar of Modena
• Balsamic Vinegar of Modena
• principal component analysis (PCA)