Applications of Machine Learning for Wine Recognition Based on 1H-NMR Spectroscopy

Abstract

The present study aims to explore the possibility of applying machine learning (ML) algorithms for the development of reliable wine authentication instruments able to encompass the exhaustive performances offered by learning-based methods, and, at the same time, to propose reference practices for improving the recognition ability of the developed models. In this regard, two ML algorithms, namely k-Nearest Neighbors (kNN) and Logistic Regression, have been utilized as supervised learning techniques applied to 1H-NMR spectral data for the development of classification models able to recognize the variety, geographical origin, and vintage of wine samples. Due to the complexity of the experimental data, which was characterized by a high number of variables, special attention was given to the preprocessing phase in order to identify the most relevant input space for each envisaged classification criterion. The obtained results have shown that 1H-NMR spectroscopy in conjunction with Logistic Regression represents a reliable approach for wine traceability, leading, for all investigated classification criteria, to accuracy scores of the developed models greater than 98% in cross-validation and up to 100% in testing.