Authenticity assessment and protection of high-quality Nebbiolo-based Italian wines through machine learning.

This paper discusses an intelligent data analysis approach, based on machine learning techniques, and aimed at the definition of methods for chemical data analysis assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont (Italy). This is an important and very relevant issue in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros. The objective is twofold: to show that the problem can be addressed without expensive and hyper-specialized wine chemical analyses, and to demonstrate the actual usefulness of classification algorithms for data mining and machine learning on the resulting chemical profiles. Following Wagstaff's proposal for practical exploitation of machine learning approaches, we describe how data have been collected and prepared for the production of different datasets, how suitable classification models have been identified and how the interpretation of the results suggests the emergence of an active role of machine learning classification techniques, based on standard chemical profiling, for the assesment of the authenticity of the wines target of the study. Experiments have been performed with both datasets of real samples and with syntethic datasets which have been artificially generated from real data. [ABSTRACT FROM AUTHOR]