Wine Characterisation with Spectral Information and Predictive Artificial Intelligence

TL;DR

This study combines UV-Vis spectroscopy with machine learning to predict grape juice attributes and classify wine origin, achieving over 91% accuracy, and offers insights for integrating AI into smart winery practices.

Contribution

It introduces a spectroscopic and AI-based method for wine analysis that simplifies traditional sensory and origin classification techniques, enhancing efficiency and accuracy.

Findings

Support Vector Machine outperformed other models in accuracy and robustness.

Origin prediction accuracy and F1 score exceeded 91%.

Lower wavelength range (250-420 nm) is most influential for feature selection.

Abstract

The purpose of this paper is to use absorbance data obtained by human tasting and an ultraviolet-visible (UV-Vis) scanning spectrophotometer to predict the attributes of grape juice (GJ) and to classify the wine's origin, respectively. The approach combined machine learning (ML) techniques with spectroscopy to find a relatively simple way to apply them in two stages of winemaking and help improve the traditional wine analysis methods regarding sensory data and wine's origins. This new technique has overcome the disadvantages of the complex sensors by taking advantage of spectral fingerprinting technology and forming a comprehensive study of the employment of AI in the wine analysis domain. In the results, Support Vector Machine (SVM) was the most efficient and robust in both attributes and origin prediction tasks. Both the accuracy and F1 score of the origin prediction exceed 91%. The feature ranking approach found that the more influential wavelengths usually appear at the lower end of the scan range, 250 nm (nanometers) to 420 nm, which is believed to be of great help for selecting appropriate validation methods and sensors to extract wine data in future research. The knowledge of this research provides new ideas and early solutions for the wine industry or other beverage industries to integrate big data and IoT in the future, which significantly promotes the development of 'Smart Wineries'.