Near-infrared spectroscopy analysis of wines through bottles to assess quality traits and provenance

Digital Agriculture, Food and Wine Sciences Group. School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia

Abstract

Due to increased fraud rates through counterfeiting and adulteration of quality wines, it is important to develop novel non-destructive techniques to assess wine quality and provenance. Therefore, our research group developed a novel method using near-infrared (NIR) spectroscopy (1596-2396 nm) coupled with machine learning (ML) modeling to assess wine vintages and quality traits based on the intensity of sensory descriptors through the bottle. These were developed using samples from an Australian vineyard for Shirazwines. Models resulted in high accuracy 97% for classification (vintages) and R=0.95 regression (sensory quality traits). The proposed method will allow to assess authenticity and sensory quality traits of any wines in the market without the need to open the bottles, which is rapid, accurate, effective, and convenient. Furthermore, currently, there are low-cost NIR devices available in the market with the required spectral range and sensitivity, which can be affordable for winemakers and retailers that can be used with the ML models proposed here.