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, an Australian research group developed a novel method using near-infrared (NIR) spectroscopy coupled with machine learning (ML) modelling 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 Shiraz wines.
The proposed method will allow the assessment of authenticity and sensory quality traits of any wine in the market without opening 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 and can be used with the ML models proposed in this research.
Background to the study
There are high complexities in the attempt to define wine quality. However, physical-chemometric techniques can objectively assess wine quality traits. Sensory analysis using trained panels could also broaden the definition of wines’ specific characteristics that may help define quality traits.
These techniques have also been implemented to assess the provenance and counterfeiting of wines or to detect mislabelling from wineries. Near-infrared spectroscopy can offer a complete chemical fingerprinting profile of wines depending on different instruments’ spectral range and sensitivity. This instrument has been used for various applications, from general quality assessment to predicting aroma profiles in beverages. Furthermore, the use of digital technologies and sensors, such as NIR, electronic noses, and tongues, allows the implementation of Artificial Intelligence (AI) and ML modelling for wine quality prediction to the specific prediction of phenolic compounds in red wines and geographical provenance, sugar content astringency, and wine authenticity.
However, most of these works are based on wine samples extracted by direct measurement from wines or by opening wine bottles for measurement purposes. There is limited research focused on measurements through the bottle that can offer non-invasive tools for physicochemical, sensory, and aroma profile assessment of wines. This work presents the implementation of NIR spectroscopy measured through the bottles of unopened wines in parallel with AI and ML techniques to assess wine quality traits and provenance and as a potential method to evaluate counterfeiting and mislabelling.
The research found that digital sensor technology, such as NIR spectroscopy, can effectively and accurately assess wine quality traits based on chemical fingerprinting and ML modelling with data obtained through the bottle. This novel application, in parallel with low-cost NIR devices, could be used from the winery to retailers to assess how these quality traits change during transport, ageing, storage, and for the detection of tampering, adulteration, and counterfeiting. A further application can be used to assess wines’ provenance and detect mislabelling.
Refer to the original article for detailed “Materials and Methods” and “Results and Discussion”.
Reference
Claudia Gonzalez Viejo, Natalie Harris and Sigfredo Fuentes (2023). Near-infrared spectroscopy analysis of wines through bottles to assess quality traits and provenance. BIO Web of Conferences 56, 02003. https://doi.org/10.1051/bioconf/20235602003
This article has been adapted from its original form to fit this platform without altering scientific facts. The original article is an open-access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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