The wine industry has been striving to achieve wine quality and consistency, which involves harvesting and selection of grapes at the optimal maturity and according to the desired traits.
Hyperspectral imaging (HSI) combined with machine learning algorithms (ML) has emerged as a promising cost-effective alternative to the traditional analytical methods to predict important enological parameters and assist on harvesting critical decisions.
However, the large amount of data generated by HSI, together with the large variability associated (grape variety, terroir), raise computational challenges for data-driven-modelling turning the selection of proper models, which best suit the problem under study and assure its generalization, a cumbersome task.
In this work, the large database collected allowed robust testing of the ML prediction models, whose performance was assessed through n-fold-Cross-Validation and independent test sets for generalization ability (GA) evaluation, using samples from different vintages, varieties and growth conditions not used in the training, addressing the issue of natural variability.
This research established models have successfully predicted the pH, sugar and anthocyanin levels of red grapes under lab conditions. The models are being tested and validated under real conditions and the results obtained are very promising.
Report presented at the SIVE OENOPPIA Awards 2019. The paper reproduced in this video-seminar was presented at the 12th edition of Enoforum (Vicenza, Italy, May 21-23, 2019).
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VIDEO SEMINARI (Pedro Melo-Pinto; streaming 20 min)
