Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra.

Autor: Armstrong CEJ; Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia; School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia., Gilmore AM; HORIBA Instruments Inc., 20 Knightsbridge Road, Piscataway, NJ 08854, United States., Boss PK; Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia; CSIRO Agriculture and Food, Locked Bag 2, Glen Osmond, SA 5064, Australia., Pagay V; Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia; School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia., Jeffery DW; Australian Research Council Training Centre for Innovative Wine Production, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia; School of Agriculture, Food and Wine, and Waite Research Institute, The University of Adelaide, PMB 1, Glen Osmond, South Australia 5064, Australia. Electronic address: david.jeffery@adelaide.edu.au.
Jazyk: angličtina
Zdroj: Food chemistry [Food Chem] 2023 Mar 01; Vol. 403, pp. 134321. Date of Electronic Publication: 2022 Sep 20.
DOI: 10.1016/j.foodchem.2022.134321
Abstrakt: Absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy was investigated as a rapid method for predicting maturity indices using Cabernet Sauvignon grapes produced under four viticulture treatments during two growing seasons. Machine learning models were developed with fused spectral data to predict 3-isobutyl-2-methoxypyrazine (IBMP), pH, total tannins (Tannin), total soluble solids (TSS), and malic and tartaric acids based on the results from traditional analysis methods. Extreme gradient boosting (XGB) regression yielded R 2 values of 0.92-0.96 for IBMP, malic acid, pH, and TSS for externally validated (Test) models, with partial least squares regression being superior for TSS prediction (R 2  = 0.97). R 2 values of 0.64-0.81 were achieved with either approach for tartaric acid and Tannin predictions. Classification of grape maturity, defined by quantile ranges for red colour, IBMP, malic acid, and TSS, was investigated using XGB discriminant analysis, providing an average of 78 % correctly classified samples for the Test model.
Competing Interests: Declaration of Competing Interest Claire Armstrong, Paul Boss, Vinay Pagay, and David Jeffery declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Adam Gilmore reports a relationship with Horiba Instruments Inc that includes: employment.
(Copyright © 2022 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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