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Type: Journal article
Title: Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra
Author: Armstrong, C.E.J.
Gilmore, A.M.
Boss, P.K.
Pagay, V.
Jeffery, D.W.
Citation: Food Chemistry, 2023; 403:1-10
Publisher: Elsevier
Issue Date: 2023
ISSN: 0308-8146
Statement of
Claire E.J. Armstrong, Adam M. Gilmore, Paul K. Boss, Vinay Pagay, David W. Jeffery
Abstract: 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 R² 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 (R² = 0.97). R² 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.
Keywords: A-TEEM
Data fusion
Discriminant analysis
Description: Available online 20 September 2022
Rights: © 2022 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.foodchem.2022.134321
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Appears in Collections:Agriculture, Food and Wine publications
ARC Training Centre for Innovative Wine Production publications

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