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

Simple item page
dc.contributor.authorArmstrong, C.E.J.
dc.contributor.authorGilmore, A.M.
dc.contributor.authorBoss, P.K.
dc.contributor.authorPagay, V.
dc.contributor.authorJeffery, D.W.
dc.date.issued2023
dc.descriptionAvailable online 20 September 2022
dc.description.abstractAbsorbance-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.
dc.description.statementofresponsibilityClaire E.J. Armstrong, Adam M. Gilmore, Paul K. Boss, Vinay Pagay, David W. Jeffery
dc.identifier.citationFood Chemistry, 2023; 403:1-10
dc.identifier.doi10.1016/j.foodchem.2022.134321
dc.identifier.issn0308-8146
dc.identifier.issn1873-7072
dc.identifier.orcidArmstrong, C.E.J. [0000-0001-5486-3619]
dc.identifier.orcidBoss, P.K. [0000-0003-0356-9342]
dc.identifier.orcidJeffery, D.W. [0000-0002-7054-0374]
dc.identifier.urihttps://hdl.handle.net/2440/136972
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/IC170100008
dc.rights© 2022 Elsevier Ltd. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.foodchem.2022.134321
dc.subjectA-TEEM
dc.subjectChemometrics
dc.subjectData fusion
dc.subjectDiscriminant analysis
dc.subjectRegression
dc.subjectXGBoost
dc.subject.meshVitis
dc.subject.meshTannins
dc.subject.meshWine
dc.subject.meshMachine Learning
dc.titleMachine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra
dc.typeJournal article
pubs.publication-statusPublished

Files

Collections

Agriculture, Food and Wine publications
ARC Training Centre for Innovative Wine Production publications