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Use of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine

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dc.contributor.authorArmstrong, C.E.J.
dc.contributor.authorNiimi, J.
dc.contributor.authorBoss, P.K.
dc.contributor.authorPagay, V.
dc.contributor.authorJeffery, D.W.
dc.date.issued2023
dc.description.abstractGenerations of sensors have been developed for predicting food sensory profiles to circumvent the use of a human sensory panel, but a technology that can rapidly predict a suite of sensory attributes from one spectral measurement remains unavailable. Using spectra from grape extracts, this novel study aimed to address this challenge by exploring the use of a machine learning algorithm, extreme gradient boosting (XGBoost), to predict twenty-two wine sensory attribute scores from five sensory stimuli: aroma, colour, taste, flavour, and mouthfeel. Two datasets were obtained from absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy with different fusion methods: variable-level data fusion of absorbance and fluorescence spectral fingerprints, and feature-level data fusion of A-TEEM and CIELAB datasets. The results for externally validated models showed slightly better performance using only A-TEEM data, predicting five out of twenty-two wine sensory attributes with R2 values above 0.7 and fifteen with R2 values above 0.5. Considering the complex biotransformation involved in processing grapes to wine, the ability to predict sensory properties based on underlying chemical composition in this way suggests that the approach could be more broadly applicable to the agri-food sector and other transformed foodstuffs to predict a product’s sensory characteristics from raw material spectral attributes.
dc.description.statementofresponsibilityClaire E. J. Armstrong, Jun Niimi, Paul K. Boss, Vinay Pagay, and David W. Jeffery
dc.identifier.citationFoods, 2023; 12(4)
dc.identifier.doi10.3390/foods12040757
dc.identifier.issn2304-8158
dc.identifier.issn2304-8158
dc.identifier.orcidArmstrong, C.E.J. [0000-0001-5486-3619]
dc.identifier.orcidNiimi, J. [0000-0002-2642-283X]
dc.identifier.orcidBoss, P.K. [0000-0003-0356-9342]
dc.identifier.orcidJeffery, D.W. [0000-0002-7054-0374]
dc.identifier.urihttps://hdl.handle.net/2440/137594
dc.language.isoen
dc.publisherMDPI AG
dc.relation.granthttp://purl.org/au-research/grants/arc/IC170100008
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
dc.source.urihttps://doi.org/10.3390/foods12040757
dc.subjectA-TEEM; CIELAB; chemometrics; regression; wine; extreme gradient boosting
dc.titleUse of Machine Learning with Fused Spectral Data for Prediction of Product Sensory Characteristics: The Case of Grape to Wine
dc.typeJournal article
pubs.publication-statusPublished

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