Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/129306
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dc.contributor.authorSummerson, V.en
dc.contributor.authorViejo, C.G.en
dc.contributor.authorSzeto, C.en
dc.contributor.authorWilkinson, K.L.en
dc.contributor.authorTorrico, D.D.en
dc.contributor.authorPang, A.en
dc.contributor.authorDe Bei, R.en
dc.contributor.authorFuentes, S.en
dc.date.issued2020en
dc.identifier.citationSensors (Switzerland), 2020; 20(18):1-24en
dc.identifier.issn1424-8220en
dc.identifier.issn1424-8220en
dc.identifier.urihttp://hdl.handle.net/2440/129306-
dc.description.abstractWildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.en
dc.description.statementofresponsibilityVasiliki Summerson, Claudia Gonzalez Viejo, Colleen Szeto, Kerry L. Wilkinson, Damir D. Torrico, Alexis Pang ... et al.en
dc.language.isoenen
dc.publisherMDPIen
dc.rights© 2020 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 (http://creativecommons.org/licenses/by/4.0/).en
dc.subjectSmoke taint; remote sensing; climate change; near-infrared spectroscopy; volatile phenolsen
dc.titleClassification of smoke contaminated cabernet sauvignon berries and leaves based on chemical fingerprinting and machine learning algorithmsen
dc.typeJournal articleen
dc.identifier.rmid1000026147en
dc.identifier.doi10.3390/s20185099en
dc.relation.granthttp://purl.org/au-research/grants/arc/ICI70100008en
dc.identifier.pubid547170-
pubs.library.collectionElectrical and Electronic Engineering publicationsen
pubs.library.teamDS10en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidSzeto, C. [0000-0002-3560-9399]en
dc.identifier.orcidWilkinson, K.L. [0000-0001-6724-9837]en
Appears in Collections:Electrical and Electronic Engineering publications

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