Classification of smoke contaminated Cabernet Sauvignon berries and leaves based on chemical fingerprinting and machine learning algorithms
dc.contributor.author | Summerson, V | |
dc.contributor.author | Gonzalez Viejo, C | |
dc.contributor.author | Szeto, C | |
dc.contributor.author | Wilkinson, KL | |
dc.contributor.author | Torrico, Damir | |
dc.contributor.author | Pang, A | |
dc.contributor.author | De Bei, R | |
dc.contributor.author | Fuentes, S | |
dc.coverage.spatial | Switzerland | |
dc.date.accessioned | 2020-09-28T20:38:42Z | |
dc.date.available | 2020-09-07 | |
dc.date.issued | 2020-09 | |
dc.date.submitted | 2020-09-05 | |
dc.description.abstract | Wildfires 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. | |
dc.format.extent | 23 pages | |
dc.format.medium | Electronic | |
dc.identifier | s20185099 | |
dc.identifier | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=elements_prod&SrcAuth=WosAPI&KeyUT=WOS:000580061900001&DestLinkType=FullRecord&DestApp=WOS_CPL | |
dc.identifier.doi | 10.3390/s20185099 | |
dc.identifier.eissn | 1424-8220 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.other | 32906800 (pubmed) | |
dc.identifier.uri | https://hdl.handle.net/10182/12826 | |
dc.language.iso | en | |
dc.publisher | MDPI | |
dc.relation | The original publication is available from MDPI - https://doi.org/10.3390/s20185099 - http://dx.doi.org/10.3390/s20185099 | |
dc.relation.isPartOf | Sensors | |
dc.relation.uri | https://doi.org/10.3390/s20185099 | |
dc.rights | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. | |
dc.rights.ccname | Attribution | |
dc.rights.ccuri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | smoke taint | |
dc.subject | remote sensing | |
dc.subject | climate change | |
dc.subject | near-infrared spectroscopy | |
dc.subject | volatile phenols | |
dc.subject.anzsrc | ANZSRC::090806 Wine Chemistry and Wine Sensory Science | |
dc.subject.anzsrc | ANZSRC::0908 Food Sciences | |
dc.subject.anzsrc2020 | ANZSRC::4008 Electrical engineering | |
dc.subject.anzsrc2020 | ANZSRC::4009 Electronics, sensors and digital hardware | |
dc.subject.anzsrc2020 | ANZSRC::4606 Distributed computing and systems software | |
dc.title | Classification of smoke contaminated Cabernet Sauvignon berries and leaves based on chemical fingerprinting and machine learning algorithms | |
dc.type | Journal Article | |
lu.contributor.unit | LU | |
lu.contributor.unit | LU|Agriculture and Life Sciences | |
lu.contributor.unit | LU|Agriculture and Life Sciences|WFMB | |
lu.identifier.orcid | 0000-0003-1482-2438 | |
pubs.article-number | 5099 | |
pubs.issue | 18 | |
pubs.notes | Date of acceptance: 5 Sept 2020 | |
pubs.publication-status | Published online | |
pubs.publisher-url | http://dx.doi.org/10.3390/s20185099 | |
pubs.volume | 20 |
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