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dc.contributor.authorFuentes, S
dc.contributor.authorSummerson, V
dc.contributor.authorViejo, CG
dc.contributor.authorTongson, E
dc.contributor.authorLipovetzky, N
dc.contributor.authorWilkinson, KL
dc.contributor.authorSzeto, C
dc.contributor.authorUnnithan, RR
dc.date.accessioned2020-11-17T03:36:24Z
dc.date.available2020-11-17T03:36:24Z
dc.date.issued2020-09-01
dc.identifierpii: s20185108
dc.identifier.citationFuentes, S., Summerson, V., Viejo, C. G., Tongson, E., Lipovetzky, N., Wilkinson, K. L., Szeto, C. & Unnithan, R. R. (2020). Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach. Sensors, 20 (18), https://doi.org/10.3390/s20185108.
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/11343/251555
dc.description.abstractBushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R2 = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R2 = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R2 = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R2 = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires
dc.languageEnglish
dc.publisherMDPI AG
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleAssessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach
dc.typeJournal Article
dc.identifier.doi10.3390/s20185108
melbourne.affiliation.departmentAgriculture and Food Systems
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleSensors
melbourne.source.volume20
melbourne.source.issue18
melbourne.identifier.arcLP160101475
dc.rights.licenseCC BY
melbourne.elementsid1464309
melbourne.contributor.authorFuentes Jara, Sigfredo Augusto
melbourne.contributor.authorLipovetzky, Nir
melbourne.contributor.authorGonzalez Viejo Duran, Claudia
melbourne.contributor.authorTongson, Eden
melbourne.contributor.authorSummerson, Vasiliki
dc.identifier.eissn1424-8220
melbourne.identifier.fundernameidAustralian Research Council, LP160101475
melbourne.accessrightsOpen Access


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