Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms

The number and intensity of wildfires are increasing worldwide, thereby raising the risk of smoke contamination of grapevine berries and the development of smoke taint in wine. This study aimed to develop five artificial neural network (ANN) models from berry, must, and wine samples obtained from g...

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Main Authors: Vasiliki Summerson, Claudia Gonzalez Viejo, Damir D. Torrico, Alexis Pang, Sigfredo Fuentes
Format: Article
Language:English
Published: International Viticulture and Enology Society 2020-11-01
Series:OENO One
Subjects:
Online Access:http://revues.u-bordeaux.fr/ojs32/index.php/oeno-one/article/view/4501
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spelling doaj-39bb628c4fd6414aa1133989a28066862021-04-02T19:51:54ZengInternational Viticulture and Enology SocietyOENO One2494-12712020-11-01544Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithmsVasiliki Summerson0Claudia Gonzalez Viejo1Damir D. TorricoAlexis PangSigfredo Fuentes2Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Building 142, Parkville 3010, Victoria, Australia Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Building 142, Parkville 3010, Victoria, Australia University of Melbourne The number and intensity of wildfires are increasing worldwide, thereby raising the risk of smoke contamination of grapevine berries and the development of smoke taint in wine. This study aimed to develop five artificial neural network (ANN) models from berry, must, and wine samples obtained from grapevines exposed to different levels of smoke: (i) Control (C), i.e., no misting or smoke exposure; (ii) Control with misting (CM), i.e., in-canopy misting, but no smoke exposure; (iii) low-density smoke treatment (LS); (iv) high-density smoke treatment (HS) and (v) a high-density smoke treatment with misting (HSM). Models 1, 2, and 3 were developed using the absorbance values of near-infrared (NIR) berry spectra taken one day after smoke exposure to predict levels of 10 volatile phenols (VP) and 18 glycoconjugates in grapes at either one day after smoke exposure (Model 1: R = 0.98; R2 = 0.97; b = 1) or at harvest (Model 2: R = 0.98; R2 = 0.97; b = 0.97), as well as six VP and 17 glycoconjugates in the final wine (Model 3: R = 0.98; R2 = 0.95; b = 0.99). Models 4 and 5 were developed to predict the levels of six VP and 17 glycoconjugates in wine. Model 4 used must NIR absorbance spectra as inputs (R = 0.99; R2 = 0.99; b = 1.00), while Model 5 used wine NIR absorbance spectra (R = 0.99; R2 = 0.97; b = 0.97). All five models displayed high accuracies and could be used by grape growers and winemakers to non-destructively assess at near real-time the levels of smoke-related compounds in grapes and/or wine in order to make timely decisions about grape harvest and smoke taint mitigation techniques in the winemaking process. http://revues.u-bordeaux.fr/ojs32/index.php/oeno-one/article/view/4501Remote sensingClimate changeArtificial neural networksSmoke taint
collection DOAJ
language English
format Article
sources DOAJ
author Vasiliki Summerson
Claudia Gonzalez Viejo
Damir D. Torrico
Alexis Pang
Sigfredo Fuentes
spellingShingle Vasiliki Summerson
Claudia Gonzalez Viejo
Damir D. Torrico
Alexis Pang
Sigfredo Fuentes
Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms
OENO One
Remote sensing
Climate change
Artificial neural networks
Smoke taint
author_facet Vasiliki Summerson
Claudia Gonzalez Viejo
Damir D. Torrico
Alexis Pang
Sigfredo Fuentes
author_sort Vasiliki Summerson
title Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms
title_short Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms
title_full Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms
title_fullStr Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms
title_full_unstemmed Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms
title_sort detection of smoke-derived compounds from bushfires in cabernet-sauvignon grapes, must, and wine using near-infrared spectroscopy and machine learning algorithms
publisher International Viticulture and Enology Society
series OENO One
issn 2494-1271
publishDate 2020-11-01
description The number and intensity of wildfires are increasing worldwide, thereby raising the risk of smoke contamination of grapevine berries and the development of smoke taint in wine. This study aimed to develop five artificial neural network (ANN) models from berry, must, and wine samples obtained from grapevines exposed to different levels of smoke: (i) Control (C), i.e., no misting or smoke exposure; (ii) Control with misting (CM), i.e., in-canopy misting, but no smoke exposure; (iii) low-density smoke treatment (LS); (iv) high-density smoke treatment (HS) and (v) a high-density smoke treatment with misting (HSM). Models 1, 2, and 3 were developed using the absorbance values of near-infrared (NIR) berry spectra taken one day after smoke exposure to predict levels of 10 volatile phenols (VP) and 18 glycoconjugates in grapes at either one day after smoke exposure (Model 1: R = 0.98; R2 = 0.97; b = 1) or at harvest (Model 2: R = 0.98; R2 = 0.97; b = 0.97), as well as six VP and 17 glycoconjugates in the final wine (Model 3: R = 0.98; R2 = 0.95; b = 0.99). Models 4 and 5 were developed to predict the levels of six VP and 17 glycoconjugates in wine. Model 4 used must NIR absorbance spectra as inputs (R = 0.99; R2 = 0.99; b = 1.00), while Model 5 used wine NIR absorbance spectra (R = 0.99; R2 = 0.97; b = 0.97). All five models displayed high accuracies and could be used by grape growers and winemakers to non-destructively assess at near real-time the levels of smoke-related compounds in grapes and/or wine in order to make timely decisions about grape harvest and smoke taint mitigation techniques in the winemaking process.
topic Remote sensing
Climate change
Artificial neural networks
Smoke taint
url http://revues.u-bordeaux.fr/ojs32/index.php/oeno-one/article/view/4501
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