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 gr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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International Viticulture and Enology Society
2020-11-01
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| Series: | OENO One |
| Subjects: | |
| Online Access: | https://oeno-one.eu/article/view/4501 |
| _version_ | 1819282077361635328 |
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| author | Vasiliki Summerson Claudia Gonzalez Viejo Damir D. Torrico Alexis Pang Sigfredo Fuentes |
| author_facet | Vasiliki Summerson Claudia Gonzalez Viejo Damir D. Torrico Alexis Pang Sigfredo Fuentes |
| author_sort | Vasiliki Summerson |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-24T01:09:51Z |
| format | Article |
| id | doaj.art-11fc37805afe466790d740958bae22da |
| institution | Directory Open Access Journal |
| issn | 2494-1271 |
| language | English |
| last_indexed | 2024-12-24T01:09:51Z |
| publishDate | 2020-11-01 |
| publisher | International Viticulture and Enology Society |
| record_format | Article |
| series | OENO One |
| spelling | doaj.art-11fc37805afe466790d740958bae22da2022-12-21T17:23:02ZengInternational Viticulture and Enology SocietyOENO One2494-12712020-11-0154410.20870/oeno-one.2020.54.4.4501Detection 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 MelbourneThe 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.https://oeno-one.eu/article/view/4501Remote sensingClimate changeArtificial neural networksSmoke taint |
| 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 |
| title | 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_short | 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 |
| topic | Remote sensing Climate change Artificial neural networks Smoke taint |
| url | https://oeno-one.eu/article/view/4501 |
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