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Open AccessArticle

Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

1
Digital Agriculture, Food, and Wine Group, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
2
School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1, Glen Osmond, SA 5064, Australia
3
The Australian Research Council Training Centre for Innovative Wine Production, PMB 1, Glen Osmond, SA 5064, Australia
4
Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, Canterbury, New Zealand
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5099; https://doi.org/10.3390/s20185099
Received: 22 August 2020 / Revised: 5 September 2020 / Accepted: 5 September 2020 / Published: 7 September 2020
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. View Full-Text
Keywords: smoke taint; remote sensing; climate change; near-infrared spectroscopy; volatile phenols smoke taint; remote sensing; climate change; near-infrared spectroscopy; volatile phenols
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MDPI and ACS Style

Summerson, V.; Gonzalez Viejo, C.; Szeto, C.; Wilkinson, K.L.; Torrico, D.D.; Pang, A.; De Bei, R.; Fuentes, S. Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms. Sensors 2020, 20, 5099. https://doi.org/10.3390/s20185099

AMA Style

Summerson V, Gonzalez Viejo C, Szeto C, Wilkinson KL, Torrico DD, Pang A, De Bei R, Fuentes S. Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms. Sensors. 2020; 20(18):5099. https://doi.org/10.3390/s20185099

Chicago/Turabian Style

Summerson, Vasiliki; Gonzalez Viejo, Claudia; Szeto, Colleen; Wilkinson, Kerry L.; Torrico, Damir D.; Pang, Alexis; De Bei, Roberta; Fuentes, Sigfredo. 2020. "Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms" Sensors 20, no. 18: 5099. https://doi.org/10.3390/s20185099

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