Journal article

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

Vasiliki Summerson, Claudia Gonzalez Viejo, Colleen Szeto, Kerry L Wilkinson, Damir D Torrico, Alexis Pang, Roberta De Bei, Sigfredo Fuentes

Sensors | MDPI | Published : 2020

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 ..

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Grants

Awarded by ARC's Industrial Transformation Research Program


Funding Acknowledgements

This research was supported through the Australian Government Research Training Program Scholarship, as well as the Digital Viticulture program funded by the University of Melbourne's Networked Society Institute, Australia. C.S. was supported by the Australian Research Council Training Centre for Innovative Wine Production (www.arcwinecentre.org.au), which is funded as part of the ARC's Industrial Transformation Research Program (Project No. ICI70100008), with support from Wine Australia and industry partners. The authors greatly acknowledge the Digital Agriculture, Food, and Wine Group.