Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms
Vasiliki Summerson, Claudia Gonzalez Viejo, Damir D Torrico, Alexis Pang, Sigfredo Fuentes
OENO One | VIGNE ET VIN PUBLICATIONS INT | Published : 2020
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 (NI..View full abstract
Awarded by Australian Research Council Training Centre for Innovative Wine Production as part of the ARC's Industrial Transformation Research Program
This research was supported by 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. The authors gratefully acknowledge the Digital Agriculture, Food, and Wine Group. They also gratefully acknowledge Kerry Wilkinson and Colleen Szeto for the opportunity to collaborate in the field trials, providing data on levels of volatile phenols and their glycoconjugates and supplying wine samples. Colleen Szeto was supported by the Australian Research Council Training Centre for Innovative Wine Production (www.arcwinecentre.org.au) funded as part of the ARC's Industrial Transformation Research Program (Project No. ICI70100008), with support from Wine Australia and industry partners.