Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach
AuthorFuentes, S; Summerson, V; Viejo, CG; Tongson, E; Lipovetzky, N; Wilkinson, KL; Szeto, C; Unnithan, RR
University of Melbourne Author/sFuentes Jara, Sigfredo Augusto; Lipovetzky, Nir; Gonzalez Viejo Duran, Claudia; Tongson, Eden; Summerson, Vasiliki
AffiliationAgriculture and Food Systems
Computing and Information Systems
Document TypeJournal Article
CitationsFuentes, S., Summerson, V., Viejo, C. G., Tongson, E., Lipovetzky, N., Wilkinson, K. L., Szeto, C. & Unnithan, R. R. (2020). Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach. Sensors, 20 (18), https://doi.org/10.3390/s20185108.
Access StatusOpen Access
ARC Grant codeARC/LP160101475
Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R2 = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R2 = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R2 = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R2 = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires
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