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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) and 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 7 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); and 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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Machine learning detects grape contamination from bush fires within one hour after exposure. Image credit: Dr. Eden Tongson