Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/128498
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Type: | Journal article |
Title: | 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 |
Author: | Fuentes, S. Summerson, V. Gonzalez Viejo, C. Tongson, E. Lipovetzky, N. Wilkinson, K.L. Szeto, C. Unnithan, R.R. |
Citation: | Sensors, 2020; 20(18):1-22 |
Publisher: | MDPI |
Issue Date: | 2020 |
ISSN: | 1424-8220 1424-8220 |
Statement of Responsibility: | Sigfredo Fuentes, Vasiliki Summerson, Claudia Gonzalez Viejo, Eden Tongson, Nir Lipovetzky, Kerry L. Wilkinson ... et al. |
Abstract: | 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. |
Keywords: | climate change electronic nose machine learning smoke taint wine sensory |
Rights: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
DOI: | 10.3390/s20185108 |
Grant ID: | http://purl.org/au-research/grants/arc/LP160101475 |
Published version: | http://dx.doi.org/10.3390/s20185108 |
Appears in Collections: | Aurora harvest 8 Environment Institute publications |
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