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.

Sigfredo Fuentes,Claudia Gonzalez Viejo,Ranjith R Unnithan,Eden Tongson,Kerry L Wilkinson,Vasiliki Summerson,Colleen Szeto,Nir Lipovetzky

doi:10.3390/s20185108

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.

Highlights

When bushfires occur within the grape growing season, vineyards can be affected at critical stages [1], which could result in different levels of smoke contamination in berries and smoke taint in wines [2,3]
This study evaluated the potential for low-cost e-noses to be used to assess wines made from grapes exposed to different levels of smoke
This paper described how the e-nose was implemented for the different treatments and wine samples used and the specific machine learning algorithms used to develop five machine learning models with their respective analyses for accuracy and performance

Summary

Introduction

When bushfires occur within the grape growing season, vineyards can be affected at critical stages (véraison to harvest) [1], which could result in different levels of smoke contamination in berries and smoke taint in wines [2,3]. The growing concerns in Australia regarding bushfire scale and frequency are shared by wine regions around the world, including the USA, Canada, South Africa, Portugal, Chile, and others [5]. To assess the potential risk of smoke taint, the industry typically relies on the analysis of grape samples by commercial laboratories to quantify smoke taint marker compounds (i.e., volatile phenols and their glycoconjugates), but this can be prohibitively expensive for some producers [6,7]. Grapes can be harvested and vinified so that sensory analysis can be conducted in-house. Depending on the timing of smoke exposure, these approaches may not inform decision-making within the time-constraints of vintage

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Discussion

Conclusion