Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

Vasiliki Summerson,Kerry L Wilkinson,Roberta De Bei,Alexis Pang,Colleen Szeto,Sigfredo Fuentes,Claudia Gonzalez Viejo,Damir D Torrico

doi:10.3390/s20185099

Abstract

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.

Highlights

The incidence and intensity of wildfires are increasing worldwide, mainly due to the effects of climate change [1,2,3,4,5]
The trial, described previously by Szeto and colleagues [60], involved the application of smoke and/or in-canopy misting to Cabernet Sauvignon grapevines and comprised five different treatments: a control (C), i.e., neither misting nor smoke exposure; (ii) a control with misting (CM), i.e., in-canopy misting but no smoke exposure; (iii) a high-density smoke treatment (HS); (iv) a high-density smoke treatment with misting (HSM); and (v) a low-density smoke treatment without misting (LS)
A previous study by Bell et al [6] found that gs of potted Cabernet Sauvignon grapevines had returned to 60% of pre-smoke exposure rate following fifteen min exposure to smoke using Tasmanian blue gum (Eucalyptus globulus L.) leaves as fuel, while rates had returned to 80% of pre-smoke values following exposure to smoke derived from Coast Live

Summary

Introduction

The incidence and intensity of wildfires are increasing worldwide, mainly due to the effects of climate change [1,2,3,4,5]. Bushfires that occur near wine regions can result in grapevine smoke exposure, which can alter the chemical composition of grape berries. Wine produced from these smoke-affected grapes may exhibit unpalatable smoky aromas and flavors, such as “burnt wood”, “ashy”, and “burnt rubber” [6,7,8,9]. These undesirable characters have been attributed to smoke-derived volatile phenols (VPs), including guaiacol, 4-methylguaiacol, cresols, and syringol [7,10,11].

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