Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms

Abstract

The number and intensity of wildfires are increasing worldwide, thereby raising the risk of smoke contamination of grapevine berries and the development of smoke taint in wine. This study aimed to develop five artificial neural network (ANN) models from berry, must, and wine samples obtained from grapevines exposed to different levels of smoke: (i) Control (C), i.e., no misting or smoke exposure; (ii) Control with misting (CM), i.e., in-canopy misting, but no smoke exposure; (iii) low-density smoke treatment (LS); (iv) high-density smoke treatment (HS) and (v) a high-density smoke treatment with misting (HSM). Models 1, 2, and 3 were developed using the absorbance values of near-infrared (NIR) berry spectra taken one day after smoke exposure to predict levels of 10 volatile phenols (VP) and 18 glycoconjugates in grapes at either one day after smoke exposure (Model 1: R = 0.98; R2 = 0.97; b = 1) or at harvest (Model 2: R = 0.98; R2 = 0.97; b = 0.97), as well as six VP and 17 glycoconjugates in the final wine (Model 3: R = 0.98; R2 = 0.95; b = 0.99). Models 4 and 5 were developed to predict the levels of six VP and 17 glycoconjugates in wine. Model 4 used must NIR absorbance spectra as inputs (R = 0.99; R2 = 0.99; b = 1.00), while Model 5 used wine NIR absorbance spectra (R = 0.99; R2 = 0.97; b = 0.97). All five models displayed high accuracies and could be used by grape growers and winemakers to non-destructively assess at near real-time the levels of smoke-related compounds in grapes and/or wine in order to make timely decisions about grape harvest and smoke taint mitigation techniques in the winemaking process.

Review of the Effects of Grapevine Smoke Exposure and Technologies to Assess Smoke Contamination and Taint in Grapes and Wine

“…For example, research by Summerson et al [5] developed ANN models that accurately classified either berry (Model 1: 98.00% overall accuracy) or leaf (Model 2: 97.40% overall accuracy) NIR readings according to the different smoke treatments applied. Furthermore, several studies have demonstrated great success in predicting levels of smoke-derived volatile phenols and their glycoconjugates in grapes and wine using NIR spectroscopy and ANN modeling [6,88]. A study by Fuentes et al [88] developed a model that accurately predicts levels of guaiacol glycoconjugates in berries as well as guaiacol and guaiacol glycoconjugates for seven different grapevine cultivars using NIR berry measurements between 700 and 1100 nm.…”

“…A study by Fuentes et al [88] developed a model that accurately predicts levels of guaiacol glycoconjugates in berries as well as guaiacol and guaiacol glycoconjugates for seven different grapevine cultivars using NIR berry measurements between 700 and 1100 nm. In further research, Summerson et al [6] developed five different models that displayed high accuracy in predicting levels of volatile phenols and their glycoconjugates in Cabernet Sauvignon grapes and wine. The first three models were developed using NIR berry spectra taken one day after grapevine smoke exposure as inputs to predict levels of 10 volatile phenols and 18 glycoconjugates in grapes at either one day after smoke exposure (Model 1: R = 0.98; R 2 = 0.97; b = 1) or harvest (Model 2: R = 0.98; R 2 = 0.97; b = 0.97), as well as six volatile phenols and 17 glycoconjugates in the final wine (Model 3: R = 0.98; R 2 = 0.95; b = 0.99).…”

“…They reportedly add complexity to wine flavor at low levels, producing desirable aromas such as "vanilla" and "oak" [26,44]. High concentrations of volatile phenols appear to produce undesirable smoky aromas; however, the minimum amount required to be able to detect these aromas varies depending on the wine [6,34]. According to Simos [44], the struc-Figure 1.…”

“…They reportedly add complexity to wine flavor at low levels, producing desirable aromas such as "vanilla" and "oak" [26,44]. High concentrations of volatile phenols appear to produce undesirable smoky aromas; however, the minimum amount required to be able to detect these aromas varies depending on the wine [6,34]. According to Simos [44], the structure/body of the wine strongly influences its susceptibility to smoky aromas; for example, in delicate sparkling white wines, winemakers have observed smoky characters with levels of guaiacol as low as 6-10 µg L −1 , while in medium-bodied red wines, thresholds range between 15-25 µg L −1 , and in fuller-bodied Shiraz styles, the threshold range is much higher at 30-40 µg L −1 [44].…”

“…Bushfires are a common occurrence globally, in places such as Australia, South Africa, Mediterranean Europe, and North and South America [1][2][3][4][5][6]. Unfortunately, climate change effects, such as increases in temperature, winds, and drought, have led to more favorable bushfire conditions [7][8][9][10][11][12][13].…”