Introduction

Recent climate change forecasts have predicted an increase in the number and intensity of wildfires, as well as a lengthening of the fire season in many grape-growing regions throughout the world, including Australia, Greece, California, Chile, and South Africa (CSIRO & Australian Government Bureau of Meteorology, 2018; Favell et al., 2019; Fuentes et al., 2019; Hughes & Alexander, 2017; Noestheden et al., 2018b; Simos, 2008). As a consequence, the incidence of grapevine smoke exposure and the subsequent development of objectional smoky aromas in wine known as smoke taint is also likely to increase, resulting in significant financial losses to the wine industry (Bell et al., 2013; Kelly et al., 2012; Kennison et al., 2011; Noestheden et al., 2018a; Noestheden et al., 2017). It has been found that the exposure of grapevines to smoke during the critical period of approximately seven days post-veraison - which also corresponds to the period at highest risk of wildfire development - leads to the exposure of fruit to volatile phenols (VP), including guaiacol, 4-methylguaiacol, syringol and cresols, which then accumulate in glycoconjugate forms in grape berries and leaves (Fuentes & Tongson, 2017; Ristic et al., 2017; van der Hulst et al., 2019). During fermentation, these glycoconjugates are hydrolysed back into their free, sensorially active forms, which express smoky aromas in the produced wine to the point of taint; however, a significant pool of glycoconjugates remains in the wine. It is believed that both the free VP and their glycoconjugates contribute to the smoky aromas (such as ‘burnt', ‘earthy', and ‘ashy' notes) in wine; therefore, they must both be measured in order to ascertain the level of smoke taint (Hayasaka et al., 2013; Kennison et al., 2008; Kennison et al., 2007; Ristic et al., 2017; Singh et al., 2011; van der Hulst et al., 2019). While low levels of these compounds may add complexity to wine flavour and aroma, high levels above the detection threshold result in the unpleasant aromas associated with smoke taint (Singh et al., 2011). Some aroma detection thresholds for VP in red wines have been reported, with guaiacol, m-cresol, and 4-methylguaiacol (believed to be key contributors to smoke-taint aromas) having the lowest detection thresholds of all the VP at 23&nbspµg/L, 20&nbspµg/L, and 30 µg/L respectively (Härtl & Schwab, 2018; Parker et al., 2013). In comparison, wines matured in oak barrels reportedly contain 10–100&nbspµg/L guaiacol and 1–20&nbspµg/L 4-methylguaiacol (Pollnitz et al., 2004). Others have also reported that a possible synergistic effect may occur when smoke-taint characteristics are perceived, despite individual concentrations of VP being below detection thresholds (De Vries et al., 2016; Kennison et al., 2009). These heavily smoke-tainted wines are unpalatable and unprofitable, costing the wine industry millions of dollars in lost wine revenue. For example, it is estimated that the 2006/2007 bushfires in Victoria, Australia, resulted in approximately 75-90 billion AU$ in lost revenue, while the 2009 Black Saturday bushfires resulted in a loss of approximately 300 million AU$ to the local Australian wine industry (Department of Primary Industries, 2009; Favell et al., 2019; Fudge et al., 2011; Kennison et al., 2007; Noestheden et al., 2017; Singh et al., 2011).

In order to ascertain the level of smoke taint in wine, concentrations of both free VP and bound glycoconjugates must be determined (Allen et al., 2013; van der Hulst et al., 2019). Techniques like gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) are often used for quantifying levels of free and glycosidically bound VP in both grapes and wine (Hayasaka et al., 2010a; Hayasaka et al., 2010b; Hayasaka et al., 2013; Pollnitz et al., 2004). Unfortunately, there are several drawbacks to these chromatographic techniques; for example, time-consuming sample preparation, reagent and instrumentation costs, need for trained personnel, destructive sampling techniques. In addition, there is often a long waiting time between taking the sample and obtaining the results (Fudge et al., 2012b, Fudge et al., 2013; Kemps et al., 2010): growers may face significant delays in receiving results for smoke contamination analysis from commercial laboratories, particularly during affected periods or the winemaking process, when there are many samples requiring analysis from affected vineyards within a region (Fudge et al., 2012b; Fudge et al., 2013). Therefore, research is required on alternative methods of detection that provide rapid results and hence allow immediate action to be taken to reduce affected grapes or to modify the winemaking processes.

The use of spectroscopic methods for quantitative and qualitative analysis has gained popularity due to their rapid results, ease of use, non-destructive nature permitting repeated measurements, and the portability of devices for in-field use (Fudge et al., 2012b; Fudge et al., 2013; Hall, 2018; Kemps et al., 2010; Teixeira dos Santos et al., 2013). Some spectroscopic techniques in the ultraviolet (UV; 250-400&nbspnm), visible (Vis; 400-700&nbspnm), near-infrared (NIR; 700-2500&nbspnm), and mid-infrared (MIR; 2500-25000&nbspnm) regions have been used for various grape and wine assessments, including the classification of grape juice based on the grape variety, the determination of polyphenolic compounds in red wines, and the assessment of the aroma potential of Tannet grapes by measuring their glycosylated aroma compound content (Boido et al., 2013; Cozzolino et al., 2012; Martelo-Vidal & Vázquez, 2014; Pirie et al., 2005). Most notably, the use of MIR spectroscopy has demonstrated great potential in classifying smoke-tainted wines; however, it does not provide details about the levels of glycoconjugates and VP in the wine, and classification rates have been found to be impacted by grape variety, oak maturation and degree of smoke taint (Fudge et al., 2012b).

Increasing research is being conducted to investigate the use of artificial neural networks (ANNs) for the analysis of UV-Vis-NIR spectroscopy, particularly since ANNs are better able to analyse non-linear data than conventional chemometric techniques, such as principal component analysis (PCA) (Diamantopoulou & Milios, 2010; Martelo-Vidal & Vázquez, 2015; Yu et al., 2018). ANN models can predict the physical properties of food products from a learning algorithm trained using experimentally-derived data or values from validated real-life models (Dieulot & Skurtys, 2013; Martelo-Vidal & Vázquez, 2015). Coupled with UV-Vis-NIR spectroscopy, ANN has been used to develop reliable models for predicting levels of malic acid, tartaric acid, and ethanol in wine (Martelo-Vidal & Vázquez, 2015). There has also been significant research investigating the use of ANNs for the detection of grapevine smoke contamination and smoke taint compounds in wine. Using grapevine berry and leaf NIR readings as inputs, ANN models have been developed to classify the spectral readings according to smoke exposure levels with high accuracy (Summerson et al., 2020). Other ANN models have been developed using readings obtained from a low-cost electronic nose (E-nose) to accurately predict levels of smoke compounds in wines (Fuentes et al., 2020). Furthermore, other research has used NIR spectroscopy within the region of 700-1100&nbspnm to develop a model for quantifying levels of guaiacol glycoconjugates in berries and wine and levels of guaiacol in wine (Fuentes et al., 2019).

This paper presents the use of NIR spectroscopy as a rapid method for assessing grapes, must, and wine with different levels of smoke contamination and with or without in-canopy misting. Five ANN regression models were developed to predict levels of smoke taint markers in grape berries or wine. All five models had high accuracy and could be used by grape growers and winemakers as a non-destructive method for assessing near real-time smoke contamination levels in grapes and wine. This would allow them to make timely decisions about which fruit to sample for further chemical analysis and/or which fruit to harvest to maintain wine quality or to apply smoke taint mitigation techniques to must/wine, such as through the use of activated carbon.

Materials and methods

4. Machine learning modelling and statistical analysis

Five ANN machine learning regression models were developed using a customised code written in MATLAB® R2020b (Mathworks, Inc., Natick, MA, USA) to test 17 different training algorithms. The best results for the first three models were obtained using the Levenberg Marquardt algorithm to predict levels of volatile phenols and their glycoconjugates in grape berries one day after smoke exposure (Model 1) and at harvest (Model 2), as well as in the wine (Model 3). Berry NIR readings taken one day after smoke exposure were used as inputs for these three models, with ten volatile phenols and 18 glycoconjugates used as targets in Models 1 and 2, and six volatile phenols and 17 glycoconjugates used for Model 3, as shown in Table 1. The Bayesian regularisation algorithm was chosen for Models 4 and 5 to predict levels of 17 glycoconjugates and six volatile phenols in the wine. Model 4 used the must NIR spectra as inputs, while Model 5 used the wine NIR spectra (Figure 1b).

A random data division was used to develop all five models. Models 1, 2, and 3 used 70&nbsp% of the data for the training stage, 15&nbsp% for validation with a mean squared error (MSE) performance algorithm, and 15&nbsp% for testing, while Models 4 and 5 used 70&nbsp% for training and 30&nbsp% for testing. All ANN models consisted of a two-layer feedforward network with the hidden layer using a tan-sigmoid function and the output layer using a linear transfer function, as shown in Figure 1. Ten hidden neurons were selected for Models 1 to 3 and seven for Models 4 and 5 after conducting a trimming exercise with three, seven, and ten neurons to see which yielded the best performance. Models were assessed for over- or underfitting using statistical data consisting of the correlation coefficient (R), slope (b), MSE, and determination coefficient (R²).

Table 1. List of volatile phenols and their glycoconjugates used as inputs.

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	Smoke compound	Abbreviation	Sample tested
Volatile phenols	guaiacol	Gu	Berries/Must/Wine
	4-methylguaiacol	4MG	Berries/Must/Wine
	phenol	Ph	Berries
	syringol	Sy	Berries/Must/Wine
	4-methylsyringol	Msy	Berries
	m-cresol	m-Cr	Berries/Must/Wine
	p-cresol	p-Cr	Berries/Must/Wine
	Total m/p-cresol	m/p-Cr	Berries
	o-cresol	o-Cr	Berries/Must/Wine
	total cresols	Cr	Berries
Glycoconjugates	Guaiacol pentosylglucosides	GuPG	Berries/Must/Wine
	Guaiacol gentiobioside	GuGG	Berries/Must/Wine
	Guaiacol rutinoside	GuRG	Berries/Must/Wine
	Guaiacol monoglucoside	GuMG	Berries/Must/Wine
	Methylguaiacol pentosylglucosides	MGuPG	Berries/Must/Wine
	Methylguaiacol rutinoside	MGuRG	Berries/Must/Wine
	Phenol rutinoside	PhRG	Berries/Must/Wine
	Phenol gentiobioside	PhGG	Berries/Must/Wine
	Phenol pentosylglucosides	PhPG	Berries/Must/Wine
	Phenol monoglucoside	PhMG	Berries/Must/Wine
	Syringol gentiobioside	SyGG	Berries/Must/Wine
	Syringol monoglucoside	SyMG	Berries/Must/Wine
	Syringol pentosylglucosides	SyPG	Berries/Must/Wine
	Methylsyringol gentiobioside	MSyGG	Berries/Must/Wine
	Methylsyringol pentosylglucosides	MSyPG	Berries/Must/Wine
	Cresol glucosylpentosides	CrPG	Berries/Must/Wine
	Cresol gentiobioside	CrGG	Berries
	Cresol rutinoside	CrRG	Berries/Must/Wine

Figure 1. Two-layer feedforward networks for (a) Models 1 and 2 to predict levels of 10 volatile phenols and 18 glycoconjugates in grape berries and (b) Models 3, 4, and 5 to predict levels of six volatile phenols and 17 glycoconjugates in the final wine.

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Results

1. NIR absorbance spectra for berries, must, and wine

Figures 2, 3, and 4 illustrate the average berry, must, and wine NIR absorbance spectra. Clear differences in spectral readings across the entire wavelength range for each smoke treatment was observed for the berry absorbance spectra (Figure 2a). A large peak was originally observed at approximately 1910&nbspnm, as well as a smaller one at approximately 1790&nbspnm. In the transformed data (Figure S1), large peaks were observed between approximately 1596-1650&nbspnm and 1820-1950&nbspnm.

Figure 2. Raw berry absorbance spectra for the five smoke treatments.

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Abbreviations: C = control without misting; CM = control with misting; HS = high-density smoke without misting; HSM = high-density smoke with misting; and LS = low-density smoke

Again, clear differences in spectral readings across the entire wavelength range for each smoke treatment were observed for the must absorbance spectra (Figure 3). A large peak was originally observed at approximately 1927&nbspnm and two smaller peaks at approximately 1784 and 2090&nbspnm (Figure 3a), while for the transformed data, large peaks could be seen between approximately 1818-1902&nbspnm and 2246-23322&nbspnm (Figure S2).

Figure 3. Raw must absorbance spectra for the five smoke treatments.

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Abbreviations: C = control without misting; CM = control with misting; HS = high-density smoke without misting; HSM = high-density smoke with misting; and LS = low-density smoke

For the wine absorbance spectra (Figure 4), the largest difference in absorbance values amongst the different smoke treatments was observed at the 1927&nbspnm peak or overtone. Further peaks were also observed at approximately 2090, 2270, and 2340&nbspnm, while in the transformed data (Figure S3), large peaks were observed between approximately 1835–1950&nbspnm and 2220–2300&nbspnm.

Figure 4. Raw wine absorbance (a) and second derivative spectra (b) for the five smoke treatments.

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Abbreviations: C = control without misting; CM = control with misting; HS = high-density smoke without misting; HSM = high-density smoke with misting; and LS = low-density smoke

3. Machine learning modelling

Statistical data for the five regression models are shown in Table 2. Model 1 had high overall correlation and determination coefficients (R&nbsp=&nbsp0.98; R^2&nbsp=&nbsp0.97; Figure 5a.), with values of the validation correlation coefficient (R&nbsp=&nbsp0.97) being close to the training correlation coefficients (R&nbsp=&nbsp0.95). In addition to this, performance values for validation (MSE&nbsp=&nbsp80.28) and testing (MSE&nbsp=&nbsp83.14) were similar among them and higher than that of the training stage (MSE&nbsp=&nbsp16.96), which further indicates no sign of under- or overfitting. Models 2 and 3 also had high correlation and determination coefficients (R&nbsp=&nbsp0.98 for each; R^2&nbsp=&nbsp0.97 for Model 2 and 0.95 for Model 3; Figures 5b and 5c). Again, there were no signs of under- or overfitting as values for the validation and training coefficients were close, and the values for the performance of the training stage were lower than those of the validation and testing stages, with the latter values being similar. Models 4 and 5 displayed very high correlation and determination coefficients (R&nbsp=&nbsp0.99 for each; R^2&nbsp=&nbsp0.99 for Model 4 and R^2&nbsp=&nbsp0.97 for Model 5; Figures 5d and 5e), with performance values of the training stage lower than the testing stage, which does not indicate any over- or underfitting.

Table 2. Statistical results from the five developed artificial neural network regression models, which estimate the levels of volatile phenols and their glycoconjugates in grapes one day after smoking (Model 1), at harvest (Model 2), and wine (Models 3-5) showing the correlation coefficient (R), determination of coefficient (R2), slope (b) and performance based on mean squared error (MSE) for each stage.

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Stage	Samples	Observations	R	R2	b	Performance (MSE)
Model 1
Training	378	10584	0.99	0.99	1.00	16.96
Validation	81	2268	0.97	0.93	1.00	80.28
Testing	81	2268	0.95	0.91	0.99	83.14
Overall	540	15120	0.98	0.97	1.00
Model 2
Training	378	10584	0.99	0.99	0.96	332.26
Validation	81	2268	0.96	0.93	0.95	1533.75
Testing	81	2268	0.96	0.93	1.00	1716.87
Overall	540	15120	0.98	0.97	0.97
Model 3
Training	378	8694	0.99	0.97	1.00	79.07
Validation	81	1863	0.95	0.91	0.98	299.77
Testing	81	1863	0.94	0.89	0.93	290.90
Overall	540	12420	0.98	0.95	0.99
Model 4&nbsp
Training	31	713	0.99	0.99	1	0.43
Testing	14	322	0.99	0.98	1	88.14
Overall	45	1035	0.99	0.99	1
Model 5
Training	31	713	0.99	0.99	1	8.03
Testing	14	322	0.96	0.92	0.92	258.70
Overall	45	1035	0.99	0.97	0.97	&nbsp

Figure 5. Overall correlation of the models to predict ten volatile phenols and 18 glycoconjugates (Table 1) of (a) Model 1: berries one day after smoking, (b) Model 2: berries at harvest, six volatile phenols, and 17 glycoconjugates of (c) Model 3: wine, (d) Model 4: wine and (e) Model 5: wine.

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Discussion

The NIR wavelength range used to develop the five models was between 1596 and 2396&nbspnm (Figures 2, 3, and 4), which includes several key overtones. The C-H stretch first overtone associated with aromatic compounds can be seen between 1680 and 1690&nbspnm, while O-H stretching associated with glucose, cellulose, water, and alcohol can be observed at approximately 1930, 2090, 2270, and 2330&nbspnm. Lastly, the region between 1900 and 1910&nbspnm corresponds to C=O stretching associated with carboxylic acids and water (Boido et al., 2013; Burns & Ciurczak, 2007; Gonzalez Viejo et al., 2018). Thus, this region was found to be effective for assessing levels of smoke contamination. For the berry absorption spectra (Figure 2), the observed peaks (1790 and 1910&nbspnm) were in the NIR regions associated with C&nbsp=&nbspO and O-H overtones in carboxylic acid or water, while for the must and wine absorption spectra (Figures 3 and 4) the observed peaks (1784, 1927, 2090 and 2270&nbspnm) were in the regions associated with C-H and O-H stretching of starch and alcohol (Boido et al., 2013; Burns & Ciurczak, 2007).

Only the HS treatment berries one day after smoke exposure contained average free guaiacol levels above the aroma detection threshold (Table S1); m-cresol and 4-methylguaiacol average levels were below the aroma detection thresholds for all smoke treatments at this time period. Berries at harvest, however, did not contain any VP above the aroma detection thresholds for all smoke treatments (Figure S2), showing that most VP had formed glycoconjugates. In the final wine, both the HS and HSM treatments had average free guaiacol levels above or at the aroma detection threshold (Figure S3), with m-cresol and 4-methylguauaicol levels below their aroma detection thresholds. This highlights the importance of assessing VP and glycoconjugates levels in both grape berries and wine to obtain an idea of the level of smoke contamination and smoke taint in the final wine. Furthermore, in addition to a possible synergistic effect of smoke compounds to the overall smoky aroma, it has been reported that the structure/body of the juice or wine strongly influences the level and detection of smoke taint, with medium-bodied red wines having a lower guaiacol detection threshold (15-25&nbspµg/L) than full-bodied style wines such as Shiraz (30-40&nbspµg/L) (Simos, 2008). It is, therefore, important to consider the style of wine when assessing levels of smoke compounds. At present, the only available means that grape growers and winemakers have for determining the levels of volatile phenols and their glycoconjugates in grapes and wine are to send samples to a commercial laboratory, which is time-consuming and requires destructive sample preparation (Fudge et al., 2012b; Fudge et al., 2013; Kemps et al., 2010; Summerson et al., 2020). Models 1-3 may, therefore, offer grape growers and winemakers a rapid and non-destructive in-field measurement technique for assessing levels of smoke compounds in grapes and potential wine produced from them, with a high level of accuracy and precision. Growers would then be able to make timely decisions, such as avoiding heavily contaminated grapes for winemaking or sending a smaller selection of berry samples for further chemical analysis, depending on the results obtained. Furthermore, Models 4 and 5 offer winemakers near-real-time measurements of the levels of volatile phenols and their glycoconjugates in must and wine, which indicate the level of smoke taint in wine. Winemakers can then decide to apply smoke taint mitigation techniques, such as through treatment with activated carbon (Fudge et al., 2012a). Research by Fudge et al. (2012a) found that treating Cabernet-Sauvignon smoke-affected wines with activated carbon reduced all volatile phenols' concentration by 56 to 71&nbsp%. Specifically, levels of guaiacol were reduced from an average of 18&nbspµg/L in the control wine to 8&nbspµg/L following treatment with activated carbon, while 4-methylguaiacol was reduced from an average of 3&nbspµg/L to 1&nbspµg/L, and total cresols from an average of 7&nbspµg/L to 2&nbspµg/L. It can, therefore, be concluded that the treatment with activated carbon is effective at ameliorating smoke taint. Moreover, as this assessment method is non-destructive, repeated measurements can be made, which is particularly useful as bottle aging has been demonstrated to increase levels of both naturally occurring and smoke-derived VP (Ristic et al., 2017). Hence, repeated measurements over time could be performed to assess levels of both free volatile phenols and their glycoconjugates in wine and must. There is also the possibility of developing models that can assess wine non-destructively while in the bottle (Cozzolino et al., 2007). Research by Cozzolino et al. (2007) found great promise in the use of Vis-NIR spectroscopy to assess wine composition in the bottle, as well as to detect problems in the wine that may have occurred during or after bottling and before selling.

While the five models developed in this study accurately predicted levels of smoke compounds in berries and wine, further research is required to assess whether these models can be used for other grape and wine varieties. Differences in berry and wine composition may affect the accuracy in predicting volatile phenols' levels and their glycoconjugates in other varieties (Fudge et al., 2012b). Research by Fudge et al. (2012b) found that compositional differences due to grape variety had greater influence in classifying smoke-affected wine over wine exposed to low levels of smoke when using MIR spectroscopy as an assessment technique. However, in another study, Fuentes et al. (2019) 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 at between 700 and 1100&nbspnm. Therefore, there is great potential for developing future models that can predict levels of smoke compounds in multiple grape varieties and wines using the NIR regions between 1680 and 1690&nbspnm, as was done in this study. Further research by Fuentes et al. (2020) also found the use of a low-cost E-nose to be effective in assessing levels of smoke compounds in wine, as well as the intensity of 12 wine descriptors, via consumer sensory testing. Therefore, NIR spectroscopy, together with an E-nose, could potentially be used to assess levels of smoke compounds non-destructively. Lastly, different winemaking techniques may affect the levels of smoke compounds in the wine; for example, both the type of yeast used and the duration of skin contact time during fermentation can influence levels of volatile phenols and their glycoconjugates in the final wine (Kennison et al., 2008; Ristic et al., 2011; Simos, 2008). Models 3 and 4 would thus need to be adjusted for different winemaking techniques, including the use of different yeasts, length of fermentation on skins, and the addition of malolactic fermentation. In this experiment, wines did not undergo malolactic fermentation, which reduces the pH of the wine and may, therefore, affect the hydrolysis of glycoconjugates into free VP and hence the level of smoke taint.

Conclusions

The use of NIR spectroscopy, coupled with machine learning, has demonstrated great potential as a non-destructive tool for measuring levels of smoke compounds in Cabernet-Sauvignon grapes and wine. The models developed could be used by grape growers and winemakers either in-field to take repeated assessments of grapes or in the winery to assess must and wine. This could assist them in making informed decisions about berry sampling and winemaking and application of smoke taint amelioration techniques, such as treatment with activated carbon to minimise levels of volatile phenols in wine. Further research is required to assess whether the models developed in this study could be used for other grape and wine varieties and winemaking techniques, such as using different yeast strains and duration of skin contact time during fermentation.

Acknowledgements

This research was supported by the Australian Government Research Training Program Scholarship, as well as the Digital Viticulture program funded by the University of Melbourne’s Networked Society Institute, Australia. The authors gratefully acknowledge the Digital Agriculture, Food, and Wine Group. They also gratefully acknowledge Kerry Wilkinson and Colleen Szeto for the opportunity to collaborate in the field trials, providing data on levels of volatile phenols and their glycoconjugates and supplying wine samples. Colleen Szeto was supported by the Australian Research Council Training Centre for Innovative Wine Production (www.arcwinecentre.org.au) funded as part of the ARC’s Industrial Transformation Research Program (Project No. ICI70100008), with support from Wine Australia and industry partners.

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