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Sensors 2020, 20, 5108; doi:10.3390/s20185108 www.mdpi.com/journal/sensors
Article
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 1, Vasiliki Summerson 1, Claudia Gonzalez Viejo 1,*, Eden Tongson 1,
Nir Lipovetzky 2, Kerry L. Wilkinson 3,4, Colleen Szeto 3,4 and Ranjith R. Unnithan 5
1 Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food,
Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010,
Australia; sfuentes@unimelb.edu.au (S.F.); vsummerson@student.unimelb.edu.au (V.S.);
eden.tongson@unimelb.edu.au (E.T.)
2 School of Computing and Information Systems, Melbourne School of Engineering,
The University of Melbourne, Parkville, VIC 3010, Australia; nir.lipovetzky@unimelb.edu.au
3 School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, PMB 1, Glen Osmond,
SA 5064, Australia; kerry.wilkinson@adelaide.edu.au (K.L.W.); colleen.szeto@adelaide.edu.au (C.S.)
4 The Australian Research Council Training Centre for Innovative Wine Production, PMB 1, Glen Osmond,
SA 5064, Australia
5 School of Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne,
Parkville, VIC 3010, Australia; r.ranjith@unimelb.edu.au
* Correspondence: cgonzalez2@unimelb.edu.au; Tel.: +61-412055704
Received: 24 August 2020; Accepted: 04 September 2020; Published: 8 September 2020
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; machine learning; electronic nose; smoke taint; wine sensory
1. 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
Sensors 2020, 20, 5108 2 of 15
berries and smoke taint in wines [2,3]. The intensity, number, and severity of bushfires are increasing
due to climate change as well as the window of opportunity [4].
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].
Alternatively, grapes can be harvested and vinified so that sensory analysis can be conducted in-
house. However, depending on the timing of smoke exposure, these approaches may not inform
decision-making within the time-constraints of vintage.
To date, there has been little research into the use of affordable in-field technology to assess
grapevine smoke contamination. Recently, the authors’ group published a study evaluating short-
range remote sensing in the thermal and near-infrared spectrum, combined with machine learning,
as a novel approach to assessing smoke contamination in grapevine leaves, berries, and wines, with
high levels of accuracy [5]. These tools may support rapid decision-making, enabling the
implementation of management strategies that reduce the risk of contamination carrying over into
wine, as smoke taint.
Electronic noses (e-nose) are comprised of an array of metal oxide semiconductor sensors (MOS)
sensitive to different gases that can measure a variety of volatiles in the environment [8]. Early
developments of e-noses involve arrays of 5–8 tin-oxide type of MOS sensors, requiring the use of sealed
chambers and/or a complete setup of different devices to heat the sample and obtain headspace to be
injected in the e-nose chamber, which has made the e-noses non-portable as they require a laboratory
setup [9,10]. Some studies have explored different signal extraction methods, such as the Lorentzian
model, which has resulted in a powerful and rapid-response technique [11]. Ayhan et al. [12] explored
the fluctuation-enhanced sensing method to detect and classify gases with improved accuracy when
developing classification models using machine learning algorithms. Some applications include
medical diagnostics [13], space shuttles and stations [14–16], crime and security [17], and food and
beverages, such as rapeseed to detect volatile compounds in pressed oil [18], wine [19], and beer [20],
among others. The latter study describes a low-cost e-nose developed with nine gas sensors to assess
the aroma profile of beers coupled with machine learning modeling. Examples of the implementation
of e-noses for food science can be found from early literature reviews [21] through the
implementation of disease diagnostics [22], more recent applications to assess food quality [23], meat
quality assessment [24], for food control [23], assessment of food authentication and adulteration [25],
and for the wine industry [26–30]. However, the e-noses used in the past range in complexity,
accessibility to users, and cost.
Low-cost e-noses can be used in the field to assess smoke contamination levels coupled with the
internet of things (IoT) for data transmission and analysis from different locations or nodes within
vineyards. However, a more efficient approach could be to mount e-noses to assess gases in different
parts of vineyards and to generate geo-referenced maps of these gases on unmanned terrestrial
vehicles (UTV), robots [31], or unmanned aerial vehicles (UAV) [32]. The levels of smoke-related
contaminants could be modeled using machine learning algorithms to infer the levels of
contaminants in berries, and therefore, the risk of smoke taint in the final wine. However, they could
not be used to directly “sniff” these contaminants from bunches since smoke-derived volatile
compounds are rapidly metabolized in berries, leading to the formation of glycoconjugates, which
are odorless [2,5–7,33–35].
This study evaluated the potential for low-cost e-noses to be used to assess wines made from
grapes exposed to different levels (densities) of smoke. The e-nose measurements were used as inputs
in machine learning modeling strategies, and the concentrations of smoke taint marker compounds
in berries and wines used as targets. Further, targets were obtained from a sensory analysis trial,
during which consumers assessed the wines made from each treatment. In total, five machine
learning models were created based on e-nose data to assess (i) the level of contamination in
grapevines related to smoke exposure from wine samples using classification models (Model 1);
Sensors 2020, 20, 5108 3 of 15
(ii) to evaluate smoke-related compounds from wines, such as 20 glycoconjugates and 10 volatile
phenols in berries after 1 h smoke (Model 2), (iii) smoke-related compounds in berries measured at
harvest (Model 3), (iv) for wines made from treatments (Model 4), and (v) consumer sensory analysis
using 12 wine descriptors (Model 5; Figure 1). The models obtained were of high accuracy, which
could allow the implementation of this artificial intelligence (AI) technology in the winemaking
process to assess the effect of ameliorating management techniques in the field (Model 1) through
micro-vinifications, to assess the best timing for skin contact during fermentation for red wines, the
addition of activated carbon to adsorb smoke-related compounds, wine filtration using membranes,
reverse osmosis, and other commercial fining agents, among others [34,35].
Not only could the implementation of this technique help winemakers evaluate the different
amelioration techniques mentioned above, but it could also monitor almost real-time changes in the
aroma profiles of wine and assess which technology could best maintain a certain quality or style target.
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. A discussion on potential
applications of the e-nose and models was also described for the wine industry to monitor and reduce
smoke taint in wines.
2. Materials and Methods
2.1. Description of Treatments and Wine Samples
Field trials involving the application of smoke and/or in-canopy misting to Cabernet Sauvignon
grapevines have been reported previously [3]. Briefly, three different smoke treatments were applied
to vines (at approximately 7 days post-véraison): (i) low-density smoke exposure (LS), (ii) high-
density smoke exposure (HS), and (iii) high-density smoke exposure, with in-canopy misting (HSM).
Two controls were also included: (iv) a control without misting (C; no smoke treatment) and (v) a
control with misting (CM; no smoke treatment). Treatments were applied to six adjacent vines, except
for HSM, which was applied to five adjacent vines (i.e., one vine was missing). Smoke treatments
involved exposure of grapevines to straw-derived smoke using a purpose-built tent for 1 h. At least
one buffer vine separated treatments. The wine was subsequently produced on a small scale (i.e., ~5
kg per fermentation, performed in triplicate for each treatment), as described previously [3].
2.2. Electronic Nose
Wine samples were measured (in triplicate) using a portable, user-friendly, and low-cost e-nose,
comprising nine different sensors, which were sensitive to different gases, as mentioned in Table 1,
plus a humidity and temperature sensor (AM2320; Guangzhou Aosong Electronics Co., Ltd.,
Guangzhou, China). Sensor details have already been reported [20]. A total of 100 mL of wine was
poured into a 500 mL beaker, and the e-nose was placed on top of the container for 1 min to capture
the gases present in the sample. The e-nose was calibrated for 20–30 s before and after measuring
each sample to reset the readings to baseline. Values from all sensors were automatically recorded in
a comma-separated values (.csv) file to facilitate analysis.
Table 1. Sensors, attached to the electronic nose, and the gasses they are sensitive to.
Sensor Name
Gases
Manufacturer
MQ3
Ethanol
Henan Hanwei Electronics Co., Ltd., Henan,
China
MQ4
Methane
MQ7
Carbon monoxide (CO)
MQ8
Hydrogen
MQ135
Ammonia, alcohol, and
benzene
MQ136
Hydrogen sulfide
Sensors 2020, 20, 5108 4 of 15
MQ137
Ammonia
MQ138
Benzene, alcohol, and
ammonia
MG811
Carbon dioxide (CO2)
2.3. Chemical Analysis of Glycoconjugates and Volatile Phenols
Volatile phenols (Table 2) were evaluated in wine samples using stable isotope dilution analysis
(SIDA) methods, as previously described [15,17–19]. Isotopically labeled standards of d3-guaiacol, d3-4-
methylguaiacol, d7-o-cresol, and d3-syringol were prepared in house by the Australian Wine Research
Institute’s (AWRI) Commercial Services Laboratory (Adelaide, Australia) using published methods
[15,17,18]. Measurements were performed using an Agilent 6890 gas chromatography coupled to a
5973 mass-spectrometer (Agilent Technologies, Forest Hill, VIC, Australia). The limit of quantitation
for volatile phenols was 1–2 µg L−1.
A range of volatile phenol glycoconjugates (Table 2) was measured using high-performance
liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) according to stable isotope
dilution analysis (SIDA) methods previously described [18,20]. The analysis was performed using an
Agilent 1200 high-performance liquid chromatography (HPLC) equipped with a 1290 binary pump,
coupled to an AB SCIEX Triple QuadTM 4500 tandem mass spectrometer, with a Turbo VTM ion
source (Framingham, MA, USA). The preparation of the isotopically labeled internal standard d3-
syringol gentiobioside has been previously reported [18,20]. The limit of quantitation for volatile
phenol glycosides was 1 µg kg−1.
Table 2. List of glycoconjugates and volatile phenols, their abbreviation, and the sample in which
they were measured.
Compound
Abbreviation/Label
Sample
Glycoconjugates
Syringol gentiobiosides
SyGG
Berries/Wine
Syringol glucosides
SyMG
Berries/Wine
Syringol pentosylglucosides
SyPG
Berries/Wine
Cresol glucosylpentosides
CrPG
Berries/Wine
Cresol gentiobioside
CrGG
Berries
Cresol glucosides
CrMG
Berries
Cresol rutinosides
CrRG
Berries/Wine
Guaiacol pentosylglucosides
GuPG
Berries/Wine
Guaiacol gentiobiosides
GuGG
Berries/Wine
Guaiacol rutinosides
GuRG
Berries/Wine
Guaiacol glucosides
GuMG
Berries/Wine
Methylguaiacol pentosylglucosides
MGuPG
Berries/Wine
Methylguaiacol rutinosides
MGuRG
Berries/Wine
Methylguaiacol glucosides
MGuMG
Berries
Methylsyringol gentiobiosides
MSyGG
Berries/Wine
Methylsyringol pentosylglucosides
MSyPG
Berries/Wine
Phenol rutinosides
PhRG
Berries/Wine
Phenol gentiobiosides
PhGG
Berries/Wine
Phenol pentosylglucosides
PhPG
Berries/Wine
Phenol glucosides
PhMG
Berries/Wine
Volatile Phenols
Guaiacol
Guaiacol
Berries/Wine
4-Methylguaiacol
4-Methylguaiacol
Berries/Wine
Phenol
Phenol
Berries
o-Cresol
o-Cresol
Berries/Wine
Total m/p-cresols
Total m/p-cresol
Berries
m-Cresol
m-Cresol
Berries/Wine
p-Cresol
p-Cresol
Berries/Wine
Sensors 2020, 20, 5108 5 of 15
Syringol
Syringol
Berries/Wine
4-Methylsyringol
4-Methylsyringol
Berries/Wine
Total cresols
Cresols
Berries
2.4. Sensory Evaluation-Consumer Test
A consumer test was conducted with participants (N = 31; age range: 21–59 years; 77% female
and 23% male) constituted of staff and students from The University of Melbourne (UoM; Ethics ID:
1545786.2) that had been recruited via e-mail. According to the power analysis conducted using the
SAS® Power and Sample Size v. 14.1 software (SAS Institute Inc., Cary, NC, USA), the number of
participants was enough to find significant differences between samples (power: 1 − β > 0.99). The
session was carried out in the sensory laboratory of the Faculty of Veterinary and Agricultural
Sciences (FVAS) in individual booths with uniform white light-emitting diode (LED) lights. Each
booth was equipped with a tablet PC in which the Bio-Sensory Application (The University of
Melbourne, Parkville, VIC, Australia) was set up with the questionnaire to gather consumer
responses. The appearance, overall aroma, smoke aroma, bitterness, sweetness, acidity, astringency,
a warming sensation, and overall liking were assessed on a likeness scale (i.e., dislike extremely—
neither like nor dislike—like extremely). The levels of smoke aroma and perceived quality were rated
on an intensity scale (i.e., absent-intense). Both liking and intensity measures were presented on a 15
cm non-structured continuous scale. In addition, emotional responses were recorded, using a 0–100
FaceScale, where 0 = sad ☹, 50 = neutral 😐, and 100 = happy 😊. Samples were randomly assigned
a 3-digit code, and 10 mL samples were served at room temperature (20 °C) in International Standard
Wine Tasting Glasses (Bormioli Luigi, Fidenza, Italy). Samples were served in random order to avoid
bias. Plain water and water crackers were used as palate cleansers between samples.
2.5. Statistical Analysis and Machine Learning Modeling
Analysis of variance (ANOVA) was conducted on e-nose data using XLSTAT (ver. 19.3.2,
Addinsoft Inc., New York, NY, USA), and Tukey’s honest significant difference test (HSD; α = 0.05)
was used to assess significant differences between treatments.
Machine learning modeling was performed based on artificial neural networks (ANN) for both
pattern recognition and regression models, using codes written in Matlab® R2019b (Mathworks, Inc.,
Natick, MA, USA) developed to test 17 different training algorithms. Five distinct models were
developed using 20 data points from the peak of the e-nose outputs (nine sensors) as inputs. Model
1 (pattern recognition) used the scaled conjugate gradient training algorithm to classify the wine
samples into the five different treatments: (i) LS, (ii) HS, (iii) HSM, (iv) C, and (v) CM. All four
regression models were developed using the Levenberg Marquardt algorithm. Model 2 consisted of
the use of the 20 glycoconjugates and 10 volatile phenols (Table 2) found in berries one hour after
being exposed to smoke as targets. In comparison, Model 3 used the same 20 glycoconjugates and 10
volatile phenols in berries but measured at harvest. The targets used for Model 4 were 17
glycoconjugates and seven volatile phenols analyzed in the wine samples (Table 2). On the other
hand, Model 5 was developed to predict 12 sensory responses, using the liking of (i) appearance, (ii)
overall aroma, (iii) smoke aroma, (iv) bitterness, (v) sweetness, (vi) acidity, (vii) astringency, (viii) a
warming sensation, (ix) overall liking, and (x) the intensity of (i) smoke aroma, (ii) perceived quality,
and (iii) the FaceScale emotional response as targets.
All inputs and targets were normalized from −1 to 1. Data were divided randomly for all ANN
models, with 60% of the data being used for the training stage, 20% for validation, and 20% for testing.
Model 1 used a cross-entropy loss to test performance, while Models 2–5 were based on means
squared error (MSE). Figure 1 shows the diagrams for Model A (Figure 1a), Models 2–4 (Figure 1b),
and Model 5 (Figure 1c); all models consisted of a two-layer feedforward network with the hidden
layer using a tan-sigmoid function and the output layer using softmax neurons (Model 1) and a linear
transfer function (Models 2–5). A trimming test (data not shown) was performed to find the optimal
number of neurons (3, 5, 7, 10) to get the best performance. Statistical data reported for regression
Sensors 2020, 20, 5108 6 of 15
models to assess under- or overfitting consist of the correlation coefficient (R), slope (b), MSE, and
determination coefficient (R2); the latter was calculated using the curve fitting tool found in Matlab®.
(a)
(b)
(c)
Figure 1. Model diagrams of the two-layer feedforward networks for (a) Model 1 for pattern
recognition to classify samples into the five treatments using seven neurons, (b) Models 2–4 for
regression to predict 20 glycoconjugates and 10 volatile phenols (Table 2) in Model 2: berries 1 h after
smoke, Model 3: berries at harvest, and Model 4: wine, and (c) Model 5 for regression to predict 12
different sensory responses using 10 neurons. Abbreviations: W: weights, b: bias.
3. Results
3.1. Electronic Nose Results
Figure 2 shows the results from the ANOVA for the e-nose responses. It can be observed that
there were significant differences (p < 0.05) between samples in the outputs from all nine sensors that
integrated the e-nose. Ethanol gas (MQ3) presented the highest values for all wine samples with CM
(mean = 4.07 V) being significantly different from HSM (mean = 3.85 V), HS (mean = 3.82 V), and C
Sensors 2020, 20, 5108 7 of 15
(mean = 3.92 V), and these from LS (mean = 3.66 V). Hydrogen sulfide (MQ136) was the lowest for all
samples, and CM (mean = 0.34 V) was significantly different from all other samples (means = 0.23–
0.27 V). The CO2 sensor readings are inverse; therefore, higher Volts mean lower concentration; it can
be observed that all the samples with smoke treatments (LS, HS, and HSM) had the lowest CO2 and
presented significant differences with control samples (CM and C).
Figure 2. Mean values of the electronic nose outputs showing the letters of significance from the
ANOVA and Tukey post hoc test (α = 0.05). Sensors: MQ3 = ethanol, MQ4 = methane, MQ7 = carbon
monoxide, MQ8 = hydrogen, MQ135 = ammonia/alcohol/benzene, MQ136 = hydrogen sulfide, MQ137
= ammonia, MQ138 = benzene/alcohol/ammonia, MG811 = carbon dioxide.
Table 3 shows the minimum, maximum, and average values of the glycoconjugates and volatile
phenols detected in berries one hour after smoking, in berries at harvest, and wine. It can be observed
that there was a wide range of values for all of the compounds, which indicated these were adequate
samples to be used for machine learning modeling and to detect smoke contamination.
Table 3. Minimum (Min), maximum (Max), and mean values of the glycoconjugates (berries: µg kg−1;
wine: µg L−1) and volatile phenols (µg L−1) detected in berries and wine.
Compound
Berries
1 h After Smoking
Berries
at Harvest
Wine
Min
Max
Mean
Min
Max
Mean
Min
Max
Mean
Syringol gentiobioside
2.37
56.93
15.42
6.30
772.81
186.55
10.43
582.11
152.58
Syringol
monoglucoside
0.14
26.97
6.38
2.65
68.34
19.22
0.36
14.54
4.26
Syringol
pentosylglucosides
0.76
4.52
1.79
6.41
369.14
88.76
1.70
103.37
27.73
Cresol
glucosylpentosides
8.07
47.12
18.13
41.69
1395.52
382.63
0.40
17.67
5.28
Sensors 2020, 20, 5108 8 of 15
Cresol gentiobioside
0.18
0.71
0.45
1.94
6.46
3.55
NA
NA
NA
Cresol monoglucoside
0.24
61.87
16.36
0
35.47
8.70
NA
NA
NA
Cresol rutinoside
1.62
13.34
4.90
3.11
122.07
38.35
2.91
133.85
40.55
Guaiacol
pentosylglucosides
2.29
25.61
7.57
15.76
1233.46
268.39
5.30
330.36
80.47
Guaiacol gentiobioside
0.05
1.38
0.40
0.54
67.44
16.33
0.30
2.81
0.99
Guaiacol rutinoside
0
1.35
0.48
1.13
32.03
9.97
0
48.60
15.24
Guaiacol
monoglucoside
0.03
30.04
7.07
1.22
30.25
7.15
0.12
12.60
3.46
Methylguaiacol
pentosylglucosides
0.55
11.51
3.29
6.79
266.50
57.32
1.43
51.79
12.72
Methylguaiacol
rutinoside
0.60
5.58
1.89
6.45
153.06
44.36
0.79
40.92
11.97
Methylguaiacol
monoglucoside
0
0
0
0.94
11.52
3.89
NA
NA
NA
Methylsyringol
gentiobioside
0.33
13.34
3.49
2.53
302.51
72.52
0.15
30.69
7.41
Methylsyringol
pentosylglucosides
0.07
0.39
0.17
1.57
34.84
10.36
0.20
8.35
2.46
Phenol rutinoside
0.31
3.78
1.26
3.75
175.57
53.28
1.42
77.58
23.40
Phenol gentiobioside
0.01
0.61
0.15
0
28.54
6.57
0.08
6.22
1.70
Phenol
pentosylglucosides
1.44
24.97
7.02
16.21
812.10
215.13
0.53
22.59
6.31
Phenol monoglucoside
0.04
2.55
0.63
0.99
21.52
5.65
0.74
43.48
11.86
Guaiacol
2.39
139.72
41.57
2.06
12.97
5.08
0
39.00
11.73
4-Methylguaiacol
3.54
27.72
9.50
3.52
4.45
3.80
0
5.00
1.40
Phenol
1.40
85.68
21.12
1.26
26.38
9.61
NA
NA
NA
o-Cresol
1.65
54.02
16.31
1.74
8.08
4.02
0
14.00
4.87
Total m/p-cresol
0.56
63.07
16.01
0.52
7.71
2.99
NA
NA
NA
m-Cresol
1.90
45.07
12.08
1.84
5.89
3.24
0
14.00
4.53
p-Cresol
0
18.00
4.38
0
2.04
0.44
0
9.00
2.60
Syringol
5.17
180.31
47.67
9.32
13.77
11.73
1.00
6.00
3.13
4-Methylsyringol
1.83
24.36
6.62
1.75
2.11
1.83
0
0
0
Total cresols
2.22
117.08
32.32
2.26
15.79
7.01
NA
NA
NA
Abbreviations: NA: Not applicable. Values <1 (µ g L−1 and µ g kg−1) are considered as below the limit
of detection. However, actual values were included in the modeling strategies.
Table 4 shows the minimum, maximum, and average values of the responses from the sensory
session conducted with consumers when evaluating the wines. It can be observed that the results
from all attributes were within the whole range of the scales used for liking and appearance (0–15)
and FaceScale (0–100), which made the data suitable to be used for machine learning modeling.
Table 4. Minimum (Min), maximum (Max), and mean values of the sensory session responses for
wine tasting.
Data/Sensory Attribute
Min
Max
Mean
Appearance liking
0.45
15.00
7.19
Overall aroma liking
0.30
14.85
6.21
Smoke aroma intensity
0
15.00
4.98
Smoke aroma liking
0
15.00
4.72
Bitter liking
0.30
15.00
5.98
Sweet liking
0
14.70
6.16
Acidity liking
0
14.70
6.23
Astringency liking
0.30
15.00
6.27
Warming liking
0.30
15.00
6.20
Overall liking
0.30
14.85
6.07
Sensors 2020, 20, 5108 9 of 15
Perceived quality
0
14.85
5.66
FaceScale
0
99.00
42.15
3.2. Machine Learning Models
Table 5 shows the statistical results from Model 1 for the classification of the samples into the
five different treatments. It can be observed that there was a high accuracy for all stages (>90%) and
97% for the overall model. According to the performance values, there were no signs of overfitting,
as the training stage had a cross-entropy value lower than the validation and testing, and these two
had similar performance. In Figure 3, the results from the receiver operating characteristic (ROC)
curve are shown. This graph depicted the sensitivity (true positive rate) and specificity (false positive
rate) of the overall model, with optimal operating points of 98%, 100%, 93%, 93%, and 98% for C, CM,
LS, HS, and HSM, respectively.
Table 5. Statistical results from the pattern recognition model (Model 1) to classify samples into five
different treatments (control, control with mist, low smoke, high smoke, and high smoke with mist).
Stage
Model 1
Samples
Accuracy
Error
Performance
(Cross-Entropy)
Training
180
99%
1%
0.01
Validation
60
93%
7%
0.04
Testing
60
92%
8%
0.05
Overall
300
97%
3%
-
Figure 3. Receiver operating characteristic (ROC) curve for Model 1 to classify wine samples into the
five different smoke treatments.
Table 6 depicts the statistical data for the four regression models. Model 2 had very high overall
correlation and determination coefficients (R = 0.98; Figure 4a; R2 = 0.95). The close value of the
validation and training correlation coefficients (R = 0.96 and R = 0.98, respectively), along with the
fact that the performance of the training stage (MSE = 0.01) was lower than that of the validation and
testing (MSE = 0.03 and MSE = 0.02, respectively), showed that there were no signs of under- or
overfitting. Models 3 and 4 had similar statistical values, both with high accuracy (Model 3: R = 0.99;
Sensors 2020, 20, 5108 10 of 15
Figure 4b; R2 = 0.97; Model 4: R = 0.99; Figure 4c; R2 = 0.98). These models also showed no signs of
under- or overfitting. On the other hand, Model 5 also had a very high overall accuracy (R = 0.98;
Figure 4d; R2 = 0.96) with similar performance values for validation and testing (MSE = 0.04) and
higher than that of the training stage (MSE = 0.02). All models presented a slope close to the unity (b
~ 1) for all stages (Figure 4).
Table 6. Statistical results from the four regression models (Models 2–4: glycoconjugates and volatile
phenols; Model 5: sensory) showing the correlation coefficient (R), determination coefficient (R2),
slope (b), and performance based on means squared error (MSE) for each stage.
Stage/
Model 2
(Berries 1 h Smoke)
Samples
Observations
R
R2
b
Performance
(MSE)
Training
180
5400
0.98
0.96
0.96
0.01
Validation
60
1800
0.96
0.92
0.97
0.03
Testing
60
1800
0.97
0.95
0.97
0.02
Overall
300
9000
0.98
0.95
0.97
-
Stage/
Model 3
(Berries at Harvest)
Samples
Observations
R
R2
b
Performance
(MSE)
Training
180
5400
0.99
0.98
0.97
0.01
Validation
60
1800
0.98
0.95
0.96
0.02
Testing
60
1800
0.98
0.97
0.95
0.01
Overall
300
9000
0.99
0.97
0.96
-
Stage/
Model 4
(Wine)
Samples
Observations
R
R2
b
Performance
(MSE)
Training
180
4320
0.99
0.99
0.99
<0.01
Validation
60
1440
0.98
0.95
0.96
0.02
Testing
60
1440
0.98
0.96
0.95
0.01
Overall
300
7200
0.99
0.98
0.98
-
Stage/
Model 5
(Wine Sensory)
Samples
Observations
R
R2
b
Performance
(MSE)
Training
180
2160
0.98
0.97
0.97
0.02
Validation
60
720
0.97
0.94
0.97
0.04
Testing
60
720
0.97
0.94
0.97
0.04
Overall
300
3600
0.98
0.96
0.97
-
Sensors 2020, 20, 5108 11 of 15
(a)
(b)
(c)
(d)
Figure 4. The overall correlation of the models to predict 20 glycoconjugates and 10 volatile phenols
(Table 2) of (a) Model 2: berries after 1 h smoking, (b) Model 3: berries at harvest; (c) 17
glycoconjugates and seven volatile phenols of Model 4: wine. (d) Shows the Model 5 to predict 12
sensory descriptors obtained in a consumer test (Figure 1c).
4. Discussion
Nowadays, the only alternative for grape growers is to apply potential amelioration techniques
before the bushfires and hope for the best since there are limited tools that can be applied in the field or
at the winemaking stage, which can render results in near real-time for proper decision-making [35,36].
Recently, non-invasive devices have been proposed using infrared thermal imaging to assess
contaminated grapevine canopies in the field and smoke taint in berries and wines using near-
infrared spectroscopy [5,33]. The research presented in this paper has contributed to the potential
implementation of new and emerging sensor technologies and modeling strategies using machine
learning in the viticulture and winemaking industry. These low-cost e-noses could become a game-
changer for the management of smoke contamination and taint in berries and wines due to bushfires.
In general, previous applications of e-noses in the wine industry have been implemented mainly
for the analysis of grapes and crushing methods [37], improvement of maceration and fermentation
Sensors 2020, 20, 5108 12 of 15
processes [38], to monitor the aging of wine in barrels [39–41], geographical classification [42], wine
spoilage [28,43,44], and to assess correlations with human perception through sensory evaluation
[27,29,45]. However, most of these studies have been based on multivariate data analysis and
correlation analysis.
Low-cost sensors presented in this research, developed by integrating an array of gas sensors [20],
could be used in the winery to assess the level of grapevine smoke exposure. In the present study,
models were developed to evaluate the effects of different amelioration techniques (Model 1) for
berries immediately after the bushfire event (Model 2), at harvest time (Model 3), and in the actual
wines (Model 4). Since smoke-derived glycoconjugates in berries are difficult to detect using e-noses
due to the binding of these compounds with sugars in the berries, these assessments need to be
performed after the winemaking process, in which the compounds are released through the
maceration and fermentation processes.
A further model (Model 5) developed to assess sensory characteristics of wines rapidly and
objectively, which can be implemented in parallel with successful amelioration techniques to reduce
smoke taint, such as the addition of activated carbon to wines or fining agents [2,34]. For the latter
case, Model 5 will offer a near real-time assessment of the techniques used.
The advantages of implementing these models coupled with low-cost sensor technology are that
grape growers and winemakers will not depend on random sampling, which may not render
representative results, or external laboratory services, which may not deliver results in a timely
manner due to being overwhelmed by large sample volumes that are delivered when concurrent
bushfires occur. Knowing the levels of smoke-derived compounds and the effects on consumer
appreciation in the winemaking process offer the following advantages: (i) rapid and user-friendly
smoke taint determination; (ii) potential implementation of techniques to reduce smoke taint using
activated carbon or fining agents on samples and re-test using the e-nose and models developed; (iii)
sensory panel not required for assessments/modifications, minimizing the time for the commercial
release of wines and economic impacts of smoke taint.
Further applications of these low-cost e-noses can be implemented to assess the maturity of
grapes in the field, specifically through the alcohol-based sensors. The latest research has shown that
ethanol is released from grape berries when they become oxygen stressed [46]. So, being able to assess
when cell death begins would be a useful tool in monitoring berry health and fruit ripening potential.
These processes of berry cell death assessment can be done non-destructively by near-infrared
spectroscopy and machine learning modeling [47] or by tracking ethanol release from grapevine
bunches through the implementation of low-cost e-noses in the field using sensor networks or as a
payload of low altitude unmanned aerial vehicle (UAV) surveys [48,49].
5. Conclusions
Low-cost e-nose sensor technology coupled with machine learning offers the advantage of easy
implementation in field conditions using sensor networks or in the winery. Machine learning models
obtained could make available valuable information to winemakers and winegrowers for the
decision-making process to produce commercial wines by minimizing smoke taint. An artificial
intelligence system can be implemented based on sensor technology and machine learning developed
here to obtain the least tainted wine or to target specific sensory aroma profiles to take advantage of
the decontamination process to maximize the likability of wines.
Author Contributions: Conceptualization, S.F. and C.G.V.; Data curation, S.F. and C.G.V.; Formal analysis,
C.G.V.; Funding acquisition, R.R.U.; Investigation, S.F., V.S., C.G.V., and K.L.W.; Methodology, V.S., C.G.V.,
K.L.W., C.S., and R.R.U.; Project administration, S.F., C.G.V., and K.L.W.; Resources, S.F., and R.R.U.; Software,
S.F., C.G.V., and R.R.U.; Supervision, S.F.; Validation, S.F., C.G.V., and N.L.; Visualization, S.F., C.G.V., and E.T.;
Writing—original draft, S.F. and C.G.V.; Writing—review and editing, S.F., V.S., C.G.V., E.T., N.L., K.W, C.S.,
and R.R.U. All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by the Australian Research Councils Linkage Projects funding scheme
(LP160101475).
Sensors 2020, 20, 5108 13 of 15
Acknowledgments: The authors would like to acknowledge Bryce Widdicombe, Mimi Sun, and Jorge Gonzalez
for their collaboration in the electronic nose development. C.S. was supported by the Australian Research
Council Training Centre for Innovative Wine Production (www.arcwinecentre.org.au), which is part of the
ARC’s Industrial Transformation Research Program (Project No. ICI70100008), with support from Wine
Australia and industry partners.
Conflicts of Interest: The authors declare no conflict of interest.
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