Article

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

Abstract and Figures

The number and intensity of wildfires are increasing worldwide, thereby also 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 with different levels of smoke exposure (i) Control (C), i.e., neither misting nor 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 around grape harvest and smoke taint mitigation techniques in the winemaking process.
Content may be subject to copyright.
VINE AND WINE
OPEN ACCESS JOURNAL
OENO One 2020, 4, 1105-1119 1105
© 2020 International Viticulture and Enology Society - IVES
Detection of smoke-derived compounds from bushres in Cabernet-Sauvignon
grapes, must, and wine using Near-Infrared spectroscopy and machine learning
algorithms
Vasiliki Summerson1, Claudia Gonzalez Viejo1, Damir D. Torrico2, Alexis Pang1 and Sigfredo Fuentes1,*
1Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural
Sciences, The University of Melbourne, Building 142, Parkville 3010, Victoria, Australia
2Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln
University, Lincoln 7647, Canterbury, New Zealand
*corresponding author: sigfredo.fuentes@unimelb.edu.au
a b s t r a c t
The number and intensity of wildres 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 ve articial 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 nal 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 ve 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.
k e y w o r d s
remote sensing, climate change, articial neural networks, smoke taint
Received: 29 September 2020 y Accepted: 20 October 2020 y Published: 27 November 2020
DOI:10.20870/oeno-one.2020.54.4.4501
Supplementary data can be downloaded through: https://oeno-one.eu/article/view/4501
© 2020 International Viticulture and Enology Society - IVES
1106 OENO One 2020, 4, 1105-1119
Vasiliki Summerson et al.
INTRODUCTION
Recent climate change forecasts have predicted an
increase in the number and intensity of wildres,
as well as a lengthening of the re 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
signicant nancial 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 wildre 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 signicant 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 avour 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 µg/L, 20 µg/L, and 30
µg/L respectively (Härtl & Schwab, 2018;
Parker et al., 2013). In comparison, wines matured
in oak barrels reportedly contain 10–100 µg/L
guaiacol and 1–20 µg/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 unprotable,
costing the wine industry millions of dollars in
lost wine revenue. For example, it is estimated
that the 2006/2007 bushres in Victoria, Australia,
resulted in approximately 75-90 billion AU$ in lost
revenue, while the 2009 Black Saturday bushres
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 signicant 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-eld 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
OENO One 2020, 4, 1105-1119 1107
© 2020 International Viticulture and Enology Society - IVES
(UV; 250-400 nm), visible (Vis; 400-700 nm),
near-infrared (NIR; 700-2500 nm), and mid-
infrared (MIR; 2500-25000 nm) regions have
been used for various grape and wine assessments,
including the classication 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 classication 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 articial 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 signicant
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 nm 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 ve 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
1. Field application of smoke treatments to
grapevines and winemaking
The experimental site is located at the University
of Adelaide’s Waite campus in Urrbrae, South
Australia (34°58’S, 138°38’E). The experiment
was conducted during the 2018/2019 season, as
previously described by Szeto et al. (2020) and
Summerson et al. (2020). The grapevines were
planted at 2.0 and 3.3 m spacing between vines
and rows, respectively, and trained to a bilateral
cordon and vertical shoot-positioned trellis system
(VSP). Grapevines were hand-pruned to a two-
node spur system, with drip irrigation under the
vine (twice weekly for 6 hours from fruit set
to pre-harvest) with a drip distance of 0.75 m
and a dripper water rate of 1.6 L/h (8.5 mm/
week). Three smoke treatments with or without
in-canopy misting were applied to ve to six
Cabernet-Sauvignon grapevines (within two
adjacent panels) at approximately seven days
post-veraison. Smoke treatments consisted of
i) low-density smoke without misting (LS), ii)
high-density smoke with misting (HSM), and
high-density smoke without misting (HS). There
were also two control treatments: i) control
without misting (C) and control with misting
(CM). Each treatment consisted of six adjacent
grapevines. Smoke treatments involved the
application of smoke for one hour using purpose-
built tents (6 m x 2.5 m x 2 m) constructed from
galvanised steel framing and greenhouse-grade
Solarweave plastic (Gale Pacic, Australia) that
enables plant photosynthesis under experimental
conditions similar to those described previously
(Kennison et al., 2008; Kennison et al., 2009,
Kennison et al., 2011; Ristic et al., 2016). Vines
were enclosed in the tents with openings at the
© 2020 International Viticulture and Enology Society - IVES
1108 OENO One 2020, 4, 1105-1119
Vasiliki Summerson et al.
extremities to avoid damaging the vines. Low
and high-density smoke exposure was achieved
by burning approximately 1.5 and 5.0 kg of
barley straw, respectively. Control vines were
separated by at least one buffer vine so as not
to be exposed to smoke. Misting treatments
involved the continuous application of ne water
droplets (65 µm) to the grapevine bunch zone,
using a purpose-built sprinkler system (delivering
water at 11 L/h), as previously described
(Caravia et al., 2017; Szeto et al., 2020). Average
ambient temperature and humidity were 31 °C and
36 %, respectively, with minimum cloud coverage.
The wine was produced on a small-scale using
approximately 5 kg bunches per fermentation,
performed in triplicate for each treatment, as set
out by Szeto et al. (Szeto et al., 2020). Bunches
were de-stemmed and crushed, 50 mg/L of sulphur
dioxide (SO2) was added to the must, and pH then
adjusted to 3.5 with tartaric acid. The must was
then inoculated with PDM yeast (150 mg/mL;
Maurivin, AB Biotek, Sydney, NSW, Australia)
and fermented with skins. Fermentation took place
at ambient temperature (25-27 °C), and the cap
was plunged twice daily until wines approached
dryness; they were then pressed and held at 25 °C
until fermentation was complete. The wines did not
undergo malolactic fermentation. The wines were
then racked from gross lees and cold stabilised
before the pH, and free SO2 were adjusted
(3.5 and 30 mg/L, respectively) prior to bottling.
2. Chemical analysis of volatile phenols and their
glycoconjugates in grape juice/homogenate and
wine
Levels of VP and their glycoconjugates were
determined in grape juice/homogenate, as well
as in the nal wine (Table 1) using previously
published stable isotope dilution analysis (SIDA)
methods (Hayasaka et al., 2010a; Y. Hayasaka
et al., 2013; Pollnitz et al., 2004; Szeto et al.,
2020). Volatile phenols were measured using an
Agilent 6890 gas chromatograph coupled to a 5973
mass spectrometer (Agilent Technologies, Forest
Hill, Vic., Australia), with isotopically labelled
standards of d4-guaiacol and d3-syringol prepared
by the Australian Wine Research Institute’s
(AWRI) Commercial Services Laboratory in
Adelaide, Australia as previously reported
(Dungey et al., 2011; Hayasaka et al., 2010a;
Pollnitz et al., 2004). The limit of quantitation
for volatile phenols was 1-2 µg/L. Volatile phenol
glycoconjugates were measured using liquid
chromatography-tandem mass spectrometry
(HPLC–MS/MS) applying previously outlined
methods (Hayasaka et al., 2010a; Hayasaka et al.,
2013). An Agilent 1200 high-performance liquid
chromatograph (HPLC) was used. It was 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). An isotopically labelled internal standard of
d3-syringol gentiobioside was prepared according
to published methods (Hayasaka et al., 2010a;
Hayasaka et al., 2013). The limit of quantitation
for volatile phenol glycosides was 1 µg/kg.
3. NIR absorbance patterns for grape berries,
must, and wine samples
Grape berry spectra were collected 24 hours after
smoke treatments were applied, as previously
described by Summerson et al. (2020), using the
microPHAZIRTM RX Analyzer (Thermo Fisher
Scientic, Waltham, MA, USA), which had a
spectral range of 1596 to 2396 nm at intervals of
7-9 nm. The device's calibration was carried out
as required using a white background calibration
standard (included with the device) prior to
starting and after every ten readings. From each
treatment, two vines were selected for analysis.
Two bunches were selected from each vine,
and nine berries were measured in triplicate
(36 berries per treatment measured three times
each; total n = 540). All measurements were
conducted at ambient temperature between 9:00
and 18:00 with berries still attached to the bunch.
Must and wine samples were measured using
a modied procedure previously described by
Gonzalez Viejo et al. (2018). A Whatman® lter
paper (Whatman plc., Maidstone, UK) of quality
grade three and 7.0 cm diameter was used as the
holding medium for the must and wine samples.
The dry lter paper was rst analysed using the
microPHAZIRTM RX Analyzer by placing it
directly onto the front of the 5 mm measuring
region and placing the white background standard
behind it to prevent signal noise inclusion due to
environmental factors. The lter paper was then
submerged and soaked with the specic must/
wine sample and analysed immediately. The
spectral readings obtained from the dry lter paper
were then subtracted from the readings obtained
from the lter paper soaked in the specic
sample to obtain only the sample spectral reading
results. Each treatment was measured three times
in triplicate for both must and wine samples
(total n = 45), with measurements conducted at
room temperature (20-23 oC).
OENO One 2020, 4, 1105-1119 1109
© 2020 International Viticulture and Enology Society - IVES
TABLE 1. List of volatile phenols and their glycoconjugates used as inputs.
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 pentosylgluco-
sides 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 pentosylgluco-
sides MSyPG Berries/Must/Wine
Cresol glucosylpentosides CrPG Berries/Must/Wine
Cresol gentiobioside CrGG Berries
Cresol rutinoside CrRG Berries/Must/Wine
© 2020 International Viticulture and Enology Society - IVES
1110 OENO One 2020, 4, 1105-1119
Vasiliki Summerson et al.
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 rst 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
ve models. Models 1, 2, and 3 used 70 % of the
data for the training stage, 15 % for validation
with a mean squared error (MSE) performance
algorithm, and 15 % for testing, while Models
4 and 5 used 70 % for training and 30 % 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 undertting
using statistical data consisting of the correlation
coefcient (R), slope (b), MSE, and determination
coefcient (R2).
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
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 nal wine.
OENO One 2020, 4, 1105-1119 1111
© 2020 International Viticulture and Enology Society - IVES
wavelength range for each smoke treatment
was observed for the berry absorbance spectra
(Figure 2a). A large peak was originally observed
at approximately 1910 nm, as well as a smaller one
at approximately 1790 nm. In the transformed data
(Figure S1), large peaks were observed between
approximately 1596-1650 nm and 1820-1950 nm.
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 nm and two
smaller peaks at approximately 1784 and 2090 nm
(Figure 3a), while for the transformed data, large
peaks could be seen between approximately 1818-
1902 nm and 2246-23322 nm (Figure S2).
For the wine absorbance spectra (Figure 4), the
largest difference in absorbance values amongst
the different smoke treatments was observed
at the 1927 nm peak or overtone. Further peaks
were also observed at approximately 2090, 2270,
FIGURE 2. Raw berry absorbance spectra for the ve smoke treatments.
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
FIGURE 3. Raw must absorbance spectra for the ve smoke treatments.
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
© 2020 International Viticulture and Enology Society - IVES
1112 OENO One 2020, 4, 1105-1119
Vasiliki Summerson et al.
and 2340 nm, while in the transformed data
(Figure S3), large peaks were observed between
approximately 1835–1950 nm and 2220–2300 nm.
2. Levels of smoke taint compounds in grape
juice/homogenate and wine
Differences in volatile phenol and glycoconjugate
levels amongst the different smoke treatments
could be seen at both one day after smoke exposure
(Figure S1) and at harvest (Figure S2), except for
syringol and 4-methylsyringol, in which there
were no signicant differences (p > 0.05) amongst
the ve smoke treatments at harvest. Signicant
differences (p < 0.05) in volatile phenol and
glycoconjugate levels were also observed amongst
the wine samples produced from grapes exposed
to the different smoke treatments (Table S3).
3. Machine learning modelling
Statistical data for the ve regression models
are shown in Table 2. Model 1 had high overall
correlation and determination coefcients
(R = 0.98; R2 = 0.97; Figure 5a.), with values of
the validation correlation coefcient (R = 0.97)
being close to the training correlation coefcients
(R = 0.95). In addition to this, performance
values for validation (MSE = 80.28) and
testing (MSE = 83.14) were similar among
them and higher than that of the training stage
(MSE = 16.96), which further indicates no sign
of under- or overtting. Models 2 and 3 also had
high correlation and determination coefcients
(R = 0.98 for each; R2 = 0.97 for Model 2 and 0.95
for Model 3; Figures 5b and 5c). Again, there were
no signs of under- or overtting as values for the
validation and training coefcients 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 coefcients (R = 0.99 for each;
R2 = 0.99 for Model 4 and R2 = 0.97 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 undertting.
DISCUSSION
The NIR wavelength range used to develop the ve
models was between 1596 and 2396 nm (Figures
2, 3, and 4), which includes several key overtones.
The C-H stretch rst overtone associated with
aromatic compounds can be seen between 1680
and 1690 nm, while O-H stretching associated
with glucose, cellulose, water, and alcohol can
be observed at approximately 1930, 2090, 2270,
and 2330 nm. Lastly, the region between 1900
and 1910 nm 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 nm)
FIGURE 4. Raw wine absorbance (a) and second derivative spectra (b) for the ve smoke treatments.
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
OENO One 2020, 4, 1105-1119 111 3
© 2020 International Viticulture and Enology Society - IVES
Stage Samples Observations R R2bPerformance
(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
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
TABLE 2. Statistical results from the ve developed articial 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 coefcient (R), determination
of coefcient (R2), slope (b) and performance based on mean squared error (MSE) for each stage.
© 2020 International Viticulture and Enology Society - IVES
1114 OENO One 2020, 4, 1105-1119
Vasiliki Summerson et al.
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.
OENO One 2020, 4, 1105-1119 111 5
© 2020 International Viticulture and Enology Society - IVES
were in the NIR regions associated with C = O 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 nm) 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 nal 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 nal 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 inuences the level and detection
of smoke taint, with medium-bodied red wines
having a lower guaiacol detection threshold
(15-25 µg/L) than full-bodied style wines such as
Shiraz (30-40 µg/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-eld
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 %. Specically, levels of guaiacol were
reduced from an average of 18 µg/L in the control
wine to 8 µg/L following treatment with activated
carbon, while 4-methylguaiacol was reduced
from an average of 3 µg/L to 1 µg/L, and total
cresols from an average of 7 µg/L to 2 µg/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 ve 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 inuence 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 nm.
Therefore, there is great potential for developing
future models that can predict levels of smoke
© 2020 International Viticulture and Enology Society - IVES
1116 OENO One 2020, 4, 1105-1119
Vasiliki Summerson et al.
compounds in multiple grape varieties and wines
using the NIR regions between 1680 and 1690 nm,
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 inuence levels of volatile phenols and their
glycoconjugates in the nal 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-
eld 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 eld 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.
REFERENCES
Allen, D., Bui, A. D., Cain, N., Rose, G., & Downey, M.
(2013). Analysis of free and bound phenolics in wine
and grapes by GC-MS after automated SPE. Analytical
and Bioanalytical Chemistry, 405(30), 9869-9877.
https://doi.org/10.1007/s00216-013-7405-0
Bell, T., Stephens, S., & Moritz, M. (2013). Short-term
physiological effects of smoke on grapevine leaves.
International Journal of Wildland Fire, 22(7), 933-946.
https://doi.org/10.1071/WF12140
Boido, E., Fariña, L., Carrau, F., Dellacassa, E., &
Cozzolino, D. (2013). Characterization of glycosylated
aroma compounds in Tannat grapes and feasibility of
the near infrared spectroscopy application for their
prediction. Food Analytical Methods, 6(1), 100-111.
https://doi.org/10.1007/s12161-012-9423-5
Burns, D., & Ciurczak, E. (2007). Handbook of Near-
infrared Analysis. Boca Raton, FL: CRC Press. https://
doi.org/10.1201/9781420007374
Caravia, L., Pagay, V., Collins, C., & Tyerman, S. D.
(2017). Application of sprinkler cooling within the
bunch zone during ripening of Cabernet-Sauvignon
berries to reduce the impact of high temperature.
Australian Journal of Grape and Wine Research, 23(1),
48-57. https://doi.org/10.1111/ajgw.12255
Cozzolino, D., Cynkar, W., Shah, N., & Smith, P.
(2012). Varietal differentiation of grape juice based
on the analysis of near-and mid-infrared spectral data.
Food Analytical Methods, 5(3), 381-387. https://doi.
org/10.1007/s12161-011-9249-6
Cozzolino, D., Kwiatkowski, M. J., Waters, E. J., &
Gishen, M. (2007). A feasibility study on the use of
visible and short wavelengths in the near-infrared
region for the non-destructive measurement of wine
composition. Analytical and Bioanalytical Chemistry,
387(6), 2289-2295. https://doi.org/10.1007/s00216-
006-1031-z
CSIRO and Australian Government Bureau of
Meteorology. (2018). State of the Climate 2018
Retrieved from
De Vries, C., Mokwena, L., Buica, A., & McKay, M.
(2016). Determination of volatile phenol in Cabernet-
Sauvignon wines, made from smoke-affected grapes,
by using HS-SPME GC-MS. South African Journal
of Enology and Viticulture, 37(1), 15-21. https://doi.
org/10.21548/37-1-754
OENO One 2020, 4, 1105-1119 111 7
© 2020 International Viticulture and Enology Society - IVES
Department of Primary Industries. (2009). Impacts of
smoke on grapes and wine in Victoria. Retrieved from
http://wine.wsu.edu/research-extension/les/2012/10/
DPI-fact-sheet_Impacts-of-smoke-on-grapes-and-
wine-in-Victoria_nal.pdf
Diamantopoulou, M. J., & Milios, E. (2010). Modelling
total volume of dominant pine trees in reforestations
via multivariate analysis and articial neural network
models. Biosystems Engineering, 105(3), 306-315.
https://doi.org/10.1016/j.biosystemseng.2009.11.010
Dieulot, J. Y., & Skurtys, O. (2013). Classication,
modeling and prediction of the mechanical behavior
of starch-based lms. Journal of Food Engineering,
119 (2), 188-195. https://doi.org/10.1016/j.
jfoodeng.2013.05.028
Dungey, K. A., Hayasaka, Y., & Wilkinson, K. L.
(2011). Quantitative analysis of glycoconjugate
precursors of guaiacol in smoke-affected grapes using
liquid chromatography–tandem mass spectrometry
based stable isotope dilution analysis. Food
Chemistry, 126(2), 801-806. https://doi.org/10.1016/j.
foodchem.2010.11.094
Favell, J. W., Noestheden, M., Lyons, S.-M., &
Zandberg, W. F. (2019). Development and evaluation
of a vineyard-based strategy to mitigate smoke-taint
in wine grapes. Journal of Agricultural and Food
Chemistry. https://doi.org/10.1021/acs.jafc.9b05859
Fudge, A., Ristic, R., Wollan, D., & Wilkinson, K.
(2011). Amelioration of smoke taint in wine by reverse
osmosis and solid phase adsorption. Australian Journal
of Grape and Wine Research, 17(2), S41-S48. https://
doi.org/10.1111/j.1755-0238.2011.00148.x
Fudge, A., Schiettecatte, M., Ristic, R., Hayasaka, Y.,
& Wilkinson, K. (2012a). Amelioration of smoke taint
in wine by treatment with commercial ning agents.
Australian Journal of Grape and Wine Research,
18(3), 302-307. https://doi.org/10.1111/j.1755-
0238.2012.00200.x
Fudge, A., Wilkinson, K., Ristic, R., & Cozzolino, D.
(2012b). Classication of smoke tainted wines using
mid-infrared spectroscopy and chemometrics. Journal
of Agricultural and Food Chemistry, 60(1), 52-59.
https://doi.org/10.1021/jf203849h
Fudge, A., Wilkinson, K., Ristic, R., & Cozzolino, D.
(2013). Synchronous two-dimensional MIR correlation
spectroscopy (2D-COS) as a novel method for screening
smoke tainted wine. Food Chemistry, 139(1–4), 115-
119. https://doi.org/10.1016/j.foodchem.2013.01.090
Fuentes, S., Summerson, V., Gonzalez Viejo, C.,
Tongson, E., Lipovetzky, N., Wilkinson, K. L.,
... Unnithan, R. R. (2020). Assessment of Smoke
Contamination in Grapevine Berries and Taint in
Wines Due to Bushres Using a Low-Cost E-Nose and
an Articial Intelligence Approach. Sensors, 20(18),
5108. https://doi.org/10.3390/s20185108
Fuentes, S., & Tongson, E. (2017). Advances in smoke
contamination detection systems for grapevine canopies
and berries. Wine & Viticulture Journal, 32(3), 36.
Fuentes, S., Tongson, E. J., De Bei, R., Gonzalez Viejo,
C., Ristic, R., Tyerman, S., & Wilkinson, K. (2019).
Non-Invasive Tools to Detect Smoke Contamination
in Grapevine Canopies, Berries and Wine: A Remote
Sensing and Machine Learning Modeling Approach.
Sensors, 19(15), 3335. https://doi.org/10.3390/
s19153335
Gonzalez Viejo, C., Fuentes, S., Torrico, D.,
Howell, K., & Dunshea, F. R. (2018). Assessment
of beer quality based on foamability and chemical
composition using computer vision algorithms, near
infrared spectroscopy and machine learning algorithms.
Journal of the Science of Food and Agriculture, 98(2),
618-627. https://doi.org/10.1002/jsfa.8506
Hall, A. (2018). Remote Sensing Applications for
Viticultural Terroir Analysis. Elements, 14(3), 185-190.
https://doi:10.2138/gselements.14.3.185
Härtl, K., & Schwab, W. (2018). Smoke Taint in
Wine-How smoke-derived volatiles accumulate in
grapevines. Wines & Vines(99/02), 62-64.
Hayasaka, Y., Baldock, G., Parker, M., Pardon, K.,
Black, C., Herderich, M., & Jeffery, D. (2010a).
Glycosylation of smoke-derived volatile phenols
in grapes as a consequence of grapevine exposure
to bushre smoke. Journal of Agricultural and
Food Chemistry, 58(20), 10989-10998. https://doi.
org/10.1021/jf103045t
Hayasaka, Y., Dungey, K., Baldock, G., Kennison, K.,
& Wilkinson, K. (2010b). Identication of a β-d-
glucopyranoside precursor to guaiacol in grape juice
following grapevine exposure to smoke. Analytica
Chimica Acta, 660(1–2), 143-148. http://dx.doi.
org/10.1016/j.aca.2009.10.039
Hayasaka, Y., Parker, M., Baldock, G. A.,
Pardon, K. H., Black, C. A., Jeffery, D. W., &
Herderich, M. J. (2013). Assessing the impact of smoke
exposure in grapes: development and validation of a
HPLC-MS/MS method for the quantitative analysis of
smoke-derived phenolic glycosides in grapes and wine.
Journal of Agricultural and Food Chemistry, 61(1), 25-
33. https://doi.org/10.1021/jf305025j
Hughes, L., & Alexander, D. (2017). Climate
Change and the Victoria Bushre Threat: Update
2017. Climate Council Report. Retrieved from
https://www.climatecouncil.org.au/uploads/98c
26db6af45080a32377f3ef4800102.pdf
Kelly, D., Zerihun, A., Singh, D., Vitzthum von
Eckstaedt, C., Gibberd, M., Grice, K., & Downey, M.
(2012). Exposure of grapes to smoke of vegetation
with varying lignin composition and accretion of lignin
derived putative smoke taint compounds in wine. Food
Chemistry, 135(2), 787-798. https://doi.org/10.1016/j.
foodchem.2012.05.036
© 2020 International Viticulture and Enology Society - IVES
1118 OENO One 2020, 4, 1105-1119
Vasiliki Summerson et al.
Kemps, B., Leon, L., Best, S., De Baerdemaeker, J.,
& De Ketelaere, B. (2010). Assessment of the quality
parameters in grapes using VIS/NIR spectroscopy.
Biosystems Engineering, 105(4), 507-513. https://doi.
org/10.1016/j.biosystemseng.2010.02.002
Kennison, K., Gibberd, M., Pollnitz, A., &
Wilkinson, K. (2008). Smoke-Derived Taint in Wine:
The Release of Smoke-Derived Volatile Phenols during
Fermentation of Merlot Juice following Grapevine
Exposure to Smoke. Journal of Agricultural and Food
Chemistry, 56(16), 7379-7383. https://doi:10.1021/
jf800927e
Kennison, K., Wilkinson, K., Pollnitz, A., Williams, H.,
& Gibberd, M. (2009). Effect of timing and duration
of grapevine exposure to smoke on the composition
and sensory properties of wine. Australian Journal of
Grape and Wine Research, 15(3), 228-237. https://doi.
org/10.1111/j.1755-0238.2009.00056.x
Kennison, K., Wilkinson, K., Pollnitz, A., Williams, H.,
& Gibberd, M. (2011). Effect of smoke application to
eld-grown Merlot grapevines at key phenological
growth stages on wine sensory and chemical
properties. Australian Journal of Grape and Wine
Research, 17(2), 5-12. https://doi.org/10.1111/j.1755-
0238.2011.00137.x
Kennison, K., Wilkinson, K., Williams, H., Smith, J.,
& Gibberd, M. (2007). Smoke-derived taint in wine:
Effect of postharvest smoke exposure of grapes on
the chemical composition and sensory characteristics
of wine. Journal of Agricultural and Food Chemistry,
55(26), 10897-10901. https://doi.org/10.1021/
jf072509k
Martelo-Vidal, M., & Vázquez, M. (2014).
Determination of polyphenolic compounds of red wines
by UV–VIS–NIR spectroscopy and chemometrics tools.
Food Chemistry, 158, 28-34. https://doi.org/10.1016/j.
foodchem.2014.02.080
Martelo-Vidal, M. J., & Vázquez, M. (2015).
Application of articial neural networks coupled to
UV–VIS–NIR spectroscopy for the rapid quantication
of wine compounds in aqueous mixtures. CyTA-Journal
of Food, 13(1), 32-39. https://doi.org/10.1016/j.
foodchem.2014.02.080
Noestheden, M., Dennis, E. G., & Zandberg, W. F.
(2018a). Quantitating Volatile Phenols in Cabernet
franc Berries and Wine after On-Vine Exposure to
Smoke from a Simulated Forest Fire. Journal of
Agricultural and Food Chemistry, 66(3), 695-703.
https://doi:10.1021/acs.jafc.7b04946
Noestheden, M., Noyovitz, B., Riordan-Short, S.,
Dennis, E. G., & Zandberg, W. F. (2018b). Smoke
from simulated forest re alters secondary metabolites
in Vitis vinifera L. berries and wine. Planta. https://
doi:10.1007/s00425-018-2994-7
Noestheden, M., Thiessen, K., Dennis, E. G., Tiet, B.,
& Zandberg, W. F. (2017). Quantitating Organoleptic
Volatile Phenols in Smoke-Exposed Vitis vinifera
Berries. Journal of Agricultural and Food Chemistry,
65(38), 8418-8425. https://doi:10.1021/acs.
jafc.7b03225
Parker, M., Baldock, G., Hayasaka, Y., Mayr, C.,
Williamson, P., Francis, I. L., . . . Johnson, D. (2013).
Seeing through smoke. Wine Vitic. J, 28, 42-46.
Pirie, A., Singh, B., & Islam, K. (2005). Ultra-violet,
visible, near-infrared, and mid-infrared diffuse
reectance spectroscopic techniques to predict several
soil properties. Soil Research, 43(6), 713-721. https://
doi.org/10.1071/SR04182
Pollnitz, A. P., Pardon, K. H., Sykes, M.,
& Sefton, M. A. (2004). The effects of sample
preparation and gas chromatograph injection
techniques on the accuracy of measuring guaiacol,
4-methylguaiacol and other volatile oak compounds
in oak extracts by stable isotope dilution analyses.
Journal of Agricultural and Food Chemistry, 52(11),
3244-3252. https://doi.org/10.1021/jf035380x
Ristic, R., Fudge, A., Pinchbeck, K., De Bei, R.,
Fuentes, S., Hayasaka, Y., . . . Wilkinson, K. (2016).
Impact of grapevine exposure to smoke on vine
physiology and the composition and sensory properties
of wine. Theoretical and Experimental Plant
Physiology, 28(1), 67-83. https://doi:10.1007/s40626-
016-0054-x
Ristic, R., Osidacz, P., Pinchbeck, K., Hayasaka, Y.,
Fudge, A., & Wilkinson, K. (2011). The effect of
winemaking techniques on the intensity of smoke
taint in wine. Australian Journal of Grape and Wine
Research, 17(2), S29-S40. https://doi.org/10.1111/
j.1755-0238.2011.00146.x
Ristic, R., van der Hulst, L., Capone, D., &
Wilkinson, K. (2017). Impact of Bottle Aging on
Smoke-Tainted Wines from Different Grape Cultivars.
Journal of Agricultural and Food Chemistry, 65(20),
4146-4152. https://doi:10.1021/acs.jafc.7b01233
Simos, C. (2008). The implications of smoke taint and
management practices. Australian Viticulture Jan/Feb,
77-80.
Singh, D., Chong, H., Pitt, K., Cleary, M., Dokoozlian, N.,
& Downey, M. (2011). Guaiacol and 4-methylguaiacol
accumulate in wines made from smoke-affected fruit
because of hydrolysis of their conjugates. Australian
Journal of Grape and Wine Research, 17(2), S13-S21.
https://doi.org/10.1111/j.1755-0238.2011.00128.x
Summerson, V., Gonzalez Viejo, C., Szeto, C.,
Wilkinson, K. L., Torrico, D. D., Pang, A., ... Fuentes, S.
(2020). Classication of SmokeContaminated
Cabernet-Sauvignon Berries and Leaves Based on
Chemical Fingerprinting and Machine Learning
Algorithms. Sensors, 20(18), 5099. https://doi.
org/10.3390/s20185099
Szeto, C., Ristic, R., Capone, D., Puglisi, C., Pagay,
V., Culbert, J., . . . Wilkinson, K. (2020). Uptake and
Glycosylation of Smoke-Derived Volatile Phenols by
OENO One 2020, 4, 1105-1119 111 9
© 2020 International Viticulture and Enology Society - IVES
Cabernet-Sauvignon Grapes and Their Subsequent
Fate during Winemaking. Molecules, 25(16), 3720.
https://doi.org/10.3390/molecules25163720
Teixeira dos Santos, c., Lopo, M., Ricardo, N., &
Lopes, J. (2013). A Review on the Applications of
Portable Near-Infrared Spectrometers in the Agro-Food
Industry. Applied Spectroscopy, 67(11), 1215-1233.
https://doi:10.1366/13-07228
van der Hulst, L., Munguia, P., Culbert, J. A., Ford,
C. M., Burton, R. A., & Wilkinson, K. L. (2019).
Accumulation of volatile phenol glycoconjugates in
grapes following grapevine exposure to smoke and
potential mitigation of smoke taint by foliar application
of kaolin. Planta, 249(3), 941-952. https://doi.
org/10.1007/s00425-018-03079-x
Yu, J., Wang, H., Zhan, J., & Huang, W. (2018). Review
of recent UV–Vis and infrared spectroscopy researches
on wine detection and discrimination. Applied
Spectroscopy Reviews, 53(1), 65-86. https://doi:10.10
80/05704928.2017.1352511
... The study was conducted at the vineyards located at the University of Adelaide Waite Campus, Urrbrae, South Australia, Australia (34° 58' S, 138° 38' E) in the 2018-2019 season [5][6][7][8]. As described by Summerson et al. [6,7], a total of five treatments of Cabernet Sauvignon grapevines were prepared. ...
... The study was conducted at the vineyards located at the University of Adelaide Waite Campus, Urrbrae, South Australia, Australia (34° 58' S, 138° 38' E) in the 2018-2019 season [5][6][7][8]. As described by Summerson et al. [6,7], a total of five treatments of Cabernet Sauvignon grapevines were prepared. Two of the treatments consisted of control samples (i) with and (ii) without a fine mist of water at canopy level, while three treatments were smoked using barley straw at different levels (iii) low-density smoke (1.5 kg straw), (iv) high-density smoke (5 kg straw), (v) high-density smoke with a fine mist of water (5 kg straw) during one h at seven days post veraison using an individual tent per treatment. ...
... A total of 5 kg of berry bunches were collected in triplicates per treatment to produce wine samples using micro-vinification techniques described by Summerson et al. [7]. Samples of must from berries one day after application of smoke treatment and at harvest, along with the wine samples, were analyzed for glycoconjugates and volatile phenols using stable isotope dilution analysis (SIDA) and gas chromatography-mass spectroscopy, respectively, as described in previous publications [7,9,10]. ...
Conference Paper
Due to climate change, the higher incidence and severity of bushfires is a major challenge for wine producers worldwide as an increase in smoke contamination negatively affects the physicochemical components that contribute to lower quality of both fresh produce and final products (smoke taint in wines). This results in reduced prices and consumer acceptability, impacting the producers and manufacturers. Current methods available to winemakers for the assessment of contamination in berries and wine consist of costly laboratory analyses which require skilled personnel, are time-consuming, cost prohibitive and destructive. Therefore, novel rapid, cost-effective, and reliable methods using digital technologies such as the use of near-infrared (NIR) spectroscopy, electronic nose (e-nose) and machine learning (ML) have been developed by our research group. Several ML models have been developed for smoke taint detection and quantification in berries and wine from different varieties using NIR absorbance values or e-nose outputs as inputs to predict glycoconjugates, volatile phenols, aromatic volatile compounds, smoke-taint amelioration techniques efficacy and sensory descriptors, all models with > 97% accuracy. These methods and models may be integrated and automated as digital twins to assess smoke contamination in berries and smoke taint in wine from the vineyard for early decision-making.
... The exposure of grapevines to smoke during the critical period between veraison and harvest can result in the uptake and accumulation of volatile phenols and their glycoconjugates in grape berries, which can negatively affect the composition and sensory properties of wines [1][2][3]. Once absorbed, volatile phenols from smoke are rapidly glycosylated and stored in the skin and pulp of grape berries [4,5]. During winemaking, many of these glycoconjugates are hydrolyzed back into their free active forms where they can impart their unpleasant 'smoky' aromas, with a large proportion of the glycoconjugate pool remaining in the final wine [5][6][7][8]. ...
... To assess the levels of the smoke compounds (both volatile phenols and their glycoconjugates), growers and winemakers are required to send samples of grapes and/or wines to commercial laboratories or conduct mini harvests to perform a sensory analysis. However, the high cost associated with laboratory testing may prevent this form of analysis for many producers, and a sensory analysis is time-consuming and may not allow for timely actions within the time constraints of a vintage [4,18]. There is, therefore, a need for a rapid and affordable alternative method for assessing smoke contamination. ...
... To date, traditional methods for assessing wine quality and the degree of smoke taint have involved the use of chromatographic techniques for the identification of aroma volatiles and trained panels [4,[39][40][41]. However, there are several drawbacks to these techniques, as they can be time-consuming in terms of sample preparation, as well as training sensory experts, which is expensive, as chromatographic techniques require costly reagents and training, and maintaining trained panels can also be expensive, as well as being destructive in their forms of assessment. ...
Article
Full-text available
The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke tainted and non-smoke tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke-taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following aging and hydrolysis of glycoconjugates.
... 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]. Recent climate research predicts an increase of 15-70% in the number of days of "very high" or "extreme" fire danger by 2050 and a lengthening of the fire season, resulting in more frequent and intense bushfires [12][13][14][15][16]. ...
... 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- [11,[40][41][42]. ...
... 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, Beverages 2021, 7, 7 4 of 21 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]. ...
Article
Full-text available
Grapevine smoke exposure and the subsequent development of smoke taint in wine has resulted in significant financial losses for grape growers and winemakers throughout the world. Smoke taint is characterized by objectional smoky aromas such as "ashy", "burning rubber", and "smoked meats", resulting in wine that is unpalatable and hence unprofitable. Unfortunately, current climate change models predict a broadening of the window in which bushfires may occur and a rise in bushfire occurrences and severity in major wine growing regions such as Australia, Mediterranean Europe, North and South America, and South Africa. As such, grapevine smoke exposure and smoke taint in wine are increasing problems for growers and winemakers worldwide. Current recommendations for growers concerned their grapevines have been exposed to smoke are to conduct pre-harvest mini-ferments for sensory assessment and send samples to a commercial laboratory to quantify levels of smoke-derived volatiles in the wine. Significant novel research is being conducted using spectroscopic techniques coupled with machine learning modeling to assess grapevine smoke contamination and taint in grapes and wine, offering growers and winemakers additional tools to monitor grapevine smoke exposure and taint rapidly and non-destructively in grapes and wine.
... Digital technologies using the low-cost e-nose in the crushing and fermentation process coupled with AI models can accurately quantify the levels of smoke-related compounds at different stages of the winemaking process 7,12 . The implementation of NIR and AI also showed high accuracy in detecting and quantifying smoke-related compounds in wine 10,11 . ...
... Previously mentioned novel digital technologies, such as low-cost e-noses and NIR, can offer accurate and rapid assessment of wine samples in real-time to determine the effectiveness of amelioration techniques implemented through microvinifications [11][12][13] . The latter will provide winemakers with the opportunity to finetune different methodologies to determine which one is the most effective for their wines according to their style and quality requirements. ...
Article
While starting a new 2021-22 grape-growing season in Australia, potential climatic anomalies such as bushfires are in the consciousness of many grape growers and winemakers. Increasing ambient temperatures has resulted in associated climatic anomalies, such as extreme wildfires in Australia, California, Siberia, Greece and Turkey as forecasted and reviewed by the latest Intergovernmental Panel on Climate Change report (IPPC-AR6). The latest IPCC report also claims a “virtually certain” increase in the frequency and intensity of heatwaves due to greenhouse emissions from burning fossil fuels. Many of these wildfires or bushfires occur in Mediterranean countries that cultivate grapes for winemaking. Recent review papers on smoke taint in wines related to mitigation techniques have assessed the state of the art research efforts related to smoke taint and developed methods for mitigation and incorporating some of the latest digital technologies for its assessment. Both reviews agreed that the best practices to remediate smoke taint in wine are activated carbon fining and reverse osmosis treatments. This article focuses on the latest advances to monitor the levels of smoke contamination in grapevines and grapes and smoke taint in wines using novel digital and non-invasive technologies. Much of these latest efforts are the product of research from the Digital Agriculture, Food and Wine group from The University of Melbourne. The latest research has produced tools, and artificial intelligence (AI) models with high accuracy (Table 1) to be deployed in near-real-time using affordable technologies either in the field between veraison to harvest and in the winery that can be accessible to winegrowers and winemakers.
... Numerous factors can influence the volatile aromatic compound composition of wines, including environmental conditions, viticultural practices, such as crop-level reduction, and drying of fruit, canopy management, and winemaking practices, such as yeast selection and the use of malolactic bacteria [7][8][9][10]. Furthermore, it has been predicted that the effects of climate change may have profound impacts on the aromatic potential of grapes and hence wine quality [11], particularly the increased risk and incidence of bushfires, resulting in grapevine smoke exposure and smoke taint in wines [12][13][14]. Grapevine exposure to smoke during the critical stages between veraison and harvest has been shown to alter the volatile aromatic composition of grapes and lead to the development of smoke taint in wine, resulting in objectionable smoky characters and reduced wine quality [12][13][14][15][16]. Volatile phenols present in smoke, including guaiacol and 4-methylguaiacol, are responsible for the development of smoke aromas in smoke tainted wines such as burnt wood, burning rubber, medicinal, and smoked meats [13,15,16]. In addition to this, increases in temperature and drought brought on by climate change can also affect the aromatic compounds in grapes and hence wine quality [11,17]. ...
... Furthermore, it has been predicted that the effects of climate change may have profound impacts on the aromatic potential of grapes and hence wine quality [11], particularly the increased risk and incidence of bushfires, resulting in grapevine smoke exposure and smoke taint in wines [12][13][14]. Grapevine exposure to smoke during the critical stages between veraison and harvest has been shown to alter the volatile aromatic composition of grapes and lead to the development of smoke taint in wine, resulting in objectionable smoky characters and reduced wine quality [12][13][14][15][16]. Volatile phenols present in smoke, including guaiacol and 4-methylguaiacol, are responsible for the development of smoke aromas in smoke tainted wines such as burnt wood, burning rubber, medicinal, and smoked meats [13,15,16]. ...
Article
Full-text available
Wine aroma is an important quality trait in wine influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, in-cluding the water status of grapevines, canopy management and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p<0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = -0.93), deca-noic acid, ethyl ester (r = -0.94) and octanoic acid, 3-methylbutyl ester (r = -0.89). The two models developed in this study may offer winemakers a rapid, cost-effective and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.
... Even though some significant correlations were observed between e-nose outputs of cooked rice and volatile compounds detected in cooked rice samples, it will be convenient to develop a method to assess cooked rice quality traits using the raw rice samples to avoid tedious sample preparation and destructive analysis. Additionally, previous studies showed promising potential through emerging trends of using sensor technology combined with the chemometrics technique to indirectly assess cooked rice quality traits using raw rice samples [73][74][75]. Table 3 shows the statistical results of the pattern recognition models developed to classify 17 types of commercial rice using the ANN algorithms based on the first derivative of NIR absorbances (Model 1) and e-nose readings (Model 2) as inputs. All of the classification models had high accuracy in all three stages of training, validation, and testing. ...
Article
Full-text available
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R=0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.
... Quantitative NIR measurements are usually based on the correlation between sample composition, as determined by defined reference methods, and the absorption of light at different wavelengths in the near infrared region. Several works have reported the use of these techniques in oenology for the determination of diverse compounds, including aromatic compounds and sensory characteristics Fernández-Novales, Garde-Cerdán, Tardáguila, Gutiérrez-Gamboa, Pérez-Álvarez, & Diago, 2019;Garde-Cerdán, Lorenzo, Alonso, & Rosario Salinas, 2010;Le Moigne, Maury, Bertrand, & Jourjon, 2008;Liu et al., 2008;Lorenzo, Garde-Cerdán, Pedroza, Alonso, & Salinas, 2009;Smyth et al., 2008;Summerson, Viejo, Torrico, Pang, & Fuentes, 2020). Schneider et al. (2004) applied Fouriertransform infrared (FT-IR) spectrometry on a grape extract in order to evaluate the concentrations of the different glycosylated compounds. ...
Article
Grape variety, vinification, and ageing are factors conditioning the aroma of a wine, with volatile secondary metabolites responsible for the so-called grape varietal character. Particularly, grape glycosylated norisoprenoids are mostly responsible for the sensory profile of Tannat wines, making relevant the use of fast instrumental tools to evaluate their concentration, allow classifying grapes and defining the optimum maturity for harvest. NIR spectroscopy is a fast, non-destructive technique, which requires minimal sample preparation. However, its quantitative applications need chemometric models for interpretation. In this work, a NIR-ANN calibration was developed to quantify norisoprenoids in Vitis vinifera cv. Tannat grapes during maturation and harvesting. Glycosidated norisoprenoids were determined by GC-MS. The ANN adjustments showed better performance than linear models such as PLS, while the best calibration was obtained by homogenising grape samples when comparing to grape juice; making possible to fit a model with an error of 146 μg/kg.
... This method uses independent sets of samples for each stage and evaluates the overall accuracy by including all samples. A similar data division to develop ANN models was used in previous studies [35,59,74]. Besides, several retraining attempts were conducted to assess the consistency of the results, obtaining similar results in every attempt. ...
Article
Full-text available
Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess com-mercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Further-more, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.
... Smoke affecting vineyards can contaminate berries passing these smoke-related compounds to the wine known as smoke taint [35]. Different digital and non-destructive sensors coupled with AI, such as infrared thermography for canopies, have been used to detect smoke contaminated vines and near-infrared spectroscopy to detect smoke-related compounds in grapes and final wine [36][37][38]. Low-cost e-noses have also been developed to detect smoke taint in berries and wine [39]. ...
Conference Paper
Full-text available
Climate change has posed major risks for viticulture and winemaking around the world, related to increased ambient temperatures, the variability of rain events, higher occurrence and intensity of climatic anomalies, such as frosts and bushfires. These changes have directly impacted grapevine phenology by compressing stages and pushing forward in time harvest to hottest months, producing a dual warming effect. Bushfires also directly impact berry smoke contamination, which can be passed to the wine in the winemaking process producing smoke taint. Due to these events' complexities and their effects on viticulture and winemaking, a smarter approach is required to obtain relevant information and process it efficiently for more appropriate decision-making by different practitioners. In the last 10 years, artificial intelligence has offered various applications to be included in viticultural and winemaking operations, which has rendered important advances and information to deal with climate change adversities.
Book
Full-text available
In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products’ quality traits and international competitiveness, especially within the past five years. Fermented beverages have been one of the most researched industries to implement these technologies to assess product composition and improve production processes and product quality. This Special Issue (SI) focused on the latest research on the application of digital technologies on beverage fermentation monitoring and the improvement of processing performance, product quality and sensory acceptability.
Article
Full-text available
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.
Article
Full-text available
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) and 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 7 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); and 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.
Article
Full-text available
Wine made from grapes exposed to bushfire smoke can exhibit unpleasant smoky, ashy characters, which have been attributed to the presence of smoke-derived volatile phenols, in free or glycosylated forms. Here we report the uptake and glycosylation of volatile phenols by grapes following exposure of Cabernet Sauvignon vines to smoke, and their fate during winemaking. A significant delay was observed in the conversion of volatile phenols to their corresponding glycoconjugates, which suggests sequestration, the presence of intermediates within the glycosylation pathway and/or other volatile phenol storage forms. This finding has implications for industry in terms of detecting smoke-affected grapes following vineyard smoke exposure. The potential for an in-canopy sprinkler system to mitigate the uptake of smoke-derived volatile phenols by grapes, by spraying grapevines with water during smoke exposure, was also evaluated. While “misting” appeared to partially mitigate the uptake of volatile phenols by grapes during grapevine exposure to smoke, it did not readily influence the concentration of volatile phenols or the sensory perception of smoke taint in wine. Commercial sensors were used to monitor the concentration of smoke particulate matter (PM) during grapevine exposure to low and high density smoke. Similar PM profiles were observed, irrespective of smoke density, such that PM concentrations did not reflect the extent of smoke exposure by grapes or risk of taint in wine. The sensors could nevertheless be used to monitor the presence of smoke in vineyards during bushfires, and hence, the need for compositional analysis of grapes to quantify smoke taint marker compounds.
Article
Full-text available
Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available practical in-field tools available for detection of smoke contamination or taint in berries. This research proposed a non-invasive / in-field detection system for smoke contamination in grapevine canopies based on predictable changes in stomatal conductance patterns based on infrared thermal image analysis and machine learning modeling based on pattern recognition. A second model was also proposed to quantify levels of smoke-taint related compounds as targets in berries and wines using near-infrared spectroscopy (NIR) as inputs for machine learning fitting modeling. Results showed that the pattern recognition model to detect smoke contamination from canopies had 96% accuracy. The second model to predict smoke taint compounds in berries and wine fit the NIR data with a correlation coefficient (R) of 0.97 and with no indication of overfitting. These methods can offer grape growers quick, affordable, accurate, non-destructive in-field screening tools to assist in vineyard management practices to minimize smoke taint in wines with in-field applications using smartphones and unmanned aerial systems (UAS).
Article
Full-text available
Main conclusion The exposure of Vitis vinifera L. berries to forest fire smoke changes the concentration of phenylpropanoid metabolites in berries and the resulting wine. The exposure of Vitis vinifera L. berries (i.e., wine grapes) to forest fire smoke can lead to a wine defect known as smoke taint that is characterized by unpleasant “smoky” and “ashy” aromas and flavors. The intensity of smoke taint is associated with the concentration of organoleptic volatile phenols that are produced during the combustion-mediated oxidation of lignocellulosic biomass and subsequently concentrated in berries prior to fermentation. However, these same smoke-derived volatile phenols are also produced via metabolic pathways endogenous to berries. It follows then that an influx of exogenous volatile phenols (i.e., from forest fire smoke) could alter endogenous metabolism associated with volatile phenol synthesis, which occurs via the shikimic acid/phenylpropanoid pathways. The presence of ozone and karrikins in forest fire smoke, as well as changes to stomatal conductance that can occur from exposure to forest fire smoke also have the potential to influence phenylpropanoid metabolism. This study demonstrated changes in phenylpropanoid metabolites in Pinot noir berries and wine from three vineyards following the exposure of Vitis vinifera L. vines to simulated forest fire smoke. This included changes to metabolites associated with mouth feel and color in wine, both of which are important sensorial qualities to wine producers and consumers. The results reported are critical to understanding the chemical changes associated with smoke taint beyond volatile phenols, which in turn, may aid the development of preventative and remedial strategies.
Article
Full-text available
Smoke-taint is a wine defect linked to organoleptic volatile phenols (VPs) in Vitis vinifera L. berries that have been exposed to smoke from wildland fires. Herein, the levels of smoke-taint-associated VPs are reported in Cabernet Franc berries from veraison to commercial maturity and in wine after primary fermentation following on-vine exposure to simulated wildland fire smoke. VPs increased after smoke exposure, were rapidly stored as acid-labile conjugates, and the levels of both free VPs and conjugated forms remained constant through ripening to commercial maturity. An increase in total VPs after primary fermentation suggested the existence of VP-conjugates other than the acid-labile VP-glycosides already reported. This conclusion was supported with base hydrolysis on the same samples. Relative to published results, the data suggested a multifactorial regional identity for smoke-taint and they inform efforts to produce a predictive model for perceptible smoke-taint in wine based on the chemical composition of smoke-exposed berries.
Article
Smoke-taint is a wine defect that may occur when ripening grape crops absorb volatile phenols (VPs), compounds associated with the negative sensory attributes of smoke-taint, due to exposure of the grapes to wildfire smoke. This study examined potential methods to reduce the impact that smoke-exposure has on wine grapes. Specifically, agricultural sprays normally used to protect grapes from fungal pathogens and a spray used to prevent cracking in soft-fleshed fruits were assessed for their capacity to inhibit increases in VP concentrations in wine grapes following on-vine smoke-exposure. The results indicated that an artificial grape cuticle applied one week before exposure to simulated forest fire smoke (at 1-2 weeks after veraison) can significantly hinder an increase in VP concentrations in smoke-exposed grapes at commercial maturity. This reduction in VP concentrations may mitigate the crop losses experienced globally by the wine industry due to exposure of grapes on-vine (at key phenological stages) to wildfire smoke.
Article
Main conclusion The accumulation of volatile phenol glycoconjugates in smoke-exposed grapes was monitored following grapevine exposure to smoke, with different glycoconjugate profiles observed for fruit sampled 1 and 7 days after smoke exposure, and at maturity. Foliar application of kaolin reduced the concentration of volatile phenol glycoconjugates in smoke-exposed fruit, but efficacy depended on the rate of application and extent of coverage. Smoke taint can be found in wines made from grapes exposed to smoke from bushfires or prescribed burns. It is characterized by objectionable smoky and ashy aromas and flavors, which have been attributed to the presence of smoke-derived volatile phenols, in free and glycoconjugate forms. This study investigated: (1) the accumulation of volatile phenol glycoconjugates in grapes following the application of smoke to Sauvignon Blanc, Chardonnay and Merlot grapevines at approximately 10 days post-veraison; and (2) the potential mitigation of smoke taint as a consequence of foliar applications of kaolin (a clay-based protective film) prior to grapevine smoke exposure. Varietal differences were observed in the glycoconjugate profiles of smoke-exposed grapes; the highest glycoconjugate levels were found in Merlot grapes, being pentose-glucosides of guaiacol, cresols, and phenol, and gentiobiosides of guaiacol and syringol. Changes in volatile phenol glycoconjugate profiles were also observed with time, i.e., between fruit sampled 1 day after smoke exposure and at maturity. The application of kaolin did not significantly affect the glycoconjugate profiles of Sauvignon Blanc and Chardonnay grapes, but significantly lower volatile phenol glycoconjugate levels were observed in Merlot fruit that was treated with kaolin prior to smoke exposure. The potential for control and smoke-exposed grapes to be differentiated by measurement of spectral reflectance was also demonstrated.
Article
With the rise of remotely piloted aircraft systems, increasing computing power and advances in image processing software, the opportunities for vineyard observations through remote sensing are increasing. Remote sensing and image analysis techniques that are becoming more available include object-based image analysis, spatiotemporal analysis, hyperspectral analysis, and topoclimatology. Each of these techniques are described and discussed as potential for development within a viticulture and terroir context. While remote sensing applications are well established at the smaller precision viticulture scale, the larger spatial scale of terroir analysis requires adaptation and new models of analysis.
Article
Find full text here: https://winesvinesanalytics.com/features/article/195118/Smoke-Taint-in-Wine