Download APPLICATION OF NIR SPECTROSCOPY FOR WHISKY

APPLICATION OF NIR SPECTROSCOPY
FOR WHISKY IDENTIFICATION
AND DETERMINATION THE CONTENT OF ETHANOL
Krzysztof Wójcicki
Department of Technology and Instrumental Analysis, Faculty of Commodity Science,
The Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań,
Poland.
[email protected]
Abstract: The aim of this paper was to distinguish whisky samples and to determine
their percentage content of ethanol. Whisky samples have been subjected to NIR
at a broad range of wave numbers (12500-4000 cm-1). The characteristic differences
for the various whisky samples occurred in the bands at about 6793 cm-1
and in the region 4892-4491 cm-1.
Principal component analysis (PCA) performed on the near infrared spectra
allowed to define three distinctive groups of whisky: (1) single malt, (2) blended
and (3) “commercial blends”.
The regression analysis using the partial least squares (PLS) and principal
component regression (PCR) methods were applied to examine the correlation
between the content of ethanol in whisky and the respective spectra. Obtained models
predicted the concentration in an accuracy from 0.9% to 1.3%. The results indicate
that the NIR spectroscopy offers a promising approach for quality evaluation
of whisky.
Keywords: whisky, spectroscopy, NIR, principal component analysis (PCA),
principal component regression (PCR), partial least squares (PLS) regression.
INTRODUCTION
Whisky (whiskey) is one of the most popular distilled drinks made
from malted or saccharified grains, which should mature (in most cases)
for at least three years in wooden barrels. Whisky must be distilled
at a minimum of 40.0% and maximum of 94.8% alcohol by volume [Bathgate
2003]. The difference between the various kinds of whisky relies mostly
on the type of grain used for the mash. There are three common types
of whisky: single malt, grain and blended. Single malt whisky represents
a whisky that is made from one batch of grain mash and produced in a single
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distillery. Grain whisky is made from at least one type of grain other than
malted barley. Blended whisky (the most popular) is a mix of malt whisky
and grain whisky, sourced from several different distilleries [Bathgate 2003].
The increase in consumption (from 8.8% in 2014 to predicted 19.3% in 2018),
a fast-growing category of bourbon (type of American whisky), new distilleries
and meetings devoted to whisky, are just some examples of the increasing
interest in the subject of whisky (study by the International Wine and Spirit
Research) [Vinexpo Report 2016]. For this reason the quality evaluation
and standardization of alcoholic beverages is an important issue in the spirits
production industry.
For analysis of whisky composition the chromatographic techniques,
mass spectrometry and electronic nose are mainly used. Poisson and Schieberle
[2008] analysed the composition of American whisky (bourbon) by applying
HRGC-O (high resolution gas chromatography - olfactometry). This technique
allowed the detection of 23 active aroma substances. Wanikawa et al. [2002]
used the multidimensional gas chromatography coupled with mass
spectrometry and olfactometry to identify green note compounds in malt
whiskies. The applied technique allowed researches the detection of two
aldehydes and three alcohols which are responsible for the green note aroma.
To analyse ethyl esters, which have greater influence on the aroma of whisky,
the two-dimensional gas chromatography coupled with mass spectrometer
(GC-GC-MS) was used by Camp et al. [2007]. The electronic nose was used
by Wongchoosuk et al. [2010] to investigate the samples of whisky tainted
with different concentration of methanol. The principal component analysis
(PCA) was used to graphical identification of studied samples.
For comparison between whisky and other alcoholic beverages
the spectroscopic techniques are mainly used. Ashok et al. [2011] used the near
infrared (NIR) spectroscopic analysis on an optofluidic chip for classification
of whisky samples. Researchers found that the combination of Raman spectra
and fluorescent background information coupled with principal component
analysis (PCA) could be used to classify different types of whisky. Whiskies
were properly classified based on their aroma features, age and cask type.
The near infrared spectra with radial basis function network was used
by Backhaus et al. [2012] to classify Scotch whiskies. Six commercially
available Scotch whisky brands and their variants were used to build the data
set. There were four data sets which were used for classification: age, cask
distillery and product. The applied technique distinguish whisky samples
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dependent on the dataset with high accuracy from 70% to over 90%.
Sujka et al. [2012] applied the FT-IR spectroscopy in combination
with multivariate data analysis for discrimination and identification of some
spirit beverages. The discriminant analysis (DA) was used to distinguish: gin,
brandy, cognac and whisky. For each type of beverages the discriminant model
was build based on their spectral properties. For whisky samples the spectral
range between 1240-1109 cm-1 and 842-390 cm-1 was investigate. Obtained
models predicted with satisfactory precision membership of given unknown
spirit beverage to proper group of beverages. The UV-Vis spectroscopy
for authentication and discrimination of whiskies coupled with chemometrics
was presented by Cantarelli et al. [2015]. Fifteen samples of whisky
with different years of aging were investigated. The pattern recognition was
performed using principal component analysis (PCA), linear discriminant
analysis (LDA), and partial least squares discriminant analysis (PLS-DA)
to obtain models that allowed the classification and discrimination of whiskies
based in trademarks and years of aging, respectively. The 99% (by LDA)
and 100% (by PLS-DA) of correct classification and discrimination
was achieved by obtained models which showed that the UV-Vis spectroscopy
and chemometric data analysis could be implemented in the routine analysis.
In this work the application of NIR spectroscopy for whisky
identification and determination the content of ethanol is proposed.
MATERIALS AND METHODS
Samples and mixture preparation
As a research material were used twenty five samples of whisky
from different origin. For classification twelve samples of whisky were used
- six samples of single malt whisky (SM) and six samples of blended whisky
(BD). For ethanol quantification thirteen samples of whisky with different
ethanol concentration were used. Samples were stored in the dark at room
temperature until the day of analysis.
For ethanol calibration model two sets of binary mixtures were prepared
by mixing deionized water with pure ethanol (99.8%), at amounts in the range
35-50 %, with 1% step.
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Spectroscopic measurements
The near infrared (NIR) spectra were performed on a MPA\FT-NIR
spectrometer (Bruker) using transmitted light. Single beam spectra
of the samples were collected and rationed against a background of water
or air. For each sample, the NIR spectra were recorded from 12500
to 4000 cm-1 by co-adding 16 interferograms at a resolution of 4 cm-1.
Measurements were performed in a 2 mm glass cuvette.
Data Analysis
Principal component analysis (PCA) was performed on the NIR spectra
of whisky to distinguish samples. PCA is a multivariate technique that linearly
transforms an original set of variables into a substantially smaller set
of uncorrelated variables that represents most of the information in the original
data set. Data for PCA are arranged in two-way matrix, in which column
vectors represent variables a row vectors represent “objects” of which
the variables are measured [Bower 2009, Esbensen and Geladi 2009, Forina
et al. 2009]. Ten replicated spectra for each of the whisky were analyzed.
To determine the relation between the spectra of studied whisky
and the content of ethanol, the PLS1 and PCR regression method were used.
Mixtures were used to build a regression model between spectral data
and ethanol concentration. The set of independent variables X were the NIR
spectra and the set of dependent variables Y were ethanol content. Full crossvalidation was applied to the regression model. The regression models were
evaluated using the adjusted R2 and the root mean-square error of crossvalidation (RMSECV), as the term indicating the prediction error of the model.
The obtained regression models were subjected to external validation
by predicting the ethanol content in the prediction set (n=13) based upon their
spectral information. The predicted values were compared to the reference
values. The quality models were evaluated by the ratio of the standard
deviation of reference data for the validation samples to the RMSEP
(RPD).The RPD provides a means for standardizing the RMSEP
and evaluating the robustness of the model [Huang et al. 2001].
The data analysis was carried out using Unscrambler 9.7 (CAMO, Oslo,
Norway) software.
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RESULTS AND DISCUSSION
Whisky spectrum in near infrared range
The NIR absorption spectra of studied whisky measured against air,
figure 1A, are similar to the spectra of other alcohol beverages (beer, vodka
and wine) published by other authors [Inon et al. 2006, Cozzolino et al. 2006,
Kolomiets et al. 2010]. Two large absorption bands were observed
corresponding to the OH bonds around 7000 cm-1 and 5200 cm-1. As noted
+ !"
by Kolomiets et al. [2010] these bands are characteristic of the
and !# + !" combination bands, respectively, of water which are superimposed on the 2 × !($%) overtone and !($%) + &($%) combination bands,
respectively, of alcohol. The 2 × !('%) and !('%) + &('%) overtone
and combination vibrations, respectively, can be observed in the 5500
-600 cm-1 region and below 4500 cm-1 [Kolomiets et al. 2010].
The bands originating from ethanol revealed in the spectra measured
against a background of water. Negative band is due to water absorption
compensation while positive bands correspond to the other components,
figure 1B [Inon et al. 2006]. The spectral range between 6000-5660 cm-1
corresponds to the first overtone of the C-H stretching vibrations in CH3
and CH2 group. The spectral range between 4580-4200 cm-1 corresponds
to the combination of CH vibrations [Workman 1996].
NIR spectra of the same types of whisky samples were indistinguishable.
Spectra of different whiskies had a different shape and intensity of some bands.
The characteristic differences for the various whisky samples occurred
in the bands at about 6793 cm-1 and in the region 4892-4491 cm-1, figure 1B.
Principal component analysis (PCA)
There are various types of classifications proposed for whiskies based
on flavour, geographical location of origin, age and cask. The principal
component analysis (PCA) was used to cluster the near infrared spectra
obtained from different types of whisky samples. The PCA was applied
on the spectra acquired from 12 samples of whisky. After performing PCA,
the data was plotted in a graph of first principal component (PC1) vs. second
principal component (PC2) as shown in figure 2.
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A
4,0
Blended
Single Malt
3,5
Absorption
3,0
2,5
2,0
1,5
1,0
0,5
0,0
12000
11000
10000
9000
8000
7000
6000
5000
4000
-1
wavenumber (cm )
B
1,0
Blended
Single Malt
-1
Absorption
0,5
4892-4491 cm
0,0
-0,5
-1,0
-1
6793 cm
-1,5
12000
11000
10000
9000
8000
7000
6000
5000
4000
-1
wavenumber (cm )
Fig. 1. (A) Absorption spectra of whiskies in near infrared region measured against
a background of air. (B) Absorption spectra of whiskies in near infrared region
measured against a background of water. Source: Author’s own work
The first and second main component (PC1 and PC2) describes 91%
of total variation. PC1 describes 66% of total variability while the PC2 25%.
Samples spread along the PC1 axis, from negative to positive values, according
to the manufacturing process. Single malt whisky (SM) characterized
by negative value of PC1 while blended whisky (BD) by positive.
Such distribution of the samples was probably associated with the production
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process in which single malt whisky is made entirely from one type of malt,
usually barley, distilled in single distillery with alembic pot still, while blended
whiskies (BD), which were characterized by positive value of PC1,
are combination of mature whiskies from several different distilleries, distilled
in a column still [Conner, Reid and Jack 2003]. Blended whiskies may vary
from each other as shown in figure 2. Samples spread along the PC2 axis,
from negative to positive, probably according to the type of grain which was
used in the production process. “Commercial” blends (cBD) which were
characterized by positive value of PC2 are mainly a mixture of small amounts
of barley whiskies with large quantity of grain whiskies. Blends which are
made by greater amounts of barley whiskies and less grain whiskies were
characterized by negative value of PC2.
0,8
cBD
PC2 (25%)
0,4
0,0
SM
-0,4
BD
-0,8
-0,8
-0,4
0,0
0,4
0,8
PC1 (66%)
Fig. 2. Scores plot for two significant principal components, PC1 vs PC2, of a PCA
of near infrared spectra of single malt whisky (SM), blended whisky (BD)
and “commercial” blended whisky (cBD). Source: Author’s own work
Regression models for ethanol-water mixtures
In order to quantitatively evaluate the ethanol concentration of ethanolwater mixtures based on their spectral characteristics, the partial least squares
regression (PLS) and principal component regression (PCR) were used.
Complete spectra, from 12500 cm-1 to 4000 cm-1, was analysed. The PLS
and PCR regression results for the complete spectra in 12500-4000 cm-1 range
without any pretreatment pointed a significant correlation between spectra
and ethanol concentration, table 1.
To evaluate the model quality the RPD (the ratio of the standard
deviation of the Y data set to the standard error of prediction) was calculated.
The RPD values of both models were higher than 8 (10.1) which indicates their
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very good capability to predict the ethanol concentration and excellence
capability for process control, table 1.
Table 1. Partial least squares (PLS) and principal component regression (PCR)
statistics for calibration models
Model
Spectra range
Number
of spectra
Number
of variables
R2
RMSECV
[%]
RPD
PLS
12500-4000 cm-1
16
1
0.99
0.47
10.1
PCR
12500-4000 cm-1
16
1
0.99
0.47
10.1
Source: Author’s own work
Quantification of ethanol concentration in whisky samples
The results of partial least squares analysis (PLS) and principal
component regression (PCR) were presented in table 2.
Table 2. Predicted and reference concentration of ethanol in whisky samples obtained
by using PLS and PCR calibration models.
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
Reference
(product
label)
[%]
40
40
40
40
40
40
43
43
43
43
46
46
46
PLS
Predicted
concentration
[%]
40.4
40.6
40.2
40.7
40.6
40.1
43.7
43.1
43.8
43.8
45.7
45.8
45.3
Deviation
[%]
1.4
1.4
1.5
1.3
1.2
1.3
1.3
0.9
1.4
1.5
1.6
0.9
1.1
PCR
Predicted
Deviation
concentration
[%]
[%]
40.4
40.6
40.2
40.7
40.6
40.1
43.7
43.1
43.8
43.8
45.7
45.8
45.3
1.4
1.4
1.5
1.3
1.2
1.3
1.3
0.9
1.4
1.5
1.6
0.9
1.1
Source: Author’s own work
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Ethanol concentration is an important parameter in the assessment
of the quality of whisky. The ethanol concentration must be more than 40%
for authentic whisky samples (Ashok 2011).
The PLS and PCR model were used to predict the ethanol concentration
in each whisky sample. Both models successfully predicted the ethanol
concentration of the samples at about 1.3% error (0.9-1.6%) when compared
to the concentration claimed by the producers on the product label,
as shown in table 2.
CONSLUSION
Whisky spectrum in the near infrared region is mainly derived
from water and ethanol, NIR spectra of the same whiskies were indistinguishable. Spectra of different whiskies had a different shape
and intensity of some bands. The characteristic differences for the various
whisky samples occurred in the bands at about 6793 cm-1 and in the region
4892-4491 cm-1.
Principal component analysis (PCA) performed on the near infrared
spectra allowed to define three distinctive groups of whisky: (1) single malt,
(2) blended and (3) “commercial blends”. The PLS and PCR models were built
to predict the concentration of ethanol in the various whisky samples. Obtained
models predicted the concentration in an accuracy from 0.9% to 1.6%.
The results indicate that the NIR spectroscopy offers a promising
approach for quality evaluation of whisky.
REFERENCES
Ashok P.C., Praveen B.B., Dholakia K., 2011, Near Infrared Spectroscopic Analysis
of Single Malt Scotch Whisky on an Optofluidic Chip, Optics Express, 19, 22982-22992.
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Whisky from Near-Infrared Raman Spectra with a Radial Basis Function Network
with Relevance Learning, The European Symposium on Artificial Neural Networks,
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Bathgate G.N., 2003, History of the Development of Whiskey Distillation, in: Whisky
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practitioner, A Wiley-Blackwell.
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STRESZCZENIE
Celem pracy było rozróżnienie próbek whisky oraz oznaczenie w nich
procentowej zawartości alkoholu etylowego. Pomiar widm absorpcji dokonano
w całym zakresie NIR (12500-4000 cm-1). Zaobserwowano wyraźne różnice
w kształcie pasm przy liczbie falowej 6793 cm-1 oraz w zakresie 4892-4491 cm-1.
Na podstawie przeprowadzonej analizy chemometrycznej przy wykorzystaniu
metody głównych składowych (PCA) wyróżniono trzy grupy whisky: (1) jęczmienne,
(2) mieszanki i (3) “mieszanki komercyjne”. W celu zbadania możliwości określenia
stężenia procentowego alkoholu etylowego w whisky na podstawie zmierzonych
widm, przeprowadzono analizę regresji PLS i PCR. Otrzymane modele przewidywały
zawartość alkoholu z dokładnością od 0,9% do 1,6%. Uzyskane wyniki świadczą,
że spektroskopia w zakresie NIR może być wykorzystywana do oceny jakości whisky.
Słowa kluczowe: whisky, spektroskopia, NIR, analiza głównych składowych (PCA),
analiza regresji głównych składowych (PCR), analiza najmniejszych kwadratów
(PLS)
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