Journal of Analytical Methods in Chemistry
Volume 2016 (2016), Article ID 8763436, 12 pages
http://dx.doi.org/10.1155/2016/8763436

Research Article

The Verification of the Usefulness of Electronic Nose Based on Ultra-Fast Gas Chromatography and Four Different Chemometric Methods for Rapid Analysis of Spirit Beverages

Paulina Wiśniewska, Magdalena Śliwińska, Jacek Namieśnik, Waldemar Wardencki, and Tomasz Dymerski

Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, 11/12 Gabriela Narutowicza Street, 80-233 Gdańsk, Poland

Received 1 February 2016; Revised 28 April 2016; Accepted 31 May 2016

Academic Editor: Jose Carlos Marques

Copyright © 2016 Paulina Wiśniewska et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Spirit beverages are a diverse group of foodstuffs. They are very often counterfeited which cause the appearance of low quality products or wrongly labelled products on the market. It is important to find a proper quality control and botanical origin method enabling the same time preliminary check of the composition of investigated samples, which was the main goal of this work. For this purpose, the usefulness of electronic nose based on ultra-fast gas chromatography (fast GC e-nose) was verified. A set of 24 samples of raw spirits, 33 samples of vodkas, and 8 samples of whisky were analysed by fast GC e-nose. Four data analysis methods were used. The PCA was applied for the visualization of dataset, observation of the variation inside groups of samples, and selection of variables for the other three statistical methods. The SQC method was utilized to compare the quality of the samples. Both the DFA and SIMCA data analysis methods were used for discrimination of vodka, whisky, and spirits samples. The fast GC e-nose combined with four statistical methods can be used for rapid discrimination of raw spirits, vodkas, and whisky and in the same for preliminary determination of the composition of investigated samples.

1. Introduction

Electronic noses are more and more often taken into consideration as a tool used for the quality and authenticity assessment of selected products due to their numerous advantages and the principle of operation. The electronic nose is an analytical instrument used for rapid detection and differentiation between types of gaseous samples. Till now electronic nose was used for environmental monitoring (detection of air pollution, tracking of pollution pathways, and efficiency assessment of wastewater and waste gas treatment), medical sciences (identification of selected diseases, including tumors, based on odorants excreted by the infected cells and organs), the perfume industry (authentication of perfumes), the pharmaceutical industry (production control of medicines), forensic operations, and the food-processing industry [1]. This instrument mimics the principles of operation of human smell. Most of the electronic noses are based on set of sensors. Chemical sensors under the influence of the gas mixture allow for generating a characteristic odour profile, which constitutes the so-called “fingerprint” [2]. Sensors such as piezoelectric, electrochemical, and biosensors are usually used. Each chemical sensor has some limitations. For example, metal oxide semiconductor (MOS) sensors require a high working temperature, and conductive polymer (CP) sensors are susceptible to humidity. These problems can be solved by use of e-noses based on the ultra-fast gas chromatography or mass spectrometry. Example of e-nose based on the ultra-fast gas chromatography technology is Heracles II. This type of electronic nose allows for combining the features of fast gas chromatography (fast GC) with advantages of a sensor-based electronic nose. In this electronic nose system, hundreds of variables can be used to achieve reliable results. As a result of one analysis, it is possible to obtain information about the composition of investigated samples and their volatile fraction profile. In this way, complete information is obtained about the similarity of a given sample to the reference sample [3]. An electronic nose based on ultra-fast gas chromatography can be used to assess quality and authenticity and to compare samples.

Spirit beverages are a diverse group of foodstuffs, which differ in terms of their taste, odour, and appearance. Spirit beverages which are produced from grain distillates, namely vodka and whisky, belong to the most popular spirit beverages. For production of vodka and whisky is used the raw spirit which according to Regulation number 110/2008 of the European Parliament and of the Council (EC) of 15 January 2008 is obtained by the distillation, after alcoholic fermentation, which does not have the characteristics of ethyl alcohol or a spirit drink but still retains the odour and flavour from the specific agricultural materials. The composition of agricultural distillates is influenced by the material, from which it is produced, and fermentation conditions [4–6]. Raw spirits may contain a lot of harmful substances, which during the production process can pass to the vodka and whiskey. Moreover, agricultural distillates can be falsified due to their botanical origin, for example, by mixing rye distillates with corn distillates [7].

Vodka is a very popular alcoholic beverage in Eastern European countries. Ethyl alcohol of agricultural origin is one of the materials used for producing vodka. Ethanol obtained during the fermentation of various agricultural materials is distilled to selectively reduce the organoleptic properties of materials [6]. By filtering alcohol several times through charcoal and by diluting it with water, an alcoholic beverage with a mild flavour is obtained [8, 9]. Whisky, just like most of the vodkas, is a grain spirit beverage produced by the distillation of a mash made from malted cereals or cereals saccharified as a result of diastasis of malt contained in them. The flavour of whisky is closely connected with the distillation process and maturation of the intermediate product in oak barrels for at least three years, which is different from vodka. Only water and pure caramel can be added to the distillate for dilution and coloring, in contrast to vodkas, to which aromas can be added; however, in this case the name of aroma should be marked on the label, for example, if it is peach aroma on the label, it should be written as “peach vodka” [10]. The composition of vodkas and whisky is usually analysed using one-dimensional gas chromatography [11–17] or two-dimensional gas chromatography [18]. If less volatile ingredients are determined, such as coumarin, it is also possible to use the HPLC technique [19]. Quality and authenticity assessments are also conducted for these matrices using infrared spectroscopy [20–22], optical spectroscopy [23], and sensory analysis [24–26] and the use of the electronic nose based on sensors [27]. None of these methods allows the simultaneous investigation of the matrix and quick differentiation of samples. Due to their diversity and popularity, spirit beverages are very often counterfeited. Lower quality products are defined as those of better quality. Raw materials on the labelling differ from those used in reality. Because of this it is important to find a method that would enable the distinction of samples because of the quality and origin and in the same time a preliminary check of the composition of the samples. Such method should be quick which would allow its introduction to the production lines.

There is a need to find equipment that quickly delivers information about composition and authenticity of spirit beverages. These requirements are fulfilled by Heracles II electronic nose based on ultra-fast gas chromatography. Two of the most popular spirit beverages, namely, vodka and whiskey, and raw spirits used for their production were selected to check the suitability of the electronic nose for quality analysis of spirit drinks. These alcoholic beverages differ significantly from each other by methods of production but they are made from similar raw materials; due to this fact it will be possible to check whether one method is good for a variety of spirits made from grains.

2. Materials and Methods

2.1. Materials

A set of 24 samples of raw spirits made from different grains obtained from Destylarnia Sobieski S.A. (Poland, Pomeranian Province) were used for the research. Samples of commercially available vodkas and whisky were obtained from local stores in Gdańsk, Poland. A set of 33 samples of vodkas produced from different mixture of different type of rye were selected. Eight brands of whisky were selected from those available on the market, out of which 5 ones were produced from a mixture of barley, rye, and wheat distillates and three whiskies were produced with the addition of maize distillate.

2.2. Methods

2.2.1. Sample Preparation

All samples were prepared using the same analytical procedure. To the vials of 20 mL 6.25 mL of deionised water and 1.75 mL of the alcoholic sample (vodka, whisky, or raw spirit) were added. Next, vials were incubated for 20 minutes at 40°C. Samples were dispensed using a syringe kept at 100°C.

2.2.2. Instrumentation

The Heracles II electronic nose based on ultra-fast gas chromatography by Alpha MOS (Toulouse, France) was used for the research. It is equipped with a sorption trap, a dispenser allowing the introduction of a gas or liquid sample, two Flame Ionisation Detectors (FID), dedicated AlphaSoft V12 software with implemented modules for chromatographic, chemometric, and sensory analysis of characteristics of detected chemical compounds, the Arochembase V4 library, an HS100 autosampler, and a set of independent chromatographic columns with different polarity (nonpolar MXT-5 and medium polar MXT-1701, length 10 m each). Parameters of operation of the electronic nose used for the analysis of spirit beverages are presented in Table 1.

Table 1: Parameters of electronic nose operation.

Parameter
Conditions

Dispenser operation conditions

Dispensing volume
2.5 mL

Dispensing time
15 s

Dispenser temperature
200°C

Volumetric intensity of carrier gas flow
30 mL/min

Parameters of sorption trap operation

Trap temperature
40°C

Retention time
20 s

Conditions for chromatographic analysis

Temperature programme
40°C (2 s) - 3°C/s - 270°C (18 s)

Carrier gas
Hydrogen

Dispenser temperature
270°C

Detector operation conditions

Detector temperature
270°C

3. Results and Discussion

Following chemometric analysis PCA (Principal Component Analysis), DFA (Discriminant Function Analysis), SIMCA classification (SIMCA (Soft Independent Modeling of Class Analogies)), and SQC (Statistical Quality Control) were used for data analysis. For all analyses, sensors (area of the peaks) with the highest discrimination power were used. At the first stage, PCA was used. Principal Component Analysis is one of the most popular chemometric methods used for modelling, compression, and visualization of multidimensional data [28, 29]. Because of that, this method was checked for visualization of the obtained data. All beverages listed in point 2.1 were used for data analysis (Figure 1). PCA allowed for distinguishing between all the three groups of samples. However, this analysis is not typically used to distinguish between groups; it allows spotting the differences between the analysed samples. For example, in Figure 1(a), how diversified the studied groups are can be seen. Groups were created due to the type of spirit drinks (raw spirits, vodka, and whisky). On the basis of the distance between the samples, it can be concluded that the group of whisky comprises two substantially different groups of samples. In the case of raw spirit samples, the relatively low precision of results is caused by the fact that all samples were made from unknown mixture of grains. All vodkas were made from the same type of grain, namely, rye. In the case of whisky, the information about botanical origin of samples was available. Three of them were made not only from barley, wheat, and rye, but also from corn. Because of that, two separate groups of whisky of different botanical origin were created (Figure 1(b)). The variables chosen for PCA, namely, the most discriminant peak areas of specific compounds, were treated as an input dataset for statistical analysis. This dataset was presented in Figure 2 and the names of selected substances were listed in Table 2. Due to satisfactory discrimination of sample groups after application of PCA method, chosen variables were also used as input data in other utilized chemometric methods.

Table 2: Key chemical compounds (variables), which are important for sample discrimination.

Number of variables from – 1 or 2 column
Name of the compound
Type of sample (raw spirit, vodka, whisky without corn, and whisky with corn)

1—1
tert-Butylmethylether
All

2—1
Ethyl acetate
All

3—1
2-Methyl-1-butanol
All

4—1
2-Methyl-1-propanol
Raw spirit, whisky without corn, and whisky with corn

5—1
n-Butanol
Raw spirit, whisky without corn, and whisky with corn

6—1
2,3-Pentanedione
Raw spirit

7—1
Methyl butanoate
Raw spirit, whisky without corn, and whisky with corn

8—1
Pentanol
Raw spirit, whisky without corn, and whisky with corn

9—1
1-Hexanol
All

10—1
(Z)-4-Heptenal
Raw spirit and whisky without corn

11—2
Diethyl ether
Raw spirit, whisky without corn, and whisky with corn

12—2
2-Methylfuran
Raw spirit, whisky without corn, and whisky with corn

13—2
2-Methyl-1-propanol
Raw spirit, whisky without corn, and whisky with corn

14—2
Ethyl acetate
All

15—2
Methyl butanoate
Raw spirit, whisky without corn, and whisky with corn

16—2
Propyl acetate
Raw spirit, whisky without corn, and whisky with corn

17—2
1-Hydroxy-2-propanone
Raw spirit

18—2
1-Hexen-3-ol
Raw spirit, whisky without corn, and whisky with corn

19—2
2-Methylthiophene
Raw spirit

20—2
Pentanol
Raw spirit, whisky without corn, and whisky with corn

21—2
3-Methylbut-2-en-1-ol
Vodka

22—2
(Z)-3-Hexen-1-ol
Raw spirit, whisky without corn, and whisky with corn

Figure 1: (a) PCA results for vodkas, whiskies, and raw spirits; (b) PCA results for vodkas, whiskies made from corn, whisky made without corn, and raw spirits.

(a)

(b)

(c)

(d)

Figure 2: Mean bar graphs of selected peak areas used as raw data in chemometrics representing key chemical compounds, which are important for discrimination of (a) raw spirit, (b) vodka, (c) whisky without corn, and (d) whisky with corn.

Discriminant Function Analysis is one of the most commonly methods used to decide which variables allow correct classification of a set of objects [30, 31]. DFA was used for discrimination of the data obtained from e-nose analysis of spirit beverages. In Figure 3 the results of DFA analysis for groups of vodkas, whiskies, and raw spirits are shown, and in Figure 4 the results of DFA for vodkas, whiskies made from corn, whisky made without corn, and raw spirits are shown. Both DFA allowed for distinguishing between all groups of samples. As opposed to PCA, DFA is used to distinguish groups not to make the actual data visualization. In Figure 3 it can be seen that points belonging to group of whiskies are definitely more concise than in the case of PCA. The groups of samples were not divided. Using DFA data analysis method the differentiation between groups of samples is more unambiguous. On the other hand differences between individial samples are disappearing when a grup of whiskey samples is defined as a one coherent group. When two groups of whiskies are created (groups which were showed on the PCA graph (Figure 1(b)) it can be seen that the groups are distinguished very well (Figure 4). In the case of DFA, spirit beverages can be distinguishing due to the general type (raw spirits, vodka, and whisky) and due to the botanical origin if the information about their origin is known as in the case of whisky produced with and without corn. In the case of the PCA the differences between the samples within the selected groups cannot be neglected.

Figure 3: DFA of vodkas, whiskies, and raw spirits.

Figure 4: DFA results for vodkas, whiskies made from corn, whisky made without corn, and raw spirits.

In SIMCA classification, a separate model is created for each class based on the principal component method. Next, the so-called “confidence envelope” (a certain volume) is created around the model. It should include with defined probability all elements belonging to a given class [1, 32]. SIMCA analysis is method typically used for classification of the samples. It was used for classification samples into four groups. SIMCA analysis was made for each reference group: vodka (Figure 5), raw spirit (Figure 6), whisky without corn (Figure 7), and whisky with corn (Figure 8). In all cases with one exception points belonging to the reference group were located in the blue area in the confidence envelope. In the case of exception one sample was not classified properly to the reference group in case of vodka classification. This analysis is similar to DFA and it is used to distinguish the groups. In contrast to DFA, SIMCA graph is created for each group separately. It is therefore more time-consuming than DFA. On the other hand, discrimination between different groups is more visible. Due to the envelope of confidence the boundaries of analysed groups are determined. Therefore, the identification of unknown samples is unambiguous.

Figure 5: SIMCA analysis for vodkas, whiskies, and raw spirits: vodka, reference group.

Figure 6: SIMCA analysis for vodkas, whiskies, and raw spirits: raw spirit, reference group.

Figure 7: SIMCA analysis for vodkas, whiskies, and raw spirits: whisky without corn, reference group.

Figure 8: SIMCA analysis for vodkas, whiskies, and raw spirits: whisky with corn, reference group.

The SQC analysis is used for process quality control. Samples, which belong to one group, should be situated in the area designated using parameters on the -axis of the graph [33, 34]. It can be used for quality analysis of samples on production lines. SQC analysis was performed for four groups of samples, namely, for vodkas, whiskies without corn, whiskies with corn, and raw spirits to show capabilities of this method. SQC analysis allows seeing the differences between individual samples and excluding those that differ from others. It is the proper method for assessing the quality of products, because it is rapid and easy to use and the discrete differences in the studied group of samples can be determined. This method allows observing which sample is statistically different from the other ones in a given batch. It enables the possibility of exclusion of a sample during the final control of ready products. In Figure 9 it can be seen that the differences between the points assigned to a group of vodkas are small. In contrast, the differences between the points belonging to the group of whiskies without corn in Figure 10 are more noticeable. It can be noticed that the area in which points belong to the reference group should be located depending on the deviation between the samples in certain group. The greater the differences between the samples in the group, the greater the tolerance range for the whole group.

Figure 9: SQC analysis for unflavoured vodkas, raw spirits, and whiskies: vodka, reference group.

Figure 10: SQC analysis for unflavoured vodkas, raw spirits, and whiskies: whisky without corn, reference group.

Due to the presence of two chromatographic columns of different polarity, it is possible to obtain two different chromatograms and identify individual compounds included in the samples. This is the difference between electronic nose based on sensors and electronic nose based on fast GC. Figures 11 and 12 present chromatograms obtained from the analysis of vodka and whisky. It can be noticed that they differ considerably from each other. As compared to vodka, the odour profile of whiskey is much richer. Nearly twice as many compounds were detected for whisky compared to vodka. Some of chemical compounds detected in vodka, whisky, and raw spirits samples are presented in Tables 3, 4, and 5, respectively. Criterion for the selection of compounds was the similarity parameter set at 90%. Compounds present in the sample were identified on the basis of comparison of calculated linear temperature-programmed retention indices (LTPRI) for compounds from both columns with indices provided in the literature. Apart from LTPRI, the tables also include approximate retention times for the detected chemical compounds and odour descriptions contained in literature sources, which can be found in Arochembase V4 library. Compounds present in the volatile fraction of vodka can be characterized by fewer descriptors than those found in whisky and raw spirits. Mostly, the aroma of compounds identified in vodka matrix was defined as fruity or green, but some of them were also described as sulphurous, winey, alcoholic, bread, straw, corn, or alkane one. In the case of whisky, compounds are mostly characterized as fruity, green, and woody. Other compounds were described as spicy, winey, earthy, sweet, smoky, alcoholic, or pine. Regarding the aroma of raw spirits most of the constituents of this matrix were determined as fruity, fusel, and anise one. Some of the compounds were described as a green, woody, minty, floral, sweet, and solvent one. As it can be noticed the decryption of compounds present in the whisky samples was more connected with aroma of barrels, forest, and sweet fruits than in the case of vodka and raw spirit samples.

Table 3: Compounds detected in vodka samples.

Name
RT1/LTPRI1
RT1/LTPRI2
LTPRI1 LIT
LTPRI2 LIT
Descriptor

(E)-2-Heptenal
50.41/950
53.00/1049
960
1062
Sulfurous, earthy, grossy, and almond

(E)-2-Octene
38.55/813
34.72/816
815
819
—

(Z)-2-Nonenal
64.94/1134
66.97/1251
1148
1254
Cucumber, geranium and green

1-Hexanol
42.59/875
45.73/991
870
980
Dry, floral, grossy, green, woody, and fruity

1-Octen-3-one
52.82/979
54.96/1075
979
1066
Herbaceous

2-Methyl-1-butanol
32.07/739
37.35/848
739
852
Winey, buttery, and malty

3-Methylbut-2-en-1-ol
35.22/775
38.53/863
778
863
Fruity and green

3-Methylfuran
22.07/614
37.44/849
614
856
—

Ethanol
14.91/426
18.55/575
437
564
Alcoholic

Ethyl acetate
21.00/600
24.68/682
609
673
Acidic, etheral, fruity, and orange

Ethyl butyrate
37.31/799
38.42/861
799
864
Fruity and acetone

Furfural
38.49/812
46.80/967
827
972
Almond and bread

Hexyl butyrate
69.28/1193
68.24/1271
1191
1257
Apple, fruity, and green

Limonene
58.06/1044
54.82/1074
1033
1061
Citrus, fruity, and minty

Methanol
14.63/418
16.67/516
419
507
—

Methylnonanedione
73.97/1261
75.92/1396
1253
1397
Fruity and straw

Propenal
15.24/435
18.11/561
450
566
—

Pyrazine
31.11/728
35.94/831
738
822
Corn and nutty

Tert-butylmethylether
19.31/551
19.24/596
546
600
—

Tetradecane
82.64/1393
75.83/1395
1400
1400
Alkane, sweet, and mildly herbaceous

Undecane
62.56/1101
56.14/1091
1100
1100
Alkane and fusel

RT1: retention time in column 1 (MXT-5);

LTPRI1: linear temperature-programmed retention index for compounds from column 1;

RT2: retention time in column 2 (MXT-1701);

LTPRI2: linear temperature-programmed retention index for compounds from column 2;

LTPRI1 LIT: linear temperature-programmed retention index for compounds from column 1 from literature;

LTPRI2 LIT: linear temperature-programmed retention index for compounds from column 2 from literature.

The positive identification of selected compounds was highlighted in bold font.

Table 4: Compounds detected in whiskey samples.

Name
RT/LTPRI1
RT/LTPRI2
LTPRI1 LIT
LTPRI2 LIT
Descriptor

(E)-2-Decanal
73.93/1260
74.36/1370
1262
1371
Green, orange, and tallowy

(E)-2-Heptenal
51.87/967
54.44/1068
960
1062
Almond, earthy, grossy, and sulfurous

(Z)-3-Hexen-1-ol
41.95/851
45.39/949
852
960
Mossy, green, and fresh

(Z)-3-Hexen-1-ol, butanoate
62.83/1173
67.05/1252
1187
1256
Banana, green, and winey

(Z)-3-Hexenyl isobutyrate
64.91/1133
64.39/1211
1144
1208
Apple, etheral, sweet, and winey

(Z)-4-Heptenal
46.43/903
48.33/986
900
988
Creamy, sweet, and boiled potato

(Z)-Whisky lactone
76.94/1305
84.64/1550
1316
1548
Coconut

1-Hexanol
44.01/875
48.67/991
870
980
Dry, floral, grossy, green, woody, and fruity

1-Hexen-3-ol
34.00/772
38.82/865
775
850
Green

2, 4, 5-Trimethylthiazole
54.21/995
54.50/1069
996
1073
Earthy, hazelnut, moldy, and chocolate

2-Methyl-1-butanol
31.68/735
37.41/849
739
852
Fruity and malty

2-Methyl-1-propanol
23.13/628
28.76/740
628
735
Alcoholic, bitter, and winey

2-Methylfuran
21.06/601
22.64/652
602
639
Burnt, solvent, metallic, and musty

2-Methylphenol
57.98/1043
69.35/1288
1054
1283
Phenolic

3, 5-Octadien-2-one
61.96/1093
64.00/1205
7092
1203
Fruity and mushroom

3-Heptanone
45.39/891
46.91/968
888
969
Cinnamon, green, spicy, and sweet

4-Ethylguaiacol
74.66/1271
77.35/1421
1282
1430
Floral, spicy, and phenolic

6-Decenal
70.20/1206
70.60/1308
1203
1294
Green

Benzaldehyde
50.37/950
55.86/1088
959
1086
Almond, woody, and burnt sugar

Beta-ionone
88.65/1487
88.71/1623
1490
1627
Floral, woody, and raspberry

Beta-phellandrene
56.63/1026
52.75/1045
1030
1059
Fruity, minty, and herbaceous

Beta-pinene
52.71/977
48.79/992
979
994
Green, pine, sweet, and resin

Butanoic acid
38.50/812
46.84/967
816
970
Rancid, sweaty, and butter

Carvone
73.14/1249
75.94/1397
1253
1386
Minty and peppermint

Diethyl ether
16.70/476
16.90/524
491
532
—

Ethanol
15.14/432
18.28/566
437
564
Alcoholic

Ethyl acetate
22.16/615
24.05/675
609
673
Acidic, etheral, fruity, and orange

Ethyl butyrate
37.34/799
38.92/867
799
864
Banana, fruity, sweet, and acetonic

Furfural
40.86/839
47.61/977
827
972
Almond, bread, and sweet

Geosmin
85.91/1444
83.15/1523
1430
1513
Beet and earthy

Hexyl acetate
55.27/1009
55.80/1087
1011
1082
Citrus, fruity, green, and spicy

Hexyl butyrate
69.27/1193
68.18/1270
1191
1257
Apple, fruity, and green

Indole
76.04/1291
84.64/1550
1295
1549
Burnt, earthy, floral, and jasmine

Limonene
56.63/1026
53.13/1051
1033
1061
Citrus, fruity, minty, and peely

Methyl butanoate
29.59/711
31.73/778
717
784
Ester, etheral, green, and sweet

n-Butanol
26.00/655
31.69/774
651
778
Fermented and fruity

p-Cresol
60.18/1071
71.60/1325
1072
1312
Phenolic and smoky

Pentanol
33.68/757
38.87/867
767
879
Anise, fruity, and green

Propyl acetate
28.76/701
31.38/774
708
775
Fruity, ketonic, sweet, and solvent

Terpinolene
61.36/1086
58.04/1118
1088
1112
Fruity, pine, herbaceous, and sweet

tert-Butylmethylether
20.35/551
20.28/596
546
600
—

RT1: retention time in column 1 (MXT-5);

LTPRI1: linear temperature-programmed retention index for compounds from column 1;

RT2: retention time in column 2 (MXT-1701);

LTPRI2: linear temperature-programmed retention index for compounds from column 2;

LTPRI1 LIT: linear temperature-programmed retention index for compounds from column 1 from literature;

LTPRI2 LIT: linear temperature-programmed retention index for compounds from column 2 from literature.

The positive identification of selected compounds was highlighted in bold font.

Table 5: Compounds detected in raw spirit samples.

Name
RT/LTPRI1
RT/LTPRI2
LTPRI1 LIT
LTPRI2 LIT
Descriptor

(Z)-3-Hexen-1-ol
42.63/859
46.74/966
852
960
Mossy, green, and fresh

(Z)-4-Heptenal
46.13/899
48.66/991
900
988
Creamy, sweet, and boiled potato

1,2-Benzenediol
77.77/1318
78.44/1440
1324
1455
—

1-Hexanol
44.12/876
48.66/991
870
980
Dry, floral, grossy, green, woody, and fruity

1-Hexen-3-ol
34.53/770
38.32/860
775
850
Green

1-Hydroxy-2-propanone
25.24/655
35.08/820
643
810
Caramelized and sweet

1-Octen-3-one
52.11/970
54.49/1069
979
1066
Herbaceous

2,3-Pentanedione
28.79/702
33.61/802
698
788
Creamy, fresh, fruity, and sweet

2-Methyl-1-butanol
31.92/737
37.55/857
739
852
Fruity and malty

2-Methyl-1-propanol
23.26/629
28.83/741
628
735
Alcoholic, bitter, and winey

2-Methylfuran
21.06/601
22.64/652
602
639
Burnt, solvent, metallic, and musty

2-Methylphenol
58.18/1045
69.44/1290
1054
1283
Phenolic

2-Methylthiophene
35.16/774
35.92/831
775
827
Sulphurous

2-Undecanal
80.83/1365
82.07/1504
1368
1503
Fruity, geranium, green and metallic

Alpha-pinene
49.46/939
45.49/941
937
945
Green, pine, camphor, and sweet

Benzaldehyde
50.53/951
54.90/1075
959
1086
Almond, woody, and burnt sugar

Beta-ionone
88.68/1487
88.30/1615
1490
1627
Floral, woody, and raspberry

Butanoic acid
39.77/827
46.74/966
816
970
Butter, rancid, and sweaty

Carvone
73.57/1255
74.52/1373
1253
1386
Minty and peppermint

Decanal
70.67/1212
69.44/1290
1206
1293
Aldehydic, burnt, floral, and green

Decanoic acid
78.58/1330
80.10/1469
1323
1476
Fatty, rancid, and soapy

Diethyl ether
16.77/479
16.98/526
491
532
—

Ethanol
14.85/424
18.47/572
437
564
Alcoholic

Ethyl acetate
21.56/607
24.14/676
609
673
Acidic, etheral, fruity, and orange

Ethyl hexanoate
54.39/997
52.80/1046
995
1061
Anise, apple, fruity, and sweet

Ethyl octanoate
69.45/1195
68.26/1271
1196
1260
Anise, floral, fresh, and leafy

Ethyl phenylacetate
71.81/1229
73.79/1361
1243
1370
Anise, cinnamon, floral, and spicy

Furfural
40.93/840
46.94/966
827
972
Almond, bread, and sweet

Geosmin
85.79/1442
83.14/1523
1430
1513
Beet and earthy

Geranial
74.70/1272
76.83/1412
1270
1416
Citrus, floral, lemon, and minty

Hexadecane
96.54/1605
87.18/1595
1600
1600
Alkane, fusel, fruity, and sweet

Hexanal
36.62/791
41.31/897
795
883
Grassy, green, herbaceous, and leafy

Hexanoic acid
52.84/979
62.10/1177
990
1186
Fatty, rancid, and sweaty

Hexyl acetate
55.56/1012
56.21/1092
1011
1083
Citrus, fruity, green, and spicy

Hexyl butyrate
68.38/1180
67.11/1253
1191
1257
Apple, fruity, and green

Hexyl isobutyrate
65.11/1136
63.90/1203
1150
1208
Green

Indole
76.44/1297
85.12/1558
1295
1549
Burnt, earthy, floral, and jasmine

Limonene
56.84/1028
54.49/1069
1033
1061
Citrus, fruity, minty, and peely

Linalool
62.89/1106
62.96/1189
1099
1195
Floral, fruity, green, and lavender

Methyl butanoate
31.22/729
31.74/778
717
784
Ester, etheral, green, and sweet

Methyl-2-propenoate
22.20/616
24.82/687
611
680
—

n-Butanol
26.04/666
29.84/754
651
778
Fermented and fruity

Nonane
46.49/903
41.31/897
900
900
Alkane and fusel

P-Cresol
60.39/1073
71.49/1323
1072
1312
Phenolic and smoky

Pentadecane
90.40/1514
82.07/1504
1500
1500
Alkane, fusel, and mild green

Pentanol
34.68/766
39.87/873
767
879
Anise, fruity, and green

Propenal
15.61/446
18.18/563
450
566
—

Propyl acetate
29.52/710
31.36/773
708
775
Fruity, ketonic, sweet, and solvent

Terpinolene
61.46/1087
58.14/1120
1088
1112
Fruity, pine, herbaceous, and sweet

tert-Butylmethylether
19.08/545
19.79/607
546
600
—

Tridecane
77.16/1308
70.34/1304
1300
1300
Alkane, citrus, fruity, and fusel

Undecane
62.24/1097
57.03/1104
1100
1100
Alkane and fusel

RT1: retention time in column 1 (MXT-5);

LTPRI1: linear temperature-programmed retention index for compounds from column 1;

RT2: retention time in column 2 (MXT-1701);

LTPRI2: linear temperature-programmed retention index for compounds from column 2;

LTPRI1 LIT: linear temperature-programmed retention index for compounds from column 1 from literature;

LTPRI2 LIT: linear temperature-programmed retention index for compounds from column 2 from literature.

The positive identification of selected compounds was highlighted in bold font.

Figure 11: An example of a vodka sample presented in a chromatogram.

Figure 12: An example of a whisky sample presented in a chromatogram.

4. Conclusion

In this paper the usefulness of the electronic nose using ultra-fast gas chromatography for the qualitative analysis of selected spirit beverages was checked. Four chemometric methods were used to interpret obtained results. PCA and SQC analysis were used to check deviations of individual samples from groups to which they belong. PCA enables the visualization of results. As a result, it was possible to quickly determine that the group of whiskies includes two different groups of samples. Through an SQC analysis, it is possible to check the quality of the samples. Samples of vodka had a similar composition and method of production, due to this fact they are located in one line in Figures 9 and 10. The differences in composition between whisky samples are more significant due to their different method of production and different raw materials used, which was presented in Figures 9 and 10. DFA and SIMCA analysis, on the other hand, were used to group vodka, whisky, and spirits samples and to compare them. Furthermore, in contrast to traditional electronic nose based on the sensors, the electronic nose based on a fast gas chromatography allows determining the compounds included in the sample. This is possible thanks to the presence of two chromatographic columns of different polarity and well-equipped database that allows comparison of retention indexes and retention times. Additionally, each compound can be attributed to the smell which has been previously described in the literature. Summing up, the use of electronic nose based on ultra-fast gas chromatography and four applied statistical methods can be used to distinguish groups of spirit beverages. It also allows finding the differences between individual samples in one group and to determine the composition of the investigated samples. By using Arochembase V4 library it was possible to make an assignment of the odours described in the literature to the individual components present in the samples.

Competing Interests

The authors declare that they have no competing interests.

Acknowledgments

The authors acknowledge the financial support for this study by Grant no. 2012/05/B/ST4/01984 from National Science Centre of Poland.

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