USING NEURAL NETWORKS TO IDENTIFY THE REGIONAL AND VARIETAL ORIGIN OF CABERNET AND MERLOT DRY RED WINES PRODUCED IN KRASNODAR REGION
Abstract and keywords
Abstract (English):
This paper shows a possibility of establishing the authenticity and geographic origin of wines by neural networks based on multi-element analysis. The study used 144 samples of Cabernet and Merlot dry red wines pro- duced in Krasnodar Region according to traditional technologies. The wines were provided by the producers or pur- chased in retail stores. The concentrations of 20 micro- and macroelements in red wines were determined by atomic emission spectroscopy with inductively coupled plasma. The analysis of average elemental contents showed a signi- ficant dependence of wine composition on the grape variety and place of origin, which enabled us to examine inter- relations between the elements and think of a way to identify them by means of classification models. The software STATISTICA Neural Networks was used to assess a possibility of determining the grape variety and geographical origin. The neural networks constructed in the study contained five variables corresponding to the elements with sta- tistically significant correlations between the names of the regions and the wine samples, namely Fe, Mg, Rb, Ti, and Na. These predictors were able to determine the grape variety and place of growth with a sufficiently high accuracy. In the test sample set, the accuracy reached 95.24% and 100% for variety and region identification, respectively. A software product was developed to automate the calculations based on the neural networks. The program can estab- lish the grape variety from a minimal set of microelements, and then, based on the variety and the same set of micro- elements, determine its place of origin.

Keywords:
Cabernet and Merlot red wines, regional and varietal origin of wine, multi-element analysis, neural net- work technologies, Neural Network
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One of the most difficult tasks in analytical chemis- try of wine is to identify its authenticity and geographi- cal origin. Single quality assessment parameters are not sufficient to determine whether the product conforms to its labels. To establish the authenticity and geographical origin of wines, as well as changes occurring in case of their adulteration, analytical approaches are being de- veloped that aim to determine the mineral and isotopic composition, study spectral characteristics, and identify phenolic and volatile compounds using various methods of analysis [1–2]. The identification of authenticity and origin criteria is based on obtaining a large amount of data and its processing by chemometric methods, which reveal hidden relations between the wine’s components [1–9]. The combination of modern data analysis tools

 

with the capabilities of chemometric methods ensures higher accuracy in identifying the geographical origin of wines. The information on the elemental composi- tion of wines can be used to both control the technologi- cal process and, in combination with chemometric data processing methods, establish the origin of wines [10, 11]. For example, wines produced in various regions of Europe differ quite markedly in the metal content [12], which makes it a good criterion for identifying their geo- graphical origin (Table 1).

Natural variability of  wine  quality  is  determined by the grapes growing conditions, such as the climate, the microelement composition of the soil, the technolo- gy of growing grapes, the period of grape harvest, etc. The mineral composition of wines can be influenced by various factors (soil, climate, relief, etc.); therefore, for

 

 

Copyright © 2019, Chernukha et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

 

 

Table 1. The metal content of wines in different countries [12]

 

Element                                                                                           Element content, mg/dm3

 

Czech Republic

France

Germany

Italy

Spain

K

553–3056

265–426

480–1,860

338–2,032

Na

2.0–110

7.7–14.6

6–25

3.5–300

Ca

40–210

65–161

58–200

88–151

12–241

Mg

7.8–138

55–96

56–105

53–60

50–236

Al

0.56–1.27

0.57–14.3

Cu

n/d–0.48

0.02–0.71

n/d–3.1

Fe

0.9–5.2

0.81–2.51

0.4–4.2

0.4–17.4

Mn

0.28–3.26

0.63–0.96

0.5–1.3

0.1–5.5

Rb

0.56–1.20

0.64–0.72

0.2–2.9

0.50–9.90

0.1–5.3

Sr

0.34–0.53

0.22–0.47

0.12–1.28

0.40–1.16

0.28–1.50

Zn

0.44–0.74

0.3–1.5

n/d–4.63

Ba

0.09–0.12

0.025–0.24

0.04–0.26

0.07–0.14

0.01–0.35

Cd

n/d–0.0002

n/d–0.019

Co

n/d–0.018

0.004–0.011

0.004–0.005

0.003–0.006

n/d–0.040

Cr

0.032–0.037

0.030–0.057

0.022–0.078

0.023–0.034

0.025–0.029

Li

0.015–0.052

0.008–0.036

0.005–0.043

0.002–0.13

Ni

n/d–0.052

0.005–0.079

Pb

0.006–0.023

0.001–0.043

V

0.020–0.054

0.06–0.23

0.01–0.14

0.026–0.043

*n/d – not detected

 

 

 

 

 

 

 

identification purposes, many researchers study those ele- ments which are least dependent on external factors in a given geographical area [3–6, 8, 9, 13, 14]. For example, some authors [13] use Sr, Mn, Mg, Li, Co, Rb, B, Cs, Zn, Al, Ba, Si, Pb, and Ca.

The content of metals in wines is  widely  diffe- rent: 10–1000 of macroelements (Ca, K, Na, and Mg), 0.1–10 mg/dm3 of minor elements (Al, Fe, Cu, Mn, Rb, Sr, and Zn), and 0.1–1000 μg/dm3 of trace elements (Ba, Cd, Co, Cr, Li, Ni, Pb, V, etc.) [12]. Therefore, the prob- lem of ascertaining the microelement “image” of grapes is of practical, as well as scientific, interest [14–18].

In cases when wines from different grape varieties have certain organoleptic similarities, for example, co- lour or astringent, sour taste, it is important to be able to identify the grape variety from the microelement com- position of the wine [19]. In fact, the task comes down to establishing the grape variety and geographical origin based on the content of microelements in a sample of un- blended wine.

The purpose of this work was to study a possibility of identifying the authenticity and geographical origin of red wines, namely Cabernet and Merlot varietal wines, based on multi-element analysis with STATISTICA Neu- ral Network.

 

STUDY OBJECTS AND METHODS

The study used 144 samples of Cabernet (76) and

 

wineries are located in different geographic zones (sub- zones) of Krasnodar Region: the South-Piedmont zone, the Black Sea zone, the Anapa subzone, and the Taman subzone. The wines were provided by the manufacturers or purchased in retail stores.

The main vineyards of Krasnodar Region are located in five cultivation areas: Temryuk (the Taman Peninsu- la, the Taman subzone), Anapa (the Anapa subzone), the Black Sea zone (Gelendzhik and Novorossiysk), Krymsk (the South-Piedmont zone), and Novokubansk. The fre- quency distribution of Cabernet and Merlot samples by zone and variety is shown in Table 2

The elemental composition of the wine samples was established by atomic emission spectroscopy with induc- tively coupled plasma using iCAP-6000 (Thermo Scien- tific). The operating conditions of the spectrometer were optimised to detect 20 elements (Li, Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Cd, Ba, and Pb). The most sensitive analytical lines were used for most of the metals, with the exception of Al, V, Ca, Mg, and Sr, for which alternative lines were chosen due to spectral overlays. For some macroelements, we needed to reduce the signal intensity. When optimising the con- ditions for element detection, we studied how the ope- rating characteristics (generator power, argon flow rate) affected the analytical signal of elements in the model

 

Table 2. Wine sample frequencies by zone

 

Merlot (68) varietal dry wines produced from 2012 to                                                                                                               

 

2015 by the main wineries in Krasnodar Region: ZAO

 

Variety                 Two-entry table of frequencies by zone          

 

Zaporozhskoye, OOO Kuban-Vino, OAO APF Fanago- ria, OOO APK Millstream Black Sea Wines, ZAO AF

 

Taman subzone

 

Anapa subzone

 

South-Pied- mont zone

 

Black Sea zone

 

Total

 

Kavkaz,  ZAO  Abrau-Durso,  ZAO  APK  Gelendzhik,

ZAO AF Myskhako, OOO Firma Somelye, ООO AF Sauk-Dere,  and  ООО  Soyuz-Vino  (Table  2).  Theses

 

Cabernet    28             17             13                  18             76

Merlot          33            23             12                  0               68

Total           61            40             25                  18              144

 

 

and sample solutions. We also investigated the mutual influence of micro- and macroelements, as well as back- ground components, of the samples prepared for analysis in the model solutions containing variable amounts of the elements. The quantification of metals was carried out by diluting the wine samples, taking into account the data obtained [5, 14–18, 20, 21].

The following reference standards were used to study the test samples: GSO 7780-2000  (Li),  GSO 8062-94 (Na), GSO 7767-2000 (Mg), GSO 7854-2000 (Al), GSO (K), GSO 7772-2000 (Ca), GSO 7205-95 (Ti), GSO (Cr), GSO 8056-94 (Mn), GSO 8032-94 (Fe), GSO 7784-2000 (Co), GSO 7785-2000 (Ni), GSO 7836-2000 (Cu), GSO 8053-94 (Zn), GSO 7035-93 (Rb), GSO 7783-2000 (Sr), GSO 7874-2000 (Cd), GSO 7760-2000 (Ba), and GSO

7778- 2000 (Pb). All the reagents used in the work were of chemically pure (C.P.) grade.

The chemometric analysis was performed using STA- TISTICA Neural Networks [22].

 

RESULTS AND DISCUSSION

The analysis of average element contents (Tables 3, and 4) showed a significant dependence of wine compo- sition on the grape variety and place of origin. For exa-

 

 

mple, the samples from the Anapa subzone had a high content of Fe, those from the South-Piedmont zone were rich in Ba, Ti, and V, whereas the Taman wines were abundant in Na, Mg, and Rb. The Cabernet wines had significantly different contents of many elements. For example, the Cabernet samples from the South-Pied- mont zone contained the lowest concentrations of Li, Na, Al, Ca, Fe, and Sr, while the Merlot samples from the same zone had the lowest content of Al, Ca, Fe, and Li. As a rule, standard deviations did not exceed half of the average values. This suggests a small variation in the concentrations of elements, which means that an ave- rage value is a relevant characteristic of metal content in wine. The exceptions are Cu, Li, Ni, Na, Rb, and Ti; however, standard deviations exceeded the averages only in three cases: Cu (Cabernet, Anapa subzone) and Ni (Cabernet and Merlot, South-Piedmont zone).

Previously, we applied traditional statistical methods of discriminant analysis and classification trees to con- struct  probabilistic-statistical  models   that   allowed us to identify the varietal and regional origin  of  the same group of red wines using multi-element analysis data [23]. This study looked at a possibility of determi-

 

 

 

Table 3. Average elemental content and standard deviations (s.d.) in Cabernet samples from various geographical zones of Krasnodar Region, µg/dm3

 

Element

 

Merlot

 

 

Anapa

South-Piedmont

Taman

Al

1,063

926

1,307

s.d.

381

178

537

Ba

106

148

163

s.d.

41

40

42

Ca

60,117

55,630

63,212

s.d.

5,280

582

8,701

Cu

53

62

47

s.d.

45

33

33

Fe

13,248

4,073

4,883

s.d.

4,214

606

1,661

K

728,426

1264,236

695,290

s.d.

74,086

369,588

106,987

Li

25

16

29

s.d.

14

7

11

Mg

71,221

114,459

158,520

s.d.

6,553

21,715

28,073

Mn

1,181

1,410

1,519

s.d.

401

499

398

Na

20,698

51,380

62,561

s.d.

10,063

28,799

30,852

Ni

29

92

112

s.d.

17

101

96

Rb

563

2,063

6,157

s.d.

531

1786

2,707

Sr

1,242

1,453

1,389

s.d.

172

376

292

Ti

11

33

28

s.d.

11

7

15

Zn

369

755

526

s.d.

83

138

140

 

 
Element                                        Cabernet                                   

                  Anapa       South-Piedmont   Black Sea    Taman     

 

Table 4. Average elemental content and standard deviations (s.d.) in Merlot samples from various geographical zones of Krasnodar Region, µg/dm3

 

 

Al

761

668

1,074

777

s.d.

389

354

322

222

Ba

91

160

93

100

s.d.

32

69

46

29

Ca

60,042

54,707

59,864

65,516

s.d.

8,432

10,124

5,571

11,564

Cu

112

69

109

65

s.d.

128

36

43

31

Fe

8,098

3,398

4,188

3,657

s.d.

3,017

1,150

972

915

K

659,037

190,177

1064,056

983,958

s.d.

96,739

394,839

171,437

233,821

Li

19

13

25

28

s.d.

5

8

24

15

Mg

74,037

116,978

96,779

141,678

s.d.

12,846

21,053

27,030

59,802

Mn

956

1,585

1,096

1,338

s.d.

319

362

165

436

Na

21,434

31,760

35,268

33,699

s.d.

9,680

27,186

13,784

8,590

Ni

24

57

33

21

s.d.

10.3

65

31

9

Rb

514

977

817

1,515

s.d.

360

290

236

352

Sr

1,207

1,323

1,533

1,270

s.d.

207

504

720

284

Ti

7

31

16

8

s.d.

3

22

11

3

Zn

366

685

757

481

s.d.

140

222

151

144

 

 

Table 5. Average elemental contents and standard deviations (s.d.) in wine samples, µg/dm3

 

Wine

 

 

Cabernet

 

 

 

Merlot

 

Zone/ subzone

Statistic

Anapa

South-Piedmont

Black Sea

Taman

Anapa

South-Piedmont

Taman

Fe

average

8,098

3,398

4,188

3,657

13,248

4,073

4,883

 

s.d.

3,017

1,151

973

915

4,214

606

1,661

Mg

average

74,037

116,978

96,780

141,678

71,221

114,459

158,520

 

s.d.

12,846

21,053

27,030

59,802

6,553

21,715

28,073

Na

average

21,434

31,760

35,268

33,699

20,698

51,380

62,561

 

s.d.

9,680

27,186

13,784

8,590

10,064

28,798

30,852

Rb

average

514

977

817

1,515

563

2,063

6,157

 

s.d.

360

290

236

352

531

1,786

2,707

Ti

average

7

31

16

8

11

33

28

 

s.d.

3

22

11

3

11

7

15

 

 

ning the grape variety and geographical origin using STATISTICA Neural Networks, followed by a compara- tive analysis.

To select a number of elements as predictors of neu- ral network classification models, we used a Spearman’s nonparametric correlation coefficient that characterised the correlation between the names of wine samples, the region of grape origin, and the concentrations of trace elements in the samples. In particular, the elements with the largest statistically significant correlation links be- tween the names of regions and wines (Fe, Mg, Rb, Ti, and Na) were selected as predictor variables.

In Table 5, which shows average elemental contents with standard deviations in both wine varieties from different regions, we can see some significant differenc- es in the average values – the deciding factor for buil- ding classification models with neural networks. Most distinctly these differences are visualised by means of graphs. Fig. 1, for example, shows some box plots dis- playing Mg content in the Cabernet and Merlot wines from various regions. The box plots present ranges of values of a selected variable separately for groups of ob- servations defined by the values of a categorical varia- ble. The rectangles depicted around the midpoints (or squares) represent selected ranges of variation, for exam- ple, the standard error (the ratio of the standard deviation to the square root of the sample size). The segments with their ends outside the rectangles also reflect ranges of variation (average ± 1.96 × standard error). The diagram shows that the average values of Mg content, together with variation values, differ significantly between both the regions and the grape varieties.

As in [23], we were not able to build adequate neural networks that would allow us to identify the grape vari- ety and region of origin from the concentrations of se- lected elements. Therefore, the problem was divided into two parts. First, networks were built to predict the grape variety from the concentrations of Fe, Mg, Rb, Ti, and Na. Then, based on the variety predicted (qualitative pre- dictor) and the same set of elements (quantitative predic- tors), further networks were built to determine the place of grape origin. After assessing their predictive proper- ties (productivity, number of classification errors, etc.), we selected the best network. Productivity is a percentage

of correctly classified wine samples, with 100% taken as

 

maximum. The higher the productivity, the more accurate the prediction. To improve predictive accuracy, the sam- ples were divided into three groups: training, control, and validation sample. The most important were the values of adequacy criteria in the test set. By combining various network options, we tried to create a network with the best predictive capabilities; therefore, at each stage of the process, the number of networks was different.

Building a neural network to establish the varietal origin of wine. The program divided 144 wine samples into three groups: training set (102), control set (21), and test set (21). The productivity of the best network (MLP 5-5-2), selected out of 50, had high values of 99.02%, 90.48%, and 95.30% in the training, control, and test sets, respectively. MLP 5-5-2 is a combination of letters and numbers that represents a topology of a multilayer perceptron. The letters stand for the type of a neural net- work, a multilayer perceptron (MLP); the first numer-

 

Box plots for Mg content in wines

180                Cabernet                                    Merlot

 

Concentration, mg/dm3

Подпись: Concentration, mg/dm3160

 

 

140

 

120

 

100

 

80

 

Anapa

Подпись: Anapa

South-Piedmont

Подпись: South-Piedmont

Black Sea

Подпись: Black Sea

Taman

Подпись: Taman

Anapa

Подпись: Anapa

South-Piedmont

Подпись: South-Piedmont

Black Sea

Подпись: Black Sea

Taman

Подпись: Taman60

 

 

 

 

 

 

Zone

average

*

average ± standard error average ± 1.96 × standard error

 

Fig. 1. Box plots displaying Mg content in Cabernet and Merlot wines.

 

 

Caber-

net

 

Merlot

Table 7. Network sensitivity analysis

 

 

MLP 5-5-2

Fe

Rb

Mg

Na

Ti

Test

141.09

52.18

40.62

42.80

17.12

Training

21.01

8.42

4.73

2.99

1.41

 Control              6.86            3.58          5.63        3.12        2.64   

 

 

 

 

 

 

 

 

Fig. 2. Neural network to determine the variety of red wines.

 

al (5) refers to the number of predictor variables in the model, a sum of quantitative predictors and qualitative predictor values; the second (5) and the third (2) nume- rals refer to the numbers of hidden and output neurons, respectively.

The network topology is shown in schematic form in

Fig. 2, where we can see five entries of predictor vari- ables X ; five hidden neurons Y ; two output neurons rep-

 

The network sensitivity can be used to estimate a contribution of each predictor to its predictive proper- ties: in our case, a contribution of the elements to the classification model. The sensitivity values (see Table 7) indicate a decreasing sequence of Fe, Rb, Mg, Na, and Ti, which represents their contributions to the predictive properties of the network.

Building a neural network to determine the re- gional origin. The possibility of predicting the wine variety based on five microelements made it realistic to create a neural network to identify the place of grape origin using the trace elements of Fe, Mg, Rb, Ti, and Na and the varieties of Cabernet and Merlot.  In  the same way, the program divided 144 wine samples into three groups: training set (102), control set (21), and test

 

i                                                               j

 

k,

 
resenting objects of classification Q , the Cabernet and

Merlot varieties, as well as connections between them in the form of weights W , W .

 

set (21). The best out of 18 networks (MLP 7-9-4) had

productivity values of 100%, 80.95%, and 100% in the training, control, and test sets, respectively.

 

ij         jk

 

Table 6 shows the frequencies of correctly and in-

correctly classified wines in the sample sets. As we can see, one Merlot sample from the training and the test sets and two Merlot samples from the control set were erroneously classified as Cabernet. All Cabernet samples were correctly identified in all the sets. The total num- ber of erroneously classified samples was four out of 144 (app. 2.8%), i.e. the neural network identified 97.2% of the wine samples correctly. In [23], by comparison, the classification tree with seven terminal vertices only once misclassified a Merlot sample as a Cabernet, based on the concentration of seven microelements, i.e. 99.3% of the training sample was identified correctly.

 

As  can  be  seen  in  Table  8,  all  the  wine  samples

(100%) from the Black Sea zone were classified by the network correctly. The next high accuracy area was the Taman subzone with 100%, 85.71%, and 100% of cor- rectly classified samples in the training, control, and test sets, respectively.  The  lowest  accuracy  was  observed in the Anapa subzone: 100%, 71.43%, and 100%, re- spectively. The total number of misclassified  samples was four out of 144 (app. 2.8%), i.e. the neural network

 

Table 8. Wine classification results by region

 

 

Sample set

Подпись: Sample set

Classification

accuracy

Подпись: Classification
accuracy

Anapa subzone

Подпись: Anapa subzone

Taman subzone

Подпись: Taman subzone

Black Sea zone

Подпись: Black Sea zone

South-Pied- mont zone

Total

Подпись: South-Pied- mont zone
Total
Table 6. Wine classification results by variety

 

 

 

 

 

 

Sample set

Подпись: Sample set

Classifica- tion accuracy

Подпись: Classifica- tion accuracy

Cabernet

Подпись: Cabernet

Merlot

Подпись: Merlot

Total

Подпись: Total

 

Training

Total

26

46

14

16

102

 

Correct

26

46

14

16

102

 

Incorrect

0

0

0

0

0

 

Correct, %

100

100

100

100

100

 

Incorrect, %

0

0

0

0

0

Control

Total

7

7

3

4

21

 

Correct

5

6

3

3

17

 

Incorrect

2

1

0

1

4

 

Correct, %

71.43

85.71

100

75

80.95

 

Incorrect, %

28.57

14.29

0

25

19.05

Test

Total

7

8

1

5

21

 

Correct

7

8

1

5

21

 

Incorrect

0

0

0

0

0

 

Correct, %

100

100

100

100

100

 

Incorrect, %

0

0

0

0

0

 

Training          Total                      57                45               102

Correct                    57                44               101

Incorrect                  0                  1                 1

Correct, %            100              97.78          99.01

Incorrect, %          0                  2.22            0.99

Control           Total                      12                9                 21

Correct                    12                7                 19

Correct, %            100              77.78          90.48

Incorrect, %          0                  22.22          9.52

Test               Total                      7                  14               21

Correct                    7                  13               20

Incorrect                  0                  1                 1

Correct, %            100              92.86          95.24

                       Incorrect, %          0                  7.14            4.76   

 

 

 

 

Red wines

 

Fe                   7740

Mg                 62000

Na                 26700

Rb                155

Ti                   10

 

Calculate

Cabernet

Anapa

 

 

Fig. 3. The programme home screen

 

identified 97.2% of the wine samples correctly. It is note- worthy that all the samples in the test set were classified correctly, regardless of the place of origin.

The sensitivity analysis showed that the average pre- dictor contributions to the network’s predictive proper- ties decreased in the following order: Variety, Rb, Ti, Mg, Fe, and Na. We can notice that this sequence is sig- nificantly different from the one for the variety identifi- cation network.

In [23], the problem of identifying the place of grape origin was solved separately for Cabernet and Merlot wines using two methods, discriminant analysis and classification trees. The discriminant analysis of Caber- net and Merlot wines involved 13 and 14 microelements, respectively, whereas only 7 and 3 microelements were used in the classification trees. However, both methods produced 100%-accurate classifications.

The above shows that the traditional methods of clas- sification analysis, which used a larger number of ele- ments, achieved a higher predictive accuracy. However, the neural networks also showed acceptable prediction accuracy with a significantly smaller number of predic- tors (5). The results were confirmed by the classification analysis in the test sample set, with a 100% accuracy

 

of region identification and only one mistake in variety identification.

Thus, we managed to build adequate neural networks for two red wines, Merlot and Cabernet, with high pre- dictive properties, able to determine the wine variety from a minimum set of elements, and then, identify the region of grape origin from the variety and the same set of elements.

To automate the process of identifying the varietal and geographical origin of red wines, we developed a program using Visual C# (C Sharp). The network para- meters obtained during the training process, their topo- logy and weights made it possible to create an autono- mous software product that can function independent- ly of STATISTICA. The home screen of the program is shown in Fig. 3. If you enter the concentration values of the trace elements Fe, Mg, Na, Rb, and Ti into the cor- responding boxes on the interface and click ‘Calculate’, you will see the variety (Cabernet) and the place of the grape origin (Anapa) at the bottom of the screen.

 

CONCLUSION

Thus, the use of neural networks enabled us to suc- cessfully identify both the varietal and the regional ori- gin of red wines. It is equally important that a certain set of elements in wine contains information not only about the grape variety, but also about the place of its growth. Traditional and heuristic methods of classi- fication analysis used with modern data analysis tools allowed us to accurately determine the grape variety and region of  origin  from  the  “elemental”  memory of the wine.

 

CONFLICT OF INTEREST

The authors declare no conflict of interest.

 

ACKNOWLEDGEMENTS

The study was financed by the Russian Foundation for Basic Research (Project No. 18-03-00059); the scien- tific equipment was provided by the Centre for Environ- mental Analysis at the Kuban State University (unique identifier RFMEFI59317X0008).

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