APLICACIÓN DE MÉTODOS

MULTIVARIADOS PARA LA

DIFERENCIACIÓN DE VINOS PERUANOS

APPLICATION OF MULTIVARIATE METHODS

TO THE DIFFERENTIATION OF PERUVIAN WINES

Ana Paredes-Doig1*, María Sun-Kou2, Elizabeth Doig-Camino2,

Gino Picasso1& Adolfo La Rosa-Toro Gómez1

Recibido: 12 de mayo 2021 / Aceptado: 14 de diciembre 2021

DOI 10.26807/ia.v10i1.218

Palabras clave: métodos multivariados, narices electrónicas,

paladio, platino, sensores MOS, vinos peruanos

Keywords: E-noses, MOS sensors, multivariate methods,

palladium, platinum, Peruvian wines

RESUMEN

Este trabajo presenta los resultados del análisis de sensado de diez vinos pe-

ruanos, de marcas conocidas (elaboración comercial) y desconocidas (elabo-

ración artesanal), utilizando narices electrónicas (E-narices) que consisten en

un conjunto de sensores a base de óxido de estaño dopado con Pd o Pt, y al-

1Universidad Nacional de Ingeniería, Facultad de Ciencias, Lima, Perú. (*correspondencia:

anludoig@gmail.com, gpicasso@uni.edu.pe, toro@uni.edu.pe).

2Pontificia Universidad Católica del Perú, Departamento de Ciencias, Lima, Perú. (msun@pucp.edu.pe,

edoig@pucp.edu.pe)

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gunos con recubrimiento de zeolita. Las combinaciones de los sensores se re-

alizaron con la finalidad de obtener la mejor discriminación de los vinos uti-

lizando métodos multivariados con un alto nivel de confianza. Los resultados

del Análisis de Componentes Principales (PCA), clúster y factorial mostraron

que con las narices electrónicas se puede identificar eficientemente los vinos

de marca conocida de los de marca desconocida, revelando la forma en que

se han elaborado. Por otro lado, los métodos multivariados aplicados a las na-

rices electrónicas compuestas por sensores de SnO2dopado con Pd mostraron

una clara diferenciación de los vinos tipo Borgoña de los vinos de marca des-

conocida, y evidenciaron la formación de aglomeraciones entre vinos tintos

y rosados. La aplicación de PCA, clúster y factorial obtenida en este estudio

permitió obtener buenos resultados en la diferenciación de los vinos, incluso

con narices electrónicas conformadas con bajo número de sensores.

ABSTRACT

This work presents the results of the sensing analysis of Peruvian wines of

known (Commercial wines) and handmade brands, using electronic noses (E-

noses) which consist of an array of sensors based on tin oxide doped with Pd

or Pt, and some with zeolite coating. The combinations of the sensors were

performed seeking to obtain the best discrimination of the wines with the mul-

tivariate methods, with a high level of confidence and a good distribution of

the results. The Principal Component Analysis (PCA), cluster and factorial re-

sults showed that the electronic noses allowed to efficiently identify wines of

known brand from those of handmade brand, revealing the way in which the

wines have been produced. On the other hand, the multivariate methods ap-

plied to the electronic noses made up of SnO2sensors doped with palladium

showed a clear differentiation of Borgoña-type wines from wines of handmade

brand and evidenced the formation of agglomerations between red and Rosé

wines. The application of PCA, cluster and factorial obtained in this study allo-

wed to obtain good results in the differentiation of wines, even with electronic

noses formed with a low number of sensors.

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The real world is essentially full of

multivariate systems requiring a si-

multaneous analysis of the different

variables which could affect the pro-

cess. For example, to analyze a food

or a drink is necessary to consider

not only, the chemicals from which

the product is made up but also the

different variables (statistically, many

variables) that could interact with

each other. i.e., throughout a matrix

of relationships that may affect two

or more variables at the same time.

Among the most common methods

to analyze these systems, the Princi-

pal Component Analysis (PCA), is

the most frequently statistical ap-

proach for evaluating information;

converts a set of a data universe with

many variables into a set of possibly

correlated variable observations, that

is, into a set of variable values wit-

hout linear correlation called princi-

pal components. This statistical

method constructs a linear transfor-

mation that chooses a new coordi-

nate system for the original data set

in which the largest variance of the

data set is captured on the first axis

(called the First Principal Compo-

nent), the second largest variance,

the large is the second axis, and so

on (Gupta & Barbu, 2018; Johnson

&Wichern, 2007).

A key aspect in the PCA method is

the interpretation of the main factors

where the variances of the initial

data will be distributed, since there

is no general methodology applica-

ble to all types of data that may exist

but will be deduced after observing

the relationship of the main factors

with the initial data.

For this study, cluster and factorial

analysis were selected to corrobo-

rate the results with the PCA ap-

proach and to provide more

information to the results statistics

obtained in these studies for the dis-

crimination of wines according eit-

her to the brand (known and

handmade brands) or the type of

wine (red, rosé, Burgundy).

INTRODUCTION

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Principal Component Analysis

Principal Component Analysis (PCA)

(Johnson & Wichern, 2007; Aldás &

Uriel, 2017) is a statistical method

used to reduce the dimensionality of

a data set. This method is usually

used to find the causes of the varia-

tion of the data set and to order them

by prevalence. It is primarily used in

statistical exploratory data analysis

and to build predictive models. It in-

volves obtaining the eigenvalues and

vectors of the covariance matrix,

after centering the variables in rela-

tion to the mean.

In the principal component analysis,

there is the possibility of using the

correlation matrix or the covariance

matrix. In the first option, the same

value is proposed to each one of the

variables; this may be appropriate

when all variables are considered

equally important. The second form

is applied when all the variables

have the same measurement units,

and when it necessary to highlight

each one of the variables.

The main components are obtained

as linear combinations of the origi-

nal variables. The components are

ordered according to the percentage

of variance explained. One of the

advantages of the method is the re-

main of the variables of the data set

that contribute the most to its va-

riance, being the first component the

most important because it contains

the highest percentage of the va-

riance of the data.

In relation to this study, the sign of

response of the sensor (voltage) mea-

sured in time (seconds) was conside-

red for each of the objects, which in

this case were the different types of

wine, the volatile components of the

wine, the type of sensor, the dopant

metal in the sensor as well as the

coating of the sensor with zeolite.

Applying PCA method, it was possi-

ble to select those components that

would later replace the original va-

riables.

Cluster Analysis

Cluster analysis is the name of a

group of multivariate techniques

whose main purpose is to group ob-

MATERIALS AND METHODS

jects based on their characteristics.

Cluster analysis classifies objects in

such a way that each object is very

similar to the objects in the cluster,

with respect to some predetermined

selection criteria. The resulting ob-

ject clusters must show a high de-

gree of internal homogeneity (inside

the cluster) and a high degree of ex-

ternal heterogeneity (among clus-

ters). Cluster analysis is especially

useful when it is necessary to deve-

lop hypotheses concerning the na-

ture of the data or to examine

previously established hypotheses

(Johnson & Wichern, 2007; Aldás &

Uriel, 2017)

Any number of rules can be used in

cluster analysis, but the fundamental

task is to assess the mean similarity

within the clusters, so that as the

mean increases, the cluster becomes

less similar.

Factor Analysis or Analysis of Com-

mon Factors

In different research studies it is not

always possible to directly measure

the variables, as is the case of quali-

tative variables: level of intelligence,

social class, etc. In these cases, it is

necessary to collect indirect measu-

res that are related to the concepts

that interest. The variables that inte-

rest are called latent variables and

the methodology that relates them to

observed variables is called Factor

Analysis.

The Factor Analysis model is a multi-

ple regression model that relates la-

tent variables with observed

variables. This method has many

points in common with principal

component analysis, and essentially

looks for new variables or factors that

explain the data. In principal compo-

nent analysis, in fact, only orthogonal

transformations of the original varia-

bles are made, emphasizing the va-

riance of the new variables,

meanwhile, in the factor analysis, on

the contrary, it is more interesting to

explain the structure of the covarian-

ces between the variables (Johnson &

Wichern, 2007).

Materials and method of operation

of the E-noses and description of

materials

Sensors Preparation

In a previous work (Paredes-Doig et

al., 2019), the sensors based on

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SnO2doped with palladium (0.1,

0.2, 0.3 and 0.5% Pd) or platinum

(0.1, 0.2, 0.3 and 0.5% Pt) were pre-

pared by wet impregnation method.

To increase the sensitivity of the sen-

sors to contact with volatile chemi-

cals present in the aroma for the

evaluation of Peruvian wines, some

sensors were coated with Zeolite Y.

Preparation of samples

A template was formed with the ad-

hesive tape to define the area that

would cover the SnO2doped with

metal (Pd or Pt) on the surface of one

alumina plates. Subsequently, 0.1

gram of doped tin oxide was combi-

ned with (0.1, 0.2, 0.3 and 0.5 %) Pd

or Pt with 0.02 g of ethylcellulose

and 32 μL of α-terpineol, forming a

paste, which was deposited on the

one substrate of alumina, and then a

heat treatment was carried out in the

oven for 15 min at 600 °C.

Sensors with zeolite Y coating

To a beaker containing 0.05 g of tin

oxide doped with Pt or with Pd, 0.01

g of ethylcellulose and 16 μL of α-

terpineol were added. All the sub-

stances were mixed uniformly to

form a paste, which was deposited

on a surface of alumina containing

two gold electrodes, and then, it was

calcined at 600 °C for 10 min using

a heating ramp of 3 °C/min.

To prepare a thin layer of zeolite Y,

1,2-propanediol was used as a sol-

vent following the procedure de-

scribed by (Vilaseca et al., 2008).

Each mixture was constantly stirred

until the zeolite Y was dispersed in

the solvent. Once the system achie -

ved homogeneity, with the help of a

micropipette, a small quantity was

extracted and deposited by micro-

dripping on the surface of the tin

oxide, previously placed on the alu-

mina sheet. Subsequently, the sensor

was tested in the presence of volatile

compounds of wine samples.

The sensing measurements of the

volatile components contained in

the aroma of each wine were per-

formed for each sample in triplicate

using the following measurement

parameters:

Sensing temperature: 260 ºC

Initial purge time: 120 seconds

Sample drag time: 30 seconds

Reading time: 40 seconds

Final purge time: 240 seconds

Cycle time: 430 seconds

The Tables 1, 2 show the composi-

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tion of the prepared sensors. Table 3

shows the relationship of the Peru-

vian wines used in the analysis and

the nomenclature used.

Table 1. Composition of the sensors

based on palladium-doped tin oxide,

tested in this work

Sensors Description

SnO2 Tin oxide

0.1% Pd/SnO2 0.1% palladium

doped tin oxide

0.2% Pd/SnO2 0.2% palladium

doped tin oxide

0.3% Pd/SnO2 0.3% palladium

doped tin oxide

0.5% Pd/SnO2 0.5% palladium

doped tin oxide

SnO2-Z Tin oxide with

zeolite coating

0.1% Pd/SnO2-Z 0.1% palladium

doped tin oxide

with zeolite coating

0.2% Pd/SnO2-Z 0.2% palladium

doped tin oxide

with zeolite coating

0.3% Pd/Sn2-Z 0.3% palladium

doped tin oxide

with zeolite coating

0.5% Pd/SnO2-Z 0.5% palladium

doped tin oxide

with zeolite coating

Table 2. Composition of the sensors

based on tin oxide doped with

platinum, tested in this work

Sensors Description

SnO2 Tin Oxide

0.1% Pt/SnO2 0.1% platinum

doped tin oxide

0.2% Pt/SnO2 0.2% platinum

doped tin oxide

0.3% Pt/SnO2 0.3% platinum

doped tin oxide

0.5% Pt/SnO2 0.5% platinum

doped tin oxide

SnO2-Z Tin oxide with

zeolite coating

0.1% Pt/SnO2-Z 0.1% platinum

doped tin oxide

with zeolite coating

0.2% Pt/SnO2-Z 0.2% platinum

doped tin oxide

with zeolite coating

0.3% Pt/SnO2-Z 0.3% platinum

doped tin oxide

with zeolite coating

0.5% Pt/SnO2-Z 0.5% platinum

doped tin oxide

with zeolite coating

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Table 3. Description of the Peruvian

wines tested in this work

Description of

Nomenclature the well-known

brand wines

OB Ocucaje Borgoña

TB Tabernero Borgoña

TR Tabernero Rose

SQR Santiago Queirolo Rose

TGR Tabernero Gran Rose

SQM Santiago Queirolo

Magdalena

Description of

Nomenclature handmade

brand wines

BS Handmade 1,

Borgoña type

BSL Handmade 2,

orgoña type

BDN Handmade 3,

Borgoña type

DM Handmade 4,

mistela

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RESULTS

Figure 1 presents the PCA obtained

with five sensors: pure SnO2and

SnO2doped with different propor-

tions of palladium without zeolite

coating. This biplot graph presents a

variance level of 58.37 % for the first

main component (F1) and 22.9 % for

the second main component (F2),

showing a relatively good total vari-

ance of the results of the electronic

nose (observations F1 and F2 of

81.28 %, being greater than 70%),

with correlations close to zero,

which shows the independence of

the observations.

It is also observed that the PCA can

associate wines with similar charac-

teristics, in this case by the type of

wine. As can be seen in Figure 1, in

some cases the circles intersect,

showing dispersion in the results.

From the analysis of the PCA is ob-

served that the electronic nose made

up of palladium doped SnO2sensors

without zeolite coating allowed to

obtain no homogeneous distribution

by the type of wine.

An interclass variation of 66.19 %

and an intraclass variation of 33.81%

were obtained from the hierarchical

cluster. The results are not clear as in

the case of the PCA. Only OB wine

is distinguished from others. How-

ever, the interclass distance is 66%

and indicates that there is a good dif-

ferentiation of the classes that group

the wines. In other words, there is

heterogeneity between classes and

more homogeneity within classes.

After seeing the results with the hier-

archical cluster method, the k-means

cluster with only 2 fixed classes was

applied, obtaining only 13.52 % for

interclass variation and 86.48 for in-

traclass. The interclass separation is

very low; therefore, this method did

not contribute to the discrimination

of the analyzed wines.

The application of Factor Analysis is

different. With this approach, three

classes of wines are defined: 1. For

handmade wines (on the right side of

the graph), 2. For commercial Bor-

goña wines (on the lower left side of

the graph) and 3. For Red wines and

commercial Rosé (in the upper left

part of the graph). So, from this me -

thod, the electronic nose could dif-

ferentiate not only brand but also

manufacturing; that is, either if they

are commercial or handmade brands,

made by strains, if they are type Bor-

goña or Red wine.

Figure 1. PCA and Factorial methods

obtained using sensors palladium

doped tin oxide (0.1, 0.2, 0.3, 0.5% Pd)

without zeolite coating

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In the following section, multivariate

methods (PCA, Cluster and Factorial)

were applied to some combinations

of sensor arrays, but which do not

correspond to the total set of sensors

that have been used or to all sam-

ples, but rather to partial sets of such

sensors.

a) Array of sensors: SnO2-Z,

0.1%Pd/SnO2-Z

and 0.2%Pd/SnO2-Z

In Figure 2, a better differentiation by

type of wine is observed, especially

those of the Borgoña type because

the region where the Borgoña type

wines are located is clearly sepa-

rated from that of handmade brand

wines and other wines. On the other

hand, the signs corresponding to the

Rosé and red wines are located in

the same region, probably due to the

formation of agglomerates.

Better results were achieved for the

cluster method, using a shaped nose

with only 3 sensors. The interclass

percentage reaches 87 % which

means that a good class differentia-

tion is observed. Furhermore, the

Borgoña wines are grouped into a

single class and almost all of the

wines reds and rosés are in another

class. This picture has been also ob-

served with the first factorial.

The k-means cluster and factorial ap-

proach allowed to obtain 3 clases of

wines. The 3 groups observed were:

1. Commercial Borgoña Wines, 2.

Commercial Red and Rosé Wines

and 3. Handmade Wines.

Figure 2. PCA and Factorial analysis

obtained from the sensing results of the

wines using the electronic nose made

up of the sensors: SnO2-Z, 0.1%

Pd/SnO2-Z and 0.2% Pd/SnO2-Z

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b) Array of sensors: SnO2-Z,

0.1%Pt/SnO2-Z,

0.2%Pt/SnO2-Z,

0.3%Pt/SnO2-Z

and 0.5%Pt/SnO2-Z

Figure 3 presents the PCA obtained

with the combination of five sensors:

SnO2-Z, SnO2doped with platinum

and all those with zeolite coating.

The total variance level is 93.06%.

In this case, a clear differentiation of

the well-known brand wines from

those of the handmade brand is ob-

served. Moreover, a differentiation of

the Borgoña-type wines is observed

among the former. However, the

signs of the red and rosé-type wines

are in the same region showing ag-

glomeration, which indicates a

medium distribution of the signals.

The interclass percentage is quite

high (86.07%), which indicates that

there is a good separation between

classes and closer proximity of ob-

jects within the agglomerates.

From the results obtained of this

method, the wines are separated into

three groups: 2 classes for handmade

wines and 1 class for commercial

wines. Therefore, with this nose it was

possible to differentiate commercial

wines from handmade ones. Thus, the

brand is related to the composition of

the wine that has been monitored

with the electronic nose.

The factor analysis was applied to

corroborate the results obtained with

the PCA and the cluster method. Up

to three groups can be observed and

the differentiation of the commer-

cials from the handmade ones is

quite clear.

Figure 3. PCA and Factorial analysis ob-

tained from the sensing results of the

wines using the electronic nose made

up of the sensors: SnO2-Z, 0.1% Pt/SnO2-

Z, 0.2% Pt/SnO2-Z, 0.3% Pt /SnO2-Z and

0.5% Pt/SnO2-Z

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c) Array of sensors: SnO2-Z,

0.1%Pt/SnO2-Z, 0.2%Pt/SnO2-Z

The PCA obtained with the combi-

nation of three sensors: SnO2-Z,

0.1% Pt/SnO2-Z and 0.2 % Pt/SnO2-

Z (all with zeolite coating), is pre-

sented in Figure 4. The total variance

level is 94.40 %, with a confidence

level of 85.17 % for the first main

component (F1) and 9.23 % for the

second main component (F2).

As is in the previous nose, the appli-

cation of a cluster method has sepa-

rated the wines into three classes: 2

classes for handmade wines and 1

for commercial wines.

With a considerable interclass dis-

tance (more than 83%) obtained with

the previous electronic nose (e-nose);

the differentiation of wines was also

tested with the sensors doped with

platinum. With this nose, a good dif-

ferentiation between handmade

wines and commercial was observed

in a good way. With the two e-noses

with platinum sensors

Figure 4. PCA and Factorial Analysis

obtained from the sensing results

of the wines using the electronic nose

made up of the sensors: SnO2-Z,

0.1% Pt/SnO2-Z, 0.2% Pt/SnO2-Z

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DISCUSSION

In recent years, great attention has

been paid to the application of data

analysis systems to artificial detection

systems, to integrate responses with

sensory and chemical data and to

combine data from different tech-

nologies such as electronic noses,

which serve to better replicate the

human sensory system (Baldwin et

al., 2011). This is why the present in-

vestigation has been carried out.

For the differentiation of samples in

this type of systems, chemometric

tools and analysis have been used to

extract the causes of the variance of

the readings of the electronic nose

and the multivariate distance (Casa -

grande Silvello & Alcarde, 2020). The

most applied multivariate procedures

are cluster analysis, factor analysis,

multidimensional scaling, discrimi-

nant analysis, regression analysis,

and artificial neural networks (Gar-

cía-González & Aparicio, 2002). In

the present work, three multivariate

methods have been used: PCA, Clus-

ter analysis and Factor analysis.

PCA as a technique applied to chem-

istry has been used in other studies

(Welke et al., 2013). For example, in

previous works different types of

wines such as Chardonnay, Merlot,

Cabernet Sauvignon, Sauvignon

Blanc and 50 % Chardonnay/Pinot

Noir 50 % have been achieved, find-

ing total variances of the first two

components, lower than those found

in the present work.

Welke et al. (2013), also found the

red wines, Cabernet Sauvignon and

Merlot, are in the same quadrant.

Chardonnay and Sauvignon Blanc

wines were separated by PC2, while

Merlot, Cabernet Sauvignon and

50% Chardonnay/50% Pinot Noir

wines were most influenced by vari-

ables related with PC1. In the present

study, it was observed that handmade

wines were in quadrants I and IV,

while commercial wines were found

in quadrants II and III. It is also im-

portant to appreciate that the sweet-

est wines were influenced by PC1,

whether commercial or handmade.

Sensors doped with platinum rea -

ched better results of the wines de-

tection and discrimination than tin

oxide sensors doped with palladium.

This behavior was seen in a previous

work (Paredes-Doig et al., 2019).Plat-

inum aggregation in the bulk (¨bulk¨)

of tin oxide leads to an increase in

the density of the chemisorbed oxy-

gen on the surface and in a certain

way increases the resistance of the

MOS; however, its character as a de-

hydrogenation catalyst is the one that

predominates and for which it is used

to increase the sensitivity of a sensor

(Sevastyanova et al., 2012).

The zeolite films improved the detec-

tion of wines such as in the work of

Vilaseca et al. (2008) The e-noses

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built with sensors coated with zeolite

Y shown better results when the mul-

tivariate methods were applied.

E-noses can detect the adulteration of

wines with methanol or ethanol

(Penza & Cassano, 2004; Berna,

2010). Penza & Cassano, 2004), tes

ted three red, three white and three

rosé wines from different Italian de-

nominations of origin and vintages

using a multisensor array that incor-

porated four metal oxide (WO3)

semi-conductor thin film sensors. In

this study, something similar appears

with handmade wines like with adul-

terated wines studied by Penza and

Cassano (2004).

Electronic systems can be used to

discriminate wines elaborated using

different grapes and techniques. That

can be used to verify authenticity of

the wines in comparison with tradi-

tional techniques.

Di Natale et al. (1996) employed four

MOS sensors to classify wines having

the same geographic origin but com-

ing from different vineyards. That de-

tection and differentiation of the

commercial wines from handmade

wines were also reached in the pre -

sent work.

Lozano et al. (2005) used an e-nose

combining sixteen tin oxide thin

film-based sensors to recognize aro-

mas in white and red wines. In the

present investigation, it was used

sensors arrays of five sensors maxi-

mum. And, also, it can say that the e-

noses of three sensors exposing good

results in comparison with other

studies.

Cozzolino et al. (2009) reported that

the results show that MOS sensors

can discriminate between grape and

type of wines and may become an

important tool for standardization of

wine quality. And this was found in

the present study, because with the e-

noses it could see that wines were

manufacturing with different type of

grape (like Burgundy grape) was ag-

glomerated in other class. Therefore,

the wines the better quality from

known brands evidenced a separa-

tion in the plot from the handmade

wines.

For example, cluster or cluster analy-

sis has been used to classify four

types of coffee, while (Pearce et al.,

1993) used it to distinguish two types

of lagers. Cluster analysis has also

been used to study sensor similarities

to select the sensors with the highest

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sensitivity from each batch of sensors

and thus avoid redundancy (Chaudry

et al., 2000; García-González &

Aparicio, 2002). Although, cluster

analysis is not most used technique

for this class of works like the PCA;

in the present study, cluster analysis

method contributed and corrobo-

rated to classify the wines too in a

good way. It was also observed that

with the factor analysis method, the

results obtained with the PCA, and

cluster methods were verified.

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CONCLUSION

The PCA results showed that the

electronic noses made up of the plat-

inum-doped tin oxide-based sensors

allowed an efficient identification of

wines of known brand from those of

handmade brand. The PCAs of the

electronic noses made up of SnO2

sensors doped with palladium

showed a clear differentiation of Bor-

goña-type wines from wines of hand-

made brand, and evidenced the

formation of agglomerations between

red, Rose and handmade brand

wines. The best results in the diffe -

rentiation of the wines were obtained

with the electronic noses made up of

sensors doped with platinum and

coated with zeolite. The PCAs ob-

tained in this study achieved good re-

sults in the differentiation of wines,

even with electronic noses formed

with a low number of sensors. Clus-

ter and factorial analyze corrobo-

rated and completed the results of

the PCA.

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APLICACIÓN DE MÉTODOS MULTIVARIADOS PARA LA

DIFERENCIACIÓN DE VINOS PERUANOS

Paredes et. al., 85–101