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Sensors and Actuators B 160 (2011) 916– 922

Contents lists available at SciVerse ScienceDirect

Sensors and Actuators B: Chemical

j o ur nal homep a ge: www.elsev ier .com/ locate /snb

mproving the training and data processing of an electronic olfactory system forhe classification of virgin olive oil into quality categories

anuel Canoa,∗, Javier Roalesa, Pedro Castilleroa, Palma Mendozaa, Antonia M. Calerob,arlos Jiménez-Otb, José M. Pedrosaa,∗

Departmento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide, Ctra. Utrera Km. 1, 41013 Sevilla, SpainDEOLEO S.A., Ctra. N-IV Km. 388, 14610 Córdoba, Spain

r t i c l e i n f o

rticle history:eceived 16 March 2011eceived in revised form 31 August 2011ccepted 2 September 2011vailable online 21 September 2011

eywords:irgin olive oillectronic olfactory systemetal oxide semiconductor sensors

CA

a b s t r a c t

A large amount of virgin olive oil samples and different feature selections have been employed in orderto improve the classification capacity of an electronic olfactory system, based on thin film metal oxidesemiconductor, to be applied into the virgin olive oil industrial analysis, according to the current Euro-pean Union regulation. The basic ability of the employed equipment for aroma discrimination was testedby using pure aromatic compounds some of them usually present in the virgin olive oil. For these sam-ples, the employed system shows well different responses and identification patterns. After that, morethan three hundred virgin olive oil samples from different origins were analysed. The fingerprints of suchsamples anticipate good discrimination between higher and lower quality samples. Multivariate analysis(principal component analysis and discriminate factorial analysis) was used in order to obtain the bestpattern recognition algorithms and the better classification of unknown samples. The real improvement

FA was obtained when an alternative feature selection named Five Contiguous Points was used. This fea-ture selection was revealed to increase the separation between the different quality categories due to abetter use of the information contained in the sensor response curves. In summary, the best results forvirgin olive oil identification were obtained by the Five Contiguous Points–discriminate factorial analysiscombination, with the increase of the number of samples demonstrating that the classification ability isclearly improved. Finally, this study also provides an overview on future application.

© 2011 Elsevier B.V. All rights reserved.

. Introduction

The juice obtained from fresh olive fruits, by means of onlyechanical and physical processes (olive milling, olive paste mix-

ng and centrifugation, and olive oil settling) is named virgin oliveil (VOO). Olive oil is widely known for its delicious taste and aromand highly prized for its contribution to the basic Mediterraneaniet [1,2].

The current European Union regulation defines three qualityategories for VOO (extra virgin, virgin and lampante) accordingo three chemical parameters (free acidity, peroxide value andbsorbance UV) and a fourth sensorial analysis [2,3]. An olive oilample must have the four parameters under the law limits to be

raded inside a category. Lampante olive oil category cannot beonsumed without a previous refining process.

∗ Corresponding authors. Tel.: +34 954 349 537; fax: +34 954 349 814.E-mail address: jmpedpoy@upo.es (J.M. Pedrosa).

925-4005/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.snb.2011.09.002

Sensory descriptors of olive oil can be classified into “posi-tive attributes”, such as fruity, bitter and pungent, and “defects”.The latter describe defects of olive oil, and they can be classifiedas fusty, mustiness, muddy sediment, metallic, rancid, heated orburnt, wood, rough, greasy, vegetable water, brine, esparto, earthy,grubby and cucumber [2]. Chemical compounds of different chem-ical groups are responsible for these defects [4,5], and they areformed by an inappropriate olive harvesting process and by inad-equate olive paste manipulation during the olive oil extractionprocess [1,6,7]. The sensory analysis plays a crucial role in the clas-sification of VOO into the designations which affect their marketprices since lampante olive oils cannot be consumed without refin-ing. Thus, the on-line detection of the different categories, duringthe oil industrial process, has a notable economical impact.

The identification of olive oil categories is usually carried outby two procedures: the use of trained assessors (panel test) [8]

and the analysis of the volatile compounds. The first one is notan error free procedure due to assessors’ subjectivity [9] and hasthe great disadvantage of being a lengthy and costly procedurethat cannot be afforded by small enterprises. The analysis of the

Actuators B 160 (2011) 916– 922 917

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Table 1Olive oil graduation: Regulation (EC) No. 1989/2003 [2].

Category Free acidity Peroxidevalue

AbsorbanceUV

Sensory analysis

K232 K270 Md Mf

Extra-virgin <0.8 ≤20 ≤2.50 ≤0.22 0 >0Virgin ≤2.0 ≤20 ≤2.60 ≤0.25 ≤2.5 >0

Lampante >2.0 – – – >2.5 –

bypasses the bottle. Both lines were regulated by a needle valve.The two lines were rejoined after the bottle to go into the electronicnose.

M. Cano et al. / Sensors and

olatile compounds by dynamic headspace (DHS) high-resolutionas chromatography (GC) [3,10,11] is an exacting technique, but its laborious and it cannot be applied on-line in the processes andottling of virgin olive oil [8].

An alternative is the use of an electronic olfactory system (EOS)hich consists of an array of electronic sensors and a mechanism

or pattern recognition [12]. It is particularly useful for quality con-rol applications in the food, beverage and cosmetics applications.

gas sensor is a device which responds by physical or chemicalroperties. Sensors do not need any pre-treatment and do not useolvents to detect the presence of volatiles.

EOS for odour recognition appeared on the market eighteenears ago (MOSES I, 1993) due to a fruitful collaboration betweenndustry and scientific world. After that, many commercial EOSsave been put on the market; this type of technology is now widelymployed in different fields; automotive, environmental monitor-ng, medical diagnostic and food processing [13–15]. So, EOS is seens an emergent technique in food quality and assessment.

In the literature, there are several examples that demonstratehe possibility of using an EOS for the characterization of vegetableils [16,17], to predict shelf life or to monitor oil oxidation undereal life storage conditions [18,19]. Other applications includeetection of defects in VOO [8,20], or monitorization of the VOOolatile compounds evolution during olive malaxation [7], whilenformation about the use of an electronic nose for the classifica-ion of different quality category VOO are not frequent [3] and theumber of analysed samples is usually very low. Moreover, thisechnology has not been yet employed to distinguish into the threeOO quality categories in industrial process. Inside the VOO manu-

acturer routine, the identification is required during the purchasef VOO coming from the mill for making olive oil to stipulate a suit-ble price [1], and the processes of storage and bottling of the finalroduct [2].

Multivariate statistics to interpret the datasets from the EOS issually employed in order to assess the classification of the mea-urements. The most common multivariate statistical techniquesre the principal component analysis (PCA), discriminate facto-ial analysis (DFA), and partial least squares analysis (PLS) [21].his paper shows a comparative study between PCA and DFA forhe classification of the VOO samples in the training set. PCA isn unsupervised learning technique that allows reduction of mul-idimensional data to a lower dimensional approximation, whileimplifying the interpretation of the data by the first and secondrincipal components (PC1 and PC2) in two dimensions and pre-erving most of the variance in the data. In addition, the samples cane classified without prior information on the samples. Conversely,FA requires prior knowledge about the samples during the train-

ng. DFA is a supervised learning technique, which classifies theample by developing a model and then identifies the unknownamples. Therefore, the objective of this study was to implementhe training and data processing of an electronic olfactory systemor the classification of virgin olive oil into quality categories, aftern exhaustive training set, in order to study its capability in then-line VOO industrial analysis. Finally, some keys to enhance thisechnology for a future application were commented.

. Materials and methods

.1. Materials

More than three hundred VOO from different cultivar origins

nd quality categories were tested to train the EOS (training set).xtra virgin, virgin and lampante samples were supplied by Car-onell which is a Spanish leader company in the olive oil market.hese samples were previously qualified by the assessors and the

≤2.5 0

Md, median for the defects; Mf , median for the positive attributes.

laboratory of the cited enterprise (according to the European Unionregulation [2]). Table 1 shows the law limits of each olive oil cat-egory. It is very important to distinguish the lampante olive oilcategory from the other two because it cannot be consumed with-out a previous refining process. All the samples were analysed bythe EOS just after supplied.

All the standard samples (acetic acid, hexanal, methanol, hex-ane, acetone, dichloromethane, ammonia and methanol) wereprepared using paraffin oil. All chemicals were obtained fromSigma–Aldrich.

2.2. Equipment

The EOS used in this work (EOS835) was built by Sacmi Industry(Sacmi Industry, S.r.l., Imola, Italy) and it is composed of a chamberwith six metal oxide semiconductor sensors previously describedby Falasconi et al. [7,22]. During the analysis each sensor is main-tained at a specific temperature in the range 350–450 ◦C. Fig. 1shows, as an example, the typical long-term variation of the initialresistance (R0) drift from our metal oxide semiconductor sensors.It was obtained by collecting the R0 value of each sensor, every dayand before starting the measurement session.

Synthetic dry air was used as carrier gas for the samples and alsoas reference air. A simple assembly of Teflon tubes was incorporatedto get always a same reference air and a constant level of relativehumidity (%RH). This operation is necessary in order to minimizethe contribution of the water vapour to the signal of the sensors.Fig. 2 shows the used assembly which was made up by a bottle withsome distilled water inside closed by a stopper with two holes. Thesynthetic dry air from the cylinder was split into lines before thehumidity regulator: one line enters the bottle, while the other half

Fig. 1. Typical time dependence of the initial resistance (R0) with each sensor.

918 M. Cano et al. / Sensors and Actua

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ig. 2. Scheme of the tube assembly used to regulate the humidity of the system.

Fig. 3 shows, as an example, the variation of the relative humid-ty (%RH) during a measurement session. A reasonably stableaseline of %RH was obtained by the combination of a simplessembly of Teflon tubes (Fig. 2) with a cylinder of synthetic dryir. Also, an important difference in the %RH between the differentamples of virgin olive oils can be observed.

All the experiments were done in an air-conditioned room at5 ◦C.

.3. Sample preparation and measuring setup

The analytical parameters (sample amount, headspace gener-tion time and temperature, flow rate and injection time) wereelected following an optimization process previously describedn the literature [3,8].

A 10-g amount of each sample was introduced in a 100 mL glassial and heated at 35 ◦C inside a controlled thermostat-samplingath for a headspace generation time (HGT) of 1800 s. With this HGT

t is ensured the saturation of the vials headspace by the volatileompounds from the olive oil samples.

The response of the system after samples injection implies aeduction in the sensors resistance and a subsequent recovery witheference air. For the measurements, this process is divided intoour steps: before sample injection, during sample exposure, afterample exposure (recovery) and a waiting time for complete signaltabilization. In our case, the time of each step was set as follows:

efore 60 s, During 60 s, After 60 s and Wait 420 s. Each samplenalysis lasted 600 s and was analysed in triplicate.

Volatile compounds were directed to the sensor chamber byhe carrier gas (synthetic dry air with the suitable humidity) at a

ig. 3. Typical variation of the relative humidity with the time and measurements.

tors B 160 (2011) 916– 922

150 cm3/min flow-rate while the temperature of the sensor cham-ber was kept at 55 ◦C.

2.4. Standard sample for the calibration

The metal oxide semiconductor sensors have a drift in time witha very slow increase of their baseline value (R0). In order to reducethe drift of the sensors (R0) a standard was measured at the begin-ning of each measurement session. The standard sample must be asubstance prepared in the same conditions as the samples and asmuch stable as possible in time. The use of the standard allowedus to perform a calibration to correct for differences in the baselineamong different days (internal standard method).

Three different standards (acetic acid, hexanal and ethanol) [23]diluted in paraffin oil at a concentration of 200 ppm have been stud-ied. The signal produced by the standards was comparable to thesignals produced by the samples. The stability in time from thedifferent standards was checked by the R/R0 ratio of the measure-ments, where R is the minimal sensor resistance value during anexposure. The repeatability study during the day was performed bymeasuring three replicates from a sample every 6 h and then theRSD (relative standard deviation), mean and maximal were calcu-lated by the R/R0 ratio obtained from the measurements every 6 h.In turn, the repeatability study between-days was performed bymeasuring three replicates from a sample every day for a weekand then the RSD, mean and maximal were calculated by the R/R0ratio obtained from the measurements every day. The best repeata-bility and reproducibility was obtained with the standard aceticacid. With this standard, the repeatability study during the dayyielded a mean %RSD of 5.4% with a maximum value of 11.3%. Forthe between-days repeatability study the maximum deviation was17.8% and the mean 9.8%. Similar values were found for the VOOswhere only the virgin samples showed higher values (7.7% meanRSD during the day and 13.1% mean RSD between-days) due totheir lower sensor response.

According to Eq. (1), the calibration was done by multiplying thenormalized R/R0 sensor response (SN,dayX) by the R/R0 ratio of thefirst standard (Sta,day1) and dividing by the R/R0 ratio of its ownstandard (Sta,dayX), where N is the number of sensor in the EOS,the first standard is the standard value measured on the first day,and the own standard is the standard value referred to the day ofmeasurement.(

R

R0

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SN ,dayX

=(

R

R0

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·(R/R0)Sta,day1

(R/R0)Sta,dayX

(1)

2.5. Data analysis

Two software applications implemented by Sacmi wereemployed for the electronic nose data processing: the “Nose PatternEditor” (NPE), which is used for data pre-processing and classifica-tion (e.g. the feature extraction from the sensor response curve andthe multivariate statistical analysis), and the “Nose Pattern Classi-fier” (NPC) which is applied for pattern recognition.

The response of the sensors yields an exponential-like shape.From each curve, we extracted different features. The most typicalis the classical feature (R/R0), where R0 is the initial resistance ofthe sensor and R is the minimal sensor resistance and correspondsto the maximum change of the response curve (for every sensorthere is an averaged value of the R/R0 ratio from the three repli-cates per sample). Other features were also tested: Delta, Fourier,Phase integral, Relative integral, Last difference, etc. [24], and a new

interesting feature named Five Contiguous Points (FCP) was alsoemployed. The FCP is calculated taking five points every 12 s (12,24, 36, 48 and 60 s) from during step in the curve of each sensor,i.e. 30 points per expositions (5 points × 6 sensors) in this case.

M. Cano et al. / Sensors and Actua

Fig. 4. Resistance decreases of S3 sensor when eight different standard samplesw

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the normalized responses obtained from the EOS835 system ofthe eight synthetic samples at the same solution concentration

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ere measured. The four steps of the response curve are shown in the graph.

In order to make a classification study, statistics multivariatenalysis was carried out by applying PCA and DFA [25,26] after theeature selection.

PCA validation was done with the traditional leave-one-outethod (cross-validation method), where each data point is

emoved from the data set and tested as unknown using the

emaining data points [27–29]. On the other hand, DFA valida-ion was done with a modified leave-one-out method, where one

ig. 5. Radar plots showing: (a) pure compound fingerprints obtained by the EOS835 (witifferent quality categories (normalized data).

tors B 160 (2011) 916– 922 919

randomly chosen sample was removed from the data set and con-sidered as an “unknown” sample [29].

3. Results and discussion

3.1. Simple aroma analysis

Eight synthetic samples were prepared by a mixture of paraffinoil as solvent and eight different analytes (acetic acid, hex-anal, methanol, hexane, acetone, dichloromethane, ammonia andethanol) as solute. Some of these compounds usually appear in thecomposition of VOO [5].

In most cases, each sensor showed a different response to theeight synthetic samples at the same concentration. Fig. 4 shows,as an example, the response of the S3 sensor to the eight differentsynthetic samples at the same solution concentration (200 ppm).Although the samples were detected with different sensibilities bythe sensor, reversibility was practically accomplished in 7 min.

A total of 48 synthetic samples were measured. Samples weredivided in three sessions with 16 samples each and with threedifferent concentrations (150, 200 and 250 ppm). During this anal-ysis, the baseline resistance of the six sensors remained stable. Thewater vapour introduced in the system was controlled with thetube assembly showed in Fig. 2.

Fig. 5a shows, as an example, the radar plots (fingerprints) with

(200 ppm). In this case, the smallest internal circle of the diagramcorresponds to the air-value (R0) and the inverse value R0/R was

h normalized data), and (b) an example of VOO fingerprints corresponding to three

920 M. Cano et al. / Sensors and Actuators B 160 (2011) 916– 922

Fig. 6. PCA plot of the first and second principal components obtained from all theda

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(e.g. the feature extraction by the sensor response curve) and explo-

ata (8 pure compounds and three concentrations, 150, 200 and 250 ppm). The aceticcid data are used for the calibration.

sed for this figures. As can be seen, there are significant differ-nces in the fingerprint of each compound. For example, hexanal,exane and dichloromethane were best detected by the S1 and S4ensors, while the detection of acetic acid and acetone is best per-ormed by the S3 and S5 sensors. All the other solvents, ammonia,

ethanol and ethanol, which are more polar compounds, were bestetected by the S2, S3 and S6 sensors.

From each sensor response, the classical feature (R/R0) waserformed and the PCA was applied to the data to reduce its dimen-ionality and also, to identify the relevant sensors in the array,nd to discard redundant information. The first two principal com-onents obtained from this analysis explain 96.33% of the dataariability. By means of the graphical representation of these tworincipal components, it is possible to interpret the results in anasier way maintaining a high proportion of the data variability.

Fig. 6 shows the PCA plot of the first two principal componentsPC1 and PC2) obtained from the three different concentrations. Theata clusters that belong to different analytes were separated fromach other. The first component tends to group the data accord-ng to their different polarity. Thus, the most polar compounds areocated on the right side, and the less polar compounds are local-zed on the left side, with the only exception of the acetic acid.he second component seems to differentiate the different analytesccording to their respective concentration, since the data corre-ponding to the more concentrate sample appears always withower PC2.

Through the comparison of Figs. 5a and 6, it can be observedhat the compounds with a high sensor response in the radar plotshowed a worse grouping in the PCA (ammonia and methanol).

These results demonstrated that the EOS is capable of giving fast and correct response, even with fewer sensors than com-ounds. Moreover, it allows the identification of any of the testedimple aromas.

.2. VOO aroma analysis

To carry out the VOO classification and identification, the firsttep was the training and validation phase, in which the algorithmas trained by VOO samples with a previously known classifica-

ion. The model developed by the training sets was applied to the

alidation dataset through the traditional leave-one-out method.fter this, the identification of the unknown VOO samples could bechieved using the obtained algorithm.

Fig. 7. PCA (top) and DFA (bottom) representation of the data obtained from theclassical feature selection from 75 known VOO samples. The acetic acid data areused as standard for the calibration.

The training of the electronic nose was a very important anddelicate phase, given that this process involves the creation of acomplete database that the instrument uses as reference for thesubsequent sample recognition. This database was created throughthe analysis of a set of olive oil samples, which should be represen-tative of the different aromas to be recognized.

Fig. 5b shows representative radar plots with the normal-ized fingerprints of each VOO category obtained with the EOS.As can be seen, there are significant differences among the fin-gerprints of the different VOO with lampante samples featuringthe highest response for all the sensors while the virgin samplesusually showed the lowest response. The S2, S4 and S6 sen-sors showed a high sensitivity in all cases, while the S1, S3 andS5 sensors were less sensitive, but they usually contribute to ahigher selectivity. In particular, S5, S3 and S4 exhibit the betterdiscrimination capacity for these samples. In any case, althoughthe radar plots can be easily interpreted, they give only partialinformation of the sensor response and a more profound datatreatment (feature extraction and multivariate analysis) is neces-sary to obtain a proper discrimination among a large number ofsamples.

In order to perform the training, 75 known VOO samples fromdifferent origins were measured by the EOS. After this, the statisti-cal program NPE was used for easy data handling, pre-processing

rative analysis (PCA or DFA).Fig. 7 shows the PCA and DFA plots obtained from the clas-

sical feature for 75 known VOO samples. The first two principal

M. Cano et al. / Sensors and Actuators B 160 (2011) 916– 922 921

Table 2Results of the VOO identification using different combination of feature and multi-variate analysis, in terms of correct prediction percentage (accuracy %).

Training Feature Analysis Accuracy %

75 samples Classical PCA 36.2Classical DFA 34.7FCP PCA 38.9FCP DFA 55.5

150 samples Classical PCA 54.2FCP PCA 62.5Classical DFA 36.1FCP DFA 66.7

300 samples Classical PCA 48.6FCP DFA 62.1

PC

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Fig. 8. PCA (top) and DFA (bottom) representation of data obtained from the FCP fea-ture selection for 150 known VOO samples. The acetic acid data are used as standard

CA, principal component analysis; DFA, discriminate factorial analysis; FCP, Fiveontiguous Points.

omponents obtained from the PCA explain 91.34% of the dataariability (Fig. 7 – top), showing the distribution of the total vari-tion between the PCs. Virgin oils data appear mixed with theampante and extra-virgin oils data, while these two last are welleparate. Moreover, lampante oils data are better grouped thanxtra-virgins data. On the other hand, through the DFA (Fig. 7 –ottom), the first two factors explain 99.36% of the data variabil-

ty, showing the distribution of the class separation between theimensions, and a slight enhancement in the classification and sep-ration among different categories is obtained. Therefore, a bettereparation of the VOO data was achieved by means of the DFA,emonstrating that DFA is better than PCA particularly when theumber of samples is high producing a data overlapping in thelusters.

Increasing the number of samples (from 75 to 150), serious limi-ations of the PCA and DFA from the classical feature were observedue to data overlapping. Therefore a study of the training dataet was done considering the following alternative features: delta,hase integral, relative integral, last difference [24] and Five Con-iguous Points (FCP). The best results were obtained by the FCPeature because it increases the available information per sensor (5oints per 6 sensors).

Fig. 8 shows the PCA and DFA plots obtained from the FCP featureor 150 VOO known samples. In this case, it was necessary to usehe first three principal components to explain 92.01% of the dataariability by applying PCA (Fig. 8 – top). Virgin oils data appearixed with the other categories like those obtained from the clas-

ical feature for 75 samples, while lampante and extra-virgin oilsre well separate. Moreover, lampante oils data are better groupedhan extra-virgin oils data. On the other hand, using the DFA byhree components (Fig. 8 - bottom) an important improvement inhe classification and separation among different categories wasbtained. The combination of the FCP feature and the DFA yieldedhe best separation of the VOO data.

Increasing the training set (from 150 to 300 samples) resultedn a decreasing of the separation among the categories and in a

orsening of the classification. This could be due to the internaltandard method, which is not able to minimize the drift of theensors when the number of VOO samples is very large.

The pattern recognition was made using the NPC software.hree different training sets were employed increasing the num-er of samples (75, 150 and 300), and 75 unknown VOO sampless external test set were used to check the algorithm. Table 2hows a summary of the percentage of accuracy employing differ-nt algorithms. The results were rather average, mainly due to the

ntermediate position of the virgin oil category, while extra-virginnd lampante oil categories appear well separate. Increasing theumber of samples in the training set (from 75 to 150) resulted

n an improvement of the accuracy obtained for any analysis.

for the calibration.

Moreover, the results practically were not improved when thenumber of samples in the training was bigger than 150. Asexpected, the best combination was obtained with FCP–DFA,because supervised learning techniques give better recognitionthan unsupervised learning methods.

In order to further improve these results we are working in sev-eral ways. The first one is the training by defects or attributes, giventhat it was observed that extra-virgin VOO with different attributescould have different aroma and the classification in the same groupby any multivariate analysis was very difficult (see Fig. 7 – top).

The second way is by obtaining a specific training set for eachgeographical origin, because it would be desirable to use trainingsamples containing more similar composition that the unknownsamples. It is clear that the algorithm can only classify unknownsamples if we match an optimum training set [30].

And finally, the third way is the use of an easy sample treatment(addition of desiccant or dilution with refined oil) to reduce thedifference in %RH between the different VOO samples (see Fig. 3).

Thus, another possible reason for the misclassification could beeliminated.

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22 M. Cano et al. / Sensors and

. Conclusions

EOS allows rapid discrimination between simple aromas, evenith fewer sensors than compounds.

The training, feature selection and multivariate analysis meth-ds are essential for the application of the EOS to the VOOuality classification. The increase of the number of samples clearly

mproves the classification ability of the system. The best results forhe VOO identification is obtained by the FCP–DFA combination.irgin samples (which appeared mixed with extra-virgin and lam-ante categories) and the humidity difference among samples fromhe same category is detrimental for the discrimination of VOOs.

EOS could one day be used in realistic application for the on-ine industrial VOO analysis. However, more experimental work iseeded.

cknowledgements

We thank the Ministry of Science and Education of Spain (projectET2007 0363 01/ 02), and SOS Corporación alimentaria S.A. fornancial support.

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Biographies

Manuel Cano received his BS degree in general Chemistry from Cordoba Universityin 2004. After graduate, he was appointed as researching assistant in the Departmentof Physical Chemistry and Applied Thermodynamics from Cordoba University. In2008, he received Doctor of science degree from Córdoba University for his studieson amperometric sensors and biosensors based on organic conducting molecules.He is now postdoctoral researcher in the Department of Physical, Chemical, andNatural System from Pablo de Olavide University. His researching interest is thedevelopment of gas sensors.

Javier Roales received his BS degree in Environmental Science from Pablo de OlavideUniversity in 2007. He is now a PhD student in colloids and interfaces, Pablo deOlavide University. His main interest is the development of gas sensors.

Pedro Castillero received his BS degree in general Physics from Sevilla Universityin 2005. He is a PhD student in colloids and interfaces, Pablo de Olavide University.

Palma Mendoza received her BS degree in Environmental Science from Pablo deOlavide University in 2009. She is a PhD student in colloids and interfaces, Pablo deOlavide University.

Antonia M. Calero received her BS degree in general chemistry from Cordoba Uni-versity in 2000. She is now the person in charge of R+D+I of oil, vinegar and saucesin the company DEOLEO S.A.

Carlos Jiménez-Ot received his BS degree in general chemistry from CordobaUniversity in 1996. In 2008, he received Doctor of science degree from CórdobaUniversity. He is now manager of Innovation in the company DEOLEO S.A.

Jose María Pedrosa received his BS degree in general chemistry from CordobaUniversity in 1996. In 2002, he received Doctor of science degree from CórdobaUniversity. He is now a professor at Department of Physical, Chemical, and Nat-ural System from Pablo de Olavide University since 2003. His main interests aremolecular devices for chemical sensors and green materials.