Comparative study of sampling systems combined with gas sensors for wine discrimination

Sensors and Actuators B 126 (2007) 616–623

Comparative study of sampling systems combined with gas sensors for wine discrimination J. Lozano, J.P. Santos, J. Guti´errez, M.C. Horrillo ∗ Laboratorio de Sensores (IFA-CSIC), C/Serrano 144, 28006 Madrid, Spain Received 6 December 2006; received in revised form 10 April 2007; accepted 11 April 2007 Available online 19 April 2007

Abstract A comparison among several sampling systems usually employed in an electronic nose is performed in this paper in order to improve the performance of this instrument for wine discrimination. Three different sampling methods have been studied: static headspace with dynamic injection (HS), purge and trap (P&T) and solid-phase micro-extraction (SPME). These electronic noses have been developed in order to discriminate five different Spanish wines coming from different grape varieties and elaboration processes. Linear techniques as principal component analysis (PCA) and nonlinear ones as probabilistic neural networks (PNN) have been used for pattern recognition. Results show that the best discrimination is achieved with P&T and SPME, although the highest response of sensors is obtained by the HS method. © 2007 Elsevier B.V. All rights reserved. Keywords: Electronic nose; Sampling methods; Wine discrimination; Multivariate analysis

1. Introduction One of the main problems that researchers have to face when studying the volatile compounds responsible for wine aroma is the choice of a suitable extraction procedure to qualitatively and quantitatively represent the wine original aroma. In other words, it can be difficult to obtain a representative extract of the wine which has not been altered or degraded in any way. Several methods have been developed with the aim of achieving that goal. All of them present different advantages and disadvantages [1]. Wine is one of the most complex alcoholic beverages, the aroma contributing much of this complexity. Describing the richness of the wine features and the great variety of wine aromas is not an easy task for researchers. Some of the reasons for such complexity are: (1) more than 800 components have been identified in the volatile fraction of the wine, (2) volatile components have a different chemical nature covering a wide range of polarity, solubility, volatility and pH, (3) a large number of the volatile components in wine can only be found at very low concentration (␮g/L) and therefore, the samples need to be highly concentrated for them to be accurately quantified, and (4) many

∗

Corresponding author. E-mail address: [email protected] (M.C. Horrillo).

0925-4005/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2007.04.018

of the aromatic components are unstable and they may be easily oxidized in contact with air or degraded by heat or extreme pH, giving rise to the appearance of artifacts. While the discrimination or classification of wines is difficult, it is also very important due to the high economic value of the product and the labelling of the wine according to origin denominations (OD). The OD assignment is important for protecting the quality of the wine, preventing illegal adulteration of wine and to control the processing of the wine. Generally, the sensory analysis based on the trained experts panel test is useful in the wine classification task, but it is not always feasible because of its high cost, time and subjectivity. Therefore, it is interesting to use other methods for wine discrimination essentially based on instrumental analytical techniques. In fact, the common methods of chemical analysis such as gas and liquid chromatography, mass spectrometry, nuclear magnetic resonance and spectrophotometry have higher reliability but take longer to give a result, are often not capable of in situ measurements and have high costs associated with them. During the last few years, the so-called electronic noses or artificial olfactometers have been developed as an alternative method to perform this aromatic analysis [2–4]. They give an overall response to a mixture without identifying the single components. This approach to wine characterization is completely different to the classical techniques.

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Fig. 1. Typical block diagram of an electronic nose.

An electronic nose generally consists of an aroma extraction system, a sensor array, a control and measurement system and a pattern recognition method. A simple flow chart of the typical structure of an electronic nose is shown in Fig. 1. The aroma extraction system or sampling method carries the aromatic compounds from the wine to the sensor chamber. Several aroma extraction techniques are usually used for electronic noses: static headspace (HS) [5], purge and trap (P&T) [6] and solid-phase micro-extraction (SPME) [7] are the most common techniques. A chemical sensor is a device that is capable of converting a chemical change into an electric signal and responds to the concentration of specific particles such as atoms, molecules, or ions in gases or liquids [8]. Chemical sensors can be based on electrical, thermal, mass or optical principles. Several examples of chemical sensors used in electronic noses are conducting polymers [9], quartz resonators [10], surface acoustic sensors (SAW) [11] and semiconductor devices [12]. The electronic nose device has the advantage of high portability for making in situ and on-line measurements with lower costs and good reliability. The control and measurement system includes all electronic circuits needed for the measurements of signals generated by the sensors such as interface circuits, signal conditioning and A/D converters. The aim of an electronic nose is the detection, identification, or quantification of volatile compounds. The multivariate response of an array of chemical gas sensors with broad and partially overlapping selectivities can be utilized as an “electronic fingerprint” to characterize a wide range of odors or volatile compounds by means of pattern recognition techniques. It means signal processing and pattern recognition system. However, those two steps may be subdivided into the following steps: preprocessing, feature extraction, classification, and decisionmaking [13]. Preprocessing compensates for sensor drift, compresses the transient response of the sensor array, and reduces sampleto-sample variations. Feature extraction has two purposes: to reduce the dimensionality of the measurement space, and to extract information relevant for pattern recognition. The most used method of performing the classification task is artificial neural networks (ANNs). An ANN is an information processing system that has certain performance characteristics in common with biological neural networks. It allows the elec-

tronic nose to function in the way of a brain function when it interprets responses from olfactory sensors in the human nose. The classifier produces an estimate of the class for an unknown sample along with an estimate of the confidence placed on the class assignment. A final decision-making stage may be used if any application-specific knowledge is available, such as confidence thresholds or risk associated with different classification errors. 2. Experimental An electronic nose based on a tin oxide array has been used for analysis of the samples using the three different sampling methods. It has been developed and fabricated in-house for wine aroma purposes [14,15]. 2.1. Sampling methods The sampling methods used in the experiments were the following: (1) Static HS followed by a dynamic injection. Two possible ways were established for the carrier gas: directly from the gas bottle to the sensors cell or carrying the volatile compounds of the wine sample. Solution (10 mL) was kept in a 50 mL Dreschel bottle at 30 ◦ C for 30 min. Then the electrovalves were switched and nitrogen fluxed by the flask, carrying the volatile compounds from the sample to the sensor cell for 20 min. After that, the electrovalves were switched again to allow the sensors to desorb sorbed substances. This procedure was repeated several times, at least eight, for each sample. Fig. 2 shows the apparatus used for the HS method. (2) The P&T unit (Tekmar 3100) was directly coupled to the sensors cell as shown in Fig. 3. The P&T operating procedure was as follows. A 1 mL sample of each wine was poured into the purge vessel. Volatile compounds were then extracted by purging and trapping in the Tenax trap. The adsorbent material was subsequently heated so that the desorbed analytes were directly transferred into the sensors cell. The parameters used in P&T analysis are shown in Table 1. (3) Solid-phase micro-extraction (SPME) is a well-known technique that has been devised for the preparation of samples

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J. Lozano et al. / Sensors and Actuators B 126 (2007) 616–623 Table 1 Purge and trap operating conditions Parameter

Value

Purge time (min) Purge temperature (◦ C) Purge-gas flow rate (ml/min) Desorption temperature (◦ C) Desorption time (min) Carrier gas flow rate (ml/min)

15 30 50 225 5 20

for gas chromatography. The sensors cell was mounted inside the chromatograph oven by replacing the chromatographic column and connected to the back injector (see Fig. 4). An aliquot (8 mL) of each sample was placed in 15 ml vials and heated at 30 ◦ C with magnetic shaking. An SPME-fibre coated with 65 ␮m of polydimethylsiloxane/divinylbenzene (supplied by Supelco) was placed in the gas-phase of the vial for 30 min. Then the SPME-fibre was situated in a heated injection port (250 ◦ C) for 10 min in order to ensure that all the volatile compounds present in the sorbent were brought to the sensor chamber. The fibre coating and operating conditions have been previously reported [15]. 2.2. Sensors Fig. 2. Headspace sampling apparatus.

The sensor array was prepared in our laboratory by RF sputtering onto an alumina substrate. The array composition is 16 thin film sensors with thicknesses between 200 and 800 nm. Some sensors were doped with chromium and indium either as surface or intermediate layer. Array composition is described in Table 2. The operation temperature of the sensors was controlled at 250 ◦ C with a PID controller. The array was placed in a 24 cm3 stainless steel cell with a heater and a thermocouple. The carrier used gas was 99.998% pure nitrogen. Gas line tubes were of stainless steel covered with fused silica in order to minimize gas adsorption in the line.

Fig. 3. Purge and trap apparatus.

Fig. 4. SPME sampling apparatus.

J. Lozano et al. / Sensors and Actuators B 126 (2007) 616–623 Table 2 Sensor array composition Sensor

SnO2 thickness (nm)

Dopant (time, s)

Dopant layer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

200 400 600 800 450 450 450 450 450 450 450 450 450 450 450 450

– – – – Cr (8) Cr (16) Cr (24) Cr (32) In (8) In (16) In (24) In (32) Cr (8) Cr (16) In (8) In (16)

– – – – Intermediate Intermediate Intermediate Intermediate Intermediate Intermediate Intermediate Intermediate Surface Surface Surface Surface

2.3. Control and measurement system The resistances of the sensors were measured with a 71/2 digits digital multimeter (Keithley 2700) with 40-channel multiplexer, connected to a personal computer through a GPIB interface. The measurement system was fully automated and controlled with a program developed in TestpointTM . More details of the system are shown in Ref. [16]. 2.4. Data processing The data obtained from the 16 sensors was stored by columns in disk before data processing. The typical electronic signal supplied by the electronic nose is shown in Fig. 5. Responses of the individual sensors were defined with respect to the minimum resistance to 12% (v/v) of ethanol for all the measurements: Rwine r= Rcalibration where Rwine is the minimum resistance of the sensor for the measurement with wine and Rcalibration is the minimum resistance of the sensor exposed to a solution of 12% of ethanol. This calibra-

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tion is performed in order to compensate the aging of sensors to the main component of wine [13]. The data collected were analyzed using a commercial software package (Matlab 6.1) for programming the feature extraction and the pattern recognition techniques (principal component analysis (PCA) and artificial neural networks (ANNs)). PCA was carried out on the sensor array data. PCA is a signal representation technique that applies a linear transformation to the data and results in a new space of variables called principal components. PCA reduces the dimensionality of feature space by restricting attention to those directions along which the scatter of the cloud data points is greatest [17]. The first two components are represented in the PCA plots. Usually these first two components carry most of the information of the old variables. Data were preprocessed before analysis. The preprocessing included a rescaling dividing by the standard deviation. For classification purposes, a probabilistic neural network (PNN) [17] has been used. This type of network is composed by four layers. In the input layer there are three elements that correspond with the first three components of the PCA that carry almost 100% of the old variables. The next layer is the pattern layer. This layer has a number of neurons equal to the training pattern vectors, grouped by classes, where the distance between the test vector and a learning pattern is assessed. The purpose of this layer is to measure and weight with a radial function the distance of the input layer vector with each training set element. The third layer, the summation layer, contains one neuron for each class. This layer adds the outputs of the pattern neurons belonging to the same class. Finally, the “output layer” is simply a thresholder that seeks for the maximum value of the summation layer. Then the highest one is selected and takes one as a result. The other outputs are set to 0 [17]. In Fig. 6 a schematic diagram of the PNN network is shown. A leave one out (LOO) cross validation method was applied to the network in order to check the performance of the network [18]. The LOO consisted of training N distinct nets (in this case, N is the number of measurements) by using N − 1 training vectors; while the validation of the trained net was carried out by using the remaining vector, excluded from the training set. This procedure was repeated N times until all vectors were validated [19,20]. 2.5. Wine samples

Fig. 5. Typical transient response of four sensors of the array.

Samples of five different wines manufactured with the majority varieties in the origin denomination (OD) “Vinos de Madrid” were used for testing the discrimination capability of the different sampling methods of the electronic nose. Grapes were grown in the same vineyard, and they were harvested at commercial maturity, Air´en and Malvar (20◦ Brix), Grenache and Tempranillo (25◦ Brix). The harvested grapes were quickly transported in plastic boxes (30 kg) to the cellar where they were processed following classic techniques of elaboration of white and red wines. The grapes were de-stemmed and crushed and 50 mg/L de SO2 was added. The alcoholic fermentation was carried out in small steel tanks (100 L), and the fermentation temperature was <30◦ in red vinification and <20◦ in white

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Fig. 6. A probabilistic neuronal network (PNN) with three input variables, five classes and 30 training examples (six belonging to each class).

vinification. The end of fermentation was taken when reducing sugar concentration decreased bellow 2 g/L. Two months later the wines were clarified, bottled in green glass bottles and stored at 15 ◦ C. The wine from Tempranillo variety was kept in American oak barrels during 6 months for aging. To summarize, there are five different wines: four young wines (from Malvar (Mal), Airen (Air), Grenache (Gre) and Tempranillo (Temp) variety) and one aged (Tempranillo variety aged in oak barrels). 3. Results and discussion 3.1. Sensor response Fig. 5 shows the typical transient responses of four chemoresistive sensors, operating at 250 ◦ C, exposed towards the headspace of the blank wine. The response of the sensors corresponds to several pulses of 20 min of exposition to the tested wine flavour, followed by a pure nitrogen purge for 40 min. Fig. 7 shows the radial plot of the average responses of the sensors to the five wines measured using the three different sampling methods. HS is the sampling method that provides the highest response, followed by P&T and SPME. This is due to the elimi-

Fig. 7. Sensor responses using different sampling methods (HS, P&T and SPME) with the same wine.

nation of water and ethanol realized by P&T and SPME methods which result in a lower response of the gas sensors. 3.2. Discrimination capability The discrimination capability of the sampling methods used was evaluated by means of the PCA, which provided a better representation of the measured data in a reduced dimension. Figs. 8–10 show the PCA score plot for the five wines measured using HS, P&T and SPME methods, respectively. The percentage of variance explained by each principal component is in brackets. The measurements with HS technique show a partial overlapping between “Airen” and “Tempranillo” wines as well as between “Malvar” and “Grenache” with P&T technique. Anyway, the datasets of the different wines are well separated. In case of P&T and SPME methods, clusters are more concentrated and separated than in the case of HS technique. The loads associated with the PCA score plots of Figs. 8–10 are shown in Figs. 11–13. It can be seen that the contribution of the sensors are quite different for each sampling technique. For example for HS there is a high correlation between sensors S9 and S11. For P&T, sensor S9 is highly correlated with sensor S16. For SPME however the most correlated sensors are S13 and S14.

Fig. 8. PCA score plot of wine measurements using HS.

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Fig. 12. Sensor contribution to PCA using P&T.

Fig. 9. PCA score plot of wine measurements using P&T.

Fig. 13. Sensor contribution to PCA using SPME.

Fig. 10. PCA score plot of wine measurements using SPME.

3.3. Classification of samples PCA results were confirmed with a more powerful pattern recognition method: ANNs. A PNN was trained with the above data in order to perform a classification. The net was validated with the LOO method. The confusion matrices for the neural network are shown in Tables 3–5 for the e-noses using HS, P&T and SPME as a sampling method, respectively. The success rate (the correct predicted number over the total number of measurements) obtained for these methods was 87.5%, 95.8% and 100%, respectively. Table 3 Confusion matrix (real, simulated) of classification using headspace sampling method

Air Mal Gre Temp Aged Fig. 11. Sensor contribution to PCA using HS.

Air

Mal

Gre

Temp

Aged

4 0 0 0 0

0 6 0 0 0

1 0 5 0 0

1 0 1 6 0

0 0 0 0 6

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Table 4 Confusion matrix (real, simulated) of classification using purge and trap sampling method

Air Mal Gre Temp Aged

Air

Mal

Gre

Temp

Aged

6 0 0 0 0

0 6 1 0 0

0 0 5 0 0

0 0 0 6 0

0 0 0 0 6

Table 5 Confusion matrix (real, simulated) of classification using SPME sampling method

Air Mal Gre Temp Aged

Air

Mal

Gre

Temp

Aged

6 0 0 0 0

0 6 0 0 0

0 0 6 0 0

0 0 0 6 0

0 0 0 0 6

4. Conclusions Discrimination of different wines elaborated with different grapes has been performed with a thin film semiconductor sensor array based electronic nose using three different sampling methods. Although the highest response is obtained by the HS method, due to the amount of ethanol extracted by this method, the best discrimination is achieved with the two other methods. Using P&T and SPME, the separation of different clusters with PCA and the classification performed with PNN is major (100% and 95.8% success rates for SPME and P&T, respectively). The choice of the technique of extraction could be determined by the final application of the prototype: HS and P&T could be more suitable for in situ and portable systems whereas SPME could be use in laboratory. Acknowledgements Authors want to thank “Instituto Madrile˜no de Investigaci´on y Desarrollo Rural Agrario y Alimentario” (IMIDRA) for the wine samples and Spanish Science and Technology Ministry for supporting the project TIC2002-04588-C02-01.

[4] C. Di Natale, F.A.M. Davide, A. D’Amico, P. Nelli, S. Groppelli, G. Sberveglieri, An electronic nose for the recognition of the vineyard of a red wine, Sens. Actuators B: Chem. 33–35 (1996) 83–88. [5] M. Penza, G. Cassano, Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks, Food Chem. 86 (2004) 283–296. [6] L. Pillonel, J.O. Bosset, R. Tabacchi, Rapid preconcentration and enrichment techniques for the analysis of food volatile. A review, Lebensmittel-Wissenschaft und-Tecnology 35 (2002) 1–14. [7] J.A. Guadarrama, M. Fern´andez, J. Iniguez, J.A. Souto, de Saja, Discrimination of wine aroma using an array of conducting polymer sensors in conjunction with solid-phase micro-extraction (SPME) technique, Sens. Actuators B: Chem. 77 (2001) 401–408. [8] T.C. Pearce, S.S. Schiffman, H.T. Nagle, J.W. Gardner, Handbook of machine olfaction, Wiley–VCH, Germany, 2003. [9] J.A. Guadarrama, M. Fern´andez, J. I˜niguez, J.A. Souto, de Saja, Array of conducting polymer sensors for the characterisation of wines, Anal. Chim. Acta 411 (2000) 193–200. [10] Y. Yang, P. Yang, X. Wang, Electronic nose based on SAWS array and its odor identification capability, Sens. Actuators B: Chem. 66–69 (2000) 167–170. [11] J.P. Santos, M.J. Fern´andez, J.L. Fontecha, J. Lozano, M. Aleixandre, M. Garc´ıa, J. Guti´errez, M.C. Horrillo, SAW sensor array for wine discrimination, Sens. Actuators B: Chem. 107 (2005) 291–295. [12] C. Di Natale, A. Macagnano, S. Nardis, R. Paolesse, C. Falconi, E. Proietti, P. Siciliano, R. Rella, A. Taurino, A. D’Amico, Comparison and integration of arrays of quartz resonators and metal-oxide semiconductor chemoresistors in the quality evaluation of olive oils, Sens. Actuators B: Chem. 78 (2001) 303–309. [13] R. Gutierrez-Osuna, Pattern analysis for machine olfaction: a review, IEEE Sens. J. 2 (2002) 189–202. [14] J.P. Santos, J. Lozano, M. Aleixandre, I. Sayago, M.J. Fern´andez, L. Ar´es, J. Guti´errez, M.C. Horrillo, Discrimination of different aromatic compounds in water, ethanol and wine with a thin film sensor array, Sens. Actuators B: Chem. 103-104 (2004) 98–103. [15] J. Lozano, J.P. Santos, J. Guti´errez, M.C. Horrillo, Comparison among several fiber coating for a SPME based electronic nose applied to wine discrimination, in: Proceedings of the IEEE Sensors 2005, Irvine, California, USA, 2005. [16] J. Lozano, J.P. Santos, M.C. Horrillo, Classification of white wine aromas with an electronic nose, Talanta 67 (2005) 610–616. [17] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, Wiley– Interscience, 2001. [18] E.P. Derks, M.L. Beckers, W.J. Melssen, L.M. Buydens, Parallel processing of chemical information in a local area network. Part II. A parallel cross-validation procedure for artificial neural networks, Comput. Chem. 20 (1995) 439–448. [19] C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1999. [20] B.G.M. Vandeginste, D.L. Massart, L.M.C. Buydens, S. de Jong, P.J. Lewi, J. Smeyers-Verbeke, Handbook of Chemometrics and Qualimetrics: Part B, Elsevier, 1998, pp. 238-239.

Biographies References [1] M. Ortega-Heras, M.L. Gonz´alez-SanJos´e, S. Beltr´an, Aroma composition of wine studied by different extraction methods, Anal. Chim. Acta 458 (2002) 85–93. [2] M. Penza, G. Cassano, Recognition of adulteration of Italian wines by thinfilm multisensor array and artificial neural networks, Anal. Chim. Acta 509 (2004) 159–177. [3] J.P. Santos, T. Arroyo, M. Aleixandre, J. Lozano, I. Sayago, M. Garc´ıa, M.J. Fern´andez, L. Ar´es, J. Guti´errez, J.M. Cabellos, M. Gil, M.C. Horrillo, A comparative study of sensor array and GC–MS: application to Madrid wines characterization, Sens. Actuators B: Chem. 102 (2004) 299– 307.

´ Lozano received the B.Sc. (2001) and Ph.D. (2005) degree in electronic Jesus engineering from University Complutense of Madrid, Spain. He has worked at the Electronics Department of the University Complutense de Madrid and at the Sensors Laboratory of Consejo Superior de Investigaciones Cient´ıficas (CSIC), Madrid. Presently he works as an assistant professor at Universidad Polit´ecnica de Madrid. His research interests include gas sensors, electronic instrumentation, modelling and measurement systems. Jos´e Pedro Santos received his B.Sc. (1987) and Ph.D. (1995) in physics from the University Complutense of Madrid. He has worked at the University of Milan (Italy), at the Institute of Advanced Materials of the European Commission’s Joint Research Centre (Ispra, Italy) and at the Electronics Department of the University Complutense of Madrid. Presently he works at the Instituto de F´ısica

J. Lozano et al. / Sensors and Actuators B 126 (2007) 616–623 Aplicada (IFA-CSIC) on several projects related to the development of sensors for volatile-compounds and pollutants detection. Javier Guti´errez is Director of the Instituto de F´ısica Aplicada (IFA-CSIC), received the Ph.D. in physics from the University Complutense of Madrid in 1977. His research interest is chemical sensors: semiconductor sensors, gravimetric sensors and optoelectronic devices applied to artificial olfactometrics for environmental monitoring, consumer protection and safety of aliments.

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Carmen Horrillo received her Ph.D. in Chemistry from the University Complutense of Madrid in 1992. From 1993 to 1995 she was working at the Institute for Advanced Materials of the European Commission’s Joint Research Centre (Ispra, Italy). Since then she has been working at the Instituto de F´ısica Aplicada (CSIC) on R&D of chemical microsensors and electronic noses for environmental protection and quality control of foods. Since 1999 she is head of the Department of Tecnolog´ıa de Gases y Superficies.