- Email: [email protected]
Wine classification with a zinc oxide SAW sensor array
Sensors and Actuators B 120 (2006) 166–171
Wine classification with a zinc oxide SAW sensor array J. Lozano a , M.J. Fern´andez a , J.L. Fontecha a , M. Aleixandre a , J.P. Santos a , I. Sayago a , T. Arroyo b , J.M. Cabellos b , F.J. Guti´errez a , M.C. Horrillo a,∗ b
a Laboratorio de Sensores (IFA-CSIC), Serrano, 144, Madrid 28006, Spain Dpto. Agroalimentaci´on (IMIDRA), Finca El Enc´ın. Ctra. N II Km 38,2. 28800, Alcal´a de Henares, Madrid, Spain
Received 22 November 2005; accepted 4 February 2006 Available online 23 March 2006
Abstract An array of surface acoustic wave (SAW) sensors using Zinc oxide films has been developed in order to discriminate Spanish wine coming from different grape varieties and elaboration processes. Sensor array is composed by eight surface acoustic wave sensors, one of them used as reference sensor and the others coated with diverse polymers by spray coating technique. Linear techniques as principal component analysis (PCA) and non-linear ones as probabilistic neural networks (PNN) have been used for pattern recognition. A classification success rate (correct predicted number over total number of measurements) of 90% has been achieved. © 2006 Elsevier B.V. All rights reserved. Keywords: ZnO SAW sensor; Polymer films; Wine discrimination
1. Introduction Surface acoustic wave (SAW) devices have shown promising characteristics as chemical vapour sensors due to their compact structures, small size, low cost, high sensitivity and fast response. The basic principle of SAW gas sensors is the reversible sorption of chemical vapours by a sorbent coating which is sensitive to the vapour to be detected. The vapour is sorbed by the sensitive layer resulting in a mass increase of the coating, which alters the surface wave velocity in the device. The velocity changes are measured indirectly using the device as the resonant element in a delay line oscillator circuit and measuring the frequency shifts due to the vapour sorption. It is well known that this sensor type shows good characteristics for VOCs detection [1–5] and also have been used for wine discrimination in some applications [6–8]. But, the need in many sensing applications of miniaturized systems, highly sensitive and of low cost has motivated the desire to integrate on the same substrate the SAW structure with the connected electronic circuitry. Piezoelectric materials commonly used in SAW
∗
Corresponding author. Tel.: +34 915618806; fax: +34 915631794. E-mail address: [email protected] (M.C. Horrillo). URL: http://www.ifa.csic.es.
0925-4005/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2006.02.014
devices are single crystalline substrates of quartz, lithium tantalite (LiTaO3 ) and lithium niobite (LiNbO3 ), but these materials are incompatible with the integrated circuit (IC) technology. As the 90% of the propagating SAW energy is focused at a depth of one wavelength from the surface, one of the alternative methods for fabricating SAW devices is to use layered structures containing piezoelectric thin films deposited on nonpiezoelectric substrates [9]. This is possible because of the availability of the techniques of deposition of thin piezoelectric films and by the compatibility of the technological process of fabrication of SAW sensors, with those of planar integrated circuits [10,11]. Thus, the research in this field is centred on the development of materials with good electroacoustic properties allowing the fabrication of high frequency devices on silicon substrates with improved performances and at the same time with a low fabrication cost. The realization of acoustic devices on silicon requires a piezoelectric overlay for the generation and detection of acoustic waves. For SAW device applications ZnO is one of the most attractive materials because it has a high electromechanical coupling coefficient, an excellent bonding on various substrate materials, in particular silicon, silicon dioxide and silicon nitride, temperature stability and high electrical resistivity [12,13]. It belongs to the hexagonal wurtzite class and can be fabricated easily through sputtering, the most common deposition method
J. Lozano et al. / Sensors and Actuators B 120 (2006) 166–171
for making polycrystalline ZnO thin films in acoustic applications because it allows to control the preferred crystalline c-axis orientation and in addition it is compatible with IC technology. To perform an analysis of complex samples it is necessary to use an array of several gas sensors with different sensitive layers and partial selectivities to various gaseous components [14]. The appropriate choice of coatings coupled with an adequate pattern recognition method lead to a selective chemical analysis. As it is well known, polymer films are the best chemical interfaces to detect organic vapours because of their high sensitivity, fast vapour diffusion, reversible responses and good ability to work at room temperature. Several polymers are used as sensitive layers such as phtalocianines [15], ciclodextrins [16], organometallic compounds [17] and rubber polymers [18]. Metals and semiconductors are also employed as sensitive layers [19]. Due to the economic importance of the wine market for many countries, instruments for quality control are very useful for the wine industry and regulator organizations. Recently, several artificial olfactory systems based on metal oxide resistive sensors have been used for this task [20,21], but those sensors are not as fast as SAW sensors. The aim of this work is to demonstrate the potentiality of ZnO SAW sensor arrays in differentiating several types of wine in order to integrate in a future most of the circuitry in the same substrate for developing miniaturized and portable sensory systems.
167
Fig. 1. AFM image of the ZnO film.
2. Experimental 2.1. Sensors The SAW sensors used in this study consist of delay lines (DL) with different polymer thin films of various thicknesses. They were fabricated on P-type silicon wafers ((1 0 0) orientation, 3–5 k cm resistivity) of 4 in. of diameter, with a SiO2 thermally grown layer of 1 m thickness. Zinc oxide films were deposited by RF magnetron sputtering using a 3 in. diameter zinc oxide target (99.99% purity) in reactive plasma. The oxygen concentration in Ar plasma was established to 10%. The ratio of argon (99.999% purity) and oxygen (99.999% purity) was controlled by the electronic mass flow controllers. The RF power was 50 W and the distance between target and substrate was 50 mm. For the deposition, the substrate temperature was maintained at 250 ◦ C and monitored with a thermocouple near the substrate [22]. At this temperature the film morphology is characterized by fine uniform grains and smooth surfaces [23]. The film thickness was measured with an interferometer (Nanospec AFT200), being approximately 2 m. The surface morphology and roughness of the film were characterized by an AFM (Digital Instruments-Nano Scope III). In Fig. 1 is shown a three-dimensional AFM image of the ZnO film. The interdigited transducers (IDTs) were made of aluminium deposited by RF sputtering using photolithographic techniques, being the thickness 200 nm and the finger width and the spacing between them of 5 m, what means a wavelength, λ, of 20 m. Fig. 2 shows a cross-section of the SAW layered structure. Five different designs of delay lines were realized modifying the acoustic aperture, W, distance between IDTs, L, and finger
Fig. 2. Schematic diagram of the layered SAW structure.
pair number, N, to optimize the parameters of design [22]. The optimal parameters are shown in Fig. 3 over an image of the ZnO SAW sensor. The central frequency of the delay line was 210 MHz and therefore the propagation velocity of the SAW (v = fλ) is 4200 m/s. Several polymers were chosen as sensitive films: OV-225 and OV-275 silicones, polyisobutylene (PIB), polyetherurethane (PEUT), and polyepichlorohydrin (PECH). They are well known commercial polymers for SAW sensor applications with excellent properties as low static glass transition temperature in order to obtain fast vapour diffusion and reversible response. The films were deposited by spray coating (see Fig. 4) using an airbrush with a solution of the polymer and the solvent. The mass change of the coating caused by the deposition of the film is detected by a
Fig. 3. SAW device with IDTs (Al)–ZnO–SiO2 –Si structure.
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array operated at a constant flow of 200 ml/min of pure nitrogen. The oscillator circuit for each device was connected to one side of the chamber. 2.2. Measurement system The sensor response, given by the frequency shifts, was measured with a frequency counter HP 53131, connected to microwave switch system, Keithley S46, which allow the frequency display of the eight sensors. The software homedeveloped for data acquisition, gives the frequency of each device and the frequency difference between each one and the reference. It was used for real-time visualization, storing and analysis of these data. The experimental set-up of the SAW sensor system is shown in Fig. 6. Fig. 4. Sensitive film deposition with simultaneous frequency monitoring.
2.3. Samples Five wines from the Madrid region were measured with the sensor array. They were elaborated with the four most common grape varieties of the region, two reds (Grenache and Tempranillo) and two whites (Air´en and Malvar). Four of them correspond to the 2003 harvest and the other to the 2002 harvest and have been elaborated with Tempranillo variety and aged for a year in American oak barrels. 2.4. Young wine
Fig. 5. View of the sensor array in the measuring cell.
reduction of the surface velocity and measured as a proportional frequency shift of the oscillation frequency. This frequency was simultaneously monitored with a HP 8510B vector network analyzer during film deposition. Frequency shifts were in the range between 200 and 500 kHz [24]. The polymers and frequency shifts of each sensor of the array are shown in Table 1. Fig. 5 shows the array of sensors. The sensors were placed in two rows on a printed circuit board over a Peltier device. A thin film platinum resistance (Pt100) was glued to the PCB to measure the temperature. Using a PID controller, the Pt100 as sensor and Peltier as actuator, the operation temperature of sensors was kept at 23 ± 0.01 ◦ C. The measurement chamber containing the sensors was made in anodized aluminium. The
Grapes grown in the same vineyard 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 ◦ C in red vinification and <20 ◦ C in white vinification. The end of fermentation was taken when reducing sugar concentration decreased bellow 2 g/l. Two months later the wines were clarified and bottled in green glass bottles and stored at 15 ◦ C. 2.5. Aged Tempranillo wine Before ageing the wine of variety Tempranillo, malolactic fermentation was induced by addition of a commercial Leuconostoc oenos strain until the malic acid level got under 1 g/l. Then the wine was clarified and the SO2 concentration adjusted around 50 mg/l. The wine was introduced in wood barrels (225 l)
Table 1 Type and frequency shift of the polymer deposited in each sensor of the array
Polymer h (kHz)
Sensor 0 (reference)
Sensor 1
Sensor 2
Sensor 3
Sensor 4
Sensor 5
Sensor 6
Sensor 7
0
OV-275 300
PIB 300
PEUT 200
OV-225 300
PEUT 300
PECH 300
PECH 435
J. Lozano et al. / Sensors and Actuators B 120 (2006) 166–171
Fig. 6. Measurement set-up schematics.
from American oak and the ageing was conducted in a special chamber during 6 months at 14 ◦ C. 3. Results Wine samples of 10 ml were kept at 30 ± 0.1 ◦ C for 30 min in a 50 ml flask. After that time the headspace was injected in the measurement chamber for 10 min. At least eight replicates of each wine measurement were made. Responses of the individual sensors are defined respect to the minimum frequency to 12% (v/v) of ethanol for all the measurements: r=
fwine fcalibration
where fwine is the difference between equilibrium frequency in nitrogen and minimum frequency of the sensor in presence of wine and fcalibration is the difference between equilibrium frequency in nitrogen and minimum frequency of the sensor exposed to a solution of 12% of ethanol. The objective of this data pre-processing is compensating for sensor drift, extracting descriptive parameters from the sensor array response and preparing the feature vector for further processing [25]. A periodic recalibration is performed once a week with a 12% ethanol solution used as reference because it is chemically stable over time and highly correlated with the target analytes in terms of sensor behavior [26]. The response to the five types of wine is shown in Fig. 7. This polar plot shows the differences among the studied samples. The variation in response intensity is due to different headspace composition of the wines. Each sample has its characteristic organic volatile compounds profile. The data obtained were pre-processed by centering about the mean and scaling with the standard deviation before performing a principal component analysis (PCA). The PCA applies a linear transformation to the data and results in a new space of variables called principal components [27]. The PCA results of the measurements are illustrated in Fig. 8. It can be seen that all
Fig. 7. Polar plot of sensor responses to different wines.
Fig. 8. PCA score plot for the measurements of the five wines.
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Table 2 Confusion matrix for the PNN classification Real/predicted
Malvar
Air´en
Grenache
Tempranillo
Temp. aged
Malvar Air´en Grenache Tempranillo Temp. aged
7 1 0 0 0
1 7 0 0 0
0 0 6 0 0
0 0 2 8 0
0 0 0 0 8
types of wines are well separated except a partial overlapping between zones corresponding to white wines (Malvar and Air´en wines). A probabilistic neural network (PNN) was used for classification purposes. The PNN was composed by three layers: the input one had three neurons, corresponding to the three principal components; the hidden layer, with radial basis transfer functions, had the same number of neurons that number of training vectors and a competitive layer in the output [27]. Leave one out (LOO) cross validation method was applied to the network in order to check the performance of the network [28]. LOO consists of training N distinct nets (in this case, N is number of measurements) by using N − 1 training vectors; while the validation of the trained net is carried out by using the remaining vector, excluded from the training set. This procedure is repeated N times until all vectors are validated [29]. The confusion matrix for the neural network is shown in Table 2. The success rate (correct predicted number over total number of measurements) was 90%. All the samples corresponding to Tempranillo grapes are well-classified. The network only confuses one sample of Malvar and Air´en and two of Grenache that classify like Tempranillo. 4. Conclusions We have demonstrated the possibility of application of a SAW sensor array, using Si technology to realize Si–SiO2 –ZnO structure, for the discrimination of wines from different grape varieties and ageing processes. Although PCA showed partial overlapping between white wine datasets the classification performance by PNN is satisfactory, showing a 90% confusion matrix. Optimization of coating thickness and composition and measurement parameters (operation temperature, vapour generation times, etc.) could improve the classification rate. Acknowledgements This work is being supported by Ministerio de Ciencia y Tecnolog´ıa from Spain under projects: TIC 2001-0554-C03-01 and TIC 2002-04588-C02-01. References [1] H. Wohltjen, Mechanism of operation and design considerations for surface acoustic wave device vapour sensors, Sens. Actuators B: Chem. 5 (1984) 307. [2] D. Amati, D. Arn, N. Blom, M. Ehrat, J. Saunois, H.M. Widmer, Sensitivity and selectivity of surface acoustic wave sensors for organic solvent vapour detection, Sens. Actuators B: Chem. 7 (1992) 587–591.
[3] M. Fang, K. Vetelino, M. Rothery, J. Hines, G.C. Fyre, Detection of organic chemicals by SAW sensor array, Sens. Actuators B: Chem. 56 (1999) 155–157. [4] J. Reibel, U. Stahl, T. Wessa, M. Rapp, Gas analysis with SAW sensor systems, Sens. Actuators B: Chem. 65 (2000) 173–175. [5] J. Wagner, M. Von Schickfus, Inductively coupled, polymer coated surface acoustic wave sensor for organic vapors, Sens. Actuators B: Chem. 76 (2001) 58–63. [6] Y.-M. Yang, P.-Y. Yang, X.-R. Wang, Electronic nose based on SAWS array and its odor identification capability, Sens. Actuators B: Chem. 66 (2000) 167–170. [7] M. Garc´ıa, M.J. Fern´andez, J.L. Fontecha, J. Lozano, J.P. Santos, M. Aleixandre, I. Sayago, J. Guti´errez, M.C. Horrillo, Differentiation of red wines using an electronic nose based on surface acoustic wave devices, Talanta 68 (4) (2006) 1162–1165. [8] 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. [9] F.S. Hickernell, Zinc oxide films for acoustoelectric device applications, IEEE Trans. Son. Ultrason. 5 (1985) 621–629. [10] M.J. Vellekoop, C.G. Visser, P.M. Sarro, A. Venema, Compatibility of zinc oxide with silicon IC processing, Sens. Actuators A: Phys. 21–23 (1990) 1027–1030. [11] T. Xu, G. Wu, G. Zhang, Y. Hao, The compatibility of ZnO piezoelectric film with micromachining process, Sens. Actuators B: Chem. 104 (2003) 61–67. [12] K.B. Sundaram, A. Khan, Characterization and optimization of zinc oxide films by r.f. magnetron sputtering, Thin Solid Films 295 (1997) 87– 91. [13] R. Ondo-Ndong, G. Ferblantier, M. Kalfioui, A. Boyer, A. Foucaran, Properties of RF magnetron sputtered zinc oxide thin films, J. Cryst. Growth 255 (2003) 130–135. [14] J.W. Gardner y, P.N. Bartlett, Electronic Noses: Principles and Applications, Oxford University Press Inc., New York, 1999. [15] W.P. Jakubik, M.W. Urbanczyk, S. Kochowski, J. Bodzenta, Bilayer structure for hydrogen detection in a surface acoustic wave sensor system, Sens. Actuators B: Chem. 82 (2002) 265–271. [16] D. Li, M. Ma, Surface acoustic wave microsensors based on cyclodextrin coatings, Sens. Actuators B: Chem. 69 (2000) 75–84. [17] M. Penza, G. Cassano, A. Sergi, C.L. Sterzo, M.V. Russo, SAW chemical sensing using poly-ynes and organometallic polymer films, Sens. Actuators B: Chem. 81 (2001) 88–98. [18] K. Bodenh¨ofer, A. Hierlemann, G. Noetzel, U. Weimar, W. G¨opel, Performances of mass-sensitive devices for gas sensing: thickness shear mode and surface acoustic wave transducers, Anal. Chem. 68 (1996) 2210– 2218. [19] V.I. Anisimkin, E. Verona, New properties of SAW gas sensing, IEEE Trans. Ultrason. Ferroelectrics Freq. Control 45 (1998) 1347– 1354. [20] J.P. Santos, M. Aleixandre, M.C. Horrillo, I. Sayago, M. Garc´ıa, M.J. Fern´andez, L. Ar´es, J. Guti´errez, A comparative analysis of sensor arrays for the implementation of an electronic nose: application to Madrid wines characterization, in: Proceedings of the ISOEN’ 2002, Rome, Italy, September 29–October 2, 2002, pp. 303–307. [21] J. Lozano, J.P. Santos, M.C. Horrillo, Classification of white wine aromas with an electronic nose, Talanta 67 (3) (2005) 610–616. [22] M.C. Horrillo, M.J. Fern´andez, J.L. Fontecha, I. Sayago, M. Garc´ıa, M. Aleixandre, J. Guti´errez, I. Gr`acia, C. Can´e, Optimization of SAW sensors with a structure ZnO–SiO2 –Si to detect volatile organic compounds, in: Proceedings of the Eurosensors XIX, Barcelona, Spain, September 11–14, 2005. [23] W. Water, S.-Y. Chu, Physical and structural properties of ZnO sputtered films, Mater. Lett. 55 (2002) 67–72. [24] J.L. Fontecha, M.J. Fern´andez, I. Sayago, J.P. Santos, J. Guti´errez, M.C. Horrillo, I. Gr`acia, C. Can´e, E. Figueras, Fine-tuning of the resonant frequency using a hybrid coupler and fixed components in SAW oscillators for gas detection, Sens. Actuators B: Chem. 103 (2004) 139–144.
J. Lozano et al. / Sensors and Actuators B 120 (2006) 166–171 [25] R. Gutierrez-Osuna, Pattern analysis for machine olfaction: a review, IEEE Sens. J. 2 (3) (2002) 189–202. [26] J.E. Haugen, O. Tomic, K. Kvaal, A calibration method for handling the temporal drift of solid-state gas sensors, Anal. Chim. Acta 407 (2000) 23–39. [27] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, WileyInterscience, 2001, pp. 115–117. [28] E.P.P.A. Derks, M.L.M. Beckers, W.J. Melssen, L.M.C. Buydens, Parallel processing of chemical information in a local area network. II. A parallel cross-validation procedure for artificial neural networks, Comput. Chem. 20 (1995) 439–448. [29] C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1999.
Biographies J. Lozano received the B.Sc. degree in Industrial Engineer from the Universidad de Extremadura, Spain in 1998 and in Electronic Engineer from Universidad Complutense de Madrid, Spain in 2001. He is currently pursuing the Ph.D. degree in electronic noses for analysis of wines at Laboratorio de Sensores, Consejo Superior de Investigaciones Cient´ıficas (CSIC), Madrid. His research interests include pattern recognition techniques, artificial neural networks, feature selection, extraction algorithms and aroma extraction techniques applied to electronic noses, instrumentation and measurement systems and chemical sensors. M.J. Fern´andez graduated from the University Complutense of Madrid, Spain and received her Ph.D. degree in 1992, both in Physics. She has worked in RF and microwave devices in R&D companies and in Consejo Superior de Investigaciones Cient´ıficas (CSIC). Now she is working at the Instituto de F´ısica Aplicada (CSIC). Her current research interests are on electronic noses and gas sensors. J.L. Fontecha graduated in Physics from the University Complutense of Madrid in 1972 and received his Ph.D. degree in Physics in 1986. He was involved long time in the RF antennae research field both at the Consejo Superior de Investigaciones Cient´ıficas (CSIC) and in R&D companies. Now he is working at the Instituto de F´ısica Aplicada of the CSIC as tenured scientist. His current major research interests involve optical metrology, mainly UV radiometry, and SAW sensors.
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M. Aleixandre received his B.Sc. in Physics from the Universidad Autonoma of Madrid, Spain, in 1999. At present he is a Ph.D. student at the Laboratorio de Sensores (IFA-CSIC). His research interests include gas sensors, fiber optic sensors, neural networks, statistics and multivariate data analysis. J.P. 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 Aplicada (IFA-CSIC) on several projects related to the development of sensors for volatile-compounds and pollutants detection. I. Sayago received the Dr. degree from the Universidad Complutense of Madrid in 1993. Since then, she has been working on chemical sensor at the Laboratorio de Sensores (IFA-CSIC). She has worked on microsensor at the CNR-LAMEL Institute (Bologna, Italy). Her main research interests are on the preparation and characterization of gas sensors (chemoresistive, SAW and cantilevers). T. Arroyo received her Ph.D. in Biology in 2000. She is a researcher of the Instituto Madrile˜no de Investigaci´on y Desarrollo Rural Agrario y Alimentario (IMIDRA). Her research interests are in the field of quality and composition of vineyard and wine. J.M. Cabellos received his Ph.D. in Chemistry in 2002. He is a researcher of the Instituto Madrile˜no de Investigaci´on y Desarrollo Rural Agrario y Alimentario (IMIDRA). His research interests are in the field of quality and composition of vineyard and wine. F.J. Guti´errez is the 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. M.C. 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 I + D of chemical microsensors and electronic noses for environmental protection and quality control of foods. Since 1999 is head of the Department of Tecnolog´ıa de Gases y Superficies.
Wine classification with a zinc oxide SAW sensor array J. Lozano a , M.J. Fern´andez a , J.L. Fontecha a , M. Aleixandre a , J.P. Santos a , I. Sayago a , T. Arroyo b , J.M. Cabellos b , F.J. Guti´errez a , M.C. Horrillo a,∗ b
a Laboratorio de Sensores (IFA-CSIC), Serrano, 144, Madrid 28006, Spain Dpto. Agroalimentaci´on (IMIDRA), Finca El Enc´ın. Ctra. N II Km 38,2. 28800, Alcal´a de Henares, Madrid, Spain
Received 22 November 2005; accepted 4 February 2006 Available online 23 March 2006
Abstract An array of surface acoustic wave (SAW) sensors using Zinc oxide films has been developed in order to discriminate Spanish wine coming from different grape varieties and elaboration processes. Sensor array is composed by eight surface acoustic wave sensors, one of them used as reference sensor and the others coated with diverse polymers by spray coating technique. Linear techniques as principal component analysis (PCA) and non-linear ones as probabilistic neural networks (PNN) have been used for pattern recognition. A classification success rate (correct predicted number over total number of measurements) of 90% has been achieved. © 2006 Elsevier B.V. All rights reserved. Keywords: ZnO SAW sensor; Polymer films; Wine discrimination
1. Introduction Surface acoustic wave (SAW) devices have shown promising characteristics as chemical vapour sensors due to their compact structures, small size, low cost, high sensitivity and fast response. The basic principle of SAW gas sensors is the reversible sorption of chemical vapours by a sorbent coating which is sensitive to the vapour to be detected. The vapour is sorbed by the sensitive layer resulting in a mass increase of the coating, which alters the surface wave velocity in the device. The velocity changes are measured indirectly using the device as the resonant element in a delay line oscillator circuit and measuring the frequency shifts due to the vapour sorption. It is well known that this sensor type shows good characteristics for VOCs detection [1–5] and also have been used for wine discrimination in some applications [6–8]. But, the need in many sensing applications of miniaturized systems, highly sensitive and of low cost has motivated the desire to integrate on the same substrate the SAW structure with the connected electronic circuitry. Piezoelectric materials commonly used in SAW
∗
Corresponding author. Tel.: +34 915618806; fax: +34 915631794. E-mail address: [email protected] (M.C. Horrillo). URL: http://www.ifa.csic.es.
0925-4005/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2006.02.014
devices are single crystalline substrates of quartz, lithium tantalite (LiTaO3 ) and lithium niobite (LiNbO3 ), but these materials are incompatible with the integrated circuit (IC) technology. As the 90% of the propagating SAW energy is focused at a depth of one wavelength from the surface, one of the alternative methods for fabricating SAW devices is to use layered structures containing piezoelectric thin films deposited on nonpiezoelectric substrates [9]. This is possible because of the availability of the techniques of deposition of thin piezoelectric films and by the compatibility of the technological process of fabrication of SAW sensors, with those of planar integrated circuits [10,11]. Thus, the research in this field is centred on the development of materials with good electroacoustic properties allowing the fabrication of high frequency devices on silicon substrates with improved performances and at the same time with a low fabrication cost. The realization of acoustic devices on silicon requires a piezoelectric overlay for the generation and detection of acoustic waves. For SAW device applications ZnO is one of the most attractive materials because it has a high electromechanical coupling coefficient, an excellent bonding on various substrate materials, in particular silicon, silicon dioxide and silicon nitride, temperature stability and high electrical resistivity [12,13]. It belongs to the hexagonal wurtzite class and can be fabricated easily through sputtering, the most common deposition method
J. Lozano et al. / Sensors and Actuators B 120 (2006) 166–171
for making polycrystalline ZnO thin films in acoustic applications because it allows to control the preferred crystalline c-axis orientation and in addition it is compatible with IC technology. To perform an analysis of complex samples it is necessary to use an array of several gas sensors with different sensitive layers and partial selectivities to various gaseous components [14]. The appropriate choice of coatings coupled with an adequate pattern recognition method lead to a selective chemical analysis. As it is well known, polymer films are the best chemical interfaces to detect organic vapours because of their high sensitivity, fast vapour diffusion, reversible responses and good ability to work at room temperature. Several polymers are used as sensitive layers such as phtalocianines [15], ciclodextrins [16], organometallic compounds [17] and rubber polymers [18]. Metals and semiconductors are also employed as sensitive layers [19]. Due to the economic importance of the wine market for many countries, instruments for quality control are very useful for the wine industry and regulator organizations. Recently, several artificial olfactory systems based on metal oxide resistive sensors have been used for this task [20,21], but those sensors are not as fast as SAW sensors. The aim of this work is to demonstrate the potentiality of ZnO SAW sensor arrays in differentiating several types of wine in order to integrate in a future most of the circuitry in the same substrate for developing miniaturized and portable sensory systems.
167
Fig. 1. AFM image of the ZnO film.
2. Experimental 2.1. Sensors The SAW sensors used in this study consist of delay lines (DL) with different polymer thin films of various thicknesses. They were fabricated on P-type silicon wafers ((1 0 0) orientation, 3–5 k cm resistivity) of 4 in. of diameter, with a SiO2 thermally grown layer of 1 m thickness. Zinc oxide films were deposited by RF magnetron sputtering using a 3 in. diameter zinc oxide target (99.99% purity) in reactive plasma. The oxygen concentration in Ar plasma was established to 10%. The ratio of argon (99.999% purity) and oxygen (99.999% purity) was controlled by the electronic mass flow controllers. The RF power was 50 W and the distance between target and substrate was 50 mm. For the deposition, the substrate temperature was maintained at 250 ◦ C and monitored with a thermocouple near the substrate [22]. At this temperature the film morphology is characterized by fine uniform grains and smooth surfaces [23]. The film thickness was measured with an interferometer (Nanospec AFT200), being approximately 2 m. The surface morphology and roughness of the film were characterized by an AFM (Digital Instruments-Nano Scope III). In Fig. 1 is shown a three-dimensional AFM image of the ZnO film. The interdigited transducers (IDTs) were made of aluminium deposited by RF sputtering using photolithographic techniques, being the thickness 200 nm and the finger width and the spacing between them of 5 m, what means a wavelength, λ, of 20 m. Fig. 2 shows a cross-section of the SAW layered structure. Five different designs of delay lines were realized modifying the acoustic aperture, W, distance between IDTs, L, and finger
Fig. 2. Schematic diagram of the layered SAW structure.
pair number, N, to optimize the parameters of design [22]. The optimal parameters are shown in Fig. 3 over an image of the ZnO SAW sensor. The central frequency of the delay line was 210 MHz and therefore the propagation velocity of the SAW (v = fλ) is 4200 m/s. Several polymers were chosen as sensitive films: OV-225 and OV-275 silicones, polyisobutylene (PIB), polyetherurethane (PEUT), and polyepichlorohydrin (PECH). They are well known commercial polymers for SAW sensor applications with excellent properties as low static glass transition temperature in order to obtain fast vapour diffusion and reversible response. The films were deposited by spray coating (see Fig. 4) using an airbrush with a solution of the polymer and the solvent. The mass change of the coating caused by the deposition of the film is detected by a
Fig. 3. SAW device with IDTs (Al)–ZnO–SiO2 –Si structure.
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array operated at a constant flow of 200 ml/min of pure nitrogen. The oscillator circuit for each device was connected to one side of the chamber. 2.2. Measurement system The sensor response, given by the frequency shifts, was measured with a frequency counter HP 53131, connected to microwave switch system, Keithley S46, which allow the frequency display of the eight sensors. The software homedeveloped for data acquisition, gives the frequency of each device and the frequency difference between each one and the reference. It was used for real-time visualization, storing and analysis of these data. The experimental set-up of the SAW sensor system is shown in Fig. 6. Fig. 4. Sensitive film deposition with simultaneous frequency monitoring.
2.3. Samples Five wines from the Madrid region were measured with the sensor array. They were elaborated with the four most common grape varieties of the region, two reds (Grenache and Tempranillo) and two whites (Air´en and Malvar). Four of them correspond to the 2003 harvest and the other to the 2002 harvest and have been elaborated with Tempranillo variety and aged for a year in American oak barrels. 2.4. Young wine
Fig. 5. View of the sensor array in the measuring cell.
reduction of the surface velocity and measured as a proportional frequency shift of the oscillation frequency. This frequency was simultaneously monitored with a HP 8510B vector network analyzer during film deposition. Frequency shifts were in the range between 200 and 500 kHz [24]. The polymers and frequency shifts of each sensor of the array are shown in Table 1. Fig. 5 shows the array of sensors. The sensors were placed in two rows on a printed circuit board over a Peltier device. A thin film platinum resistance (Pt100) was glued to the PCB to measure the temperature. Using a PID controller, the Pt100 as sensor and Peltier as actuator, the operation temperature of sensors was kept at 23 ± 0.01 ◦ C. The measurement chamber containing the sensors was made in anodized aluminium. The
Grapes grown in the same vineyard 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 ◦ C in red vinification and <20 ◦ C in white vinification. The end of fermentation was taken when reducing sugar concentration decreased bellow 2 g/l. Two months later the wines were clarified and bottled in green glass bottles and stored at 15 ◦ C. 2.5. Aged Tempranillo wine Before ageing the wine of variety Tempranillo, malolactic fermentation was induced by addition of a commercial Leuconostoc oenos strain until the malic acid level got under 1 g/l. Then the wine was clarified and the SO2 concentration adjusted around 50 mg/l. The wine was introduced in wood barrels (225 l)
Table 1 Type and frequency shift of the polymer deposited in each sensor of the array
Polymer h (kHz)
Sensor 0 (reference)
Sensor 1
Sensor 2
Sensor 3
Sensor 4
Sensor 5
Sensor 6
Sensor 7
0
OV-275 300
PIB 300
PEUT 200
OV-225 300
PEUT 300
PECH 300
PECH 435
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Fig. 6. Measurement set-up schematics.
from American oak and the ageing was conducted in a special chamber during 6 months at 14 ◦ C. 3. Results Wine samples of 10 ml were kept at 30 ± 0.1 ◦ C for 30 min in a 50 ml flask. After that time the headspace was injected in the measurement chamber for 10 min. At least eight replicates of each wine measurement were made. Responses of the individual sensors are defined respect to the minimum frequency to 12% (v/v) of ethanol for all the measurements: r=
fwine fcalibration
where fwine is the difference between equilibrium frequency in nitrogen and minimum frequency of the sensor in presence of wine and fcalibration is the difference between equilibrium frequency in nitrogen and minimum frequency of the sensor exposed to a solution of 12% of ethanol. The objective of this data pre-processing is compensating for sensor drift, extracting descriptive parameters from the sensor array response and preparing the feature vector for further processing [25]. A periodic recalibration is performed once a week with a 12% ethanol solution used as reference because it is chemically stable over time and highly correlated with the target analytes in terms of sensor behavior [26]. The response to the five types of wine is shown in Fig. 7. This polar plot shows the differences among the studied samples. The variation in response intensity is due to different headspace composition of the wines. Each sample has its characteristic organic volatile compounds profile. The data obtained were pre-processed by centering about the mean and scaling with the standard deviation before performing a principal component analysis (PCA). The PCA applies a linear transformation to the data and results in a new space of variables called principal components [27]. The PCA results of the measurements are illustrated in Fig. 8. It can be seen that all
Fig. 7. Polar plot of sensor responses to different wines.
Fig. 8. PCA score plot for the measurements of the five wines.
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Table 2 Confusion matrix for the PNN classification Real/predicted
Malvar
Air´en
Grenache
Tempranillo
Temp. aged
Malvar Air´en Grenache Tempranillo Temp. aged
7 1 0 0 0
1 7 0 0 0
0 0 6 0 0
0 0 2 8 0
0 0 0 0 8
types of wines are well separated except a partial overlapping between zones corresponding to white wines (Malvar and Air´en wines). A probabilistic neural network (PNN) was used for classification purposes. The PNN was composed by three layers: the input one had three neurons, corresponding to the three principal components; the hidden layer, with radial basis transfer functions, had the same number of neurons that number of training vectors and a competitive layer in the output [27]. Leave one out (LOO) cross validation method was applied to the network in order to check the performance of the network [28]. LOO consists of training N distinct nets (in this case, N is number of measurements) by using N − 1 training vectors; while the validation of the trained net is carried out by using the remaining vector, excluded from the training set. This procedure is repeated N times until all vectors are validated [29]. The confusion matrix for the neural network is shown in Table 2. The success rate (correct predicted number over total number of measurements) was 90%. All the samples corresponding to Tempranillo grapes are well-classified. The network only confuses one sample of Malvar and Air´en and two of Grenache that classify like Tempranillo. 4. Conclusions We have demonstrated the possibility of application of a SAW sensor array, using Si technology to realize Si–SiO2 –ZnO structure, for the discrimination of wines from different grape varieties and ageing processes. Although PCA showed partial overlapping between white wine datasets the classification performance by PNN is satisfactory, showing a 90% confusion matrix. Optimization of coating thickness and composition and measurement parameters (operation temperature, vapour generation times, etc.) could improve the classification rate. Acknowledgements This work is being supported by Ministerio de Ciencia y Tecnolog´ıa from Spain under projects: TIC 2001-0554-C03-01 and TIC 2002-04588-C02-01. References [1] H. Wohltjen, Mechanism of operation and design considerations for surface acoustic wave device vapour sensors, Sens. Actuators B: Chem. 5 (1984) 307. [2] D. Amati, D. Arn, N. Blom, M. Ehrat, J. Saunois, H.M. Widmer, Sensitivity and selectivity of surface acoustic wave sensors for organic solvent vapour detection, Sens. Actuators B: Chem. 7 (1992) 587–591.
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Biographies J. Lozano received the B.Sc. degree in Industrial Engineer from the Universidad de Extremadura, Spain in 1998 and in Electronic Engineer from Universidad Complutense de Madrid, Spain in 2001. He is currently pursuing the Ph.D. degree in electronic noses for analysis of wines at Laboratorio de Sensores, Consejo Superior de Investigaciones Cient´ıficas (CSIC), Madrid. His research interests include pattern recognition techniques, artificial neural networks, feature selection, extraction algorithms and aroma extraction techniques applied to electronic noses, instrumentation and measurement systems and chemical sensors. M.J. Fern´andez graduated from the University Complutense of Madrid, Spain and received her Ph.D. degree in 1992, both in Physics. She has worked in RF and microwave devices in R&D companies and in Consejo Superior de Investigaciones Cient´ıficas (CSIC). Now she is working at the Instituto de F´ısica Aplicada (CSIC). Her current research interests are on electronic noses and gas sensors. J.L. Fontecha graduated in Physics from the University Complutense of Madrid in 1972 and received his Ph.D. degree in Physics in 1986. He was involved long time in the RF antennae research field both at the Consejo Superior de Investigaciones Cient´ıficas (CSIC) and in R&D companies. Now he is working at the Instituto de F´ısica Aplicada of the CSIC as tenured scientist. His current major research interests involve optical metrology, mainly UV radiometry, and SAW sensors.
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M. Aleixandre received his B.Sc. in Physics from the Universidad Autonoma of Madrid, Spain, in 1999. At present he is a Ph.D. student at the Laboratorio de Sensores (IFA-CSIC). His research interests include gas sensors, fiber optic sensors, neural networks, statistics and multivariate data analysis. J.P. 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 Aplicada (IFA-CSIC) on several projects related to the development of sensors for volatile-compounds and pollutants detection. I. Sayago received the Dr. degree from the Universidad Complutense of Madrid in 1993. Since then, she has been working on chemical sensor at the Laboratorio de Sensores (IFA-CSIC). She has worked on microsensor at the CNR-LAMEL Institute (Bologna, Italy). Her main research interests are on the preparation and characterization of gas sensors (chemoresistive, SAW and cantilevers). T. Arroyo received her Ph.D. in Biology in 2000. She is a researcher of the Instituto Madrile˜no de Investigaci´on y Desarrollo Rural Agrario y Alimentario (IMIDRA). Her research interests are in the field of quality and composition of vineyard and wine. J.M. Cabellos received his Ph.D. in Chemistry in 2002. He is a researcher of the Instituto Madrile˜no de Investigaci´on y Desarrollo Rural Agrario y Alimentario (IMIDRA). His research interests are in the field of quality and composition of vineyard and wine. F.J. Guti´errez is the 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. M.C. 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 I + D of chemical microsensors and electronic noses for environmental protection and quality control of foods. Since 1999 is head of the Department of Tecnolog´ıa de Gases y Superficies.