A measurement system for odor classification

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 52, NO. 4, AUGUST 2003

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A Measurement System for Odor Classification Based on the Dynamic Response of QCM Sensors Claudia Di Nucci, Ada Fort, Santina Rocchi, Luca Tondi, Valerio Vignoli, Fabio Di Francesco, and M. Belén Serrano Santos

Abstract—In this paper, an innovative measurement system for odor classification, based on quartz crystal microbalances (QCMs), is presented. The application proposed in this paper is the detection of typical wine aroma compounds in mixtures containing ethanol. In QCM sensors, the sensitive layer is, e.g., a polymeric layer deposited on a quartz surface. Chemical mixtures are sorbed in the sensitive layer, inducing a change in the polymer mass and, therefore, in the quartz resonance frequency. In this paper, the frequency shift is measured by a dedicated, fully digital front-end hardware implementing a technique that allows reducing the measurement time while maintaining a high-frequency resolution [1]. The developed system allows, therefore, measuring variations of the QCM resonance frequency shifts during chemical transients obtained with abrupt changes in odor concentration. Hence, the reaction kinetics can be exploited to enhance the sensor selectivity. In this paper, some measurements obtained with an array of four sensors with different polymeric sensitive layers are presented. An exponential fitting of the transient responses is used for feature extraction. Finally, to reduce data dimensionality, principal component analysis is used. Index Terms—Chemical transients, electronic nose, frequency meter, quartz crystal microbalance (QCM).

I. INTRODUCTION

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LECTRONIC noses are complex systems based on sensor arrays, able to perform odorant classification. The arrays consist of chemical sensors that are moderately selective and respond to a wide variety of chemical compounds. Nevertheless, each different gas is detected with a different sensitivity. Each chemical mixture is, therefore, characterized by a different pattern of sensor responses [2], [3]. Different technological solutions can be used to obtain chemical sensors for electronic noses. In particular, several working principles, based on the variation of a physical property (e.g., mass [4], [5] or conductivity [6], [7]) of the gas sensitive material when exposed to the gas, are exploited. The main characteristic of these sensors is the reversibility of the chemical process; in fact, removing the gas, the sensors turn back to the initial state. In this paper, quartz crystal microbalances (QCMs), chemical sensors subjected to a mass variation in the presence of a gas, are used to detect wine aroma compounds. In literature, different kinds of mass variation sensors are used: QCMs, superficial acoustic wave sensor (SAWs), and bulk acoustic wave Manuscript received June 15, 2002; revised January 17, 2003. C. Di Nucci, A. Fort, S. Rocchi, L. Tondi, V. Vignoli, and M. B. S. Santos are with the Department of Information Engineering, Siena, Italy (e-mail: [email protected]). F. Di Francesco is with the National Research Council, Institute of Clinical Physiology, Pisa, Italy (e-mail: [email protected]). Digital Object Identifier 10.1109/TIM.2003.814826

sensors (BAWs). All of these are built up on a quartz piezoelectric substrate and usually the gas sensitive material is a polymeric layer, deposited on one side of the substrate. In particular, polymers are widely used for gas sensing because they provides some advantages over other technologies, such as deposition simplicity, low cost, room-temperature operations, and a wide choice of parameters to modulate the sensor selectivity to different chemical species. In the case of QCMs, the mass variation due to the sorption of molecules in the polymeric layer induces a variation of the quartz resonance frequency. The problem of detecting typical wine aroma compounds is considered in this paper. It is well known that the high concentration of ethanol tends to saturate the sensor response. A possible solution to this problem is to pretreat the wine samples by organophilic pervaporation (a membrane separation process) in order to reduce the ethanol content with respect to the aroma compounds content [8]. Due to the higher affinity of the membrane for the aroma compounds, the permeate obtained is selectively enriched in aroma compounds and the original ratio between ethanol and aroma content in the feed solution (wine) is shifted. As an example, this shift can be of roughly an order of magnitude. Thus, in this work we have investigated the possibility of detecting odorants with concentrations of 500 ppm (namely ethyl acetate, isoamyl acetate, linalool, and hexanol) in standard water solutions with 1% wt ethanol. Up until now, in literature, a large part of the works based on QCM sensors focuses on measurements carried out when the sensors are in chemical steady conditions. Nevertheless, it could be of great interest to study the QCM behavior during chemical transients due to abrupt changes in the chemical condition of the atmosphere in which the sensors are placed [9]. In fact, in regard to what happens to electrical resistance measurements for sensors fabricated from poly(alkoxy-bithiophenes) [10], [11], the dynamic response can also be explained for the enhancing of the sensor selectivity. A chemical transient duration in the range of a few seconds, depending on the specific odorant, is typical for the QCMs used. Therefore, to accurately sample the chemical transient, the frequency-shift estimation must be performed in a short measurement time (less than 1/10 s), and with a high-frequency accuracy (a few hertz). For such an application, a system able to sample the QCM chemical transients with a measurement time of 20 ms and with an uncertainty of 5 Hz in the range of 30 kHz was developed. The system is flexible, fully digital, and programmable, and it is characterized by an uncertainty that can be reduced by increasing the measurement time via software reprogramming.

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II. PROPOSED METHOD A. Frequency Shift Measurement Technique In a generic QCM-based measurement system, the quartz sensors are placed in oscillator circuits. The value of the quartz resonance frequency depends, in chemical reference conditions, on the characteristics both of the crystal and the sensitive layer. The crystal has a characteristic resonance frequency , usually in the megahertz range, whereas the film deposition on the bulk ) of the resonance frequency up to a few huncauses a shift ( dreds of ppm. QCM sensors are usually placed in an isolated and thermally-stabilized measurement chamber. The gas is injected (in most cases with a flow controlled by mass-flow controllers) directly on the sensitive layer surface. When the gas gets into contact with the layer, the molecules are sorbed in the sensitive film. This fact causes a mass variation of the sensor of the resonance frequency. and, consequently, a shift An estimate of the initial gas concentration is the result of a measure of this frequency shift. The working principle of the device in the presence of gases is, therefore

where is the gas concentration variation, is the mass is the frequency variation. If layer variation, and is the resonance frequency of the QCM in reference and , conditions, the relationship between for an AT-cut quartz, is [6]

without a polymeric layer and in stable conditions oscillates at the known frequency . Depositing the polymeric layer only on one side of the bulk, the quartz oscillates at frequency lower than . When a gas (or odor mixture) is present, the QCM os) further decreases up to 0.2% of ; cillation frequency ( overall, the frequency shift is 3000 ppm of . If the signal at is 1 bit sampled at the known frethe unknown frequency , it is proved that the event quency , where “two following samples are equal” (slip event) occurs with a fre. By counting quency proportional to the difference the number of slip events occurring during the measurement , a measurement of the frequency shift of the untime with respect to the sampling frequency known frequency is achieved, with a resolution . In this context, the term resolution is used to indicate the maximum error affecting the measurement result in ideal conditions, i.e., when the error depends only on the unknown initial phase difference and . This solution between the two signals at frequencies has therefore resolution versus measurement-time performance exactly equivalent to a standard counter based frequency meter. Nevertheless, the resolution can be improved by using in parallel different issues of the slip event counter, described above in detail, if sample streams are considered, obtained by sampling at the sampling frequency the unknown frequency signal , with each stream shifted by a time period , the av) is given by erage number of slip events of streams ( (3)

(1) is measured in hertz, is measured in megahertz, where (the electrode surface) is measured in cm , and is measured in grams. The relationship between the mass variation and the gas concentration (mol/m ) follows a simple, reversible, Langmuir binding isotherm

where riod ( that

is the slip event number in each reporting pe) registered by the stream . Cantoni proved is a quantization of the phase difference , where is the fictitious fre-

quency (4)

(2) where (in grams) is the sensitivity coefficient and (m /mol) is the binding constant for the polymeric layer, like shown in [7], where analytical models and experimental evidence are reported. In literature, several systems for the measurement of the sensor resonance frequency shift are presented. For example, Luklum et al. [12], Vanysek [13], Martin et al. [14], and Krause and Hemmon [15] calculate the resonance frequency variation on the basis of the impedance variation by means of a vectorial impedance meter; Zhang and Feng [16] accomplish a frequency variation measurement via analog phase measurements; Hook et al. [17] measure both the resonance frequency and the quality factor Q with a data acquisition system. None of these measurement systems is suitable for frequency-shift measurement during chemical transients. In this paper, a fully digital method is used to detect the QCM oscillation frequency via an electronic front end that implements a technique presented by Cantoni [1]. A summary of this technique is given in what follows, as already said, a quartz sensor

where

is given by the following equation: (5)

Under these hypotheses, the resolution of the estimation is [1] (6) Therefore, with this method, the resolution improves of a . Provided that the initial phases of factor for a given the considered streams are uniformly distributed [1]. B. Measurement Technique Performance The presented algorithm critical issue is to have an almost uniform distribution of the initial phases of the streams. In [1], some sufficient conditions to ensure the uniform distribution of the initial phases are provided, and one of these is a specific relationship between the sampling frequency and the measured fre. Of course, this relationship can be achieved only for quency

DI NUCCI et al.: MEASUREMENT SYSTEM FOR ODOR CLASSIFICATION

thenominalfrequency .Asthefrequencyshifts,evenafewhertz from , this condition is no more fulfilled, and the measurement system performance gets worse. In this case, the error committed by the system increases. It must be noted that the measurement range is given by the accuracy we want to achieve, and it depends , where is number of streams used in (3) [1]. on For our purposes, a measurement range of 30 kHz (variation using 10 MHz QCMs) was necessary. This of 3000 ppm of requirement is derived from the maximum frequency expected shift in the experimental conditions used in this paper. In the whole measurement range, an uncertainty lower than 5 Hz had to be granted. This requirement was based on the performance we expected from our oscillators (short term stability lower than 1 ppm) so that the system uncertainty depends on the oscillator performance and not on the frequency meter. The uncertainty was estimated by simulations (A type and B type contributions). , was set to 20 ms. This choice The measurement time, allows for a fine sampling of the chemical transients that exhibit typical time constants of the order of some seconds. It is possible to note that to obtain a mean error lower than 5 Hz in the whole measurement range, the value of the parameter , , and must be appropriately set. From Fig. 1, it appears 97 ( 4) gives a high theoretical resothat a value of lution (about 0.6 Hz) at the nominal frequency (shift 0 Hz), but also that the mean error increases rapidly when increasing 37, the theoretical resolution is the frequency shift. When about 1.8 Hz at the nominal frequency, but the error remains well below 5 Hz in the whole considered frequency range. Therefore, 8 and 37 is the parameter set used for the measurements presented in the next section. The standard deviation of the error is less than 2 Hz for all the parameter sets and remains almost constant in the whole frequency range. The numerical simulation presented in Fig. 1 was carried out by considering a nominal frequency of 10 MHz with the addition of a clock jitter uniformly distributed in the range [0 ns, 10 ns] for all the l channels. III. MEASUREMENT SYSTEM The four QCM sensors used in this work are AT-cut quartz discs with a thickness of 0.18 mm and a diameter of 13.97 mm. The oscillation frequency is equal to 10 MHz. The gold electrodes have a diameter of 7.36 mm. The characteristics of the sensitive layers deposited on the QCMs are shown in Table I. The deposition is obtained by dropping with a micro-pipette 1 l of the polymeric suspension on the sensor support plate, and by evaporating the liquid medium to a uniform coating polymer layer, in a controlled environment and at ambient temperature [18]. The sensitive layers cause a frequency shift in air of about 15 kHz with respect to the nominal resonance frequency . Previous data analysis from poly(alkoxy-bithiophenes) sensors [10], [11] demonstrated that sensor kinetics depends on the chemical nature of both the sensors and the odorants, but also on the dopant, the dopant mole ratio, the sensor aging, and the sensor fabrication procedure. In particular, the morphology of the coating polymer layer is very important in determining the sorption kinetics.

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(a)

(b) Fig. 1. Simulation results. (a) Mean and (b) standard deviation of the error on frequency shift versus the true frequency shift.

The developed measurement system is presented in Fig. 2. Four gas sensors are positioned in a Teflon measurement chamber with a circular symmetry, in order to obtain an equal exposure of each sensor to the chemical mixture. Mass-flow controllers set the gas input flow. The oscillator circuits are placed out of the measurement chamber, to avoid the presence of noninert material in the chamber itself. Odorants in both liquid and gas phase can be analyzed. The vapors from liquids to be tested were generated by bubbling by a Drechsel bottle a carrier gas under temperature-controlled conditions ( 0.1 C) through the liquid sample. The measurement chamber is placed in an incubator to control the operating temperature In this study the incubator works at 35 C. The frequency meter is implemented on a CPLD Altera Flex 10K20RC240-4. A PC controls the whole system; it sets the mass-flow controllers, configures the frequency meter and the reference frequency generator, controls the frequency measurement process, and processes data.

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TABLE I QCM SENSITIVE LAYERS’ CHARACTERISTICS (M/Ox IS THE MOLAR RATIO BETWEEN MONOMERIC UNITS AND OXIDIZING SALTS)

Fig. 2. Measurement system block diagram.

The frequency meter performance was experimentally tested and the samby generating the signal at the frequency pling signal at the frequency with two waveform generators HP33120A. A comparison of simulated with experimental results is shown in Fig. 3. IV. EXPERIMENTAL RESULTS In this section, results concerning chemical transient measurements are reported. They were obtained by analyzing five different water solutions with 1% wt ethanol concentration and 500 ppm of some of the most significant wine aroma compounds. In detail, the investigated solutions were as follows. • Water solution A, ethanol 1% wt. • Water solution B, ethanol 1% wt plus 500 ppm ethyl acetate. • Water solution C, ethanol 1% wt plus 500 ppm isoamyl acetate. • Water solution D, ethanol 1% wt plus 500 ppm linalool. • Water solution E, ethanol 1% wt plus 500 ppm hexanol. The total gas flow investing the sensors is constant and equal to 200 ml/min. In reference conditions, a 200 ml/min synthetic air flow enters the chamber. An abrupt change in the chemical

Fig. 3. Comparison of simulation results with experimental data; frequency shift mean error versus true frequency shift.

conditions is obtained by reducing the synthetic air reference flow to 130 ml/min while, simultaneously, adding a 70 ml/min


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Fig. 4. Transient responses of sensor n.1. Solution A: solid, solution B: gray solid, solution C: gray dotted, solution D: dash-dotted, and solution E: dashed.

Fig. 5. Transient responses to solution B. Sensor 4: solid, sensor 2: dotted, sensor 1: dashed, and sensor 3: dash-dotted.

synthetic air flow passing in the Drechsel bottle containing the test solution. Frequency shifts are measured each 20 ms; ten successive measurements are averaged to obtain a frequency sample. In Fig. 4, the behavior of sensor n.1 in the presence of the considered gas mixtures is reported. The different dynamic behaviors can be remarked. Figs. 5–7 show the responses of the four sensors in the array to three different solutions (B, C, and D, respectively). Note that the sensor responses (frequency shift) are normalized with respect to the baseline value in the reference gas, according to the following equation:

Fig. 6. Transient responses to solution C. Sensor 4: solid, sensor 2: dotted, sensor 1: dashed, and sensor 3: dash-dotted.

Fig. 7. Transient responses to solution D. Sensor 4: solid, sensor 2: dotted, sensor 1: dashed, and sensor 3: dash-dotted.

• The crossing of an analyte, A, over the phase boundary between the gas phase and the sensor layer, defined by the equilibrium of the phase partition (8) is the concentration of the analyte at the where is the concentration of in the gas, and is surface, the partition coefficient. • The reversible reaction between binding sites, sites , in the layer and the analyte

(7) (9) A. Gas-Polymer Interaction The sensor behavior can be interpreted by assuming that three basic processes take place [19], [20].

where and

and are the forward and backward reaction rates, denotes the analyte adsorbed at the binding sites.

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• The diffusion controlled transport of the analytes in the sensor layer that can be described by the Fick’s law for the molar flow into the layer (10) denotes the molar flow, is the area of the layer, where and is the diffusion constant. Thus, the response of the coated QCM sensors is based on the different sorption and diffusion characteristics of the individual compounds in the polymeric coating. Fig. 4 shows the transient responses of sensor n.1 (4,4′-dipentoxy-2, 2′-bithiophene doped with iron perchlorate) to standard water solutions with 1% wt ethanol and ethylacetate, isoamyl acetate, linalool, and hexanol, respectively. It can be observed how different compounds take different times to reach steadystate in the polymer. Curves in Fig. 4 show that the studied aroma compounds exhibit two different dynamic behaviors: solutions with esters reach steady state during odor exposure time, whereas solutions with alcohols do not. This can be explained by a faster sorption of esters, and a slower sorption of alcohol. So, dynamic parameters can be used to enhance the system discrimination capability as expected. An exception is given by the solution containing ethanol alone that can not be distinguished from the mixture containing also ethyl acetate. The faster response of the QCMs to ethanol, that has the smaller size among the considered compounds, can be explained by a fast diffusion in the polymer layer. In fact, at infinite dilution in the membrane, the only factor influencing the solute diffusion speed through the membrane is its size; a smaller molecule is expected to move faster than a bigger one [21], [22]. The results shown in this figure confirm the validity of the assumption at the base of this work. The chemical dynamic behavior can give useful information to enhance the QCM sensors’ ability to perform odor classification, as it will be further proved in the next section.

Fig. 8. PCA with parameters a1, b1, c1, a2, b2, and c2. Solution A: circle, solution B: square, solution C: diamond, solution D: triangle, and solution E: cross.

Fig. 9. PCA with static parameters (final shift). Solution A: circle, solution B: square, solution C: diamond, solution D: triangle, and olution E: cross.

B. Data Processing and Odor Classification The frequency shift is fitted with two exponential functions of the following type: (11) In fact, a first-order approximation was found to give a satisfactory fit of experimental data, in terms of mean square error. This indicates that usually one of the physical mechanisms involved in the sensor dynamic dominates. for the falling transient In this way, six parameters ( for the rising transient) are extracted for classifiand cation. Some of these parameters are related (e.g., represents ) due to the rethat final shift and is related to while versibility of the process. Principal component analysis (PCA) is used to reduce data dimensionality. In Fig. 8, the signatures (first and second principal components) obtained by applying this data processing technique to the measurements, obtained with the five considered solutions, are shown. Each marker represents a different measurement, while different marker types indicate measurements obtained

with different solutions. Measurements were repeated in a period of three weeks. It can be seen that solutions A and B cannot be well discriminated (that is, these sensors seem to be unable to detect 500 ppm of ethylacetate in the presence of 10 000 ppm of ethanol), while a good discrimination is obtained among the other solutions. As a comparison, in Fig. 9, the signatures obtained by applying the PCA to the steady-state frequency shift values are shown. The discrimination ability improvement for the QCM array when the dynamic behavior is kept into account is evident. V. CONCLUSION In this paper, the structure of an electronic nose based on QCM sensor is described, and its performance is discussed. The system can be applied to many real-world problems of wide interest. In this paper, the suitability of the system to the discrimination among different wine types is investigated.


In particular, some measurements performed on standard water solutions, containing ethanol and some of the principal wine aroma compounds, are used to test the system selectivity and sensitivity. With all of the considered solutions, the electronic nose provides a satisfactory selectivity, except in the case of ethylacetate that is not well discriminated. In any case, it was shown that the possibility to also study the QCM sensor dynamic response allows enhancing the selectivity. Besides selectivity enhancement, the proposed system leads to another significant advantage: the reduction of analysis time with respect to the majority of QCM-based electronic noses. In fact, by applying the chemical transient analysis, it is not necessary that the odor injection is long enough to reach a steady-state sensor response, since the asymptotic frequency shift can be obtained by the exponential fitting. Anyway, between two successive measurements, enough time must be allowed to recover the frequency baseline (typically tens of seconds). The presented results are encouraging; nevertheless, it must be noted that when analyzing pervaporated wine samples, many different chemical compounds are simultaneously present, and the interaction with the sensitive layer can be far more complex, e.g., due to the presence of competition effects. As a final comment, it must be noted that the developed system, that allows a fast and accurate tracking of the sensor dynamics, can be used to further understand the nature of the interaction between polyalkoxythiophene polymers and odorants. To this purpose, a simple but very interesting experimental activity will be devoted to the measurements of sensor response to compounds with equal functional group and molecular volume, but different structure.

REFERENCES [1] M. T. Hill and A. Cantoni, “Precise all-digital frequency detector for high frequency signals,” IEEE Trans. Commun., vol. 48, Nov. 2000. [2] E. Radeva, V. Georgiev, L. Spassov, N. Koprinarov, and S. Kanev, “Humidity adsorptive proprieties of thin fullerene layers studied by means of quartz micro-balance,” Sens. Actuators B, vol. 42, pp. 11–13, 1997. [3] M. Kampik and T. Skubis, “A method of reduction of the temperature coefficient of thermal piezoacoustic sensor operating in a differential mode,” in Proc. Eurosensors XI, vol. 2, Warsaw, Poland, 1997, pp. 793–796. [4] A. Menon, M. Haimbodi, R. Zhou, and F. Josse, “Polymer-coated quartz crystal resonators for multi-information sensing,” Electron. Lett., vol. 33, no. 4, pp. 287–289, Feb. 13, 1997. [5] S. Goka, K. Okabe, Y. Watanabe, and H. Sekimoto, “Fundamental study on multi-mode quartz crystal gas sensor,” in Proc. IEEE Ultrason. Symp., vol. 1, 1999, pp. 489–492. [6] J. Polo, E. Llobet, X. Vilanova, J. Brezmes, and X. Correig, “Spice model for quartz crystal microbalance gas sensor,” Electron. Lett., vol. 35, no. 10, pp. 772–773, May 13, 1999. [7] J. W. Gardner, P. N. Bartlett, and K. F. E. Pratt, “Modeling of gas sensitive conducting polymer device,” Inst. Elect. Eng. Proc. Circuits Devices Syst., vol. 142, no. 5, pp. 321–333, 1995. [8] C. Pinheiro, T. Schaefer, C. M. Rodrigues, A. Barros, S. Rocha, and I. Delagadillo, “Integrating pervaporation with electronic nose for monitoring the muscatel aroma production,” in Proc. 7th Int. Symp. Olfaction Electron. Nose, 2000, p. 145. [9] H. Ishida, T. Tokuhiro, T. Nakamoto, and T. Moriizumi, “Improvement of olfactory video camera: Gas/odor flow visualization system,” Sens. Actuators B, vol. 83, no. 1, pp. 256–261, Mar. 2002. [10] G. Zotti, M. C. Gallazzi, G. Zerbi, and S. V. Meille, “Conducting polymers from anodic coupling of some regiochemically defined dialkoxysubstituted thiophene oligomers,” Synth. Meth., vol. 73, pp. 217–225, 1995.

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[11] M. C. Gallazzi, L. Tassoni, C. Bertarelli, G. Pioggia, F. Di Francesco, and E. Montoneri, “Poly(alkoxy-bithiophenes) sensors for organic vapors,” Sens. Actuators B, vol. 88, pp. 178–189, 2003. [12] R. Luklum, C. Behling, and P. Hauptmann, “Signal amplification with multilayer arrangements on chemical Quartz-Crystal-Resonator Sensors,” IEEE Trans. Ultrason., Ferroelect. Freq. Contr., vol. 47, pp. 1246–1252, May. [13] P. Vanysek, “Quartz crystal microbalance in electroanalytical application,” in Proc. IEEE Int. Freq. Contr. Symp., 1997, pp. 50–55. [14] S. J. Martin, V. E. Granstaff, G. C. Frye, and A. J. Ricco, “Using quartz crystal microbalance simultaneously sense mass accunulationand solution properties,” in Int. Conf. Solid-State Sens. Actuators, 1991, pp. 785–788. [15] P. R. Kraus and D. Emmons, “A novel sensor for monitoring oilfield fouling in near-real time,” On-line Monitoring Techiniques for the OffShore Industry, pp. 3/1–3/4. [16] C. Zhang and G. Feng, “Contribution of amplitude measurement in QCM sensors,” IEEE Trans. Ultrason., Ferroelect. Freq. Contr., vol. 43, pp. 942–947, May. [17] F. Hook, M. Rodahl, C. Keller, K. Glasmastar, C. Fredriksson, P. Dahiqvist, and B. Kasemo, “The dissipative QCM-D technique: Interfacial phenomena and sensor applications for proteins, biomembranes, living cells and polymers,” in Proc. Joint Meet. EFTF IEEE IFCS, vol. 2, 1999, pp. 966–972. [18] C. Bertarelli, T. Biver, F. Di Francesco, M. C. Gallazzi, A. Moriconi, G. Pioggia, G. Serra, and L. Tassoni, “Development and testing of doped poly (3,3’-dipentoxy-2,2’-bithiophene) based conducting polymer sensors,” Proc. Eurosensors, to be published. [19] L. Lonsdale, “Transport properties of cellulose acetate osmotic membranes,” J. Appl. Polymer Sci., vol. 4, p. 1341, 1965. [20] P. Boeker, O. Wallenfang, and G. Horner, “Mechanistic model of diffusion and reaction in thin sensor layers the DIRMAS model,” Sens. Actuators B, vol. 83, no. 1–3, 15, pp. 202–208, Mar. 2002. [21] T. Schäfer, “Recovery of wine-must aroma by pervaporation,” Ph.D. dissertation, Universidade Nova Lisboa, Lisboa, Portugal, 2002. [22] J. D. Seader and E. J. Henley, Eds., Separation Process Principles. New York: Wiley, 1998.

Claudia Di Nucci received the Laurea degree in electronic engineering at the University of Pisa, Pisa, Italy, in 2000. She is currently purusing the Ph.D. degree information engineering-electronics at the University of Siena, Siena, Italy. Her main research activity is related to the development of laboratory electronic noses-based QCM sensors and, in particular, on the development of sensors front-end electronics.

Ada Fort received the Laurea degree in electronic engineering and the Ph.D. degree in nondestructive testing from the University of Florence, Florence, Italy, in 1989 and 1992, respectively. Since 1997, she has been an Assistant Professor with the Department of Information Engineering, University of Siena, Siena, Italy. Her current interests concern the development of measurement systems based on chemical sensors and the development of automatic fault diagnosis systems. Santina Rocchi received the Laurea degree in electronic engineering from the University of Florence, Florence, Italy, in 1978. From 1981 to 1992, she was an Assistant Professor of electronics at the University of Florence, where she was involved in the development of prototype systems for ultrasonic applications in medical diagnosis, nondestructive testing and robotics. In 1992, for one year, she was an Associate Professor of electronics at the University of Perugia, Perugia, Italy. Since 1993, she has been with the University of Siena, where she is currently Full Professor of electronics with the Department of Information Engineering. Her main research interest is sensor front-end and processing electronics design, with emphasis on chemical, optical, and acoustic sensors.

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Luca Tondi received the B.S. degree in telecommunication engineering from the University of Siena, Siena, Italy, in 2000. Since January 2001, he has been with the Department of Information Engineering, University of Siena, where he works in the Electronics and Electronics Measures Laboratory. His research activity is mainly focused on the design of microcontroller-based systems for data acquisition and process control.

Fabio Di Francesco received the degree in physics from University of Pisa, Pisa, Italy, in 1994. For two years, he studied the development of mercury pollution detection methods at the Institute of Biophysics, National Research Council, CNR, Pisa, then he joined Interdepartmental Research Center E. Piaggio, where he started working on electronic noses with two distinct application areas, evaluation of olfactory annoyance of industrial emissions, and detection of olive oils defects. At present, he is a Research Scientist at the Institute of Clinical Physiology, CNR, where, within the MAST group, he is responsible for the development and use of electronic noses.

Valerio Vignoli received the Laurea degree (cum laude) in electronic engineering and the Ph.D. degree in non destructive testing, both from the University of Florence, Florence, Italy, in 1989 and 1994, respectively. Since 1997, he has been with the Department of Information Engineering, University of Siena, Siena, Italy, as an Assistant Professor. His current research interests include sensor and smart sensor systems, with particular emphasis on front end electronics design.

M. Belén Serrano Santos received the M.S. degree in physical chemistry from the Complutense University of Madrid, Madrid, Spain, and the Maîtrise degree in solid state chemistry from the University of Paris-Sud, Paris, France, in 1993. She received the Ph.D. degree from the University of Siena, Siena, Italy, for her work on synthesis and structural characterization of natural and synthetic silicates with molecular sieving properties in 2000. She is currently with the University of Siena, where she works on a laboratory electronic nose as well as on the fabrication of membrane coatings to enhance the selectivity of ceramic sensors.