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Array of conducting polymer sensors for the characterisation of wines
Analytica Chimica Acta 411 (2000) 193–200
Array of conducting polymer sensors for the characterisation of wines A. Guadarrama a,∗ , J.A. Fernández b , M. Íñiguez c , J. Souto a , J.A. de Saja a a
Departamento de F´ısica de la Materia Condensada, Cristalograf´ıa y Mineralog´ıa Facultad de Ciencias, Universidad de Valladolid P. de la Magdalena s/n, 47011 Valladolid, Spain b Estación Enológica de Castilla y León. Sant´ısimo Cristo, 20, 47490 Rueda (Valladolid), Spain c Estación Enológica de la Rioja. Bretón de los Herreros, 4, 26200 Haro (La Rioja), Spain Received 8 October 1999; received in revised form 21 January 2000; accepted 27 January 2000
Abstract The response of an array of polymeric sensors to different Spanish wine varieties has been evaluated. Two different systems for the injection of the volatile compounds, based on static and dynamic headspace sampling, were used. A comparative study of the operation of the array when using one or the other methodology has been carried out. The cross sensitivity of the polymeric films to moisture and ethanol has been explicitly considered. The discrimination and classification capabilities of the sensor array have been examined by statistical analysis of the obtained data using pattern recognition techniques. © 2000 Elsevier Science B.V. All rights reserved. Keywords: Conducting polymers; 3-Methylthiophene; Aniline; Pyrrole; Wines; Electronic nose
1. Introduction The quality of foodstuff can be assessed by evaluating the colour, taste and smell of the product. Humans are capable of discriminating five different types of flavours: acid, bitter, sweet, salty and umami, whereas the aromatic and chromatic characteristics of food products give rise to a much wider spectrum of sensations. The analysis of the volatile compounds released by a certain comestible can help in classifying its specific attributes, in detecting any problems that may arise in its processing, or, more simply, in identifying any undesirable degradation during its storage [1]. The most common method to address aromatic properties is the so-called panel test. This method, nonetheless, ∗
Corresponding author.
requires the training of human specialists. Certain disadvantages, such as the high cost and the difficulty in setting standards for an objective estimation, preclude a widespread application of this procedure. Chemical analytical methods [2,3] have a higher reliability, although they require long and complicated processes, so that it is not possible to perform an in situ evaluation with common techniques. Moreover, they also imply elevated economic expenses. Therefore, there is a need for new reliable methods for the assessment of olfactory characteristics in the food industry. New strategies are focussing on systems that are fast, non-destructive and objective, at a reasonably low cost. The use of arrays of gas sensors, the now commonly termed electronic noses [4–6], with purposely-designed software for discrimination of signals, is becoming increasingly extended. The
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sensing principle, in general, is based on the measurement of the variations of the mass, optical or electrical properties of the active materials. Conducting polymer gas sensors experiment changes in their electrical resistance when exposed to different volatile species [7–9]. This kind of active unit has been utilised for the evaluation of foodstuffs such as olive oil, coffee, beer or wine [10–14]. In spite of some promising perspectives, these sensors lack specificity, show a limited reproducibility and display a marked cross-sensitivity to water vapour. Specificity can nowadays be overcome to some extent with the aid of pattern-recognition techniques. On the other hand, several improvements in the reproducibility in the preparation and properties of the sensors, which have been recently reported, encourage further work in this direction [15–18]. The most serious shortcoming of these systems to date is the influence of moisture in the recorded data. As a matter of fact, polymeric chemoresistors, unlike mass sensitive devices, are able to detect both high and low weight volatile substances. Therefore, ethanol vapours will also interfere in the measurements when analysing wine samples [19]. If this type of sensors is to be used in the wine industry, it is necessary to eliminate, or at least substantially diminish, the presence of these two components in the volatile mixture. There are certain adsorbent resins, such as Tenax TA (a 2,6-diphenylene oxide porous polymer resin), that show a low affinity towards water and a low breakthrough volume for ethanol, while, on the other hand, they are able to efficiently retain and concentrate the rest of the volatiles present in wine. The volatiles can be released afterwards by applying a temperature program to the trap. The viability of a system for the evaluation of the organoleptic characteristics of wines based on an array of polymeric sensors directly connected to a thermal desorption injector or a headspace autosampler is analysed in the present work.
2. Experimental 2.1. Samples and conditioning Different types of Spanish wines were used for the experiments: two red wines, both of them aged in oak
barrels, one from the Rioja O.D. and the other from the Ribera de Duero O.D.; and a white wine produced with the Albariño grape variety, from the R´ıas Baixas O.D. For comparison purposes, deionised water and a 15% dilution of ethanol in water were also analysed. The samples have been prepared following two alternative methodologies, both based on headspace sampling, which are well-known techniques for their extremely high reproducibility, and as such they have been extensively used in common chromatographic techniques. The dynamic headspace sampling method was performed as follows. The volatile components are extracted with a Dynamic Thermal Stripper 1000 purge system from TEKNOKROMA. This is accomplished by bubbling a helium flow of 69 ml min−1 for 20 min through a vial that contains 40 ml of wine, and by trapping the volatiles in a tube filled with Tenax TA resin. A flow of the clean helium gas is passed directly through the tube for 10 additional minutes in order to eliminate the water and ethanol vapours that may have been retained inside it in the first stage. The volatiles are afterwards injected in the measuring system with an ATD 400 thermal desorption equipment from Perkin–Elmer. In the first step of the injection process, the temperature of the Tenax tube is raised to 300◦ C, and the volatiles are driven to a cold trap (−30◦ C) by flowing helium (22.5 psi) for 5 min. Helium is used as the carrier gas in order to prevent the combustion of the Tenax TA. Afterwards, the temperature of this trap is abruptly elevated from –30 to 300◦ C, so that the volatiles can be concentrated in a narrow band. These compounds are finally injected into the sensor chamber with a helium flow of 70 ml min−1 . For the static headspace sampling, 3 ml of the wines were placed in 10 ml vials. The vials are encapsulated and placed in an HP7694e automatic headspace sampler from Hewlett–Packard. The vials are kept at a constant temperature (40◦ C) for 9 min in order to obtain a homogeneous headspace. The vial is pressurised for 8 s at 1.5 bar. The pressure gradient that builds up permits to fill a 3 ml loop in 9 s, and its content is then injected to the carrier gas that drives the volatiles to the sensors chamber. In this case, the carrier gas was high purity synthetic air (N50) and the flow rate was 140 ml min−1 .
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2.2. Preparation of polymeric sensors The poly(3-methylthiophene) (PMT) and polyaniline (PAN) films were grown electrochemically onto glass substrates covered with ITO electrodes (electrode spacing 75 m). Alumina substrates with gold electrodes (electrode spacing 50 m) where used for the polypyrrole (PPy) sensors. The substrates were cleaned with acetone prior to polymer deposition. A detailed description of the complete procedure for the preparation of the sensors has been reported elsewhere [10,17]. Electropolymerisation and electrochemical measurements were performed using an EG&G PARC Model 263 potentiostat/galvanostat, which was controlled by electrochemical software M270/250 installed in a desktop computer. The reference electrodes were Ag/AgNO3 0.1 mol l−1 (EG&G) for the non-aqueous media, and Ag/AgCl when water is used as solvent. All potentials quoted are relative to the corresponding reference. The counter electrode was a large surface area platinum gauze, which was flamed prior to use. The solutions were prepared and introduced into a cell with a thermostatic jacket (Metrohm) and with a temperature controlled liquid system (Neslab). All the polymeric films were grown at a constant temperature (25◦ C). The solutions were deoxygenated by bubbling nitrogen for 10 min prior to use. The poly(3-methylthiophene) sensors were obtained from an electrolytic solution of 3-methylthiophene (0.1 mol l−1 , purchased from Sigma), and with 0.1 mol l−1 of the corresponding salt in acetonitrile (Sigma–Aldrich, HPLC grade). Lithium perchlorate anhydrous (Fluka), lithium trifluoromethane sulfonate (Fluka), tetrabutylammonium perchlorate (Sigma) and tetrabutylammonium tetrafluoroborate (Fluka) were
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employed in the polymerisation reaction to check the effects of dopant anions. These polymeric sensors were prepared by chronopotentiometry at a constant current (0.6 mA for 60 s). Stable oxidised blue films are obtained. In a subsequent conditioning stage, a –0.5 V potential is applied to the films in order to obtain the reduced state, which is more sensible to the gaseous species. The polymer films are finally washed in acetonitrile. The polyaniline sensors are generated from a solution of aniline (1 mol l−1 , from Aldrich) and HCl (2 mol l−1 , Panreac) in deionised water (Milli-Qplus, Millipore). The films were formed by repetitive cycling (15 cycles between –0.3 and 0.9 V at a rate of 50 mV s−1 , final potential 0 V). The polypyrrole films are obtained from an aqueous solution of pyrrole (0.1 mol l−1 , Aldrich) and tetrasulfonated nickel phthalocyanine (0.01 mol l−1 , Aldrich). These sensors are formed by cronoamperometry at a constant potential (0.9 V for 120 s). A 0.0 V conditioning potential was finally applied to the polypyrrole films. All the polymeric sensors used in this work, as well as the growth techniques and conditions, are collected in Table 1. 2.3. Measuring setup The sensors were mounted in a TEFLON text box with a volume of approximately 150 ml. The connection of the ATD-400 system to the sensors box is done inside the chromatograph oven with 1/16 in. tubing. The test box, which is replacing the chromatographic column, is kept at a constant temperature (30.3◦ C) throughout the experiments. An initial flow of helium of 84 ml min−1 is set for 5 min to allow for the stabilisation of the baseline, prior to the injection of the
Table 1 Conducting polymer sensors formed by electrochemical polymerisation using different techniques: Chronoamperometry (CA), Cyclic Voltammetry (CV) and Cronopotentiometry (CP) No. S01 S02 S03 S04 S05 S06
Monomer
Electrolyte 0.1 mol l−1
3-methylthiophene 3-methylthiophene 0.1 mol l−1 3-methylthiophene 0.1 mol l−1 3-methylthiophene 0.1 mol l−1 Aniline 1 mol l−1 Pyrrole 0.1 mol l−1
0.1 mol l−1
LiClO4 LiCF3 SO3 0.1 mol l−1 TBAClO4 0.1 mol l−1 TBABF4 0.1 mol l−1 HCl 2 mol l−1 NiPcTs 0.01 mol l−1
Technique
Conditions
CP CP CP CP CV CA
i=−0.6 mA; t=60 s; Ef =−0.5 V; tf =60 s i=−0.6 mA; t=60 s; Ef =−0.5 V; tf =60 s i=−0.6 mA; t=60 s; Ef =−0.5 V; tf =60 s i=−0.6 mA; t=60 s; Ef =−0.5 V; tf =60 s 0 to 0.9 to –0.3 V; 15 cycles; 50 m V/s Epol =0.9; tpol =120 s; Ef =0.0 V; tf =60 s
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volatiles. The flow is then lowered to 70 ml min−1 as the vapours enter the chamber. This change in the flow rate produces a negligible variation in the resistance of the sensors. After the response of the sensing units is recorded, the helium flow is kept for 20 min so that the original baseline is recovered. For the static headspace sampling measurements a flow of 140 ml min−1 of dry air is set for 3 min to allow for the stabilisation of the baseline. The carrier gas passes then through the sample loop for 1 min, dragging the volatile components of the sample towards the chamber of sensors. In this step no variation in the flow is induced. Finally, the clean air flow is kept for 12 min so that the original baseline is recovered. Up to 10 sensors can be mounted simultaneously in the reaction box. A Keithley 224 Programmable Current Source was used to provide a direct current of 10 A to the sensors. The voltage drop across each one of the sensing units was monitored with a computer controlled Keithley 2000 multimeter with a Keithley 2000SCAN scanning card. The data were collected using TestPointTM software. The obtained data are exported to the GPES44 program, and the different features of the signals are analysed. The height, width at half height, area, and area of the derivative of the responses are routinely determined. The analysis has also been attempted by considering the first coefficients of the Fourier transform of the curves, which were obtained by applying a Fast Fourier Transform (FFT) algorithm. Finally, pattern recognition techniques (PARC) were used for the discrimination of the signals, for this purpose Principal Component Analysis (PCA) has been carried out using the software Matlab v4.2.
3. Results and discussion Prior to the evaluation of the wine samples, the potential of the dynamic headspace sampling technique as a means to reduce the concentration of moisture and ethanol in the vapours was checked. In particular, the importance of drying the traps with a clean helium flow was elucidated. The chromatograms shown in Fig. 1 correspond to the vapours extracted from a wine sample. The upper graph corresponds to the direct injection of the volatiles without performing this step. The most intense peak that is observed in the chromatogram corresponds to ethanol, with a retention time (RT) of 11.2 min. This indicates that, even though the resin here used has a low affinity towards this compound, it is still present at a high concentration within the tubes. The effect of the clean helium flow is evident in the lower graph. The peak that corresponds to ethanol has undergone a drastic reduction, while the rest of the peaks remain almost unaltered. Hence, this method renders ethanol a minor component in the volatile mixture. A quantitative analysis of the chromatograms performed with different types of wines reveals that over 90% of the ethanol is removed after the drying step, and that only minute differences in the final content of ethanol were found. The presence of water in the traps cannot be determined with the chromatograph, as the FID detector is
2.4. Chromatographic analysis The measurements have been carried out on an Autosystem chromatograph from Perkin Elmer, equipped with a TR-WAX capillary column phase (length: 60 m; inner diameter: 0.22 mm) and an FID detector. Data acquisition and treatments have been done with the Turbocroam v4.1 software, also from Perkin Elmer. At the beginning of the measurements, the temperature is kept at 60◦ C for 30 min. Afterwards, it is raised to 180◦ C at a rate of 1◦ C min−1 .
Fig. 1. Chromatograms of diluted (1/100) red wine (a) without drying and (b) after drying. Note the different scales in the graphs.
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Fig. 2. Response of a poly(3-methylthiophene) sensor to a water sample injected with a dynamic headspace system: (a) without drying and (b) after drying.
not sensitive to water. In order to estimate its operation, deionised water samples were directly injected inside the sensor chamber. The results, expressed in terms of the percentage resistance change (R0 : initial resistance of the sensor; R: resistance at time t) of the polymeric film, are shown in Fig. 2. The response of a polymeric sensor is clearly much weaker if the tubes are dried before the injection. This can only be attributed to the difference in the concentration of water vapours. The reduction can be estimated from the sensors signals. A decrease in the maximum signal of about 85% was measured after drying. As this type of sensors show a sublinear relation between humidity and resistance [20], this indicates that over 85% of
the water vapour was eliminated. Therefore, it can be concluded that, after the drying stage, the water and ethanol vapours that condensed in the trap could be eliminated to a high extent. The response of a polymer sensor to a Ribera del Duero red wine using this sampling technique is compared to the signal obtained from deionised water in Fig. 3. As it can be observed, not only the relative resistance changes, but also the shapes of the curves are clearly different for the two samples. Just after the injection of the volatiles, the tendencies of both curves are similar, showing a steep relative increase in the resistance of approximately 0.35% in the first few seconds. Therefore, this initial effect can be probably
Fig. 3. Response of a poly(3-methylthiophene) sensor to (a) red wine and (b) water samples obtained with the dynamic headspace system.
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Fig. 4. Response of a poly(3-methylthiophene) sensor to (a) red wine and (b) water samples obtained with the static headspace system.
assigned to the water vapours. This is further supported by the fact that the curve that corresponds to water shows an immediate decay that reflects the desorption of the vapour. On the other hand, the response to the wine is characterised by a further increase of the signal, with a global maximum change of about 1%, and a slower desorption process. As it can be clearly observed in Fig. 1, there are many components in the wine vapour. The kinetics of the adsorption and desorption of these volatiles on the polymeric surfaces can differ considerably from one to another. Hence, the upper curve shown in Fig. 3 would correspond to a superposition of the changes experimented when the different volatiles reach the reaction chamber and interact with the sensors there located. An alternative method for the evaluation of the wines, based on the static headspace sampling technique, was also probed. Fig. 4 corresponds to the same test depicted in Fig. 3, when the static sampling is used instead of the dynamic one. As it can be clearly appreciated, the shapes of the curves are very different from those in the previous figure. With this sampling technique, the vapours are injected in the reaction box in the very first seconds, and are almost immediately removed from it. Therefore, the initial increase in the resistance is much faster, and so is the recovery of the signal afterwards. The signals are more intense, as the concentrations of the volatile compounds in the chamber are higher than those attained with the gradual delivery inherent to the dynamic method.
Although the static sampling technique would allow for a faster measurement of the samples, the overall selectivity of the system is reduced. Clear evidences are obtained from the direct inspection of Figs. 3 and 4. A more detailed comparison of the data for a series of sensors, in terms of the maximum increase of the resistance, is presented in Fig. 5. For the means of clarity, the net responses to the red wine have been normalised to the maximum of the water vapour curves. More intense relative signals can be observed in all cases if the dynamic technique is used.
Fig. 5. Relative variation of the response towards wine, normalised to the water signal, using dynamic and static injection systems, for the poly(3-methylthiophene) sensors.
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Fig. 6. Principal component analysis of the responses of the array of sensors exposed to three different wines, a 15% dilution of ethanol in water and deionised water when the static headspace sampling technique is used.
Fig. 7. Principal component analysis of the responses of the array of sensors exposed to three different wines, a 15% dilution of ethanol in water and deionised water when the dynamic headspace sampling technique is used.
The potential for discrimination of wine aromas was probed by repetitively exposing the sensor array to the vapours. Even though the aforementioned results indicate a poorer selectivity if the static sampling technique is used, both injection methods were systematically tested. Several features can be chosen to characterise the response of the sensors. The reproducibility of the system is high for the peak height and the width at half height, but there is a greater dispersion in other parameters, such as the area under the curves. Principal component analysis (PCA) was applied to the data. The best results were obtained when the width at half height, measured in minutes, was the feature selected for the classification of the signals. The corresponding plots of the first two principal components for this parameter are shown in Figs. 6 and 7. In both cases, the experimental data were obtained with identical sets of samples, that is to say, they were drawn out from the same bottles. When the static headspace sampling technique is used, the sensors are only capable of distinguishing the water vapour from the rest of the samples. All the wines, as well as the 15% dilution of ethanol in water, are located in the same region of the plane. The ellipses, which correspond to the 90% confidence intervals, clearly intersect. This is a clear indication of a poor operation of the system. On the other hand, when the dynamic headspace sampling is employed, the separation of the clusters is enhanced, and the different samples can be dis-
criminated. The pure water and the diluted sample of ethanol in water are well far apart from the wines. These, in turn, are quite nicely differentiated, although there is a minor overlapping of the Ribera de Duero and the R´ıas Baixas clusters.
4. Conclusions A set of sensors for volatile compounds have been prepared by electropolymerisation of 3-methylthiophene, pyrrole and aniline with different salts. The sensors thus prepared were mounted on a box and subsequently exposed to a variety of wine vapours. Headspace sampling techniques were used to inject the vapours in the reaction box. These methods were chosen for the extremely high reproducibility they provide. The static headspace sampling technique produces very fast responses. The resistance of the polymeric sensors increase immediately after the injection of the vapours, and the recovery of the sensors is also rapid. On the other hand, the water and ethanol vapours produce strong interferences in the signals and conceal the particularities of the vapours that are connected to other components that are present in low concentrations. As a consequence, it is not possible to classify the different wines attending to the signals produced by the array of sensors.
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The dynamic headspace sampling method relies in the low affinity towards water and the low breakthrough volume for ethanol of the Tenax TA resin. The purge and trap thermal desorption system is efficient in decreasing the cross interferences if a drying step is performed after the volatile organic compounds are concentrated in the resin. This drying stage removes most of the water vapours that condense in the surface of the porous adsorbent, and is also capable of eliminating most of the trapped ethanol. On the other hand, the majority of the volatile compounds that are distinctive of the aroma of the wines remain concentrated in the trap. The signals obtained from the thermal desorption of the trapped compounds allow for a better discrimination of the different samples.
Acknowledgements Financial assistance from CICYT of Spain (1FD97-1186) is gratefully acknowledged. One of us (A.G.) would like to thank the Junta de Castilla y León for a grant. References [1] M.T. Morales, J.J. Rios, R. Aparicio, J. Agric. Food Chem. 45 (1997) 2666. [2] M.R. Salinas, G.L. Alonso, F.J. Esteban-Infantes, J. Agric. Food Chem. 42 (1994) 1328.
[3] A.L. Waterhouse, S.E. Ebeler, Chemistry of wine flavor, ACS Symposium series 714, American Chemical Society, Washington DC, 1998. [4] Y. González, J.L. Pérez, B. Moreno, C. Garc´ıa, Anal. Chim. Acta 384 (1999) 83. [5] P.N. Bartlett, J.M. Elliot, J.W. Gardner, Food Technol. 51 (1997) 44. [6] E. Schaller, J.O. Bosser, F. Escher, Chimia 53 (1999) 98. [7] D.L. Ellis, M.R. Zakin, L.S. Berstein, M.F. Rubner, Anal. Chem. 68 (1996) 817. [8] J.M. Slater, E.J. Watt, Analyst 117 (1992) 1265. [9] B.P.J. de Lacy, P. Evans, N.M. Ratcliffe, Analyst 121 (1996) 793. [10] A. Guadarrama, M.L. Rodr´ıguez-Méndez, J.A. de Saja, J.L. R´ıos, J.M. Ol´ıas, Sens. Actuators B, submitted for publication. [11] H. Ulmer, J. Mitrovics, G. Noetzel, U. Weimar, W. Göpel, Sens. Actuators B 43 (1997) 24. [12] P.N. Bartlett, N. Blair, Analyst 118 (1993) 371. [13] S. Baldacci, T. Matsuno, K. Toko, R. Stella, D. De Rosi, Sensors Mater. 10 (1998) 185. [14] C. Di Natale, F. Davide, A. D’Amico, P. Nelli, S. Groppelli, G. Sberveglieri, Sens. Actuators B 33 (1996) 83. [15] N.E. Agbor, M.C. Petty, A.P. Monkman, Sens. Actuators B 28 (1995) 173. [16] S.K. Cha, Polym. Sci. B Polym. Phys. 35 (1997) 165. [17] A. Guadarrama, C. de la Fuente, J.A. Acuña, M.D. Vázquez, M.L. Tascón, P. Sánchez Batanero, Qu´ımica Anal´ıtica 18 (1999) 209. [18] G. Schopf, G. Komehl, Polythiophenes-Electrically conductive polymers, Advances in polymer science, Vol. 129, Springer, Berlin, 1997. [19] D. Hodgins, Sens. Actuators B 26/27 (1995) 255. [20] H. Arai, T. Seiyama, in: W. Göpel, T. A. Jones, M. Kleitz, I. Lundström, T. Seiyama (Eds.), Sensors. A Comprehensive Survey. Vol. 3, VCH, Weinheim, 1992, p. 1002.
Array of conducting polymer sensors for the characterisation of wines A. Guadarrama a,∗ , J.A. Fernández b , M. Íñiguez c , J. Souto a , J.A. de Saja a a
Departamento de F´ısica de la Materia Condensada, Cristalograf´ıa y Mineralog´ıa Facultad de Ciencias, Universidad de Valladolid P. de la Magdalena s/n, 47011 Valladolid, Spain b Estación Enológica de Castilla y León. Sant´ısimo Cristo, 20, 47490 Rueda (Valladolid), Spain c Estación Enológica de la Rioja. Bretón de los Herreros, 4, 26200 Haro (La Rioja), Spain Received 8 October 1999; received in revised form 21 January 2000; accepted 27 January 2000
Abstract The response of an array of polymeric sensors to different Spanish wine varieties has been evaluated. Two different systems for the injection of the volatile compounds, based on static and dynamic headspace sampling, were used. A comparative study of the operation of the array when using one or the other methodology has been carried out. The cross sensitivity of the polymeric films to moisture and ethanol has been explicitly considered. The discrimination and classification capabilities of the sensor array have been examined by statistical analysis of the obtained data using pattern recognition techniques. © 2000 Elsevier Science B.V. All rights reserved. Keywords: Conducting polymers; 3-Methylthiophene; Aniline; Pyrrole; Wines; Electronic nose
1. Introduction The quality of foodstuff can be assessed by evaluating the colour, taste and smell of the product. Humans are capable of discriminating five different types of flavours: acid, bitter, sweet, salty and umami, whereas the aromatic and chromatic characteristics of food products give rise to a much wider spectrum of sensations. The analysis of the volatile compounds released by a certain comestible can help in classifying its specific attributes, in detecting any problems that may arise in its processing, or, more simply, in identifying any undesirable degradation during its storage [1]. The most common method to address aromatic properties is the so-called panel test. This method, nonetheless, ∗
Corresponding author.
requires the training of human specialists. Certain disadvantages, such as the high cost and the difficulty in setting standards for an objective estimation, preclude a widespread application of this procedure. Chemical analytical methods [2,3] have a higher reliability, although they require long and complicated processes, so that it is not possible to perform an in situ evaluation with common techniques. Moreover, they also imply elevated economic expenses. Therefore, there is a need for new reliable methods for the assessment of olfactory characteristics in the food industry. New strategies are focussing on systems that are fast, non-destructive and objective, at a reasonably low cost. The use of arrays of gas sensors, the now commonly termed electronic noses [4–6], with purposely-designed software for discrimination of signals, is becoming increasingly extended. The
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sensing principle, in general, is based on the measurement of the variations of the mass, optical or electrical properties of the active materials. Conducting polymer gas sensors experiment changes in their electrical resistance when exposed to different volatile species [7–9]. This kind of active unit has been utilised for the evaluation of foodstuffs such as olive oil, coffee, beer or wine [10–14]. In spite of some promising perspectives, these sensors lack specificity, show a limited reproducibility and display a marked cross-sensitivity to water vapour. Specificity can nowadays be overcome to some extent with the aid of pattern-recognition techniques. On the other hand, several improvements in the reproducibility in the preparation and properties of the sensors, which have been recently reported, encourage further work in this direction [15–18]. The most serious shortcoming of these systems to date is the influence of moisture in the recorded data. As a matter of fact, polymeric chemoresistors, unlike mass sensitive devices, are able to detect both high and low weight volatile substances. Therefore, ethanol vapours will also interfere in the measurements when analysing wine samples [19]. If this type of sensors is to be used in the wine industry, it is necessary to eliminate, or at least substantially diminish, the presence of these two components in the volatile mixture. There are certain adsorbent resins, such as Tenax TA (a 2,6-diphenylene oxide porous polymer resin), that show a low affinity towards water and a low breakthrough volume for ethanol, while, on the other hand, they are able to efficiently retain and concentrate the rest of the volatiles present in wine. The volatiles can be released afterwards by applying a temperature program to the trap. The viability of a system for the evaluation of the organoleptic characteristics of wines based on an array of polymeric sensors directly connected to a thermal desorption injector or a headspace autosampler is analysed in the present work.
2. Experimental 2.1. Samples and conditioning Different types of Spanish wines were used for the experiments: two red wines, both of them aged in oak
barrels, one from the Rioja O.D. and the other from the Ribera de Duero O.D.; and a white wine produced with the Albariño grape variety, from the R´ıas Baixas O.D. For comparison purposes, deionised water and a 15% dilution of ethanol in water were also analysed. The samples have been prepared following two alternative methodologies, both based on headspace sampling, which are well-known techniques for their extremely high reproducibility, and as such they have been extensively used in common chromatographic techniques. The dynamic headspace sampling method was performed as follows. The volatile components are extracted with a Dynamic Thermal Stripper 1000 purge system from TEKNOKROMA. This is accomplished by bubbling a helium flow of 69 ml min−1 for 20 min through a vial that contains 40 ml of wine, and by trapping the volatiles in a tube filled with Tenax TA resin. A flow of the clean helium gas is passed directly through the tube for 10 additional minutes in order to eliminate the water and ethanol vapours that may have been retained inside it in the first stage. The volatiles are afterwards injected in the measuring system with an ATD 400 thermal desorption equipment from Perkin–Elmer. In the first step of the injection process, the temperature of the Tenax tube is raised to 300◦ C, and the volatiles are driven to a cold trap (−30◦ C) by flowing helium (22.5 psi) for 5 min. Helium is used as the carrier gas in order to prevent the combustion of the Tenax TA. Afterwards, the temperature of this trap is abruptly elevated from –30 to 300◦ C, so that the volatiles can be concentrated in a narrow band. These compounds are finally injected into the sensor chamber with a helium flow of 70 ml min−1 . For the static headspace sampling, 3 ml of the wines were placed in 10 ml vials. The vials are encapsulated and placed in an HP7694e automatic headspace sampler from Hewlett–Packard. The vials are kept at a constant temperature (40◦ C) for 9 min in order to obtain a homogeneous headspace. The vial is pressurised for 8 s at 1.5 bar. The pressure gradient that builds up permits to fill a 3 ml loop in 9 s, and its content is then injected to the carrier gas that drives the volatiles to the sensors chamber. In this case, the carrier gas was high purity synthetic air (N50) and the flow rate was 140 ml min−1 .
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2.2. Preparation of polymeric sensors The poly(3-methylthiophene) (PMT) and polyaniline (PAN) films were grown electrochemically onto glass substrates covered with ITO electrodes (electrode spacing 75 m). Alumina substrates with gold electrodes (electrode spacing 50 m) where used for the polypyrrole (PPy) sensors. The substrates were cleaned with acetone prior to polymer deposition. A detailed description of the complete procedure for the preparation of the sensors has been reported elsewhere [10,17]. Electropolymerisation and electrochemical measurements were performed using an EG&G PARC Model 263 potentiostat/galvanostat, which was controlled by electrochemical software M270/250 installed in a desktop computer. The reference electrodes were Ag/AgNO3 0.1 mol l−1 (EG&G) for the non-aqueous media, and Ag/AgCl when water is used as solvent. All potentials quoted are relative to the corresponding reference. The counter electrode was a large surface area platinum gauze, which was flamed prior to use. The solutions were prepared and introduced into a cell with a thermostatic jacket (Metrohm) and with a temperature controlled liquid system (Neslab). All the polymeric films were grown at a constant temperature (25◦ C). The solutions were deoxygenated by bubbling nitrogen for 10 min prior to use. The poly(3-methylthiophene) sensors were obtained from an electrolytic solution of 3-methylthiophene (0.1 mol l−1 , purchased from Sigma), and with 0.1 mol l−1 of the corresponding salt in acetonitrile (Sigma–Aldrich, HPLC grade). Lithium perchlorate anhydrous (Fluka), lithium trifluoromethane sulfonate (Fluka), tetrabutylammonium perchlorate (Sigma) and tetrabutylammonium tetrafluoroborate (Fluka) were
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employed in the polymerisation reaction to check the effects of dopant anions. These polymeric sensors were prepared by chronopotentiometry at a constant current (0.6 mA for 60 s). Stable oxidised blue films are obtained. In a subsequent conditioning stage, a –0.5 V potential is applied to the films in order to obtain the reduced state, which is more sensible to the gaseous species. The polymer films are finally washed in acetonitrile. The polyaniline sensors are generated from a solution of aniline (1 mol l−1 , from Aldrich) and HCl (2 mol l−1 , Panreac) in deionised water (Milli-Qplus, Millipore). The films were formed by repetitive cycling (15 cycles between –0.3 and 0.9 V at a rate of 50 mV s−1 , final potential 0 V). The polypyrrole films are obtained from an aqueous solution of pyrrole (0.1 mol l−1 , Aldrich) and tetrasulfonated nickel phthalocyanine (0.01 mol l−1 , Aldrich). These sensors are formed by cronoamperometry at a constant potential (0.9 V for 120 s). A 0.0 V conditioning potential was finally applied to the polypyrrole films. All the polymeric sensors used in this work, as well as the growth techniques and conditions, are collected in Table 1. 2.3. Measuring setup The sensors were mounted in a TEFLON text box with a volume of approximately 150 ml. The connection of the ATD-400 system to the sensors box is done inside the chromatograph oven with 1/16 in. tubing. The test box, which is replacing the chromatographic column, is kept at a constant temperature (30.3◦ C) throughout the experiments. An initial flow of helium of 84 ml min−1 is set for 5 min to allow for the stabilisation of the baseline, prior to the injection of the
Table 1 Conducting polymer sensors formed by electrochemical polymerisation using different techniques: Chronoamperometry (CA), Cyclic Voltammetry (CV) and Cronopotentiometry (CP) No. S01 S02 S03 S04 S05 S06
Monomer
Electrolyte 0.1 mol l−1
3-methylthiophene 3-methylthiophene 0.1 mol l−1 3-methylthiophene 0.1 mol l−1 3-methylthiophene 0.1 mol l−1 Aniline 1 mol l−1 Pyrrole 0.1 mol l−1
0.1 mol l−1
LiClO4 LiCF3 SO3 0.1 mol l−1 TBAClO4 0.1 mol l−1 TBABF4 0.1 mol l−1 HCl 2 mol l−1 NiPcTs 0.01 mol l−1
Technique
Conditions
CP CP CP CP CV CA
i=−0.6 mA; t=60 s; Ef =−0.5 V; tf =60 s i=−0.6 mA; t=60 s; Ef =−0.5 V; tf =60 s i=−0.6 mA; t=60 s; Ef =−0.5 V; tf =60 s i=−0.6 mA; t=60 s; Ef =−0.5 V; tf =60 s 0 to 0.9 to –0.3 V; 15 cycles; 50 m V/s Epol =0.9; tpol =120 s; Ef =0.0 V; tf =60 s
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volatiles. The flow is then lowered to 70 ml min−1 as the vapours enter the chamber. This change in the flow rate produces a negligible variation in the resistance of the sensors. After the response of the sensing units is recorded, the helium flow is kept for 20 min so that the original baseline is recovered. For the static headspace sampling measurements a flow of 140 ml min−1 of dry air is set for 3 min to allow for the stabilisation of the baseline. The carrier gas passes then through the sample loop for 1 min, dragging the volatile components of the sample towards the chamber of sensors. In this step no variation in the flow is induced. Finally, the clean air flow is kept for 12 min so that the original baseline is recovered. Up to 10 sensors can be mounted simultaneously in the reaction box. A Keithley 224 Programmable Current Source was used to provide a direct current of 10 A to the sensors. The voltage drop across each one of the sensing units was monitored with a computer controlled Keithley 2000 multimeter with a Keithley 2000SCAN scanning card. The data were collected using TestPointTM software. The obtained data are exported to the GPES44 program, and the different features of the signals are analysed. The height, width at half height, area, and area of the derivative of the responses are routinely determined. The analysis has also been attempted by considering the first coefficients of the Fourier transform of the curves, which were obtained by applying a Fast Fourier Transform (FFT) algorithm. Finally, pattern recognition techniques (PARC) were used for the discrimination of the signals, for this purpose Principal Component Analysis (PCA) has been carried out using the software Matlab v4.2.
3. Results and discussion Prior to the evaluation of the wine samples, the potential of the dynamic headspace sampling technique as a means to reduce the concentration of moisture and ethanol in the vapours was checked. In particular, the importance of drying the traps with a clean helium flow was elucidated. The chromatograms shown in Fig. 1 correspond to the vapours extracted from a wine sample. The upper graph corresponds to the direct injection of the volatiles without performing this step. The most intense peak that is observed in the chromatogram corresponds to ethanol, with a retention time (RT) of 11.2 min. This indicates that, even though the resin here used has a low affinity towards this compound, it is still present at a high concentration within the tubes. The effect of the clean helium flow is evident in the lower graph. The peak that corresponds to ethanol has undergone a drastic reduction, while the rest of the peaks remain almost unaltered. Hence, this method renders ethanol a minor component in the volatile mixture. A quantitative analysis of the chromatograms performed with different types of wines reveals that over 90% of the ethanol is removed after the drying step, and that only minute differences in the final content of ethanol were found. The presence of water in the traps cannot be determined with the chromatograph, as the FID detector is
2.4. Chromatographic analysis The measurements have been carried out on an Autosystem chromatograph from Perkin Elmer, equipped with a TR-WAX capillary column phase (length: 60 m; inner diameter: 0.22 mm) and an FID detector. Data acquisition and treatments have been done with the Turbocroam v4.1 software, also from Perkin Elmer. At the beginning of the measurements, the temperature is kept at 60◦ C for 30 min. Afterwards, it is raised to 180◦ C at a rate of 1◦ C min−1 .
Fig. 1. Chromatograms of diluted (1/100) red wine (a) without drying and (b) after drying. Note the different scales in the graphs.
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Fig. 2. Response of a poly(3-methylthiophene) sensor to a water sample injected with a dynamic headspace system: (a) without drying and (b) after drying.
not sensitive to water. In order to estimate its operation, deionised water samples were directly injected inside the sensor chamber. The results, expressed in terms of the percentage resistance change (R0 : initial resistance of the sensor; R: resistance at time t) of the polymeric film, are shown in Fig. 2. The response of a polymeric sensor is clearly much weaker if the tubes are dried before the injection. This can only be attributed to the difference in the concentration of water vapours. The reduction can be estimated from the sensors signals. A decrease in the maximum signal of about 85% was measured after drying. As this type of sensors show a sublinear relation between humidity and resistance [20], this indicates that over 85% of
the water vapour was eliminated. Therefore, it can be concluded that, after the drying stage, the water and ethanol vapours that condensed in the trap could be eliminated to a high extent. The response of a polymer sensor to a Ribera del Duero red wine using this sampling technique is compared to the signal obtained from deionised water in Fig. 3. As it can be observed, not only the relative resistance changes, but also the shapes of the curves are clearly different for the two samples. Just after the injection of the volatiles, the tendencies of both curves are similar, showing a steep relative increase in the resistance of approximately 0.35% in the first few seconds. Therefore, this initial effect can be probably
Fig. 3. Response of a poly(3-methylthiophene) sensor to (a) red wine and (b) water samples obtained with the dynamic headspace system.
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Fig. 4. Response of a poly(3-methylthiophene) sensor to (a) red wine and (b) water samples obtained with the static headspace system.
assigned to the water vapours. This is further supported by the fact that the curve that corresponds to water shows an immediate decay that reflects the desorption of the vapour. On the other hand, the response to the wine is characterised by a further increase of the signal, with a global maximum change of about 1%, and a slower desorption process. As it can be clearly observed in Fig. 1, there are many components in the wine vapour. The kinetics of the adsorption and desorption of these volatiles on the polymeric surfaces can differ considerably from one to another. Hence, the upper curve shown in Fig. 3 would correspond to a superposition of the changes experimented when the different volatiles reach the reaction chamber and interact with the sensors there located. An alternative method for the evaluation of the wines, based on the static headspace sampling technique, was also probed. Fig. 4 corresponds to the same test depicted in Fig. 3, when the static sampling is used instead of the dynamic one. As it can be clearly appreciated, the shapes of the curves are very different from those in the previous figure. With this sampling technique, the vapours are injected in the reaction box in the very first seconds, and are almost immediately removed from it. Therefore, the initial increase in the resistance is much faster, and so is the recovery of the signal afterwards. The signals are more intense, as the concentrations of the volatile compounds in the chamber are higher than those attained with the gradual delivery inherent to the dynamic method.
Although the static sampling technique would allow for a faster measurement of the samples, the overall selectivity of the system is reduced. Clear evidences are obtained from the direct inspection of Figs. 3 and 4. A more detailed comparison of the data for a series of sensors, in terms of the maximum increase of the resistance, is presented in Fig. 5. For the means of clarity, the net responses to the red wine have been normalised to the maximum of the water vapour curves. More intense relative signals can be observed in all cases if the dynamic technique is used.
Fig. 5. Relative variation of the response towards wine, normalised to the water signal, using dynamic and static injection systems, for the poly(3-methylthiophene) sensors.
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Fig. 6. Principal component analysis of the responses of the array of sensors exposed to three different wines, a 15% dilution of ethanol in water and deionised water when the static headspace sampling technique is used.
Fig. 7. Principal component analysis of the responses of the array of sensors exposed to three different wines, a 15% dilution of ethanol in water and deionised water when the dynamic headspace sampling technique is used.
The potential for discrimination of wine aromas was probed by repetitively exposing the sensor array to the vapours. Even though the aforementioned results indicate a poorer selectivity if the static sampling technique is used, both injection methods were systematically tested. Several features can be chosen to characterise the response of the sensors. The reproducibility of the system is high for the peak height and the width at half height, but there is a greater dispersion in other parameters, such as the area under the curves. Principal component analysis (PCA) was applied to the data. The best results were obtained when the width at half height, measured in minutes, was the feature selected for the classification of the signals. The corresponding plots of the first two principal components for this parameter are shown in Figs. 6 and 7. In both cases, the experimental data were obtained with identical sets of samples, that is to say, they were drawn out from the same bottles. When the static headspace sampling technique is used, the sensors are only capable of distinguishing the water vapour from the rest of the samples. All the wines, as well as the 15% dilution of ethanol in water, are located in the same region of the plane. The ellipses, which correspond to the 90% confidence intervals, clearly intersect. This is a clear indication of a poor operation of the system. On the other hand, when the dynamic headspace sampling is employed, the separation of the clusters is enhanced, and the different samples can be dis-
criminated. The pure water and the diluted sample of ethanol in water are well far apart from the wines. These, in turn, are quite nicely differentiated, although there is a minor overlapping of the Ribera de Duero and the R´ıas Baixas clusters.
4. Conclusions A set of sensors for volatile compounds have been prepared by electropolymerisation of 3-methylthiophene, pyrrole and aniline with different salts. The sensors thus prepared were mounted on a box and subsequently exposed to a variety of wine vapours. Headspace sampling techniques were used to inject the vapours in the reaction box. These methods were chosen for the extremely high reproducibility they provide. The static headspace sampling technique produces very fast responses. The resistance of the polymeric sensors increase immediately after the injection of the vapours, and the recovery of the sensors is also rapid. On the other hand, the water and ethanol vapours produce strong interferences in the signals and conceal the particularities of the vapours that are connected to other components that are present in low concentrations. As a consequence, it is not possible to classify the different wines attending to the signals produced by the array of sensors.
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The dynamic headspace sampling method relies in the low affinity towards water and the low breakthrough volume for ethanol of the Tenax TA resin. The purge and trap thermal desorption system is efficient in decreasing the cross interferences if a drying step is performed after the volatile organic compounds are concentrated in the resin. This drying stage removes most of the water vapours that condense in the surface of the porous adsorbent, and is also capable of eliminating most of the trapped ethanol. On the other hand, the majority of the volatile compounds that are distinctive of the aroma of the wines remain concentrated in the trap. The signals obtained from the thermal desorption of the trapped compounds allow for a better discrimination of the different samples.
Acknowledgements Financial assistance from CICYT of Spain (1FD97-1186) is gratefully acknowledged. One of us (A.G.) would like to thank the Junta de Castilla y León for a grant. References [1] M.T. Morales, J.J. Rios, R. Aparicio, J. Agric. Food Chem. 45 (1997) 2666. [2] M.R. Salinas, G.L. Alonso, F.J. Esteban-Infantes, J. Agric. Food Chem. 42 (1994) 1328.
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