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Bread baking aromas detection by low-cost electronic nose
Available online at www.sciencedirect.com
Sensors and Actuators B 130 (2008) 100–104
Bread baking aromas detection by low-cost electronic nose A. Ponzoni a,∗ , A. Depari b , M. Falasconi a , E. Comini a , A. Flammini b , D. Marioli b , A. Taroni b , G. Sberveglieri a a
CNR-INFM Sensor Laboratory, and University of Brescia, Department of Chemistry and Physics, Via Valotti 9, 25133 Brescia, Italy b University of Brescia, Department of Electronics for Automation and INFM, Via Branze 38, 25123 Brescia, Italy Available online 31 July 2007
Abstract In this work we propose a low-cost electronic nose based on a resistance to period converter readout system, suitable to handle a wide range of resistance values (from k up to tens of G) with high accuracy (<1%). An array composed of four metal oxide based gas sensors, with baseline resistance spreading on the above range has been used to validate the system. The electronic nose has been applied to the detection of key aromas peculiar of different stages of the bread baking process has been chosen as target application, revealing the suitability of the proposed electronic nose to distinguish these volatiles in an ordered manner reflecting the different baking step they represent. © 2007 Elsevier B.V. All rights reserved. Keywords: Electronic nose; Metal oxide gas sensors; Food aromas compounds; Readout interface
1. Introduction Electronic noses were proposed some years ago as a promising technology for odor detection and discrimination [1]. After that, it has been successfully applied in different fields, such as, for example, food science [2,3], medicine [4,5] or environmental pollution control [6,7]. The performance of an electronic nose is strongly related to the different response spectra exhibited by sensors composing the sensor array, namely sensor selectivity. Focusing on conductometric gas sensors, materials scientists have developed a lot of materials exhibiting a wide range of sensing performances suitable for different applications. For example, by properly adjusting synthesis parameters of WO3 layers, high sensitivity to oxidizing gases can be obtained at room temperature [8]. The n-type sensing properties of TiO2 (resistance decreases after exposure to reducing gases such as CO) can be modified in a p-type sensing material (resistance increases after exposure to reducing gases) but its high resistivity (>1 G) makes it difficult to use [9].
∗
Corresponding author. Tel.: +39 030 3715707; fax: +39 030 2091271. E-mail address: [email protected] (A. Ponzoni).
0925-4005/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2007.07.099
In order to make these findings suitable for the development of a low-cost electronic nose, a major difficult still remains, the lack of a cheap electronic system able to read with the required accuracy the widespread resistance values (usually from k to G and higher) exhibited by different sensitive layers. A promising solution based on a resistance to period converter (RPC) has been proposed in [10], however its validation to handle a sensor array is still missing. In this work, a 4-sensor array has been used for this purpose, employing different materials with baseline resistance values ranging from hundreds of k to tens G. As target application we chose the detection of key aromas of bread baking. This process is of crucial importance at industrial level as concern the final food quality. Previous works show that monitoring the evolution of the product quality directly during the cooking process could be feasible by a gas sensor array [11]. Beside the approach used in [11], where the whole set of volatiles produced by the process is analyzed, it is of interest to get knowledge on the sensor behavior towards key odorants of the process. Indeed, despite hundreds of molecules have been identified to develop from foods and food processing, only a small fraction of these (less than 5%) revealed to stimulate aroma perception of the human nose [12]. So far, several works have been focused on identification of key aromas and development of aroma models,
A. Ponzoni et al. / Sensors and Actuators B 130 (2008) 100–104
101
Table 1 Array of metal oxides gas sensors: materials, working temperatures and baseline resistances Code
Material
Working temperature (◦ C)
Resistance
TiO2 MoW In2 O3 WO3
Titanium oxide Mixed molibdenum and tungsten oxides Indium oxide Tungsten oxide
400 275 400 300
10 G 50 M 800 k 30 M
i.e., synthetic blends with flavor very close to the one of the original food [12,13] and knowledge on sensors or instruments suitable for these studies is increasing of interest [14,15]. Here, we will consider five key odorants typical of different stages of the bread baking process, using diffusion tubes method to test sensors towards controllable concentrations of such volatiles. 2. Experimental 2.1. Sensitive layers Fig. 1. Block scheme of the RPC unit.
Materials used are reported in Table 1, where codes used in the following to identify sensors are reported together with sensor working temperature and baseline resistance. Titanium dioxide and indium oxide have been used as highly and scarcely resistive materials respectively (Table 1). Thin films based on titanium dioxide, mixed molybdenum and tungsten oxides and indium oxide have been synthesized by RF magnetron sputtering in reactive atmosphere by using metallic targets. Details on synthesis and characterization can be found in [16,17] and [18] respectively. Differently, WO3 layers have been deposited by thermal evaporation in reactive atmosphere using a metallic tungsten wire as source [19]. Sensitive layers have been synthesized on 2 mm × 2 mm × 0.25 mm alumina substrates. Interdigitated (IDC) platinum contacts and a platinum meander acting as heater have been deposited by sputtering on the top and rear face of the substrate. 2.2. Sensors readout system The electronic system used to read sensor resistance is a modular equipment able to interface resistive sensors to a personal computer. It is based on a resistance to period conversion performed by a low-noise circuit whose block scheme is shown in Fig. 1. The sensor is inserted into a ramp generator with the output period proportional to the resistive component RSENS of the sensor. V0 decreases from 0 V to the negative threshold value (Vt ) with a slope depending on the RSENS value. Once V0 reaches the Vt value, Vout switches to the 1 state while the control closes the switch, thus Vs is settled to its initial value 0 V. After the TRESET delay, a new cycle is started. The measured period T and RSENS are related by T = TRESET +
|Vt | RSENS Ci Vexc
(1)
The system acquires data with a sampling time of about 25 ms. Measuring time depends on RSENS value but properly
Fig. 2. Measuring setup.
tuning Vexc and Vt voltages allows to keep it below 250 ms. As far as accuracy is concerned, it has been proved the system capability to measure resistance values over the 10 k–3 G range within an accuracy of 1%. Further details can be found in [10,20]. 2.3. Gas standard generations Gas standards have been generated by diffusion tubes [21] (fine permeation tubes) filled with pure compounds (Sigma–Aldrich). A fixed amount (1 ml1 ) of the target compound has been placed in the diffusion tube: a steel cylinder with volume of 10 ml, provided with a small hole on the top (diameter = 2 mm, height = 10 cm), allowing vapors to exit from the cylinder. A 300 sccm flux of dry air was used as carrier. The measuring setup (Fig. 2) is provided by a humidifier system and thermostatic bath where diffusion tubes are lodged so 1 Acetylpyrazine is a solid compound at room temperature, 1 mg has been placed in the diffusion tube.
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Table 2 Tested key-aromas Name
Chemical formula
Tested concentration (ppm)
Odor quality
Acetaldehyde Diacetyl Acetylpyridine Acetylpyrazine 2-Ethyl 3-methylpyrazine
C2 H4 O C4 H6 O2 C7 H7 NO C6 H6 N2 O C7 H10 N2
1200 15 5.8 <0.001a 7
Acid Buttery Oily, pop corn Sweet roasted Roasted
a Compound concentration in air has been determined by periodically weighting the diffusion tube but no appreciable weight variations have been detected for acetylpyridine, thus its concentration can be given as an upper limit.
that the evaporation rate of each substance can be controlled by regulating the bath temperature. Both lines, the reference one and the gas one, are provided by valves that allow to work with a constant flux on both lines either they are fluxed in the test chamber or not. Otherwise, once the chamber connection has been switched from one line to the other, transitory times will occur while the flux reach its equilibrium value. In the gas line, such a flux variation would also change the concentration in the gas line (Eq. (1)). Gas concentrations (Table 2) have been determined by placing the steel cylinder in the test circuit (simulating measurements) and periodically weighting it by means of an analytical balance with resolution 0.01 mg. The slope of the weight versus time curve gives the evaporation rate r of the compound, which is related to its concentration C by C=
r RT MQ p
(2)
where C is measured in ppm, r in ng/min, M the molar weight, Q the carrier flux (sccm), R the gas constant (8.314 J/K), p the working pressure (101,300 Pa) and T is the bath temperature (K). 3. Results Response spectra of each sensor are reported in Fig. 3. It can be observed that sensors exhibit higher response towards
Fig. 3. Sensor response spectra toward the whole set of volatiles tested.
diacetyl and acetaldehyde with respect to other compounds. One reason is the high concentrations tested for these two compounds; however this is not enough to explain the response pattern obtained. Indeed, despite acetaldehyde has been tested in a concentration of 1200 ppm, which is about 200 times higher than concentrations of acetylpyridine, for example, and more than 106 times higher than the concentration of acetylpyrazine, sensors do not exhibit such a large difference in their response towards these chemicals. Despite metal oxide based gas sensors are known to exhibit non-linear response, the fact that none of the sensors reflect the large difference in concentrations between these compounds, suggest that the response spectra shape would be mainly due to the chemistry of such volatiles. The principal component analysis (PCA) is useful to better highlight this. In Fig. 4, the PCA score plot of the data against the first and second principal components (PC1 and PC2 respectively) has been reported. It can be observed that the first feature distinguished by the sensor array is the chemistry of the compound. Moving along the PC1 axis from left to right, acetaldehyde is first encountered, then diacetyl. Acetylpyridine is quite close to diacetyl and they are distinguished along the PC2 direction rather than the PC1. Moving further along the PC1 the pyrazines group is encountered. According to [22], acetaldehyde and diacetyl are produced together with alcohols and acetates during the fermentation process. As the baking process is started, the rising temperature
Fig. 4. Principal component analysis (PCA) plot of the whole data set recorded.
A. Ponzoni et al. / Sensors and Actuators B 130 (2008) 100–104
causes evaporation of such highly volatile compounds, so that their presence is peculiar of the baking process initial steps. Especially acetaldehyde is strongly smelled at this stage due to its high vapor pressure (1006 h Pa at 20 ◦ C), compared to the vapor pressure of other compounds (52 h Pa for diacetyl and 595 h Pa for ethanol, at 20 ◦ C). Volatiles produced during fermentation have also been found in toasted bread, i.e., at the end of the baking process, but only diacetyl contributes significantly to the bread smell [23]. So far diacetyl can be regarded as a key odorants identifying a baking step further the initial one. As the baking process goes on, the high temperature favors the chemical reaction between amino acids and sugars (Maillard reactions). In particular, the reactive carbonyl group of the sugar interacts with the nucleophilic amino group of the amino acid producing flavoring molecules such as, for example, pyridines, pyrazines and pyrrolines. As highlighted by Rychlick and Grosch [23], the concentration of these compounds changes according to the baking degree. They showed that 6-acetyltetrahydropyridine reach the maximum concentration almost in the middle of the process and it slowly decrease going further in the process due to its low molecular stability that cause its to degrade along time at high temperatures. On the contrary, 2-ethyl 3,5-dimethylpyrazine and acetylpyrroline, having a higher molecular stability, exhibit concentrations increasing with time and browning of bread. As far as relative concentrations are concerned, pyridines and pyrazines are present in similar amounts in wheat bread, while the diacetyl amount is quite higher [23]. Bath temperature (Fig. 2) has been settled in order to obtain key odorants in relative concentration concordant with this. Further looking at the obtained PCA plot (Fig. 4) it can be observed that compounds are ordered along the PC1 direction according to their characteristic step of the baking process. It should be pointed out that a reserve still remains if the results reported in [23] concerning 6-acetyltetrahydropyridine and 2-ethyl 3,5-dimethylpyrazine can be regarded as valid in general for pyridines and pyrazines respectively or they are valid just for the above specific molecules. If we can assume it is valid, compounds studied in the present work are distinguished along the PC1 direction according to a sequence resembling the aroma evolution of the bread baking process: acetaldehyde–diacetyl–pyridines–pyrazines. If the above assumption is not valid, the qualitative distinction of aromas still resemble the process evolution but grouping the pyridines and pyrazines classes. Referring to literature, concentrations of pyridines and pyrazines studied in [23] can change by a factor 2–10 due to the different toasting degree. With the aim to evaluate compounds concentration effects on the PCA plot, further measurements have been carried out with 2-ethyl 3-methylpyrazine by lowering the bath temperature from 20 to 10 ◦ C in order to reduce the evaporation rate and obtain a concentration of 0.8 ppm instead of 7 ppm. Sensor responses appreciably decrease (Fig. 5). MoW and TiO2 sensors are particularly sensitive to this, being their response reduced by a factor 4 and 3 respectively. However, the two data sets in the PCA are almost superimposed (Fig. 4). This confirms that varying the concentration of a given compound
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Fig. 5. Response towards 2-ethyl 3-methylpyrazine, 0.8 ppm (dashed line) and 7 ppm (continuous line).
within the range compatible with the one expected during the baking process, is less significant than the chemical nature of the compound, which thus remains the main feature distinguished by the sensor array. 4. Conclusions A low-cost readout electronic system suitable to measure resistance values within the range k–G with high accuracy (<1%) has been used to handle an array of four metal oxide based gas sensors. Monitoring key aromas of baking processes has been chosen as target application to validate the proposed approach. Results highlight the suitability of the low-cost electronic readout system to handle conductometric sensors exhibiting baseline values spreading on its wide working-range. Furthermore, the capability of the proposed electronic nose to distinguish key aromas of bread baking process primarily depending on their chemical nature has been proved. In a PCA plot these volatiles are ordered in a sequence concordant with the sequence they will be developed during the bread baking process: acetaldehyde, diacetyl, pyridines and pyrazines. Acknowledgement This work has been partly funded by the PNR FIRB 2003–2005 project “Sviluppo di microsistemi multisensoriali per applicazioni ambientali e agroalimentari”. References [1] T.C. Pearce, S.S. Shiffman, H.T. Nagle, J.W. Gardner, Handbook of Machine Olfaction, Wiley, 2002. [2] M. Falasconi, E. Gobbi, M. Pardo, M. Della Torre, A. Bresciani, G. Sberveglieri, Detection of toxigenic strains of Fusarium verticillioides in corn by electronic olfactory system, Sens. Actuator B: Chem. 108 (1/2) (2005) 250–257. [3] F. Sinesio, C. Di Natale, G.B. Quaglia, F.M. Bucarelli, E. Moneta, A. Macagnano, R. Paolesse, A. D’Amico, Use of electronic nose and trained sensory panel in the evaluation of tomato quality, J. Sci. Food Agric. 80 (1) (2000) 63–71.
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[4] J.W. Gardner, H.W. Shin, E.L. Hines, An electronic nose system to diagnose illness, Sens. Actuator B 101 (2004) 39–46. [5] C. Di Natale, A. Macagnano, R. Paolesse, E. Tarizzo, A. Mantini, A. D’Amico, Human skin odor analysis by means of an electronic nose, Sens. Actuator B 65 (2000) 216–219. [6] S. Zampolli, I. Elmi, F. Ahmed, M. Passini, G.C. Cardinali, S. Nicoletti, L. Dori, An electronic nose based on solid state sensor arrays for low-cost indoor air quality monitoring application, Sens. Actuator B 106 (2005) 199–206. [7] K. Boholt, K. Andreasen, F. den Berg, T. Hansen, A new method for measuring emission of odor from a rendering plant using the Danish Odor Sensor System (DOSS) artificial nose, Sens. Actuator B 106 (2005) 170–176. [8] L.G. Teoh, Y.M. Hon, J. Shieh, W.H. Lai, M.H. Hon, Sensitivity properties of a novel NO2 gas sensor based on mesoporous WO3 thin film, Sens. Actuator B: Chem. 96 (2003) 219–225. [9] Y. Li, W. Wlodarski, K. Galatsis, S.H. Moslih, J. Cole, S. Russo, N. Rockelmann, Gas sensing properties of p-type semiconducting Cr-doped TiO2 thin films, Sens. Actuator B 83 (2002) 160–163. [10] A. Depari, A. Flammini, D. Marioli, S. Rosa, A. Taroni, M. Falasconi, G. Sberveglieri, A new hardware approach to realize low-cost electronic noses, in: Proceedings of the IEEE Sensors, 2005, pp. 239–242. [11] D. Ward, S. Benedetti, M. Riva, Monitoring and controlling cooking processes using an electronic olfaction device, in: Proceedings of the 9th International Symposium on Olfaction and Electronic Nose (ISOEN), 2002, pp. 292–298. [12] W. Grosch, Evaluation of the key odorants of foods by dilution experiments, aroma models and omission, Chem. Sens. 26 (2001) 533–545. [13] G. Zehentbauer, W. Grosch, Crust aroma of baguettes. I. Key odorants of baguettes prepared in two different ways, J. Cereal Sci. 28 (1) (1998) 81–92. [14] T. Hoffmann, P. Schieberle, C. Krummel, A. Freiling, J. Bock, L. Heinert, D. Kohl, High resolution gas chromatography/selective odorant measurement by multisensor array (HRGC/SOMSA): a useful approach to standardize multisensor arrays for use in the detection of key foods odorants, Sens. Actuator B 41 (1997) 81–87. [15] D. Kohl, L. Heinert, J. Bock, Th. Hofmann, P. Schieberle, Gas sensors for food aroma during baking and roasting processes based on selective odorant measurements by an array, Thin Solid Films 391 (2001) 303–307. [16] V. Guidi, M.C. Carotta, M. Ferroni, G. Martinelli, L. Paglialonga, E. Comini, G. Sberveglieri, Preparation of nanosized titania thick and thin films as gas-sensors, Sens. Actuator B 57 (1999) 197–200. [17] E. Comini, M. Ferroni, V. Guidi, G. Martinelli, G. Faglia, G. Sberveglieri, Nanostructured mixed oxides compounds for gas sensing applications, Sens. Actuator B 84 (2002) 26–32. [18] E. Comini, A. Cristalli, G. Faglia, G. Sberveglieri, Light enhanced gas sensing properties of indium oxide and tin dioxide sensors, Sens. Actuator B 65 (2000) 260–263. [19] A. Ponzoni, E. Comini, M. Ferroni, G. Sberveglieri, Thin Solid Films 490 (2005) 81–85. [20] A. Depari, A. Flammini, D. Marioli, S. Rosa, A. Taroni, A low-cost circuit for high-value resistive sensors varying over a wide range, Meas. Sci. Technol. 17 (2) (2006) 353–358. [21] J.J. McKinley, R.E. Majors, LC–GC 10 (2000) 1024–1033. [22] A. Hansen, B. Hansen, The influence of wheat flour type on the production of flavor compounds in wheat sourdoughs, J. Cereal Sci. 19 (1996) 185–190. [23] M. Rychlick, W. Grosch, Identification and quantification of potent odorants formed by toasting of wheat bread, Lebensm. Wiss. Technol. 29 (1996) 515–525.
Biographies Andrea Ponzoni was born in 1976. He received the degree in physics from the University of Parma, Italy, in 2000 and the PhD degree in material engineering
from the University of Brescia in 2006 with a thesis on nanostructured metal oxides for gas sensing applications. His major research activity regards synthesis and electrical characterization of metal oxides for gas sensing applications. Alessandro Depari was born in Breno (BS) in 1976. In 2002 he graduated in electronic engineering at the University of Brescia, Italy, where, in 2006, he received the PhD degree in electronic instrumentation. His main research activities are the signal conditioning and processing for chemical sensors, in particular resonant and resistive sensors for electronic noses and the development of sensors networks for distributed measurement. Matteo Falasconi got his degree in physics from University of Pavia in 2000. In 2005, he has obtained a PhD degree in material engineering from University of Brescia with a thesis on the development of artificial olfactory systems for the food industry. At present, his research interests are electronic noses and data analysis. Elisabetta Comini was born on 21 November 1972 and she received her degree in physics at the University of Pisa in 1996. She received her PhD in material science at the University of Brescia. She is presently working on chemical sensors with particular reference to deposition of thin films by PVD technique and electrical characterisation of MOS thin films. In 2001 she has been appointed assistant professor at the University of Brescia. During her career she has published more than 80 articles on peer reviewed international journals. Alessandra Flammini was born in Brescia, Italy, in 1960. She graduated with honors in physics at the University of Rome, Italy, in 1985. From 1985 to 1995 she worked on industrial research and development on digital drive control. Since 1995 she has been a researcher at the Department of Electronics for Automation of the University of Brescia. Her main field activity is the design of digital electronic circuits (FPGA, DSP, processors) for measurement instrumentation. Daniele Marioli was born in Brescia, Italy, in 1946. He graduated electrical engineering in 1969 at the University of Pavia, Italy. From 1984 to 1989, he was an associate professor in applied electronics, and since 1989 he has been a full professor of applied electronics at Brescia University. Since 1993, he has been the director of the Department of Electronics for Automation of the Faculty of Engineering of the University of Brescia. His main field activity is the design and experimentation of analog electronic circuits for the processing of electrical signals from transducers, with particular regard to S/N ratio optimization. Andrea Taroni was born in Cotignola, Ravenna, Italy, in 1942. He received the degree in physics from the University of Bologna, Italy, in 1966. He was an associate professor at University of Modena from 1971 to 1986. Since 1986 he has been a full professor in electrical measurements at the University of Brescia. Since 1993 he has been the dean of the faculty of engineering of the University of Brescia. He has done extensive research in the field of physical quantities sensors and electronic instrumentation, both developing original devices and practical applications. Giorgio Sberveglieri is the director of the Sensor Laboratory. He received his degree in physics from the University Parma. In 1988, he established the Gas Sensor Lab, mainly devoted to the preparation and characterization of thin film chemical sensors based on metal oxide semiconductors and, since the mid 1990s, to the area of electronic noses. In 1994, he was appointed full professor in physics. He is referee of many international journals and has acted as chairman in several conferences on materials science and on sensors. During 30 years of scientific activity he published more than 200 papers in international journals; he presented more than 110 Oral Communications to international congresses. He is also an evaluator of IST and Growth Projects of EU and the coordinator of the Applied Network of INFM since April 2001.
Sensors and Actuators B 130 (2008) 100–104
Bread baking aromas detection by low-cost electronic nose A. Ponzoni a,∗ , A. Depari b , M. Falasconi a , E. Comini a , A. Flammini b , D. Marioli b , A. Taroni b , G. Sberveglieri a a
CNR-INFM Sensor Laboratory, and University of Brescia, Department of Chemistry and Physics, Via Valotti 9, 25133 Brescia, Italy b University of Brescia, Department of Electronics for Automation and INFM, Via Branze 38, 25123 Brescia, Italy Available online 31 July 2007
Abstract In this work we propose a low-cost electronic nose based on a resistance to period converter readout system, suitable to handle a wide range of resistance values (from k up to tens of G) with high accuracy (<1%). An array composed of four metal oxide based gas sensors, with baseline resistance spreading on the above range has been used to validate the system. The electronic nose has been applied to the detection of key aromas peculiar of different stages of the bread baking process has been chosen as target application, revealing the suitability of the proposed electronic nose to distinguish these volatiles in an ordered manner reflecting the different baking step they represent. © 2007 Elsevier B.V. All rights reserved. Keywords: Electronic nose; Metal oxide gas sensors; Food aromas compounds; Readout interface
1. Introduction Electronic noses were proposed some years ago as a promising technology for odor detection and discrimination [1]. After that, it has been successfully applied in different fields, such as, for example, food science [2,3], medicine [4,5] or environmental pollution control [6,7]. The performance of an electronic nose is strongly related to the different response spectra exhibited by sensors composing the sensor array, namely sensor selectivity. Focusing on conductometric gas sensors, materials scientists have developed a lot of materials exhibiting a wide range of sensing performances suitable for different applications. For example, by properly adjusting synthesis parameters of WO3 layers, high sensitivity to oxidizing gases can be obtained at room temperature [8]. The n-type sensing properties of TiO2 (resistance decreases after exposure to reducing gases such as CO) can be modified in a p-type sensing material (resistance increases after exposure to reducing gases) but its high resistivity (>1 G) makes it difficult to use [9].
∗
Corresponding author. Tel.: +39 030 3715707; fax: +39 030 2091271. E-mail address: [email protected] (A. Ponzoni).
0925-4005/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2007.07.099
In order to make these findings suitable for the development of a low-cost electronic nose, a major difficult still remains, the lack of a cheap electronic system able to read with the required accuracy the widespread resistance values (usually from k to G and higher) exhibited by different sensitive layers. A promising solution based on a resistance to period converter (RPC) has been proposed in [10], however its validation to handle a sensor array is still missing. In this work, a 4-sensor array has been used for this purpose, employing different materials with baseline resistance values ranging from hundreds of k to tens G. As target application we chose the detection of key aromas of bread baking. This process is of crucial importance at industrial level as concern the final food quality. Previous works show that monitoring the evolution of the product quality directly during the cooking process could be feasible by a gas sensor array [11]. Beside the approach used in [11], where the whole set of volatiles produced by the process is analyzed, it is of interest to get knowledge on the sensor behavior towards key odorants of the process. Indeed, despite hundreds of molecules have been identified to develop from foods and food processing, only a small fraction of these (less than 5%) revealed to stimulate aroma perception of the human nose [12]. So far, several works have been focused on identification of key aromas and development of aroma models,
A. Ponzoni et al. / Sensors and Actuators B 130 (2008) 100–104
101
Table 1 Array of metal oxides gas sensors: materials, working temperatures and baseline resistances Code
Material
Working temperature (◦ C)
Resistance
TiO2 MoW In2 O3 WO3
Titanium oxide Mixed molibdenum and tungsten oxides Indium oxide Tungsten oxide
400 275 400 300
10 G 50 M 800 k 30 M
i.e., synthetic blends with flavor very close to the one of the original food [12,13] and knowledge on sensors or instruments suitable for these studies is increasing of interest [14,15]. Here, we will consider five key odorants typical of different stages of the bread baking process, using diffusion tubes method to test sensors towards controllable concentrations of such volatiles. 2. Experimental 2.1. Sensitive layers Fig. 1. Block scheme of the RPC unit.
Materials used are reported in Table 1, where codes used in the following to identify sensors are reported together with sensor working temperature and baseline resistance. Titanium dioxide and indium oxide have been used as highly and scarcely resistive materials respectively (Table 1). Thin films based on titanium dioxide, mixed molybdenum and tungsten oxides and indium oxide have been synthesized by RF magnetron sputtering in reactive atmosphere by using metallic targets. Details on synthesis and characterization can be found in [16,17] and [18] respectively. Differently, WO3 layers have been deposited by thermal evaporation in reactive atmosphere using a metallic tungsten wire as source [19]. Sensitive layers have been synthesized on 2 mm × 2 mm × 0.25 mm alumina substrates. Interdigitated (IDC) platinum contacts and a platinum meander acting as heater have been deposited by sputtering on the top and rear face of the substrate. 2.2. Sensors readout system The electronic system used to read sensor resistance is a modular equipment able to interface resistive sensors to a personal computer. It is based on a resistance to period conversion performed by a low-noise circuit whose block scheme is shown in Fig. 1. The sensor is inserted into a ramp generator with the output period proportional to the resistive component RSENS of the sensor. V0 decreases from 0 V to the negative threshold value (Vt ) with a slope depending on the RSENS value. Once V0 reaches the Vt value, Vout switches to the 1 state while the control closes the switch, thus Vs is settled to its initial value 0 V. After the TRESET delay, a new cycle is started. The measured period T and RSENS are related by T = TRESET +
|Vt | RSENS Ci Vexc
(1)
The system acquires data with a sampling time of about 25 ms. Measuring time depends on RSENS value but properly
Fig. 2. Measuring setup.
tuning Vexc and Vt voltages allows to keep it below 250 ms. As far as accuracy is concerned, it has been proved the system capability to measure resistance values over the 10 k–3 G range within an accuracy of 1%. Further details can be found in [10,20]. 2.3. Gas standard generations Gas standards have been generated by diffusion tubes [21] (fine permeation tubes) filled with pure compounds (Sigma–Aldrich). A fixed amount (1 ml1 ) of the target compound has been placed in the diffusion tube: a steel cylinder with volume of 10 ml, provided with a small hole on the top (diameter = 2 mm, height = 10 cm), allowing vapors to exit from the cylinder. A 300 sccm flux of dry air was used as carrier. The measuring setup (Fig. 2) is provided by a humidifier system and thermostatic bath where diffusion tubes are lodged so 1 Acetylpyrazine is a solid compound at room temperature, 1 mg has been placed in the diffusion tube.
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A. Ponzoni et al. / Sensors and Actuators B 130 (2008) 100–104
Table 2 Tested key-aromas Name
Chemical formula
Tested concentration (ppm)
Odor quality
Acetaldehyde Diacetyl Acetylpyridine Acetylpyrazine 2-Ethyl 3-methylpyrazine
C2 H4 O C4 H6 O2 C7 H7 NO C6 H6 N2 O C7 H10 N2
1200 15 5.8 <0.001a 7
Acid Buttery Oily, pop corn Sweet roasted Roasted
a Compound concentration in air has been determined by periodically weighting the diffusion tube but no appreciable weight variations have been detected for acetylpyridine, thus its concentration can be given as an upper limit.
that the evaporation rate of each substance can be controlled by regulating the bath temperature. Both lines, the reference one and the gas one, are provided by valves that allow to work with a constant flux on both lines either they are fluxed in the test chamber or not. Otherwise, once the chamber connection has been switched from one line to the other, transitory times will occur while the flux reach its equilibrium value. In the gas line, such a flux variation would also change the concentration in the gas line (Eq. (1)). Gas concentrations (Table 2) have been determined by placing the steel cylinder in the test circuit (simulating measurements) and periodically weighting it by means of an analytical balance with resolution 0.01 mg. The slope of the weight versus time curve gives the evaporation rate r of the compound, which is related to its concentration C by C=
r RT MQ p
(2)
where C is measured in ppm, r in ng/min, M the molar weight, Q the carrier flux (sccm), R the gas constant (8.314 J/K), p the working pressure (101,300 Pa) and T is the bath temperature (K). 3. Results Response spectra of each sensor are reported in Fig. 3. It can be observed that sensors exhibit higher response towards
Fig. 3. Sensor response spectra toward the whole set of volatiles tested.
diacetyl and acetaldehyde with respect to other compounds. One reason is the high concentrations tested for these two compounds; however this is not enough to explain the response pattern obtained. Indeed, despite acetaldehyde has been tested in a concentration of 1200 ppm, which is about 200 times higher than concentrations of acetylpyridine, for example, and more than 106 times higher than the concentration of acetylpyrazine, sensors do not exhibit such a large difference in their response towards these chemicals. Despite metal oxide based gas sensors are known to exhibit non-linear response, the fact that none of the sensors reflect the large difference in concentrations between these compounds, suggest that the response spectra shape would be mainly due to the chemistry of such volatiles. The principal component analysis (PCA) is useful to better highlight this. In Fig. 4, the PCA score plot of the data against the first and second principal components (PC1 and PC2 respectively) has been reported. It can be observed that the first feature distinguished by the sensor array is the chemistry of the compound. Moving along the PC1 axis from left to right, acetaldehyde is first encountered, then diacetyl. Acetylpyridine is quite close to diacetyl and they are distinguished along the PC2 direction rather than the PC1. Moving further along the PC1 the pyrazines group is encountered. According to [22], acetaldehyde and diacetyl are produced together with alcohols and acetates during the fermentation process. As the baking process is started, the rising temperature
Fig. 4. Principal component analysis (PCA) plot of the whole data set recorded.
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causes evaporation of such highly volatile compounds, so that their presence is peculiar of the baking process initial steps. Especially acetaldehyde is strongly smelled at this stage due to its high vapor pressure (1006 h Pa at 20 ◦ C), compared to the vapor pressure of other compounds (52 h Pa for diacetyl and 595 h Pa for ethanol, at 20 ◦ C). Volatiles produced during fermentation have also been found in toasted bread, i.e., at the end of the baking process, but only diacetyl contributes significantly to the bread smell [23]. So far diacetyl can be regarded as a key odorants identifying a baking step further the initial one. As the baking process goes on, the high temperature favors the chemical reaction between amino acids and sugars (Maillard reactions). In particular, the reactive carbonyl group of the sugar interacts with the nucleophilic amino group of the amino acid producing flavoring molecules such as, for example, pyridines, pyrazines and pyrrolines. As highlighted by Rychlick and Grosch [23], the concentration of these compounds changes according to the baking degree. They showed that 6-acetyltetrahydropyridine reach the maximum concentration almost in the middle of the process and it slowly decrease going further in the process due to its low molecular stability that cause its to degrade along time at high temperatures. On the contrary, 2-ethyl 3,5-dimethylpyrazine and acetylpyrroline, having a higher molecular stability, exhibit concentrations increasing with time and browning of bread. As far as relative concentrations are concerned, pyridines and pyrazines are present in similar amounts in wheat bread, while the diacetyl amount is quite higher [23]. Bath temperature (Fig. 2) has been settled in order to obtain key odorants in relative concentration concordant with this. Further looking at the obtained PCA plot (Fig. 4) it can be observed that compounds are ordered along the PC1 direction according to their characteristic step of the baking process. It should be pointed out that a reserve still remains if the results reported in [23] concerning 6-acetyltetrahydropyridine and 2-ethyl 3,5-dimethylpyrazine can be regarded as valid in general for pyridines and pyrazines respectively or they are valid just for the above specific molecules. If we can assume it is valid, compounds studied in the present work are distinguished along the PC1 direction according to a sequence resembling the aroma evolution of the bread baking process: acetaldehyde–diacetyl–pyridines–pyrazines. If the above assumption is not valid, the qualitative distinction of aromas still resemble the process evolution but grouping the pyridines and pyrazines classes. Referring to literature, concentrations of pyridines and pyrazines studied in [23] can change by a factor 2–10 due to the different toasting degree. With the aim to evaluate compounds concentration effects on the PCA plot, further measurements have been carried out with 2-ethyl 3-methylpyrazine by lowering the bath temperature from 20 to 10 ◦ C in order to reduce the evaporation rate and obtain a concentration of 0.8 ppm instead of 7 ppm. Sensor responses appreciably decrease (Fig. 5). MoW and TiO2 sensors are particularly sensitive to this, being their response reduced by a factor 4 and 3 respectively. However, the two data sets in the PCA are almost superimposed (Fig. 4). This confirms that varying the concentration of a given compound
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Fig. 5. Response towards 2-ethyl 3-methylpyrazine, 0.8 ppm (dashed line) and 7 ppm (continuous line).
within the range compatible with the one expected during the baking process, is less significant than the chemical nature of the compound, which thus remains the main feature distinguished by the sensor array. 4. Conclusions A low-cost readout electronic system suitable to measure resistance values within the range k–G with high accuracy (<1%) has been used to handle an array of four metal oxide based gas sensors. Monitoring key aromas of baking processes has been chosen as target application to validate the proposed approach. Results highlight the suitability of the low-cost electronic readout system to handle conductometric sensors exhibiting baseline values spreading on its wide working-range. Furthermore, the capability of the proposed electronic nose to distinguish key aromas of bread baking process primarily depending on their chemical nature has been proved. In a PCA plot these volatiles are ordered in a sequence concordant with the sequence they will be developed during the bread baking process: acetaldehyde, diacetyl, pyridines and pyrazines. Acknowledgement This work has been partly funded by the PNR FIRB 2003–2005 project “Sviluppo di microsistemi multisensoriali per applicazioni ambientali e agroalimentari”. References [1] T.C. Pearce, S.S. Shiffman, H.T. Nagle, J.W. Gardner, Handbook of Machine Olfaction, Wiley, 2002. [2] M. Falasconi, E. Gobbi, M. Pardo, M. Della Torre, A. Bresciani, G. Sberveglieri, Detection of toxigenic strains of Fusarium verticillioides in corn by electronic olfactory system, Sens. Actuator B: Chem. 108 (1/2) (2005) 250–257. [3] F. Sinesio, C. Di Natale, G.B. Quaglia, F.M. Bucarelli, E. Moneta, A. Macagnano, R. Paolesse, A. D’Amico, Use of electronic nose and trained sensory panel in the evaluation of tomato quality, J. Sci. Food Agric. 80 (1) (2000) 63–71.
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[4] J.W. Gardner, H.W. Shin, E.L. Hines, An electronic nose system to diagnose illness, Sens. Actuator B 101 (2004) 39–46. [5] C. Di Natale, A. Macagnano, R. Paolesse, E. Tarizzo, A. Mantini, A. D’Amico, Human skin odor analysis by means of an electronic nose, Sens. Actuator B 65 (2000) 216–219. [6] S. Zampolli, I. Elmi, F. Ahmed, M. Passini, G.C. Cardinali, S. Nicoletti, L. Dori, An electronic nose based on solid state sensor arrays for low-cost indoor air quality monitoring application, Sens. Actuator B 106 (2005) 199–206. [7] K. Boholt, K. Andreasen, F. den Berg, T. Hansen, A new method for measuring emission of odor from a rendering plant using the Danish Odor Sensor System (DOSS) artificial nose, Sens. Actuator B 106 (2005) 170–176. [8] L.G. Teoh, Y.M. Hon, J. Shieh, W.H. Lai, M.H. Hon, Sensitivity properties of a novel NO2 gas sensor based on mesoporous WO3 thin film, Sens. Actuator B: Chem. 96 (2003) 219–225. [9] Y. Li, W. Wlodarski, K. Galatsis, S.H. Moslih, J. Cole, S. Russo, N. Rockelmann, Gas sensing properties of p-type semiconducting Cr-doped TiO2 thin films, Sens. Actuator B 83 (2002) 160–163. [10] A. Depari, A. Flammini, D. Marioli, S. Rosa, A. Taroni, M. Falasconi, G. Sberveglieri, A new hardware approach to realize low-cost electronic noses, in: Proceedings of the IEEE Sensors, 2005, pp. 239–242. [11] D. Ward, S. Benedetti, M. Riva, Monitoring and controlling cooking processes using an electronic olfaction device, in: Proceedings of the 9th International Symposium on Olfaction and Electronic Nose (ISOEN), 2002, pp. 292–298. [12] W. Grosch, Evaluation of the key odorants of foods by dilution experiments, aroma models and omission, Chem. Sens. 26 (2001) 533–545. [13] G. Zehentbauer, W. Grosch, Crust aroma of baguettes. I. Key odorants of baguettes prepared in two different ways, J. Cereal Sci. 28 (1) (1998) 81–92. [14] T. Hoffmann, P. Schieberle, C. Krummel, A. Freiling, J. Bock, L. Heinert, D. Kohl, High resolution gas chromatography/selective odorant measurement by multisensor array (HRGC/SOMSA): a useful approach to standardize multisensor arrays for use in the detection of key foods odorants, Sens. Actuator B 41 (1997) 81–87. [15] D. Kohl, L. Heinert, J. Bock, Th. Hofmann, P. Schieberle, Gas sensors for food aroma during baking and roasting processes based on selective odorant measurements by an array, Thin Solid Films 391 (2001) 303–307. [16] V. Guidi, M.C. Carotta, M. Ferroni, G. Martinelli, L. Paglialonga, E. Comini, G. Sberveglieri, Preparation of nanosized titania thick and thin films as gas-sensors, Sens. Actuator B 57 (1999) 197–200. [17] E. Comini, M. Ferroni, V. Guidi, G. Martinelli, G. Faglia, G. Sberveglieri, Nanostructured mixed oxides compounds for gas sensing applications, Sens. Actuator B 84 (2002) 26–32. [18] E. Comini, A. Cristalli, G. Faglia, G. Sberveglieri, Light enhanced gas sensing properties of indium oxide and tin dioxide sensors, Sens. Actuator B 65 (2000) 260–263. [19] A. Ponzoni, E. Comini, M. Ferroni, G. Sberveglieri, Thin Solid Films 490 (2005) 81–85. [20] A. Depari, A. Flammini, D. Marioli, S. Rosa, A. Taroni, A low-cost circuit for high-value resistive sensors varying over a wide range, Meas. Sci. Technol. 17 (2) (2006) 353–358. [21] J.J. McKinley, R.E. Majors, LC–GC 10 (2000) 1024–1033. [22] A. Hansen, B. Hansen, The influence of wheat flour type on the production of flavor compounds in wheat sourdoughs, J. Cereal Sci. 19 (1996) 185–190. [23] M. Rychlick, W. Grosch, Identification and quantification of potent odorants formed by toasting of wheat bread, Lebensm. Wiss. Technol. 29 (1996) 515–525.
Biographies Andrea Ponzoni was born in 1976. He received the degree in physics from the University of Parma, Italy, in 2000 and the PhD degree in material engineering
from the University of Brescia in 2006 with a thesis on nanostructured metal oxides for gas sensing applications. His major research activity regards synthesis and electrical characterization of metal oxides for gas sensing applications. Alessandro Depari was born in Breno (BS) in 1976. In 2002 he graduated in electronic engineering at the University of Brescia, Italy, where, in 2006, he received the PhD degree in electronic instrumentation. His main research activities are the signal conditioning and processing for chemical sensors, in particular resonant and resistive sensors for electronic noses and the development of sensors networks for distributed measurement. Matteo Falasconi got his degree in physics from University of Pavia in 2000. In 2005, he has obtained a PhD degree in material engineering from University of Brescia with a thesis on the development of artificial olfactory systems for the food industry. At present, his research interests are electronic noses and data analysis. Elisabetta Comini was born on 21 November 1972 and she received her degree in physics at the University of Pisa in 1996. She received her PhD in material science at the University of Brescia. She is presently working on chemical sensors with particular reference to deposition of thin films by PVD technique and electrical characterisation of MOS thin films. In 2001 she has been appointed assistant professor at the University of Brescia. During her career she has published more than 80 articles on peer reviewed international journals. Alessandra Flammini was born in Brescia, Italy, in 1960. She graduated with honors in physics at the University of Rome, Italy, in 1985. From 1985 to 1995 she worked on industrial research and development on digital drive control. Since 1995 she has been a researcher at the Department of Electronics for Automation of the University of Brescia. Her main field activity is the design of digital electronic circuits (FPGA, DSP, processors) for measurement instrumentation. Daniele Marioli was born in Brescia, Italy, in 1946. He graduated electrical engineering in 1969 at the University of Pavia, Italy. From 1984 to 1989, he was an associate professor in applied electronics, and since 1989 he has been a full professor of applied electronics at Brescia University. Since 1993, he has been the director of the Department of Electronics for Automation of the Faculty of Engineering of the University of Brescia. His main field activity is the design and experimentation of analog electronic circuits for the processing of electrical signals from transducers, with particular regard to S/N ratio optimization. Andrea Taroni was born in Cotignola, Ravenna, Italy, in 1942. He received the degree in physics from the University of Bologna, Italy, in 1966. He was an associate professor at University of Modena from 1971 to 1986. Since 1986 he has been a full professor in electrical measurements at the University of Brescia. Since 1993 he has been the dean of the faculty of engineering of the University of Brescia. He has done extensive research in the field of physical quantities sensors and electronic instrumentation, both developing original devices and practical applications. Giorgio Sberveglieri is the director of the Sensor Laboratory. He received his degree in physics from the University Parma. In 1988, he established the Gas Sensor Lab, mainly devoted to the preparation and characterization of thin film chemical sensors based on metal oxide semiconductors and, since the mid 1990s, to the area of electronic noses. In 1994, he was appointed full professor in physics. He is referee of many international journals and has acted as chairman in several conferences on materials science and on sensors. During 30 years of scientific activity he published more than 200 papers in international journals; he presented more than 110 Oral Communications to international congresses. He is also an evaluator of IST and Growth Projects of EU and the coordinator of the Applied Network of INFM since April 2001.