- Email: [email protected]
Identification of aromas from wine using quartz-resonator gas sensors in conjuction with neural-network analysis
ELSEVIER
Sensors
and Actuators
B 24-25
(1995) 194-196
Identification of aromas from wine using quartz-resonator gas sensors in conjuction with neural-network analysis Hidehito
Nanto, Shiro Tsubakino,
Mitsuo Ikeda, Fumitaka
Endo
Electron Device System Research Laboratory, Kanazawa Institute of Technology, 7-l Oogigaoka, Nonoichi-machi, PO Kanazawa-South, Ishikawa 921, Japan
Abstract Transient response curves for aromas that are generated by vapourizing wine by a heater are observed using only an epoxycoated quartz-resonator gas sensor. The pattern-recognition analysis using principal-component or neural-network analysis is carried out using nine parameters that characterize the transient response curves. The recognition probability of the neuralnetwork analysis is 100% for three kinds of wine, red, white and rosC. Keywordx Aroma sensors; Neural-nehvork
analysis; Quartz-resonator
1. Introduction A quartz-crystal resonator microbalance is known to provide a very sensitive mass-measuring device at nanogram levels, since the resonance frequency changes upon the deposition of a given mass on the electrode. Synthetic polymer-coated quartz-crystal resonators have been studied as sensors for various gases, since a quartzcrystal resonator coated with a sensing membrane works as a chemical sensor. Recently, considerable interest has arisen in the use of arrays of quartz-resonator gas sensors in conjunction with an associated pattern-recognition technique for the detection of odours, fragrances and aromas [l-S]. The success of this approach depends upon the choice of parametric expression used to define the array output. We have reported that a neural-network patternrecognition analysis using some parameters that characterize the transient response curves of the quartzresonator gas sensors for exposure to aromas is effective for identification of aromas from coffee or wine [5]. In this paper, we demonstrate a novel gas-sensing system using only one sensor in conjunction with neural-network analysis, which is useful for discriminating among aromas from different kinds of wine, and discuss the choice of parameters that characterize the transient response curves.
gas sensors
sensors. After conventional epoxy resin as a sensing membrane was dissolved in a volatile organic solvent, its solution was coated on one side of the silver electrode of the quartz resonator. The frequency of the vibrating quartz-resonator gas sensor was measured with the frequency counter of a film-thickness monitor (ULVAC, CRTM-1A) attached to a personal computer (NEC, PC-9801). The transient responses of the sensor for the aroma from each of three kinds of wine (red, white and rest) were measured using a sensing chamber as shown schematically in Fig. 1. A constant amount (20 ~1) of wine was injected into the sensing chamber using a micro syringe and vapourized on a ceramic heater
CHAMBER \
'FAN
"I' MICRO SYRINGE SHIELD\ \ SENSOR,
2. Experimental Commercially available 5 MHz quartz-crystal resonators with 0.61 cm’ silver electrodes were used as gas 09254005/95/$09.50 0 1995 Elsevier Science S.A. All rights reserved SSDI 0925-4005(94)01470-3
HEATER
Fig. 1. Schematic diagram of aroma-sensing
chamber.
H. Nanto et al. I Senmm and Actuators B 24-25 (1995) 794-796
79.5
(60 “C). The transient response curve was also measured for aromas from whisky and brandy.
3. Results and discussion Typical transient response curves of an epoxy-coated sensor for exposure to the aroma from each of three kinds of wine are shown in Fig. 2. For comparison with other liquors such as whisky and brandy, the transient response curves for the aroma from whisky and brandy are also shown in the Figure. It can be seen that the shapes of the transient response curve for the wines are clearly different from those for whisky and brandy, though the difference between the transient response curves for the three kinds of wine is not so clear. The shape of the transient response curve strongly dependent on the temperature of the heater and the amount of wine. In the present work a constant amount of wine (20 ~1) was always vapourized at 60 “C as described above, so that we were able to obtain reproducible responses for repeated measurements. We defined nine parameters, FJF,,,, FJF,,,.,, . . ., F,IF,,,., and FdF,,,,, which characterize the transient response curves shown in Fig. 3. The principal-component analysis or neuralnetwork pattern-recognition analysis using these parameters was carried out to discriminate aromas from different kinds of wine. The result of the principal-component analysis, which was reduced to the first two principal components for the visualization of multidimensional data, is given in Fig. 4. It is clear that the responses for individual aromas tend to cluster in discrete sections of space with well-defined boundaries. The neural network used had a three-layer structure that consisted of nine input units, nine hidden units and three output units, as shown in Fig. 5. After each
OUT
IN
eww
I
1
5 TINE
8
10
14
20
[minl
Fig. 3. Nine defined parameters F,IF,,, FJF,,, F,lF,,,,,, . . . , Fsl F,,,, and FdF,,,,, where F,, is the maximum frequency change and F,, Fz, F3, . . , F8 and F,, are the frequency changes 1, 2, 3, . . . ,
8 and 14 min after the onset of the response, respectively.
PRlNCiPAL
Fig. 4. Principal-component three kinds of wine.
COIP0NENT.X
I
plot using results for the aromas from
neuron unit collects the sum of weighted inputs from the former layer, its output is determined by putting the sum into a non-linear function. The network was initially trained 5000 times using data so as to obtain the desired output when a certain pattern was input from the sensor. The back-propagation algorithm was applied as the learning rule. The recognition probability was defined as the ratio of the number of right answers to the total number of trials. The recognition probability of the neural-network analysis for data from transient response curves that are measured for the aroma of each wine using only an epoxy-resin-coated sensor was 100% for 14 trials as listed in Table 1.
4. Summary and conclusions
0 TIME
10 [mini
20
Fig. 2. Typical transient response cmves of the epoxy-coated quartzresonator gas sensor for exposure to individual aromas from red, white and rose wines as well as from whisky and brandy.
Discrimination between three types of wine was successfully demonstrated using a novel gas-sensing system with the following features: (1) The aromas, which are detected using only an epoxy-coated quartz-resonator gas sensor, are generated by vapourizing the wine with a heater in the chamber. (2) Nine parameters that characterize the transient response curves of the sensor for exposure to aroma are used for the principal-component or neural-network pattern-recognition analysis.
H. Nanto et al. / Sensors and Actuators B 24-25 (1995) 794-796
796
OUTPUT UNIT ACTIVITY
INPUT PATTERN
1
0.5
0 lt#‘Ul LAYERHIDDENLAYER(WTWT u\‘fER
Fig. 5. Structure of neural network used. The network consists of nine input units, nine hidden units and three output units. Table 1 Identification results for three kids (c)
of wine: (a) red, (b) white and
rose
SZUllple
(a) Wine (rose) (b) Wine (red) (c) Wine (white)
excellent assistance in the experiments. This work was partly supported by the Foundation of the Hokuriki Sangyou Kasseika Center of Japan.
Identification (a)
(b)
(c)
5 0 0
0 4 0
0 0 5
Since the novel sensing system proposed here is very simple, it is one of the most attractive candidates for aroma sensing and identification.
Acknowledgements The author would like to thank K. Ozaki, A. Katada, S. Kitagawa, S. Hosokawa and K. Mochizuki for their
References PI W.P. Carey, K.R. Beebe, RR. Kowalski, D.L. Illman and T. Hirschfeld, Selection of adsorbates for chemical sensor arrays by pattern recognition, Anal. Chem., 58 (1986) 149-152. PI T. Nakamoto, K. Fukunishi and T. Moriizumi, Identification capability of odor sensor using quartz-resonator array and neural network pattern recognition, Sensors and Actuators, 18 (1989) 473-476. [31 R. Muller, High electronic selectivity obtainable with nonselective chemisensors, Sen.wrs andActuators B, 4 (1991) 35-39. [41 J.W. Gardner, Detection of vapours and odours from multisensor array using pattern recognition, Sen.wrs and Actuafors B, 4 (1991) 109-115. [51 H. Nanto, T. Kawai, H. Sokooshi and T. Usuda, Aroma identiiication using a quartz-resonator sensor in conjunction with pattern recognition, Sensors and Actuators B, 13-14 (1993) 718-720.
Sensors
and Actuators
B 24-25
(1995) 194-196
Identification of aromas from wine using quartz-resonator gas sensors in conjuction with neural-network analysis Hidehito
Nanto, Shiro Tsubakino,
Mitsuo Ikeda, Fumitaka
Endo
Electron Device System Research Laboratory, Kanazawa Institute of Technology, 7-l Oogigaoka, Nonoichi-machi, PO Kanazawa-South, Ishikawa 921, Japan
Abstract Transient response curves for aromas that are generated by vapourizing wine by a heater are observed using only an epoxycoated quartz-resonator gas sensor. The pattern-recognition analysis using principal-component or neural-network analysis is carried out using nine parameters that characterize the transient response curves. The recognition probability of the neuralnetwork analysis is 100% for three kinds of wine, red, white and rosC. Keywordx Aroma sensors; Neural-nehvork
analysis; Quartz-resonator
1. Introduction A quartz-crystal resonator microbalance is known to provide a very sensitive mass-measuring device at nanogram levels, since the resonance frequency changes upon the deposition of a given mass on the electrode. Synthetic polymer-coated quartz-crystal resonators have been studied as sensors for various gases, since a quartzcrystal resonator coated with a sensing membrane works as a chemical sensor. Recently, considerable interest has arisen in the use of arrays of quartz-resonator gas sensors in conjunction with an associated pattern-recognition technique for the detection of odours, fragrances and aromas [l-S]. The success of this approach depends upon the choice of parametric expression used to define the array output. We have reported that a neural-network patternrecognition analysis using some parameters that characterize the transient response curves of the quartzresonator gas sensors for exposure to aromas is effective for identification of aromas from coffee or wine [5]. In this paper, we demonstrate a novel gas-sensing system using only one sensor in conjunction with neural-network analysis, which is useful for discriminating among aromas from different kinds of wine, and discuss the choice of parameters that characterize the transient response curves.
gas sensors
sensors. After conventional epoxy resin as a sensing membrane was dissolved in a volatile organic solvent, its solution was coated on one side of the silver electrode of the quartz resonator. The frequency of the vibrating quartz-resonator gas sensor was measured with the frequency counter of a film-thickness monitor (ULVAC, CRTM-1A) attached to a personal computer (NEC, PC-9801). The transient responses of the sensor for the aroma from each of three kinds of wine (red, white and rest) were measured using a sensing chamber as shown schematically in Fig. 1. A constant amount (20 ~1) of wine was injected into the sensing chamber using a micro syringe and vapourized on a ceramic heater
CHAMBER \
'FAN
"I' MICRO SYRINGE SHIELD\ \ SENSOR,
2. Experimental Commercially available 5 MHz quartz-crystal resonators with 0.61 cm’ silver electrodes were used as gas 09254005/95/$09.50 0 1995 Elsevier Science S.A. All rights reserved SSDI 0925-4005(94)01470-3
HEATER
Fig. 1. Schematic diagram of aroma-sensing
chamber.
H. Nanto et al. I Senmm and Actuators B 24-25 (1995) 794-796
79.5
(60 “C). The transient response curve was also measured for aromas from whisky and brandy.
3. Results and discussion Typical transient response curves of an epoxy-coated sensor for exposure to the aroma from each of three kinds of wine are shown in Fig. 2. For comparison with other liquors such as whisky and brandy, the transient response curves for the aroma from whisky and brandy are also shown in the Figure. It can be seen that the shapes of the transient response curve for the wines are clearly different from those for whisky and brandy, though the difference between the transient response curves for the three kinds of wine is not so clear. The shape of the transient response curve strongly dependent on the temperature of the heater and the amount of wine. In the present work a constant amount of wine (20 ~1) was always vapourized at 60 “C as described above, so that we were able to obtain reproducible responses for repeated measurements. We defined nine parameters, FJF,,,, FJF,,,.,, . . ., F,IF,,,., and FdF,,,,, which characterize the transient response curves shown in Fig. 3. The principal-component analysis or neuralnetwork pattern-recognition analysis using these parameters was carried out to discriminate aromas from different kinds of wine. The result of the principal-component analysis, which was reduced to the first two principal components for the visualization of multidimensional data, is given in Fig. 4. It is clear that the responses for individual aromas tend to cluster in discrete sections of space with well-defined boundaries. The neural network used had a three-layer structure that consisted of nine input units, nine hidden units and three output units, as shown in Fig. 5. After each
OUT
IN
eww
I
1
5 TINE
8
10
14
20
[minl
Fig. 3. Nine defined parameters F,IF,,, FJF,,, F,lF,,,,,, . . . , Fsl F,,,, and FdF,,,,, where F,, is the maximum frequency change and F,, Fz, F3, . . , F8 and F,, are the frequency changes 1, 2, 3, . . . ,
8 and 14 min after the onset of the response, respectively.
PRlNCiPAL
Fig. 4. Principal-component three kinds of wine.
COIP0NENT.X
I
plot using results for the aromas from
neuron unit collects the sum of weighted inputs from the former layer, its output is determined by putting the sum into a non-linear function. The network was initially trained 5000 times using data so as to obtain the desired output when a certain pattern was input from the sensor. The back-propagation algorithm was applied as the learning rule. The recognition probability was defined as the ratio of the number of right answers to the total number of trials. The recognition probability of the neural-network analysis for data from transient response curves that are measured for the aroma of each wine using only an epoxy-resin-coated sensor was 100% for 14 trials as listed in Table 1.
4. Summary and conclusions
0 TIME
10 [mini
20
Fig. 2. Typical transient response cmves of the epoxy-coated quartzresonator gas sensor for exposure to individual aromas from red, white and rose wines as well as from whisky and brandy.
Discrimination between three types of wine was successfully demonstrated using a novel gas-sensing system with the following features: (1) The aromas, which are detected using only an epoxy-coated quartz-resonator gas sensor, are generated by vapourizing the wine with a heater in the chamber. (2) Nine parameters that characterize the transient response curves of the sensor for exposure to aroma are used for the principal-component or neural-network pattern-recognition analysis.
H. Nanto et al. / Sensors and Actuators B 24-25 (1995) 794-796
796
OUTPUT UNIT ACTIVITY
INPUT PATTERN
1
0.5
0 lt#‘Ul LAYERHIDDENLAYER(WTWT u\‘fER
Fig. 5. Structure of neural network used. The network consists of nine input units, nine hidden units and three output units. Table 1 Identification results for three kids (c)
of wine: (a) red, (b) white and
rose
SZUllple
(a) Wine (rose) (b) Wine (red) (c) Wine (white)
excellent assistance in the experiments. This work was partly supported by the Foundation of the Hokuriki Sangyou Kasseika Center of Japan.
Identification (a)
(b)
(c)
5 0 0
0 4 0
0 0 5
Since the novel sensing system proposed here is very simple, it is one of the most attractive candidates for aroma sensing and identification.
Acknowledgements The author would like to thank K. Ozaki, A. Katada, S. Kitagawa, S. Hosokawa and K. Mochizuki for their
References PI W.P. Carey, K.R. Beebe, RR. Kowalski, D.L. Illman and T. Hirschfeld, Selection of adsorbates for chemical sensor arrays by pattern recognition, Anal. Chem., 58 (1986) 149-152. PI T. Nakamoto, K. Fukunishi and T. Moriizumi, Identification capability of odor sensor using quartz-resonator array and neural network pattern recognition, Sensors and Actuators, 18 (1989) 473-476. [31 R. Muller, High electronic selectivity obtainable with nonselective chemisensors, Sen.wrs andActuators B, 4 (1991) 35-39. [41 J.W. Gardner, Detection of vapours and odours from multisensor array using pattern recognition, Sen.wrs and Actuafors B, 4 (1991) 109-115. [51 H. Nanto, T. Kawai, H. Sokooshi and T. Usuda, Aroma identiiication using a quartz-resonator sensor in conjunction with pattern recognition, Sensors and Actuators B, 13-14 (1993) 718-720.