- Email: [email protected]
Illumination heating and physical raking for increasing sensitivity of electronic nose measurements with black tea
Available online at www.sciencedirect.com
Sensors and Actuators B 131 (2008) 37–42
Illumination heating and physical raking for increasing sensitivity of electronic nose measurements with black tea Nabarun Bhattacharya a,∗ , Bipan Tudu b , Arun Jana a , Devdulal Ghosh a , Rajib Bandhopadhyaya b , Amiya Baran Saha a b
a Center for Development of Advance Computing (C-DAC), Kolkata 700091, India Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt Lake Campus, Kolkata 700098, India
Available online 23 December 2007
Abstract One of the most complicated components of electronic olfaction process is odour handling and delivery system capable of enabling the associated sensors to perform with acceptable sensitivity. For smell monitoring of black tea, an array of metal oxide semiconductor (MOS) sensors has been used for assessment of volatiles in the experimental set-up. In the presence of detectable vapor, the conductivity of the sensor increases depending on the concentration of odour molecules in the vapor. But, the MOS sensors are highly sensitive to moisture and water vapor. Presence of water vapor in the headspace of any sample, therefore, produces strong sensor outputs, which are essentially noise. Such overriding effect of noise caused by water vapor plays catastrophic role in terms of efficient pattern recognition by parametric and non-parametric methods. This paper presents the details of a novel sampling system based on illumination-controlled heating together with physical raking of the tea samples developed for enhancement of sensitivity of MOS sensor array. This increase in sensor outputs enhances the precision of the measurement system significantly. The efficacy of the system has been validated by comparison of performance of the system in terms of correlating electronic nose data with tea tasters’ scores using probabilistic neural network (PNN). © 2007 Elsevier B.V. All rights reserved. Keywords: Sensor array; Illumination heating; Physical raking; Probabilistic neural network (PNN)
1. Introduction Electronic nose technology has been successfully employed for recognition and quality analysis of various food and agro products, like wine [1], cola [2], meat [3], fish [4], etc. But on classification of tea quality, very few studies have been reported. Pioneering work has been done by Dutta et al. [5] for classification of tea aroma. Several experiments with black tea aroma using electronic nose have been carried out by the authors and reported in the literature. Smell profile of tea during fermentation process has been successfully monitored online [6,7]. Also successful correlation between multi-sensor array data with tea tasters’ marks has been successfully achieved [8]. During the above studies, it has been observed that brewed tea liquor could not be used due to presence of water vapor in the headspace. In all the above studies, only dry tealeaves
have been put in the sample holder for headspace generation and sampling. But the volatile emission from dry tea being considerably low, sensor outputs have been observed to be significantly small. On the other hand, tea scientists have established that optimum volatile emission takes place from the tea at around 60 ± 5 ◦ C [9]. In fact, good flavor of brewed tea is caused due to rise in temperature of the tea by addition of boiling water to it. To resolve this conflicting requirement, some new method of heating of the tea samples appears imperative. This paper describes a novel heating using optical energy from commonly used miniature halogen lamps and simple motorized mechanical agitation system of tea samples within the sample holder. 2. System description 2.1. Description of customized electronic nose
∗ Corresponding author at: Centre for Development of Advanced Computing (C-DAC), E-2/1, Block-GP, Sector-V, Salt Lake, Kolkata 700091, West Bengal, India. E-mail address: [email protected] (N. Bhattacharya).
0925-4005/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2007.12.031
The customized electronic nose system consists of (a) a sensor array; (b) a micro-pump; (c) solenoid valves with automated sequence control; (d) sample holder; (e) illumination
38
N. Bhattacharya et al. / Sensors and Actuators B 131 (2008) 37–42
2.2. Description of the heating cum raking system
Fig. 1. Customized electronic nose set-up developed.
heating arrangement; (f) olfaction software. The micro-pump generates compressed air with constant flow rate that is routed through three solenoid valves for control of headspace generation, sampling and purging in an automated sequence. The electronic nose set-up developed under the study is shown in Fig. 1. A user-friendly interactive software has been carefully designed which has got features like programmable sequence control, dynamic fermentation profile display, data logging, alarm annunciation, data archival, etc. The software has been developed in LABVIEW 8.1® of National Instruments.
One vital feature of this system is the controlled heating of the samples under test using optical energy. 35-Watts miniature halogen lamp has been fixed to the fixture for sample holder made of Teflon. Also a motorized raking arrangement has been included at the bottom of the sample holder to ensure adequate emanation of volatiles. Schematic diagram of the illumination heating cum raking arrangement is illustrated in Fig. 2. A resistance temperature detector (RTD) device, Pt100, whose resistance varies almost linearly with temperature, has been employed for monitoring the temperature of tea samples under test as shown in Fig. 2. Using an external power source, the change in resistance of the RTD is converted into a corresponding change in voltage, which is fed to one of the analogue inputs of the USB data acquisition card connected to the PC. The electronic controllers of the DC motor and the halogen lamp are connected to the digital output of the USB card and ON/OFF control signals are generated by the USB card under command control from the olfaction software. USB card model number 6009 of National Instruments, USA, has been used in the system. Two metallic blades made of stainless steel are fitted with the shaft of the motor and these rotating blades during sniffing operation of the electronic nose create physical turbulence within the sample ensuring adequate volatile emanation from them. The entire sniffing cycle consists of sequence of operations as described in Table 1. The typical sensor response curve during the sequence
Fig. 2. Illumination heating and motorized raking arrangement.
N. Bhattacharya et al. / Sensors and Actuators B 131 (2008) 37–42
39
Fig. 3. Operational sequences and typical sensor response in the experimental sniffing cycle.
of operational conditions in a sniffing cycle has been shown in Fig. 3. 3. Experimental 3.1. Monitoring of individual sensor responses at various operational modes Experimentations with tea samples had been carried with the new odour delivery system with all the possible combinations of heating and raking, namely, (a) no heating or raking; (b) only heating, no raking; (c) no heating, only raking; (d) both heating and raking. It may be noted that same samples were used for all the experimental conditions to study the comparative advantages associated with each combination. Experiments were conducted with following experimental conditions: • Amount of each sample = 50 g. • Temperature maintained during sampling = 60 ± 3 ◦ C. • Air-flow rate = 5 ml/s. Table 1 Sequence of operations in an experimental sniffing cycle Operation
Purpose
Illumination heating cycle Headspace generation Sampling
To heat black tea samples to 60 ± 3 ◦ C To accumulate adequate volatile compounds before sampling Sampling of odour molecules by the MOS sensors Concurrent with the sampling operation; ensures emanation of more volatiles during sampling Cleaning the sensor surface with blow of fresh air so that the sensor output returns to the baseline value
Motorized agitation Purging
Duration (s) 60 30 100 100
100
The sensors considered for black tea classification were TGS816, TGS-823, TGS-831, TGS-832, TGS-2600, TGS-2610, TGS-2611 and TGS-2620 of Figaro, Japan. The RS /RS values of the sensors were recorded for each combination of above variations in the odour delivery system to study the improvement of the performance of the system where RS is the change in resistance of the metal oxide semiconductor (MOS) sensor and RS is the base resistance value of the respective MOS sensor. Table 2 shows sample variation of RS /RS values at different operational modes of the system. The bar graph shown in Fig. 4 clearly demonstrates effect of various combinations of heating and raking on the responses of the sensors. 3.2. Experiments with finished tea and correlation with tea tasters’ scores The newly developed odour handling system has been evaluated to examine whether any improvement in performance of the electronic nose in terms of accuracy of correlation of the sensor array data with the tea tasters’ scores takes place. For this purpose, experimentations with 50 samples of black tea have been performed. Black tea produced at processing plants are tasted by expert human panel called “Tea Tasters” and gradation of tea is done on the basis of marks given by these tasters. Experiments were done for approximately 1-month duration at M/s. Dalmia Tea Plantation & Industries Limited, Kolkata, India, which has multiple tea gardens spread across India and the teas produced in their gardens are sent everyday to the tea tasting center for quality assessment. The tasting center had expert tea tasters and for our experiments, one expert tea taster was deputed for assigning taster’s mark to each of the samples. The identified tea taster has evaluated all these samples and assigned scores to individual samples which have been considered as taster’s mark for the correlation study with the computational network model. A sample tea taster score sheet is given in Table 3.
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Table 2 RS /RS values for individual sensors at various operational modes Sensor part no.
Target gas of the sensor
No heating or raking (RS /RS )
Only heating, no raking (RS /RS )
No heating, only raking (RS /RS )
Both heating and raking (RS /RS )
TGS 2610 TGS 2620 TGS 2611 TGS 2600 TGS 816 TGS 831 TGS 832 TGS 823
LP gas Alcohol Methane Air contaminants Methane, butane, propane CFC (R-21, R-22) CFC (R-134a) Organic solvent vapor
0.132 0.501 0.106 0.321 0.621 0.051 0.389 0.497
0.17 0.565 0.134 0.38 0.827 0.176 0.443 0.574
0.138 0.508 0.109 0.363 0.69 0.071 0.388 0.514
0.186 0.65 0.158 0.504 0.829 0.203 0.534 0.65
Fig. 4. Bar graph of responses of the sensors at various operational modes.
While “leaf quality” and “infusion” scores are based on visual inspection of the samples by the tasters, the marks given against “liquor” are the combined perception of taste, briskness and astringency of the sample. The scores assigned to “aroma”, signify the smell and flavor of the samples. We, therefore, have considered these aroma scores, for training the probabilistic neural network model discussed later. The samples arriving at the testing center on daily basis were also tested with the electronic nose system fitted with the new odour handling mechanism with illumination-based heating and
motorized agitation arrangement. All the 50 samples have been exposed to the electronic nose sensors through all the experimental combinations, namely, (a) no heating or raking; (b) only heating no raking; (c) no heating only raking; (d) both heating and raking. Four sets of data matrix having eight columns and fifty rows have, thus, been generated by these experiments from the electronic nose. 60% of each of four datasets has been used for training the neural network and rest 40% of the data have been used for testing the network. 4. Data analysis and results
Table 3 Sample tea taster’s score sheet Sample code
DALMIA 240806-01 DALMIA 260806-10 DALMIA 190706-07 DALMIA 280706-03
4.1. Comparison of sensor responses for different operational modes
Scores (1–10) Leaf quality
Infusion
Liquor
Aroma
7 6 5 7
5 5 4 5
3 5 4 6
5 8 7 6
It has been observed that there is a marked increase in the output of the sensors as soon as illumination heating is enabled in comparison with the output levels without heating. With introduction of physical raking along with heating, the sensor outputs show further increase in level and the sensor output voltage curve
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41
Table 4 Summary of results Name of the testing center
Dalmia Tea Plantation & Industries Limited
Total no. of samples
50
No. of samples used for training
30
rises more steeply compared to the previous condition. This is due to spread of heat throughout the sample and exposure of more surface area of the tea granules to the incoming pressurized airflow as well as illumination-based heating. But there is hardly any enhancement of sensor outputs with or without raking if the heating is withdrawn. Table 2 gives a sample comparative summary of individual sensor responses for different operational modes of the new odour handling system and Fig. 4 demonstrates these variations graphically. 4.2. PNN-based correlation study for system validation Illumination heating and motorized raking, as already described, results in enhanced outputs from the sensors and this unambiguous increase in sensor outputs improves the precision of the overall measurement system significantly. The performance of the classification algorithm is also better as the formed clusters are more distinct with these data. In this section, the performance of the overall system with the improved odour delivery system is presented and the probabilistic neural network (PNN) has been considered for the classification of a representative 50 tea samples. Probabilistic neural network is predominantly a classifier that is capable of mapping some input pattern to a number of classifications [10,11]. PNN is basically the implementation of a statistical algorithm called kernel discriminant analysis in which the operations are organized into a multi-layered feed forward network with four layers, namely, input layer, pattern layer, summation layer and output layer. The input pattern, which is essentially the multi-sensor output from the electronic nose, is presented to the first layer and the pattern layer computes the probability density function (PDF) from the input vector with respect to the training vectors by calculating distances. The summation layer computes sum of these contributions for each class. And the final layer computes the output by picking the maximum probabilities. The PNN algorithm is characterized by (a)
Fig. 5. Block diagram of PNN topology.
No. of samples used for testing
Operational mode of the odour handling system
% Accuracy of correlation in PNN
20
No heating or raking Only heating no raking No heating only raking Both heating and raking
85 89 85 91
fast training process; (b) parallel structure; (c) training samples can be added or removed very easily. In a previous study by the authors [8], the back propagation-multiplayer perceptron (BP-MLP) and radial basis function (RBF) topologies of neural networks have been used for correlation of multi-sensor output with tea tasters’ scores and encouraging results were obtained. But both the BP-MLP and the RBF models require huge amount of training data for accuracy of prediction. In the present study, PNN has been used to classify the tea samples and correlate the sensor array outputs to tea tasters’ scores. Block diagram of PNN topology is illustrated in Fig. 5. Here X1 , X2 , . . ., XN are the input vectors formed out of output responses of the sensor array consisting of eight sensors. Y1 , Y2 , . . ., YM are the training vectors. Since PNN is a supervised method, for each training vector, the corresponding class score is defined. Let the classes are C1 , C2 , . . ., Ck and contain I1 , I2 , . . ., Ip number of training vectors respectively so that. I1 + I2 + · · · + In = M. In this layer for each input vector, the corresponding PDF is calculated by the formula: PDF = −(Xi −Yj
2 /σ 2 )
where Xi is the input vector and Yi is the training vector, σ is the standard deviation where i = 1, 2, . . ., N and j = 1, 2, . . ., M. PDF values are represented by P1 , P2 , . . ., PM in the “P” matrix. In the summation layer, the PDF values associated with each class are averaged and the “G” matrix is formed. In the output layer, class declaration is accomplished based on the maximum value of G in the summation layer. Table 4 gives the result of PNN-based correlation between multi-sensor output from electronic nose and tea tasters’ scores obtained with different operational modes of the new odour handling system. It may be observed that maximum accuracy (>90%) has been achieved when both heating and raking is ON during sniffing operation. 5. Conclusion The sensitivity electronic nose sensor is, in general, quite poor and enhancement of the same is a challenge for the scientists. The paper describes a novel and elegant method of improving the sensitivity of electronic nose instrument for black tea classification. The effect of moisture is almost eliminated in this process and the success rate of classification of finished black tea is significantly enhanced. In addition to contributing to visible increase in the sensor responses, the system decisively demonstrates improvement in classification ability of the electronic
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nose system Therefore, this elegant method of odour handling using illumination heating and physical ranking may serve as a very useful tool for many others applications of electronic nose. Acknowledgements The authors are grateful to Mr. Surajit Ghosh, eminent tea technologist for support and guidance provided although the long study. The authors are deeply indebted to Mr. G.G. Dalmia, Dalmia Tea Plantation & Industries Limited, where experiments were performed during the study. The work has been sponsored by the National Tea Research Foundation, Tea Board, Government of India and the Department of Science & Technology, Government of India. References [1] J. Lozano, J.P. Santos, M. Aleixandre, I. Sayago, J. Gutierrez, M.C. Horrillo, Identification of typical wine aromas by means of an electronic nose, IEEE Sens. J. 6 (2006) 173–178. [2] B.G. Kermani, S.S. Schiffman, H.T. Nagle, Performance of the Levenberg–Marquardt neural network training method in electronic nose applications, Sens. Actuator B: Chem. 110 (2005) 13–22. [3] D.D.H. Boothe, J.W. Arnold, Electronic nose analysis of volatile compounds from poultry meat samples, fresh and after refrigerated storage, J. Sci. Food Agric. 82 (2002) 315–322. [4] M. O’Connell, G. Valdora, G. Peltzer, R. Martin Negri, A practical approach for fish freshness determinations using a portable electronic nose, Sens. Actuator B: Chem. 80 (2001) 149–154. [5] R. Dutta, E.L. Hines, J.W. Gardner, K.R. Kashwan, M. Bhuyan, Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach, Sens. Actuator B: Chem. 94 (2003) 228–237. [6] N. Bhattacharyya, S. Seth, B. Tudu, P. Tamuly, A. Jana, D. Ghosh, R. Bandyopadhyay, M. Bhuyan, S. Sabhapandit, Detection of optimum fermentation time for black tea manufacturing using electronic nose, Sens. Actuator B: Chem. 122 (2007) 627–634. [7] N. Bhattacharyya, S. Seth, B. Tudu, P. Tamuly, A. Jana, D. Ghosh, R. Bandyopadhyay, M. Bhuyan, Monitoring of black tea fermentation process using electronic nose, J. Food Eng. 80 (2007) 1146–1156. [8] N. Bhattacharyya, R. Bandyopadhyay, M. Bhuyan, A. Ghosh, R.K. S Mudi, Correlation of Multi-sensor Array Data with “Tasters” Panel evaluation for Objective Assessment of Black Tea Flavour Int. Proc. ISOEN-2005, Barcelona, Spain, April 13–15, 2005. [9] http://www.tocklai.net. [10] R.E. Shaffer, R.A. McGill, S.L. Rose-Pehrsson, Probabilistic neural networks for chemical sensor array pattern recognition: comparison studies,
improvements, and automated outlier detection, NRL Formal Report 611097-9879, 1998. [11] M. Garcia, M. Aleixandre, J. Gutierrez, M.C. Horrillo, Electronic nose for wine discrimination, Sens. Actuator B: Chem. 113 (2006) 911–916.
Biographies Nabarun Bhattacharyya is Additional Director in Centre for Development of Advanced Computing (C-DAC), Kolkata, India, which is a premier R&D Institute under Department of Information Technology, Government of India. He received his Bachelor of Engineering in Electronics & Telecommunication Engineering from Jadavpur University, Kolkata, India in the year 1984. His research areas are Agri-Electronics, Machine Olfaction, Soft Computing and Pattern Recognition. He has published more than 30 research papers in journals and conferences. Bipan Tudu received his MTech degree in Instrumentation and Electronics Engineering in 2004 from the Jadavpur University, Kolkata, India. He is currently a Lecturer in the Department of Instrumentation and Electronics Engineering. His main research interest includes Pattern Recognition, Artificial Intelligence and Machine Olfaction. Arun Jana is presently a Project Engineer in Centre for Development of Advanced Computing (C-DAC), Kolkata, India, which is a premier R&D Institute under Department of Information Technology, Government of India. He received his Master in Computer Applications (MCA) from the Utkal University, India in the year 2004. His research interests are machine learning and soft computing. Devdulal Ghosh is presently a Project Engineer in Centre for Development of Advanced Computing (C-DAC), Kolkata, India, which is a premier R&D Institute under Department of Information Technology, Government of India. He received his Bachelor of Engineering (BE) in Computer Science from the Burdwan University, India in the year 2004. His research interests are virtual instrumentation, software engineering and object oriented programming. Rajib Bandopadhyay received his PhD degree in the year 2001 and currently a Professor in the Department of Instrumentation Engg., Jadavpur University, Kolkata, India. His research interests are in the fields of Machine Olfaction, Intelligent Control and NQR based Instrumentation Systems. He is a fellow of IETE (India) and published more than 35 research articles in journals and conferences. Amiya Baran Saha is the Executive Director in Centre for Development of Advanced Computing (C-DAC), Kolkata, India, which is a premier R&D Institute under Department of Information Technology, Government of India. He completed his MTech (Electrical Engineering) with specialisation in Computer Science from Indian Institute of Technology (IIT), Kanpur, India in 1972. His research interests are in the fields of Computer Software Development, Real Time Software Development, RDBMS, Multimedia Content Creation and Natural Language Processing.
Sensors and Actuators B 131 (2008) 37–42
Illumination heating and physical raking for increasing sensitivity of electronic nose measurements with black tea Nabarun Bhattacharya a,∗ , Bipan Tudu b , Arun Jana a , Devdulal Ghosh a , Rajib Bandhopadhyaya b , Amiya Baran Saha a b
a Center for Development of Advance Computing (C-DAC), Kolkata 700091, India Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt Lake Campus, Kolkata 700098, India
Available online 23 December 2007
Abstract One of the most complicated components of electronic olfaction process is odour handling and delivery system capable of enabling the associated sensors to perform with acceptable sensitivity. For smell monitoring of black tea, an array of metal oxide semiconductor (MOS) sensors has been used for assessment of volatiles in the experimental set-up. In the presence of detectable vapor, the conductivity of the sensor increases depending on the concentration of odour molecules in the vapor. But, the MOS sensors are highly sensitive to moisture and water vapor. Presence of water vapor in the headspace of any sample, therefore, produces strong sensor outputs, which are essentially noise. Such overriding effect of noise caused by water vapor plays catastrophic role in terms of efficient pattern recognition by parametric and non-parametric methods. This paper presents the details of a novel sampling system based on illumination-controlled heating together with physical raking of the tea samples developed for enhancement of sensitivity of MOS sensor array. This increase in sensor outputs enhances the precision of the measurement system significantly. The efficacy of the system has been validated by comparison of performance of the system in terms of correlating electronic nose data with tea tasters’ scores using probabilistic neural network (PNN). © 2007 Elsevier B.V. All rights reserved. Keywords: Sensor array; Illumination heating; Physical raking; Probabilistic neural network (PNN)
1. Introduction Electronic nose technology has been successfully employed for recognition and quality analysis of various food and agro products, like wine [1], cola [2], meat [3], fish [4], etc. But on classification of tea quality, very few studies have been reported. Pioneering work has been done by Dutta et al. [5] for classification of tea aroma. Several experiments with black tea aroma using electronic nose have been carried out by the authors and reported in the literature. Smell profile of tea during fermentation process has been successfully monitored online [6,7]. Also successful correlation between multi-sensor array data with tea tasters’ marks has been successfully achieved [8]. During the above studies, it has been observed that brewed tea liquor could not be used due to presence of water vapor in the headspace. In all the above studies, only dry tealeaves
have been put in the sample holder for headspace generation and sampling. But the volatile emission from dry tea being considerably low, sensor outputs have been observed to be significantly small. On the other hand, tea scientists have established that optimum volatile emission takes place from the tea at around 60 ± 5 ◦ C [9]. In fact, good flavor of brewed tea is caused due to rise in temperature of the tea by addition of boiling water to it. To resolve this conflicting requirement, some new method of heating of the tea samples appears imperative. This paper describes a novel heating using optical energy from commonly used miniature halogen lamps and simple motorized mechanical agitation system of tea samples within the sample holder. 2. System description 2.1. Description of customized electronic nose
∗ Corresponding author at: Centre for Development of Advanced Computing (C-DAC), E-2/1, Block-GP, Sector-V, Salt Lake, Kolkata 700091, West Bengal, India. E-mail address: [email protected] (N. Bhattacharya).
0925-4005/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2007.12.031
The customized electronic nose system consists of (a) a sensor array; (b) a micro-pump; (c) solenoid valves with automated sequence control; (d) sample holder; (e) illumination
38
N. Bhattacharya et al. / Sensors and Actuators B 131 (2008) 37–42
2.2. Description of the heating cum raking system
Fig. 1. Customized electronic nose set-up developed.
heating arrangement; (f) olfaction software. The micro-pump generates compressed air with constant flow rate that is routed through three solenoid valves for control of headspace generation, sampling and purging in an automated sequence. The electronic nose set-up developed under the study is shown in Fig. 1. A user-friendly interactive software has been carefully designed which has got features like programmable sequence control, dynamic fermentation profile display, data logging, alarm annunciation, data archival, etc. The software has been developed in LABVIEW 8.1® of National Instruments.
One vital feature of this system is the controlled heating of the samples under test using optical energy. 35-Watts miniature halogen lamp has been fixed to the fixture for sample holder made of Teflon. Also a motorized raking arrangement has been included at the bottom of the sample holder to ensure adequate emanation of volatiles. Schematic diagram of the illumination heating cum raking arrangement is illustrated in Fig. 2. A resistance temperature detector (RTD) device, Pt100, whose resistance varies almost linearly with temperature, has been employed for monitoring the temperature of tea samples under test as shown in Fig. 2. Using an external power source, the change in resistance of the RTD is converted into a corresponding change in voltage, which is fed to one of the analogue inputs of the USB data acquisition card connected to the PC. The electronic controllers of the DC motor and the halogen lamp are connected to the digital output of the USB card and ON/OFF control signals are generated by the USB card under command control from the olfaction software. USB card model number 6009 of National Instruments, USA, has been used in the system. Two metallic blades made of stainless steel are fitted with the shaft of the motor and these rotating blades during sniffing operation of the electronic nose create physical turbulence within the sample ensuring adequate volatile emanation from them. The entire sniffing cycle consists of sequence of operations as described in Table 1. The typical sensor response curve during the sequence
Fig. 2. Illumination heating and motorized raking arrangement.
N. Bhattacharya et al. / Sensors and Actuators B 131 (2008) 37–42
39
Fig. 3. Operational sequences and typical sensor response in the experimental sniffing cycle.
of operational conditions in a sniffing cycle has been shown in Fig. 3. 3. Experimental 3.1. Monitoring of individual sensor responses at various operational modes Experimentations with tea samples had been carried with the new odour delivery system with all the possible combinations of heating and raking, namely, (a) no heating or raking; (b) only heating, no raking; (c) no heating, only raking; (d) both heating and raking. It may be noted that same samples were used for all the experimental conditions to study the comparative advantages associated with each combination. Experiments were conducted with following experimental conditions: • Amount of each sample = 50 g. • Temperature maintained during sampling = 60 ± 3 ◦ C. • Air-flow rate = 5 ml/s. Table 1 Sequence of operations in an experimental sniffing cycle Operation
Purpose
Illumination heating cycle Headspace generation Sampling
To heat black tea samples to 60 ± 3 ◦ C To accumulate adequate volatile compounds before sampling Sampling of odour molecules by the MOS sensors Concurrent with the sampling operation; ensures emanation of more volatiles during sampling Cleaning the sensor surface with blow of fresh air so that the sensor output returns to the baseline value
Motorized agitation Purging
Duration (s) 60 30 100 100
100
The sensors considered for black tea classification were TGS816, TGS-823, TGS-831, TGS-832, TGS-2600, TGS-2610, TGS-2611 and TGS-2620 of Figaro, Japan. The RS /RS values of the sensors were recorded for each combination of above variations in the odour delivery system to study the improvement of the performance of the system where RS is the change in resistance of the metal oxide semiconductor (MOS) sensor and RS is the base resistance value of the respective MOS sensor. Table 2 shows sample variation of RS /RS values at different operational modes of the system. The bar graph shown in Fig. 4 clearly demonstrates effect of various combinations of heating and raking on the responses of the sensors. 3.2. Experiments with finished tea and correlation with tea tasters’ scores The newly developed odour handling system has been evaluated to examine whether any improvement in performance of the electronic nose in terms of accuracy of correlation of the sensor array data with the tea tasters’ scores takes place. For this purpose, experimentations with 50 samples of black tea have been performed. Black tea produced at processing plants are tasted by expert human panel called “Tea Tasters” and gradation of tea is done on the basis of marks given by these tasters. Experiments were done for approximately 1-month duration at M/s. Dalmia Tea Plantation & Industries Limited, Kolkata, India, which has multiple tea gardens spread across India and the teas produced in their gardens are sent everyday to the tea tasting center for quality assessment. The tasting center had expert tea tasters and for our experiments, one expert tea taster was deputed for assigning taster’s mark to each of the samples. The identified tea taster has evaluated all these samples and assigned scores to individual samples which have been considered as taster’s mark for the correlation study with the computational network model. A sample tea taster score sheet is given in Table 3.
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Table 2 RS /RS values for individual sensors at various operational modes Sensor part no.
Target gas of the sensor
No heating or raking (RS /RS )
Only heating, no raking (RS /RS )
No heating, only raking (RS /RS )
Both heating and raking (RS /RS )
TGS 2610 TGS 2620 TGS 2611 TGS 2600 TGS 816 TGS 831 TGS 832 TGS 823
LP gas Alcohol Methane Air contaminants Methane, butane, propane CFC (R-21, R-22) CFC (R-134a) Organic solvent vapor
0.132 0.501 0.106 0.321 0.621 0.051 0.389 0.497
0.17 0.565 0.134 0.38 0.827 0.176 0.443 0.574
0.138 0.508 0.109 0.363 0.69 0.071 0.388 0.514
0.186 0.65 0.158 0.504 0.829 0.203 0.534 0.65
Fig. 4. Bar graph of responses of the sensors at various operational modes.
While “leaf quality” and “infusion” scores are based on visual inspection of the samples by the tasters, the marks given against “liquor” are the combined perception of taste, briskness and astringency of the sample. The scores assigned to “aroma”, signify the smell and flavor of the samples. We, therefore, have considered these aroma scores, for training the probabilistic neural network model discussed later. The samples arriving at the testing center on daily basis were also tested with the electronic nose system fitted with the new odour handling mechanism with illumination-based heating and
motorized agitation arrangement. All the 50 samples have been exposed to the electronic nose sensors through all the experimental combinations, namely, (a) no heating or raking; (b) only heating no raking; (c) no heating only raking; (d) both heating and raking. Four sets of data matrix having eight columns and fifty rows have, thus, been generated by these experiments from the electronic nose. 60% of each of four datasets has been used for training the neural network and rest 40% of the data have been used for testing the network. 4. Data analysis and results
Table 3 Sample tea taster’s score sheet Sample code
DALMIA 240806-01 DALMIA 260806-10 DALMIA 190706-07 DALMIA 280706-03
4.1. Comparison of sensor responses for different operational modes
Scores (1–10) Leaf quality
Infusion
Liquor
Aroma
7 6 5 7
5 5 4 5
3 5 4 6
5 8 7 6
It has been observed that there is a marked increase in the output of the sensors as soon as illumination heating is enabled in comparison with the output levels without heating. With introduction of physical raking along with heating, the sensor outputs show further increase in level and the sensor output voltage curve
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Table 4 Summary of results Name of the testing center
Dalmia Tea Plantation & Industries Limited
Total no. of samples
50
No. of samples used for training
30
rises more steeply compared to the previous condition. This is due to spread of heat throughout the sample and exposure of more surface area of the tea granules to the incoming pressurized airflow as well as illumination-based heating. But there is hardly any enhancement of sensor outputs with or without raking if the heating is withdrawn. Table 2 gives a sample comparative summary of individual sensor responses for different operational modes of the new odour handling system and Fig. 4 demonstrates these variations graphically. 4.2. PNN-based correlation study for system validation Illumination heating and motorized raking, as already described, results in enhanced outputs from the sensors and this unambiguous increase in sensor outputs improves the precision of the overall measurement system significantly. The performance of the classification algorithm is also better as the formed clusters are more distinct with these data. In this section, the performance of the overall system with the improved odour delivery system is presented and the probabilistic neural network (PNN) has been considered for the classification of a representative 50 tea samples. Probabilistic neural network is predominantly a classifier that is capable of mapping some input pattern to a number of classifications [10,11]. PNN is basically the implementation of a statistical algorithm called kernel discriminant analysis in which the operations are organized into a multi-layered feed forward network with four layers, namely, input layer, pattern layer, summation layer and output layer. The input pattern, which is essentially the multi-sensor output from the electronic nose, is presented to the first layer and the pattern layer computes the probability density function (PDF) from the input vector with respect to the training vectors by calculating distances. The summation layer computes sum of these contributions for each class. And the final layer computes the output by picking the maximum probabilities. The PNN algorithm is characterized by (a)
Fig. 5. Block diagram of PNN topology.
No. of samples used for testing
Operational mode of the odour handling system
% Accuracy of correlation in PNN
20
No heating or raking Only heating no raking No heating only raking Both heating and raking
85 89 85 91
fast training process; (b) parallel structure; (c) training samples can be added or removed very easily. In a previous study by the authors [8], the back propagation-multiplayer perceptron (BP-MLP) and radial basis function (RBF) topologies of neural networks have been used for correlation of multi-sensor output with tea tasters’ scores and encouraging results were obtained. But both the BP-MLP and the RBF models require huge amount of training data for accuracy of prediction. In the present study, PNN has been used to classify the tea samples and correlate the sensor array outputs to tea tasters’ scores. Block diagram of PNN topology is illustrated in Fig. 5. Here X1 , X2 , . . ., XN are the input vectors formed out of output responses of the sensor array consisting of eight sensors. Y1 , Y2 , . . ., YM are the training vectors. Since PNN is a supervised method, for each training vector, the corresponding class score is defined. Let the classes are C1 , C2 , . . ., Ck and contain I1 , I2 , . . ., Ip number of training vectors respectively so that. I1 + I2 + · · · + In = M. In this layer for each input vector, the corresponding PDF is calculated by the formula: PDF = −(Xi −Yj
2 /σ 2 )
where Xi is the input vector and Yi is the training vector, σ is the standard deviation where i = 1, 2, . . ., N and j = 1, 2, . . ., M. PDF values are represented by P1 , P2 , . . ., PM in the “P” matrix. In the summation layer, the PDF values associated with each class are averaged and the “G” matrix is formed. In the output layer, class declaration is accomplished based on the maximum value of G in the summation layer. Table 4 gives the result of PNN-based correlation between multi-sensor output from electronic nose and tea tasters’ scores obtained with different operational modes of the new odour handling system. It may be observed that maximum accuracy (>90%) has been achieved when both heating and raking is ON during sniffing operation. 5. Conclusion The sensitivity electronic nose sensor is, in general, quite poor and enhancement of the same is a challenge for the scientists. The paper describes a novel and elegant method of improving the sensitivity of electronic nose instrument for black tea classification. The effect of moisture is almost eliminated in this process and the success rate of classification of finished black tea is significantly enhanced. In addition to contributing to visible increase in the sensor responses, the system decisively demonstrates improvement in classification ability of the electronic
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nose system Therefore, this elegant method of odour handling using illumination heating and physical ranking may serve as a very useful tool for many others applications of electronic nose. Acknowledgements The authors are grateful to Mr. Surajit Ghosh, eminent tea technologist for support and guidance provided although the long study. The authors are deeply indebted to Mr. G.G. Dalmia, Dalmia Tea Plantation & Industries Limited, where experiments were performed during the study. The work has been sponsored by the National Tea Research Foundation, Tea Board, Government of India and the Department of Science & Technology, Government of India. References [1] J. Lozano, J.P. Santos, M. Aleixandre, I. Sayago, J. Gutierrez, M.C. Horrillo, Identification of typical wine aromas by means of an electronic nose, IEEE Sens. J. 6 (2006) 173–178. [2] B.G. Kermani, S.S. Schiffman, H.T. Nagle, Performance of the Levenberg–Marquardt neural network training method in electronic nose applications, Sens. Actuator B: Chem. 110 (2005) 13–22. [3] D.D.H. Boothe, J.W. Arnold, Electronic nose analysis of volatile compounds from poultry meat samples, fresh and after refrigerated storage, J. Sci. Food Agric. 82 (2002) 315–322. [4] M. O’Connell, G. Valdora, G. Peltzer, R. Martin Negri, A practical approach for fish freshness determinations using a portable electronic nose, Sens. Actuator B: Chem. 80 (2001) 149–154. [5] R. Dutta, E.L. Hines, J.W. Gardner, K.R. Kashwan, M. Bhuyan, Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach, Sens. Actuator B: Chem. 94 (2003) 228–237. [6] N. Bhattacharyya, S. Seth, B. Tudu, P. Tamuly, A. Jana, D. Ghosh, R. Bandyopadhyay, M. Bhuyan, S. Sabhapandit, Detection of optimum fermentation time for black tea manufacturing using electronic nose, Sens. Actuator B: Chem. 122 (2007) 627–634. [7] N. Bhattacharyya, S. Seth, B. Tudu, P. Tamuly, A. Jana, D. Ghosh, R. Bandyopadhyay, M. Bhuyan, Monitoring of black tea fermentation process using electronic nose, J. Food Eng. 80 (2007) 1146–1156. [8] N. Bhattacharyya, R. Bandyopadhyay, M. Bhuyan, A. Ghosh, R.K. S Mudi, Correlation of Multi-sensor Array Data with “Tasters” Panel evaluation for Objective Assessment of Black Tea Flavour Int. Proc. ISOEN-2005, Barcelona, Spain, April 13–15, 2005. [9] http://www.tocklai.net. [10] R.E. Shaffer, R.A. McGill, S.L. Rose-Pehrsson, Probabilistic neural networks for chemical sensor array pattern recognition: comparison studies,
improvements, and automated outlier detection, NRL Formal Report 611097-9879, 1998. [11] M. Garcia, M. Aleixandre, J. Gutierrez, M.C. Horrillo, Electronic nose for wine discrimination, Sens. Actuator B: Chem. 113 (2006) 911–916.
Biographies Nabarun Bhattacharyya is Additional Director in Centre for Development of Advanced Computing (C-DAC), Kolkata, India, which is a premier R&D Institute under Department of Information Technology, Government of India. He received his Bachelor of Engineering in Electronics & Telecommunication Engineering from Jadavpur University, Kolkata, India in the year 1984. His research areas are Agri-Electronics, Machine Olfaction, Soft Computing and Pattern Recognition. He has published more than 30 research papers in journals and conferences. Bipan Tudu received his MTech degree in Instrumentation and Electronics Engineering in 2004 from the Jadavpur University, Kolkata, India. He is currently a Lecturer in the Department of Instrumentation and Electronics Engineering. His main research interest includes Pattern Recognition, Artificial Intelligence and Machine Olfaction. Arun Jana is presently a Project Engineer in Centre for Development of Advanced Computing (C-DAC), Kolkata, India, which is a premier R&D Institute under Department of Information Technology, Government of India. He received his Master in Computer Applications (MCA) from the Utkal University, India in the year 2004. His research interests are machine learning and soft computing. Devdulal Ghosh is presently a Project Engineer in Centre for Development of Advanced Computing (C-DAC), Kolkata, India, which is a premier R&D Institute under Department of Information Technology, Government of India. He received his Bachelor of Engineering (BE) in Computer Science from the Burdwan University, India in the year 2004. His research interests are virtual instrumentation, software engineering and object oriented programming. Rajib Bandopadhyay received his PhD degree in the year 2001 and currently a Professor in the Department of Instrumentation Engg., Jadavpur University, Kolkata, India. His research interests are in the fields of Machine Olfaction, Intelligent Control and NQR based Instrumentation Systems. He is a fellow of IETE (India) and published more than 35 research articles in journals and conferences. Amiya Baran Saha is the Executive Director in Centre for Development of Advanced Computing (C-DAC), Kolkata, India, which is a premier R&D Institute under Department of Information Technology, Government of India. He completed his MTech (Electrical Engineering) with specialisation in Computer Science from Indian Institute of Technology (IIT), Kanpur, India in 1972. His research interests are in the fields of Computer Software Development, Real Time Software Development, RDBMS, Multimedia Content Creation and Natural Language Processing.