Semiconductor Sensor Array Based Electronic Nose for Milk, Rancid Milk and Yoghurt Odors Identification 1
B. Botre, 1D. Gharpure, 1A. Shaligram and 2S. Sadistap 1
Department of Electronic Science, University of Pune, Pune: 411007, India. Phone: +91-0202-25699841. Email:
[email protected] 2 CEERI, Pilani, Rajasthan.
Abstract. This paper presents the use semiconductor sensor array based electronic nose for the identification of milk, rancid milk and yoghurt odors. A low cost sensor array, serial data acquisition system and E-nose software package (ESP) tool are used to generate the database, feature extraction and normalization. The MLP NN is trained using the NeuroSolutions for the identification. The network has successfully classified milk, rancid milk and yoghurt odors with 96% success rate. A sensitivity analysis is done to test the performance of the sensor data in the trained network Keywords: electronic nose, MLP NN, milk, rancid milk, yoghurt.
PACS: 47.66. -P, 87.19.lt
INTRODUCTION In the recent years, Electronic Noses have been successfully used for different applications particularly for food and beverage industries. Monitoring quality and freshness of the dairy products is one applications for which electronic nose could be used. S. Labreche et al. [1] used Alpha MOS FOX 4000 electronic nose to determine the shelf life of milk. The evaluation of the degradation of yoghurt samples by headspace-gas chromatography-mass spectrometry based electronic nose is reported by C.Carrillo-Carrion et al. [2]. A sensor fusion method for on-line monitoring of yoghurt fermentation is used by C. Cimander et al. [3], wherein an electronic nose, a near-infrared spectrometer (NIRS) and standard bioreactor probes were used. The electronic noses are also used for classification and quality inspection of sea food like fish, fruits like banana, apple, tomato, meat, eggs, beverages like wine, coffee, juice [4-8] etc. Extending this idea further, this paper reports an attempt of using the semiconductor sensor array and Neurosoluation based electronic nose for analyzing milk, rancid milk and yoghurt odors. The first section deals with the experimentation and measurements using an electronic nose. The MLP NN training and results obtained are discussed along with sensitivity analysis in the second section.
EXPERIMENTAL AND METHODS The electronic nose system (figure 1) used for the experimentation is composed of sensor module, ADuC831 serial data acquisition (DAQ) system for measurements [9], E-nose software package (ESP) tool and NeuroSolutions 5.0 from NeuroDimension Inc. The sensor array incorporates of five commercially available Figaro gas sensors: 2 TGS2620, 1 TGS2610 and 2 TGS2600. The sensors are supplied with a 5 V circuit voltage and a 5 V sensor heating voltage providing a specific temperature by using sensor heater driver. The temperature and humidity sensors are also mounted in the sensor chamber to monitor the ambient condition while the measurements. The Aduc831 based serial DAQ is composed of 12bit, 8-channel analog to digital converters interfaced to the sensor array. A serial communication is achieved by using RS232 communication protocol to the Desktop PC. ESP tool includes data base generation, feature extraction and normalization required before the data to be analyzed using the pattern recognition system. A NeuroSolutions 5.0 is used as the pattern recognition system in electronic nose to classify the odor patterns.
Sensor Transient to Yoghurt
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FIGURE 1. Experimental setup of Electronic Nose.
Figure B
Measurements Three samples of milk, rancid milk and yoghurt were selected for identification. The response of the sensor array was obtained by injecting 10 gm of sample and keeping the sensor heater ON during the measurement of 80 sec. The data was digitized at 1 samples/second sampling rate using serial DAQ system & ESP tool. Figure 2 A. B. C. shows the sensor array response to milk, rancid milk and yoghurt. The sensor responses clearly illustrate the difference between odor patterns of the three samples which can be identified using the pattern recognition system. Sensor Transient to Milk Voltage (volts)
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FIGURE 2. Sensor response to A. milk, B. rancid milk and C. Yoghurt.
The measurements comprised three samples with eight replicates of each type of measurements. Five such data sets were taken over a period of 3 weeks. The database of 120 (3 samples, 8 replicates, 5 such data sets) measurements were taken. The feature vector for each odor sample is composed of points in the stable region i.e. voltage values at the 50th second, 60th second and 70th second forming a data base of 360 feature vector. The response patterns of features were then normalized between [0 – 1] using ESP tool and the Multilayer Perceptron Neural Network (MLP NN) was trained for identification.
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RESULTS
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Figure A Sensor Transient to Rancid milk
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All the rows were tagged as input and desired output and randomized for better training of the MLP NN. To train the network, 360 vectors were used and divided into: 214 training vectors and 146 test vectors. The architecture of MLP NN is shown in figure 3. The features consisting of seven neurons for seven sensors were applied as the input to the network. The output layer is composed of three neurons corresponding to milk, rancid milk and yoghurt samples to be identified and the number of neurons in the hidden layer was optimized to 5. The network was trained using Neurosolutions for 50,000 iterations. On satisfactory performance, the connection weights and the configuration of the MLP NN were stored and evaluated with the 146 test vectors in the test network mode. The output neurons o1, o2 and o3 of the MLP NN, were assigned to milk, rancid milk and yoghurt odors respectively. The confusion matrix in table 1 shows successful identification of milk, rancid milk and yoghurt.
neurons in the output layer was trained using the Neurosolutions. The network could able successfully classify theses samples up to 96% overall success rate. The success rate obtained using the trained network is 100%, 89% and 98% for milk, rancid milk and yoghurt samples respectively. The success rate for rancid milk is less as compared to other two sample odors. This could be due to the more or less similar odor patterns obtained for yoghurt and rancid milk odors as can be observed from the sensitivity analysis in figure 4.
FIGURE 3. Architecture of MLP NN.
TABLE 1. Confusion matrix obtained during the MLP NN test. Output / o1 o2 o3 Desired o1 47 0 0 o2 0 41 1 o3 0 5 51
It is possible to identify milk sample up to 100 % success rate, where as rancid milk and yoghurt samples are identified with success rate up to 89% and 98% respectively. The performance of the trained network is given in table 2. TABLE 2. Performance evaluation of the trained MLP NN for the test feature vector. Performance o1 o2 o3 MSE 0.0107 0.0439 0.04366 NMSE 0.0488 0.2027 0.1898 MAE 0.06150 0.0912 0.09056 Min Abs Error 0.02403 0.00321 0.00329 Max Abs Error 1.05237 1.05539 1.0550 r 0.983628 0.90587 0.912923 Percent Correct 100 89.1304 98.07692
Sensitivity
Sensitivity About the Mean 0.6 0.5 0.4 0.3 0.2 0.1 0
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FIGURE 4. Sensitivity test of the sensors in array.
It is also observed from figure 4 that the sensor data obtained from TGS2620 and TGS2610 have shown good sensitivity while training the MLP NN to the odors from the dairy products. On the other side, the data from sensor TGS2600 have list sensitivity to these odors. The temperature and humidity sensors data have equally contributed in the identification as seen from table 3. TABLE 3. Sensitivities of the sensors obtained for the trained MLP NN. Sensitivity o1 o2 o3 s1 – TGS2620 0.05807 0.46875 0.47007 s2 – TGS2610 1.31E-06 0.51167 0.51400 s3 – TGS2600 1.53E-07 0.02685 0.02679 s4 – TGS2600 1.76E-07 0.00967 0.00981 s5 – TGS2620 1.28E-06 0.38907 0.38731 s6 – TEMP 3.77E-07 0.17187 0.17659 s7 – HUMIDITY 3.09E-07 0.12196 0.12421
DISCUSSION The electronic nose has been successfully tested for the identification of milk, rancid milk and yoghurt. Three points in the stable region of the sensor response were used as feature vector without any signal preprocessing for the classification. A database of 360 feature vectors was obtained from the 3 points in the stable region of the sensors response. A network of 7 neurons at the input, 5 neurons in the hidden and 3
CONCLUSION The electronic nose is successfully employed for the identification of milk, rancid milk and yoghurt. The MLP NN in neurosolutions is trained using neurosolution for the classification of these samples. The overall performance of the electronic nose has shown 96% success rate with only 3 points in the stable region as features. The database of sensors
TGS2620 and TGS2610 have contributed more in the classification as compared to the TGS2600 one as observed from the sensitivity analysis.
ACKNOWLEDGMENTS The authors thank University of Pune, CSIR and DST for financial support.
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