The Application of Back Propagation Neural Network of Multi-channel Piezoelectric Quartz Crystal Sensor for Mixed Organic Vapours

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1 Tamang Journal of Science and Engineering, Vol. 5, No. 4, pp (2002) 209 The Application of Bac Propagation Neural Networ of Multi-channel Piezoelectric Quartz Crystal Sensor Ping Chang and Jeng-Shong Shih * Department of Chemistry National Taiwan Normal University Taipei, Taiwan 116, R.O.C. sshih@cc.ntnu.edu.tw Abstract A multi-channel piezoelectric quartz crystal sensor with a homemade computer interface was prepared and employed in the present study to detect mixture of organic molecules. Bac propagation neural networ (BPN) was used to distinguish the species in the mixture organic molecules and multivariate linear regression analysis (MLR) was used to compute the concentration of the species. A six-channel piezoelectric sensor detecting organic molecules in static system was investigated and discussed. Amine, carboxylic acid, alcohol and aromatic molecules can easily be distinguished by this system with bac propagation neural networ. Furthermore, the concentrations of the organic compounds were computed with an error of about 10% by multivariate linear regression analysis (MLR). Detection of organic mixture with amine, carboxylic acid, alcohol and aromatic molecules by this method also had good qualitative and quantitative results. In order to achieve better distinguishability, change of fault-tolerance in bac propagation neural networ was also investigated and discussed in this study. Key Words: Pizoelectric Crystal, Multichannel Sensor, Organic Vapours, Bac Propagation Neural Networ, Linear Regression Analysis 1. Introduction It is well nown that the use of gas sensor array and pattern recognition analysis has the advantage in identifying odors, organic molecules and gases because of poor selectivity of a lot of other gas sensors [1-5]. The qualitative and quantitative analysis of a gas mixture with non-selective sensor elements is achievable with a combination of several different sensor elements in an array. Piezoelectric quartz crystals were very sensitive for the changes in mass [6-12]. By coating some special materials on the surface, the frequency of the crystal can be decreased by adsorbing targets on the crystal's surface. The relationship derived for quartz crystals (AT-cut) vibrating in the thicness shear-mode is as follows [13,14]: F = f 2 m/a (1) Where F is the frequency shift due to the coating, f (MHz) is the frequency of quartz crystal, m (gram) is the mass of deposited coating and A (cm 2 ) is the area coated. Principal component analysis (PCA), a well-nown technology of statistics, is useful in selecting the classic independents of all

2 210 Ping Chang and Jeng-Shong Shih materials [15]. We can easily differentiate different analytes by taeing a view of the profile discrimination with the responses of several channels in a plot. However, it is necessary to distinguish the different gases by sensor array without subective udgment in many cases. Therefore, using of computer for the study is the best choosing. Can a computer smart as a human being? Artificial neural networ (ANN) capacitates a computer the ability of learning and thining. Chemical sensors were developed for an artificial nose in the past decade [16]. The application of ANN method proved to be particularly advantageous if the measured property is not connected exactly to the signal of the transducers of sensors. The optimum structure of neural networ is determined by a trial and error method. Bac propagation neural networ (BPN) is the most popular technology of the chemical sensor array. The bac propagation method is part of the parallel distributed processing system [17]. One-layer networs lie Hopfield and Kohonen structure, and multi-layer systems lie counter propagation and bac propagation of errors, can be used for chemical applications. Bacpropagation refers to the method for computing the gradient of the case-wise error function with respect to the weights for a feedforward networ which is straightforward and elegant application of the chain rule of elementary calculus. By definition, bacpropagation or bacprop refers to a training method that uses bacpropagation to compute the gradient. In other words, a bacprop networ is a feedforward networ trained by bacpropagation [18]. In this study, qualitative analysis of the analytes (mixture gas) was done by using PZ sensor array and BPN. Three layers networ structure of BPN was established and three units of hidden layer were used. Furthermore, the concentration of each compound of the gases was computed by MLR. 2. Experimental 2.1 Crystal Coating Piezoelectric crystals used were AT-cut spherical quartz crystals, with a radius of 4.0 mm and a thicness of 0.18 mm with a basic resonant frequency of 10 MHz and were provided with silver-plated metal electrodes on both sides (Taiwan Crystal Co.). The crystals were coated with the prepared solution via dropping method with a microsyringe. An aliquot of 2 ml of the prepared coating solution was dropped onto one side of the quartz crystals. After evaporation of the solvent, differently functioning PZ crystals were obtained. The coating materials selected with PCA were polyvinyl alcohol, fullerene, polystyrene, stearic acid, polyethylene adipate and polyvinyl pyrrolidene [8]. 2.2 Apparatus Figure 1 depicts the experimental setup of a piezoelectric quartz crystal detection system with an assembled computer interface. The multi- channel PZ sensor connected with an oscillator system was placed in a glass cell. Organic liquid was inected into the inecting port which incudes a heat plant. A home-made computer interface, including an oscillator, Altera programmable logic devices, a standard crystal and a programming peripheral interface (PPI 8255) was prepared for frequency to digital conversion. The Altera was designed with a 24 bits counter and 24 bits register to treat the 10 MHz frequency and no frequency mixing was used. Therefore, the true frequencies were obtained and hence the errors were reduced [15]. Data processing and signal acquisition were automatically performed on a microcomputer (PC/AT) with a program in Qbasic. The MLR is done with the commercial statistical software pacage SAS and the bac propagation neural networ (BPN) is a program written in Qbasic. Oscillation circuit and Ferquency counter Multi-channel sensor Sample Inecting port Heater Nitrogen Flow controller Figure 1. Experimental setup of detection system with an assembled computer interface

3 The Application of Bac Propagation Neural Networ of Multi-channel Piezoelectric Quartz Crystal Sensor 211 BPN program Training process: Step 1. Design the structure of neural networ and input parameters of the nertwor. Step 2. Get initial weights W and initial θ values from randomizing. Step 3. Input training data matrix X and output matrix T. Step 4. Compute the output vector of each neural units. (a) Compute the output vecter H of the hidden layer net = WiX i θ (2) H = f ( net ) (3) (b) Compute the output vecter Y of the output layer net = WH i θ (4) Y = f ( net ) (5) Step 5. Compute the distances d (a) Compute the distances d of the output layer ' δ = ( T Y ) f ( net ) (6) (b) Compute the distances d of the hidden layer ' δ = ( δ W) f ( net ) (7) Step 6. Compute the modification of W and θ (η is the learning rate) (a) Compute the modification of W and θ of the output layer W = ηδ H (8) θ = ηδ (9) (b) Compute the modification of W and θ of the hidden layer Wi = ηδ Xi (10) θ = ηδ (11) Step 7. Renew W and θ (a) Renew W and θ of the output layer W = W + W (12) θ = θ + θ (13) (b) Renew W and θ of the hidden layer Wi = Wi + Wi (14) θ = θ + θ (15) Step 8. Repeat step 3 to step7 until convergence. Testing process: Step 1. Input the parameters of the networ. Step 2. Input the W and θ Step 3. Input an unnown data matrix X Step 4. Compute the output vector (a) Compute the output vector H of hidden layer net = WiX i θ (16) H = f ( net ) (17) (b) Compute the output vecter Y of the output layer net = WH i θ (18) Y = f ( net ) (19)

4 212 Ping Chang and Jeng-Shong Shih 3. Results And Discussion In this study, a networ structure was used as shown in Figure 2. The response of six coated (polyvinyl alcohol, fullerene, polystyrene, stearic acid, polyethylene adipate and polyvinyl pyrrolidene) PZ crystals was the input layer matrix of the networ. First, pure organic gases such as toluene, butanol, butyl amine and acetic acid was analyzed by this system. 40 training examples and 20 testing examples were prepared for training and testing the networ respectively. In the BPN networ, the problem of classification had been solved. On the other words, using BPN networ in PZ multichannel sensor can distinguish different organic gases, toluene, butanol, butyl amine and acetic acid, rapidly and accurately. Response of channel 1 Response of channel 2 Response of channel 3 Response of channel 4 Response of channel 5 Response of channel 6 Hz Input Layer #6 Hidden Layer #3 Output Layer #4 Toluene Presence Presence(+1) (+1) Absence (-1) alcohol Acetic acid amine Figure 2. The structure of neural networ adopted in this study In the calculation of BPN, input data processing and output data reprocessing were done as in equation (20) and equation (21) DOld µ D New = (20) κ σ µ: mean of the data of the unit σ: standard deviation of the data of the unit = 2.58 (99%) D Old Min D New = ( ) (21) Max Min Min: minimum of the data of the unit Max: maximum of the data of the unit In this networ, each unit of the output layer stands for the presence (+1) or absence (-1) of the detected molecule. 0.9 as a target value of the presence and -0.9 as a target value of the unexpected answer were used for computing the bac propagation of error algorithm. When the value of output is larger than 0.9 or smaller than -0.9, we define the analytes presence or absence. However, +0.8 bias of the processing data was used for error algorithm. In this study, batch learning (change weights and threshold limits after all training samples were computed), 0.5 of learning rate, 0.1 of minimum learning rate and random initial weights and threshold limits were used. Table 1 depicts that overfiting does not occur in the training process of BPN because testing result has smaller error. NNs (Neural networs), lie other flexible nonlinear estimation methods such as ernel regression and smoothing splines, can suffer from either underfitting or overfitting. A networ that is not sufficiently complex can fail to detect fully the signal in a complicated data set, leading to underfitting. A networ that is too complex may fit the noise, not ust the signal, leading to overfitting. Overfitting is especially dangerous because it can easily lead to predictions that are far beyond the range of the training data with many of the common types of NNs. Overfitting can also produce wild predictions in multilayer perceptrons even with noise-free data18. Table 1. The error rate for BPN of organic molecules detection system Learning cycles: 5000 Number of examples Error rate/units Error rate /examples Training % 7.5 % Testing 20 0 % 0 % error-rate/units=(units total - units correct )/units correct error-rate/examples=(examples total - examples correct )/ samples correct The linear relationship between the response and concentration is shown in Figure 3. Regression analysis usalysis. Although quite high sensitivity was was founded to compute the concentration of the organic molecule. Table 2 was obtained by linear regression an in the case of stearic acid, however, relatively quite low relative coefficient (R 2 = ) was also observed in the case of stearic acid as shown in Table 2 which implied that stearic acid was not a good adsorbent for the analysis of butyl amine. Different experimental data set is used to test the regression equation as shown in Table 3. Quite good linear responses to toluene were found for all adsorbents which indicated that toluene in organic mixtures could be analyzed by multivariate linear regression analysis with these adsorbents on multi-channel quartz crystals.

5 The Application of Bac Propagation Neural Networ of Multi-channel Piezoelectric Quartz Crystal Sensor 213 target value "zero" maybe dangerous for the predictions should be tested carefully. Table 5 shows the error rate (units) of various target values of training and testing. In the Table 5, both error rates of training and testing of target value "zero" are smaller than others. It indicates that learning fault-tolerance is helpful to distinguish organic molecule in gas mixture. In this case, BPN is confusing whether butyl alcohol was in the gas mixture or not, as edvent in Table 6. However, it could be easily to distinguish toluene, butyl amine and acetic acid. Figure 3. The response of multichannel PZ sensor for toluene Table 2. Regression equations of linear regression analysis for organic gases Analytes Regression channel Regression equation Toluene Polystyrene Y=6.9075X Polyvinyl Y=7.1517X alcohol pyrrolidene Acetic Polyvinyl Y=28.003X acid pyrrolidene Polystyrene Y=7.2733X amine *Stearic Y=10.386X acid *High sensitivity, but relatively low relative coefficient (R 2 ) Furthermore, The qualitative and quantitative analysis of a gas mixture was also investigated and discussed. Five types with different concentrations of gas mixture (40 examples for training and calibration, 20 examples for testing), as shown in Table 4, had been detected (three times of each sample) by PZ mulitichannel sensor. Lie previous study, the same networ structure was used as shown in Figure 2. Also, positive output as a target value of the presence and negative output as a target value of the unexpected answer was used for computing the bac propagation of error algorithm. Figure 4 shows the relationship between the error rate and the number of training cycle. Choosing an appropriate target value in the error algorithm is very important. By comparing line A, B, C and D, it can be seen that ±0.9 is not an appropriate target value in this case. However, R 2 Table 3. Testing result of linear regression Theoretic Analytes conc.(mg/l) Testing Error conc.(mg/l) Toluene % % % % % alcohol % % % % % Acetic acid % % % % % amine % % % % %

6 214 Ping Chang and Jeng-Shong Shih Sample Table 4. Samples of gas mixtures (mg/l) Toluene alcohol Acetic acid amine A A A A B B B B C C C C D D D D E E E E Error Rate/units % Training Cycles Figure 4. Error rate curves in training process, A: target value is ±0.9; B: target value is ±0.5; C: target value is ±0.2; D: target value is ±0. Table 5. The error rate/units of BPN of gas mixture Target value Error rate/units Error rate/units Training ± Testing 0.75 Training ± Testing Training ± Testing 0.15 Training ± Testing Number of examples of training: 40(160 output units) Number of examples of testing: 20(80 output units) Learning cycles: 8000 Table 6. Testing results of BPN of gas mixtures Examples Theoretic output* Output Judgement A1 [1, -1, -1, 1] [1, -1, -1, 1] toluene and butyl amine A2 [1, -1, -1, 1] [1, -1, -1, 1] toluene and butyl amine A3 [1, -1, -1, 1] [1, -1, -1, 1] toluene and butyl amine A4 [1, -1, -1, 1] [1, -1, -1, 1] toluene and butyl amine B1 [1, -1, 1, -1] [1, -1, 1, -1] toluene and acetic acid B2 [1, -1, 1, -1] [1, -1, 1, -1] toluene and acetic acid B3 [1, -1, 1, -1] [1, -1, 1, -1] toluene and acetic acid B4 [1, -1, 1, -1] [1, -1, 1, -1] toluene and acetic acid C1 [1, 1, -1, -1] [1, -1**, -1, -1] toluene C2 [1, 1, -1, -1] [1, 1, -1, -1] toluene and butyl alcohol C3 [1, 1, -1, -1] [1, 1, -1, -1] toluene and butyl alcohol C4 [1, 1, -1, -1] [1, 1, -1, -1] toluene and butyl alcohol D1 [1, 1, 1, -1] [1, -1**, 1, -1] toluene and acetic acid D2 [1, 1, 1, -1] [1, -1**, 1, -1] toluene and acetic acid D3 [1, 1, 1, -1] [1, -1**, 1, -1] toluene and acetic acid D4 [1, 1, 1, -1] [1, -1**, 1, -1] toluene and acetic acid E1 [1, 1, -1, 1] [1, 1, -1, 1] toluene, butyl alcohol and butyl amine E2 [1, 1, -1, 1] [1, 1, -1, 1] toluene, butyl alcohol and butyl amine E3 [1, 1, -1, 1] [1, 1, -1, 1] toluene, butyl alcohol and butyl amine E4 [1, 1, -1, 1] [1, 1, -1, 1] toluene, butyl alcohol and butyl amine *[ toluene, butyl alcohol, acetic acid, butyl amine ] ** Incorrect output

7 The Application of Bac Propagation Neural Networ of Multi-channel Piezoelectric Quartz Crystal Sensor 215 Table 7. Regression equations of MLR of gas mixtures Organic molecule Regression equations R 2 Toluene Y= X X X X X X alcohol Y= X X X X X X Acetic acid Y= X X X X X X amine Y= X X X X X X X 1 : The response of polystyrene X 2 : The response of polyvinyl alcohol X 3 : The response of stearic acid X 4 : The response of fullerene X 5 : The response of polyethylene adipate X 6 : The response of polyvinyl pyrrolidene Table 8. MLR Testing results of organic mixtures Sample Toluene Acetic acid amine True Type1 Type2 True Type1 Type2 True Type1 Type2 A (difference) (error %) -13.1% -14.0% 8.7 % 9.5 % A % -19.4% -9.5% -8.8% A % -29.1% -26.8% -26.0% A % -22.9% -22.7% 22.0 % B % 0.9 % 6.7 % 8.6 % B % -0.2% -7.5% -5.8% B % -7.5% -8.1% -6.3% B % -1.4% 0.2 % -1.9% C % -12% C % -14.7% C % -15.5% C % -18.6%

8 216 Ping Chang and Jeng-Shong Shih Table 9. MLR Testing results of organic mixtures (continued from Table 8) Sample Toluene Acetic acid amine True Type1 Type2 True Type1 Type2 True Type1 Type2 D % 62.8 % 15.4 % 19.5 % D % 24.1 % -9.5% 5.8 % D % 19.8 % -15.6% -12.4% D % 29.8 % -15.7% -12.6% E % 13.8 % -3.1% -1.3% E % -10.4% -10.4% -7.6% E % 4.1 % -21.3% -18.4% E % 2.8 % -24.3% -21.4% Quantitative analysis for organic gas mixture, including toluene, butyl amine, acetic acid and butyl alcohol (interferent in this case), was calculated by multivariate linear regression analysis (MLR). Regression equations are shown in Table 7 and the testing results are shown in Table 8 and Table 9. MLR woring-out of gas mixture with butyl alcohol was shown as Type 2 (butyl alcohol is an interferent). No obvious difference was observed between these two types. In other words, the system can wor even the presence of interferent. In conclusion, various organic molecules, amine (butyl amine), carboxylic acid (acetic acid), alcohol (butyl alcohol) and aromatic molecule (toluene), can be distinguished clearly and determined by the six-channel (polyvinyl alcohol, fullerene, polystyrene, stearic acid, polyethylene adipate and polyvinyl pyrrolidene) piezoelectric detection system. A bac propagation neural networ was used to recognize the organic molecules can be distinguished very clearly. The networ also wored in gas mixture cases. Toluene, acetic acid and butyl amine were distinguished from gas mixture with interferent, alcohol. With an error of about 5-20%, we suggest, the method can be used to detect the components of organic mixtures both qualitatively and quantitatively. Acnowledgment The authors would lie to than the National Science Council of Republic of China in Taiwan for the financial support. References [1] Persaud, K.; Dodd, G. Nature 1982, 299, 352. [2] Carey, W. P.; Beebe, K. R.; Kowalsi, B. R. Anal. Chem. 1986, 58, 149. [3] Sundgren, H.; Lundstrom, I.; Winquist, F. Sensors and Actuators B 1990, 2, 115. [4] Krebs, P.; Grisel, A. Sensors and Actuators B 1993, 13, 155. [5] Wang, X.; Yee, S.; Carey, P. Sensors and Actuators B 1993, 13-14, 458. [6] Lu, C. J.; Shih, J. S. Analytica Chimica Acta 1995, 306, 129. [7] Sheng, H. J.; Shih, J. S. Analytica Chimica

9 The Application of Bac Propagation Neural Networ of Multi-channel Piezoelectric Quartz Crystal Sensor 217 Acta 1997, 350, 109. [8] Jane, Y. S.; Shih, J. S. Analyst 1995, 120, 517. [9] Chao, Y. C.; Shih, J. S. Analytica Chimica Acta 1998, 374, 39. [10] Chiou, C. S.; Shih, J. S. Analytica Chimica Acta 1998, 360, 69. [11] Chang, P.; Shih, J. S. Analytica Chimica Acta 1999, 380, 55. [12] Chang, P.; Shih, J. S. Analytica Chimica Acta 1998, 360, 61. [13] Sauerbrey, G. Z. Z. Phys. 1959, 155, 206. [14] Sauerbrey, G. Z. Z. Phys. 1964, 178, 457. [15] Chang, P.; Shih, J. S. Analytica Chimica Acta 2000, 403, 39. [16] Taylor, M.; Lisboa, P. Techniques and Application of Neural Networs 1993, Ellis Horwood. [17] Rumelhart, D. E.; McClelland, J. L. Parallel Distributed Processing; MIT Press, U.S.A [18] ftp://ftp.sas.com/pub/neural/faq.html Manuscript Received: Sept. 11, 2002 and Accepted: Oct. 11, 2002