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Development of Surface Acoustic Wave Sensor Electronic Nose for the Identification of Volatile Compound Organic Using Artificial Neural Network

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The electronic nose is an electronic instrumentation system that is made to mimic the working system of the human olfactory system. The e-nose device is designed to detect the smell of gas, which cannot be detected by the human nose due to several reasons such as safety and health. The e-nose design usually uses a semiconductor sensor to get data input. The use of semiconductor sensors has several weaknesses, including being less sensitive to low gas concentrations. In addition, one sensor can only detect certain gases, so if the system is used to detect several gas odors, many sensors are needed, which results in an expensive e-nose system. This research develops an e-nose system using a Surface Acoustic Wave sensor array that works by using an acoustic wave sensor at a resonant frequency of 46 MHz so that it is quite sensitive to changes in the smell of gas at very low concentrations and is not owned by semiconductor sensors in general. Therefore, the sensor is sensitive to the type of Volatile Compound Organic vapor flowing on the surface of the sensor. The sensor is coated with polymer. The polymers used in this study have been OV-101 on the 1st sensor, PEG-1540 on the 2nd sensor, and OV-17 on the 3rd sensor. Steam flowing on the surface of the sensor will produce different patterns depending on the type of steam. Then these patterns are analyzed and identified by using an Artificial Neural Network with backpropagation learning. The significance of this research is to offer a low-cost, high-accuracy Volatile Compound Organic identification tool. The test results show that ANN with Surface Acoustic Wave sensor provides convenience and advantages in identifying the type of vapor. Identification of the developed electronic nose achieves success of 93.3%.
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Electronic Nose; Surface Acoustic Wave; Volatile Compound Organic; Polymer; Artificial Neural Network

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Casalinuovo, I. A., Di Pierro, D., Bruno, E., Di Francesco, P., Coletta, M., Experimental use of a new surface acoustic wave sensor for the rapid identification of bacteria and yeasts, Lett. Appl. Microbiol., vol. 42, no. 1, pp. 24-29, 2006.

Mahmud, M. M. et al., A Low-Power Wearable E-Nose System Based on a Capacitive Micromachined Ultrasonic Transducer (CMUT) Array for Indoor VOC Monitoring, IEEE Sens. J., vol. 21, no. 18, pp. 19684-19696, 2021.

Ratiu, I.-A., Bocos-Bintintan, V., Monedeiro, F., Milanowski, M., Ligor, T., Buszewski, B., An optimistic vision of future: Diagnosis of bacterial infections by sensing their associated volatile organic compounds, Crit. Rev. Anal. Chem., vol. 50, no. 6, pp. 501-512, 2020.

Chilo, J., Pelegri-Sebastia, J., Cupane, M., Sogorb, T., -nose application to food industry production," IEEE Instrum. Meas. Mag., vol. 19, no. 1, pp. 27-33, 2016.

Kukade, M. V, Moshayedi, A. J., Gharpure, D. C., Electronic-nose (E-nose) for recognition of Cardamom, Nutmeg and Clove oil odor, Electron. its Interdiscip. Appl., vol. 2, 2014.

Li, X. et al., Evolution of volatile compounds and spoilage bacteria in smoked bacon during refrigeration using an E-Nose and GC-MS combined with partial least squares regression, Molecules, vol. 23, no. 12, p. 3286, 2018.

Byun, H., Persaud, K. C., Pisanelli, A. M., Wound‐State Monitoring for Burn Patients Using E‐Nose/SPME System, ETRI J., vol. 32, no. 3, pp. 440-446, 2010.

Binson, V. A., Subramoniam, M., Design and development of an e-nose system for the diagnosis of pulmonary diseases., Acta Bioeng. Biomech., vol. 23, no. 1, 2021.

Li, W., Jia, Z., Xie, D., Chen, K., Cui, J., Liu, H., Recognizing lung cancer using a homemade e-nose: A comprehensive study, Comput. Biol. Med., vol. 120, p. 103706, 2020.

Lekha, S., Suchetha, M., Recent advancements and future prospects on e-nose sensors technology and machine learning approaches for non-invasive diabetes diagnosis: A review, IEEE Rev. Biomed. Eng., vol. 14, pp. 127-138, 2020.

Seesaard, T., Sriphrapradang, C., Kitiyakara, T., Kerdcharoen, T., Self-screening for diabetes by sniffing urine samples based on a hand-held electronic nose, in 2016 9th Biomedical Engineering International Conference (BMEiCON), 2016, pp. 1-4.

Sarno, R., Sabilla, S. I., Wijaya, D. R., Electronic Nose for Detecting Multilevel Diabetes using Optimized Deep Neural Network., Eng. Lett., vol. 28, no. 1, 2020.

Choden, P., Seesaard, T., Eamsa-Ard, T., Sriphrapradang, C., Kerdcharoen, T., Volatile urine biomarkers detection in type II diabetes towards use as smart healthcare application, in 2017 9th International Conference on Knowledge and Smart Technology (KST), 2017, pp. 178-181.

Yu, H., Wang, J., Zhang, H., Yu, Y., Yao, C., Identification of green tea grade using different feature of response signal from E-nose sensors, Sensors Actuators B Chem., vol. 128, no. 2, pp. 455-461, 2008.

Omatu, S., Yano, M., E-nose system by using neural networks, Neurocomputing, vol. 172, pp. 394-398, 2016.

Adak, M. F., Yumusak, N., Classification of E-nose aroma data of four fruit types by ABC-based neural network, Sensors, vol. 16, no. 3, p. 304, 2016.

Shahid, A., Choi, J.-H., Rana, A. U. H. S., Kim, H.-S., Least squares neural network-based wireless E-Nose system using an SnO2 sensor array, Sensors, vol. 18, no. 5, p. 1446, 2018.

Baskar, C., Nesakumar, N., Rayappan, J. B. B., Doraipandian, M., A framework for analysing E-Nose data based on fuzzy set multiple linear regression: Paddy quality assessment, Sensors Actuators A Phys., vol. 267, pp. 200-209, 2017.

Herrero, J. L., Lozano, J., Santos, J. P., Fernández, J. Á., Marcelo, J. I. S., A web-based approach for classifying environmental pollutants using portable E-nose devices, IEEE Intell. Syst., vol. 31, no. 3, pp. 108-112, 2016.

Yolanda, G.M.M., Concepcion, C.O., Jose, L.P.P., Carmelo, G.P., Bernardo, M.C., Electronic Nose Based On Metal Oxide Semiconductor Sensors And Pattern Recognition Techniques: Characterisation Of Vegetable Oils. Analytica Chimica Acta, Vol. 449, pp. 69-80, 2001.

Kodogiannis, V.S., John, N.L., Andrzej, T., and Hardial, S.C., Artificial Odor Discrimination System Using Electronic Nose and Neural Networks for the Identification of Urinary Tract Infection. IEEE Transactions On Information Technology In Biomedicine, Vol. 12, No. 6, November, 2008.

Kea, T.T., Cheng, H.L., Shi, W.C., An Electronic-Nose Sensor Node Based on a Polymer Coated Surface Acoustic Wave Array for Wireless Sensor Network Applications. Sensors 11, pp 4609-4621, 2011.

Voiculescu, I., Nordin, A. N., Acoustic wave based MEMS devices for biosensing applications, Biosens. Bioelectron., vol. 33, no. 1, pp. 1-9, 2012.

Zheng, L., Shao, L., Loncar, M., Lai, K., Imaging acoustic waves by microwave microscopy: Microwave impedance microscopy for visualizing gigahertz acoustic waves, IEEE Microw. Mag., vol. 21, no. 10, pp. 60-71, 2020.

Pitanti, A. et al., High-frequency mechanical excitation of a silicon nanostring with piezoelectric aluminum nitride layers, Phys. Rev. Appl., vol. 14, no. 1, p. 14054, 2020.

Ho CK, Lindgren ER, Rawlinson KS, McGrath LK, Wright JL. Development of a Surface Acoustic Wave Sensor for In-Situ Monitoring of Volatile Organic Compounds. Sensors. 2003; 3(7):236-247.

Bambang, K., Chemical Sensors Theory, Practice and Applications. Jember: Chemistry Department, Pharmacy Study Program, UNEJ, 2008.

Tarikhul, I., Upendra, M., Nimal, A.T., Sharma, M.U., Surface Acoustic Wave (SAW) Vapour Sensor using 70 MHz SAW Oscillator. Sixth International Conference on Sensing Technology (ICST). pp 112-114, 2012.

Yuri, V., Andrew, L., Non-Selective Chemical Sensors In Analytical Chemistry: From "Electronic Nose" to "Electronic Tongue", Journal of Analytical Chemistry, 361, pp. 255-260, 2008.

Gardner, J.W., Hines, E.L., and Tang, H.C., Detection of Vapours and Odours From A Multisensor Array Using Pattern-Recognition Techniques Part 2. Artificial neural networks, Sensors and Actuators B, 9 pp. 9- I5, 2012.

Fikri, M., Sabri, O., Cheddadi, B., Using Artificial Neural Network to Speed Up the Study of the State of Electrical Systems, (2022) International Review of Electrical Engineering (IREE), 17 (5), pp. 495-503.

Elena, D., Violeta, C.N., Volatile Organic Compounds (VOCs) as Environmental Pollutants: Occurrence and Mitigation Using Nanomaterials, Int J Environ Res Public Health. Dec; 18(24): 13147, 2021.

Carlos, C., Daniel,M., Cristina, R., Isidr, .BR., Emmanuel de la O,C., Jose, M.S and Mari, C,H., Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2, Sensors 22, 1261, 2022.

Harish, K.G., Manoj, V., Solar air heaters performance prediction using multi-layer perceptron neural network- A systematic review, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 43, no. 24, pages: 1556-7036, 2021.

Shatnawi, N., Al-Omari, A., Alkhateeb, S., Prediction of Risk Factors Influencing Severity Level of Traffic Accidents Using Artificial Intelligence, (2023) International Review of Civil Engineering (IRECE), 14 (1), pp. 1-7.

Katruksa, S., Jiriwibhakorn, S., Evaluation of Mid-Term Load Forecasting Case Study Based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs), (2020) International Review of Electrical Engineering (IREE), 15 (4), pp. 283-293.

Wijaya, D., Sarno, R., Zulaika, E., Sensor Array Optimization for Mobile Electronic Nose: Wavelet Transform and Filter Based Feature Selection Approach, (2016) International Review on Computers and Software (IRECOS), 11 (8), pp. 659-671.

Qasim, M., Velkin, V., Maximum Power Point Tracking Techniques for Micro-Grid Hybrid Wind and Solar Energy Systems - a Review, (2020) International Journal on Energy Conversion (IRECON), 8 (6), pp. 223-234.

Habib, T., Abouhogail, R., Modelling of Spacecraft Orbit via Neural Networks, (2021) International Review of Aerospace Engineering (IREASE), 14 (5), pp. 285-293.

Hanandeh, S., Khliefat, I., Hanandeh, R., Alhomaidat, F., Modelling the Free Flow Speed and 85th Percentile Speed Using Artificial Neural Network (ANN) and Genetic Algorithm, (2022) International Review of Civil Engineering (IRECE), 13 (4), pp. 296-308.

Belkhiri, D., Alaoui, M., Improved Tracking of Optimal Torque by Artificial Neural Network for Wind Energy Systems, (2021) International Review on Modelling and Simulations (IREMOS), 14 (2), pp. 110-117.


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