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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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DOI: https://doi.org/10.15866/irea.v11i5.23904

Abstract


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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Keywords


Electronic Nose; Surface Acoustic Wave; Volatile Compound Organic; Polymer; Artificial Neural Network

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References


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