electronic nose; machine learning; sensors
This dataset is the original resistance data and gas response data of 11 VOCs in 8 commercial gas sensors collected by the self-developed G919 electronic nose device. Eleven types of VOCs were detected, including acetone, ethanol, butyl acetate, methanol, dimethylbenzene, isopropanol, methylbenzene, benzaldehyde, hexane, n-propanol and ethylene glycol. Eight commercial gas sensors were employed, including MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, MQ-8, MQ-9.
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Response data for ammonia and ethanol gases was collected using the electronic nose system.To meet the need for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment in livestock and poultry breeding areas, the electronic nose detection system solved the problems of poor real-time performance and low detection accuracy. An active pumping ammonia detection artificial olfactory system based on a bionic chamber is proposed. The sensing unit of the system consists of a bionic chamber and eight sensors.
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The e-nose device used in this study was constructed using a gas sensor array, LCD display, micro air pumps for inhalation and exhalation, a microcontroller, and a mini-PC. Gas samples from the sample chamber were periodically drawn into the device through a hose. Each sample underwent a 30-hour sampling process at room temperature (25°C). The sampling frequency was 15 times per hour, resulting in 60 records per sample.
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