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Gas detection is crucial in environmental monitoring, industrial safety, and public health, but conventional detection methods are limited by high costs, complex operation, and poor portability. To address these issues, this paper proposes a low-cost gas detection strategy based on machine olfaction technology. The strategy integrates an inexpensive metal oxide semiconductor (MOS) sensor array, a simple signal preprocessing module, and lightweight machine learning algorithms to mimic the biological olfactory system’s gas recognition capability. Unlike traditional expensive analytical instruments such as gas chromatography, the proposed system adopts low-cost, miniaturized components, significantly reducing hardware costs while ensuring detection accuracy. Experimental results show that the system can effectively identify multiple target gases (including volatile organic compounds and hazardous gases) with a classification accuracy of over 90%. It features fast response speed, low power consumption, and easy integration, making it suitable for on-site, real-time detection in resource-constrained scenarios. This strategy provides an economical and efficient solution for gas detection, promoting the widespread application of machine olfaction technology in environmental protection, industrial production, and daily safety monitoring.
