Rapid and Simultaneous Detection of Petroleum Hydrocarbons and Organic Pesticides in Soil Based on Electronic Nose
The rapid detection of petroleum hydrocarbons and organic pesticides is an important prerequisite for precise soil management. It is also a guarantee for soil quality, environmental safety, and human health. However, the current rapid detection methods are prone to sample matrix interference, comple...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/380 |
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| Summary: | The rapid detection of petroleum hydrocarbons and organic pesticides is an important prerequisite for precise soil management. It is also a guarantee for soil quality, environmental safety, and human health. However, the current rapid detection methods are prone to sample matrix interference, complex development processes, short lifespan, and low detection accuracy. Moreover, they face difficulties in achieving simultaneous detection of petroleum hydrocarbons and organic pesticides. In this paper, we developed an electronic nose system for the simultaneous detection of petroleum hydrocarbons and organic pesticides in soil based on gas technology, which includes a sampling module and recognition model. The developed sampling module can simultaneously acquire the odor signals of petroleum hydrocarbons and organic pesticides in soil. The established recognition model can quickly distinguish between healthy soil, soil contaminated by petroleum hydrocarbons, and soil contaminated by organic pesticides. It can also achieve specific recognition of pesticide types and petroleum types. The performance of the developed electronic nose system was verified for real soil, petroleum products, and organic pesticides. The experiment shows that the developed electronic nose system has an accuracy of 100% for three tasks: soil conditions identification, pesticide types identification, and petroleum types identification. |
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| ISSN: | 1424-8220 |