4.6 Article

Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose

期刊

APPLIED SCIENCES-BASEL
卷 11, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/app11052337

关键词

electronic nose; livestock farms; VOC detection; odor identification

资金

  1. National Natural Science Found of China [51875245, 61805099]
  2. Fundamental Research Funds for the Central Universities
  3. Science-Technology Development Plan Project of Jilin Province [20190303012SF, 20200201228JC, 20190201019JC, 20200403038SF]
  4. 13th Five-Year Plan Scientific Research Foundation of the Education Department of Jilin Province [JJKH20211119KJ, JJKH20201006KJ]

向作者/读者索取更多资源

This study utilized electronic nose technology to analyze volatile organic compounds (VOCs) in livestock farms, confirming that Support Vector Machine (SVM) is a more effective method for recognition. Gas chromatography-mass spectrometry (GC-MS) and electronic nose were used to analyze VOCs in pig and chicken houses at different time and locations, demonstrating the promise of the developed electronic nose sensor for characterizing volatile odors in livestock farms.
The composition of volatile organic compounds (VOCs) in large-scale livestock farms is complex, which seriously affects the health of livestock and is difficult to evaluate. In order to quickly analyze the pollution degree of VOCs in livestock farms, electronic nose technology was used in this study to detect and analyze the gases in pig and chicken houses, respectively. Firstly, the gas chromatography-mass spectrometry (GC-MS) and electronic nose were used to analyze the VOCs in the pig and chicken houses at different time and locations. The types and relative contents of VOCs were obtained from different livestock farms by GC-MS analysis. The sensor array response of the electronic nose showed similar results. In addition, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machine (SVM) analyses were performed on the electrical signal that was generated by the sensors of electronic nose, respectively. Finally, the classification rate of different odor sources in livestock farms was the highest (>85%), which indicates that SVM is a more effective method suitable for volatile gases recognition in livestock farms. The results have shown that the developed electronic nose sensor is a promising and feasible instrument for characterizing volatile odors in livestock farms.

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