Fast prediction of odor concentration along pig manure chain based on machine learning: Monitoring 20 instead of over 100 odorous substances

文献类型： 外文期刊

第一作者： Cao, Tiantian

作者： Cao, Tiantian;Zheng, Yunhao;Shang, Bin;Cong, Qunxin;Cao, Qitao;Dong, Hongmin;Cao, Tiantian;Zheng, Yunhao;Shang, Bin;Cong, Qunxin;Cao, Qitao;Dong, Hongmin

作者机构：

关键词： Pig manure; Odor concentration; Machine learning; Ammonia; Volatile organic compounds

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 233 卷

页码：

收录情况： SCI

摘要： Pig production is the main source of odor emissions. Owing to the complex composition of odors and the timeconsuming and laborious process of measuring odor concentration (OC), identifying odorous substances, and quantifying odor concentration are technical bottlenecks in odor reduction. This study innovatively constructed a machine learning (ML) model to predict OC based on key odorous components. Over 400 gas samples were collected from the whole pig manure management chain in different regions, and 128 odor components were determined. The prediction results showed that extreme random tree regression ML had superior predictive performance for OC, with a better determination coefficient (R-2 > 0.8) and fewer features. 20 key odor substances out of over 100 components were the important features contributing to OC based on the Shapley additive explanation. Dimethyl sulfide, ammonia, hydrogen sulfide, ethyl sulfide, acetylene, and hexaldehyde were found to have the most significant impact on OC. This method can be easily extended to other types of farms, such as cattle and chicken, and provides a scientific basis for the research and development of qualitative and quantitative odorous substances and equipment for the rapid determination of odor concentration.

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