# Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose
## 4 Conclusions  
In this paper, GC-MS and electronic nose were used to explore odor identity in livestock farms of Northeastern China. The types and distribution of VOCs in different livestock houses have been analyzed. A total of more than 70 substances and 30 substances have been detected by GC-MS in the nursery piggery and chicken house, respectively. Moreover, the highest level of VOCs was obtained during the morning at the two localities. Alkanes and aldehydes play the most important role of VOC pollution in nursery pigs according to the measured results, and the release of alkanes makes up main VOC pollution in the chicken houses. What is more, more types of gas will be produced when chickens eat. Furthermore, the response of the electronic nose sensor array shows that the main VOC components except ammonia and hydrogen sulfide in pregnancy houses and nursery houses are also different. There are more amine series and sulfur-containing odors in pregnancy houses, whereas nursery houses have more combustible gases. On the other hand, the smell characteristics in the piggery are obviously more complicated than that of the chicken house, but the proportion of ammonia and hydrogen sulfide in the chicken house is greater than that of the piggery. The electronic nose can clearly distinguish the fingerprints of pigs and chickens, as well as subtle differences between them. In addition, the electronic nose combines with SVM can evaluate the VOC odor attributes of livestock farms more comprehensively. The research results mentioned above can not only provide a reference for the treatment of VOC pollution in livestock farms, but also can prove that the electronic nose sensor we developed is a feasible instrument for characterizing the odor of livestock farms, and it is very promising for applications in husbandry, chemical analysis and the food industry.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **KNN/SVM** & **Moming** & **Afternoon** \\ \hline Average & KNN & 85.71\% & 98.72\% \\  & SVM & 97.62\% & 98.72\% \\  & KNN & 85.71\% & 100.00\% \\ Maximum & SVM & 95.24\% & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The accuracy for odor classification in different sampling locations of chicken house at the same sampling time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **KNN/SVM** & **Pig House** & **Chicken House** \\ \hline Average & KNN & 60\% & 78.33\% \\  & SVM & 74.29\% & 93.33\% \\  & KNN & 58.78\% & 83.33\% \\ Maximum & SVM & 73.47\% & 93.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The accuracy for odor classification in different sampling locations and different sampling times of pig and chicken houses.  
**Author Contributions:** Conceptualization, Z.C.; methodology, X.W. and Y.L.; software, C.K.; validation, C.L. and Y.X.; formal analysis, C.K.; investigation, X.W.; resources, Z.C.; data curation, H.J.; writing--original draft preparation, X.W. and D.C.; writing--review and editing, C.L. and L.R.; visualization, Y.L.; supervision, Z.C.; project administration, C.L.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Found of China (51875245, 61805099), the Fundamental Research Funds for the Central Universities, the Science-Technology Development Plan Project of Jilin Province (20190303012 SF, 20200201228 JC, 20190201019C, 20200403088 SF), the \"13 th Five-Year Plan\" Scientific Research Foundation of the Education Department of Jilin Province (JKH2021119 KJ, JJKH20201006 KJ).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to not have suitable server for publicly store data.  
**Conflicts of Interest**: The authors declare no conflict of interest.