Discriminate Model of Electronic Nose for Distinguishing Volatiles of Microbial Fermentation Bed in Swine House

LAN Jiang-lin; LIU Bo; CHEN Zheng; SONG Ze-qiong; LIN Juan

Volume 27 Issue 1

Mar. 2012

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Article Navigation > Fujian Journal of Agricultural Sciences > 2012 > 27(1): 77-86

LAN Jiang-lin, LIU Bo, CHEN Zheng, SONG Ze-qiong, LIN Juan. Discriminate Model of Electronic Nose for Distinguishing Volatiles of Microbial Fermentation Bed in Swine House[J]. Fujian Journal of Agricultural Sciences, 2012, 27(1): 77-86.

Citation:

LAN Jiang-lin, LIU Bo, CHEN Zheng, SONG Ze-qiong, LIN Juan. Discriminate Model of Electronic Nose for Distinguishing Volatiles of Microbial Fermentation Bed in Swine House[J]. Fujian Journal of Agricultural Sciences, 2012, 27(1): 77-86.

Citation:

LAN Jiang-lin, LIU Bo, CHEN Zheng, SONG Ze-qiong, LIN Juan. Discriminate Model of Electronic Nose for Distinguishing Volatiles of Microbial Fermentation Bed in Swine House[J]. Fujian Journal of Agricultural Sciences, 2012, 27(1): 77-86.

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Discriminate Model of Electronic Nose for Distinguishing Volatiles of Microbial Fermentation Bed in Swine House

1.
Agricultural Bioresource Research Institute, Fujian Academy of Agricultural Sciences, Fuzhou, Fujian 350003,China;
2.
College of Biological Science and Technology, Fuzhou University, Fuzhou,Fujian 350108,China

Received Date: 2011-09-21
Rev Recd Date: 2011-11-18
Publish Date: 2012-01-30

Abstract

Abstract

Volatile components from microbial fermentation bed in pig farming were analyzed with gas chromatography-mass spectrometry, results show that 22 components are separated and identified in 30 samples(matching degree≥90,relative content≥4%), in which based on the kind and content in the components 4 types were clustered, corresponding to 4 ferment level respectively e.g. First level was characterized with shallaw degree of fermentation;Second level with medium degree;Third level with deep degree;Fourth level with profound. Using cluster analysis and discrimination analysis to build up the discrimination model.The accurate rate for discrimination model was up to 96.67%. utilizing the mathematical equation to determine the fermentation index of unknown pig litter, the degree of fermentation of unknown litter were judged fastly and accurately.The model of ferment level for pig litter was built up to provide the experience for computer-based standardizing analysis for scientific management of Pig Litter in the Microbial Fermentation Bed, further more with the new idea for pig breeding.
- microbial fermentation bed,
- volatiles,
- electronic nose,
- discriminant model

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References(7)

References

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