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Citation: CHEN Zhenbang, JIN Jing. Application of Fisher discrimination in gas chromatography-mass spectrometry analysis on combustion residues of typical combustion improvers[J]. Chinese Journal of Chromatography, ;2016, 34(11): 1106-1112. doi: 10.3724/SP.J.1123.2016.06017 shu

Application of Fisher discrimination in gas chromatography-mass spectrometry analysis on combustion residues of typical combustion improvers

CHEN Zhenbang ^1, ,
JIN Jing ²

1.
Department of Graduate Student, Chinese People's Armed Police Forces Academy, Langfang 065000, China;
2.
Department of Fire Protection Engineering, Chinese People's Armed Police Forces Academy, Langfang 065000, China

Received Date: 12 June 2016

Fund Project: National Scientific and Technological Foundational Work Special Project (No. SQ2015FY3120051).

To find out a more accurate and effective pattern recognition method for the identification of combustion residues of typical combustion improvers, gas chromatography-mass spectrometry (GC-MS) was applied to analyze the combustion residues of seven combustion improvers loaded on different carriers and one submitted sample, and gasoline was identified directly by the GC-MS analysis from the unknown specimen. Two discrimination methods, including Fisher discrimination and PCA (principal component analysis) combined with Fisher discrimination, were used for the data analysis. The results showed that nitro paint thinner was discriminated from the submitted sample by PCA combined with Fisher discrimination, while gasoline was discriminated from unidentified sample by Fisher discrimination, which was in accordance with the direct GC-MS identification result. By comparing the results obtained by these two discrimination methods, Fisher discrimination is believed to be more efficient in classification of combustion residues of the seven combustion improvers and identification of the submitted sample. The results of this study provide a new analytical method for the combustion improvers identification.
- gas chromatography-mass spectrometry (GC-MS),
- principal component analysis (PCA),
- Fisher discrimination,
- combustion improvers,
- evidence identification
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