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The source identification and classification study of soot after combustion

Authors

  • Youran Zhi,

    1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui, China
    2. Suzhou Key Laboratory of Urban Public Safety, Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu, China
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  • Ruowen Zong,

    Corresponding author
    1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui, China
    • Suzhou Key Laboratory of Urban Public Safety, Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu, China
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  • Liao Guangxuan,

    1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui, China
    2. Suzhou Key Laboratory of Urban Public Safety, Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu, China
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  • Haiqiang Liu,

    1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui, China
    2. Suzhou Key Laboratory of Urban Public Safety, Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, Jiangsu, China
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  • Tan Jialei

    1. Beijing Municipal Institute of Labour Protection, Beijing, China
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Correspondence to: Ruowen Zong, State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui 230026, China.

E-mail: zongrw@ustc.edu.cn

ABSTRACT

Solid phase microextraction-gas chromatography-mass spectroscopy technology was used to extract and analyze three kinds of accelerant soot including diesel soot, gasoline soot, and diesel–gasoline mixture soot. A total of 60 spectrograms have been obtained, analyzed, and compared. It was found that these kinds of soot were quite different with each other in the difference of major target compounds and retention time span, and could be visually identified by the profile of the corresponding spectrogram. A data matrix of 60 * 41 was reached by the characteristic substances corresponding with the retention time in all these 60 spectrograms. With principal component analysis method, two major component variables were obtained to classify the attribution of soot, with perfect classification efficiency of 100%. Hierarchical cluster analysis was further applied for the dendrogram analysis. In spite of the absence of the training set, the classification of 100% accuracy of these kinds of soot could be achieved. Copyright © 2012 John Wiley & Sons, Ltd.

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