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Compound Identification Using Random Projection for Gas Chromatography-Mass Spectrometry Data

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Advanced Intelligent Computing Theories and Applications (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9227))

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Abstract

In general, compound identification through library searching is performed on original mass spectral space by using some developed similarity measure. In this paper, the original mass spectral space was transformed into binary space by random projection. The hamming distance between query and reference the vector of binary space are calculated. The Mass Spectral Library 2005 (NIST05) main library is used as reference database and the replicate library is used as query data. With the number of binary digits increasing, the accuracy of compound identification is also increased. When the number set as 2076 bits, random projection achieve better identification performance than corresponding three similarity measures.

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Acknowledgments

This work was supported by National Natural Science Foundation of China under grant nos. 61271098 and 61032007, and Provincial Natural Science Research Program of Higher Education Institutions of Anhui Province under grant no. KJ2012A005.

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Authors and Affiliations

  1. School of Electronic Engineering and Automation, Anhui University, Hefei, 230601, Anhui, China

    Li-Li Cao, Zhi-Shui Zhang, Peng Chen & Jun Zhang

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  1. Li-Li Cao
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  2. Zhi-Shui Zhang
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  3. Peng Chen
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  4. Jun Zhang
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Corresponding author

Correspondence to Jun Zhang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Incheon, Korea (Republic of)

    Kyungsook Han

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Cao, LL., Zhang, ZS., Chen, P., Zhang, J. (2015). Compound Identification Using Random Projection for Gas Chromatography-Mass Spectrometry Data. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_71

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  • DOI: https://doi.org/10.1007/978-3-319-22053-6_71

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22052-9

  • Online ISBN: 978-3-319-22053-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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