Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions

    Authors

    J. L. Frisch-Daiello; M. R. Williams; E. E. Waddell;M. E. Sigman

    Comments

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    Abbreviated Journal Title

    Forensic Sci.Int.

    Keywords

    Ignitable liquids; Fire debris; Self-organizing feature maps; Gas; chromatography-mass spectrometry; Total ion spectrum; Extracted ion; spectrum; ARTIFICIAL NEURAL-NETWORKS; FIRE DEBRIS ANALYSIS; CLASSIFICATION; Medicine, Legal

    Abstract

    The unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) is applied to spectral data of ignitable liquids to visualize the grouping of similar ignitable liquids with respect to their American Society for Testing and Materials (ASTM) class designations and to determine the ions associated with each group. The spectral data consists of extracted ion spectra (EIS), defined as the time-averaged mass spectrum across the chromatographic profile for select ions, where the selected ions are a subset of ions from Table 2 of the ASTM standard E1618-11. Utilization of the EIS allows for interlaboratory comparisons without the concern of retention time shifts. The trained SOFM demonstrates clustering of the ignitable liquid samples according to designated ASTM classes. The EIS of select samples designated as miscellaneous or oxygenated as well as ignitable liquid residues from fire debris samples are projected onto the SOFM. The results indicate the similarities and differences between the variables of the newly projected data compared to those of the data used to train the SOFM. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

    Journal Title

    Forensic Science International

    Volume

    236

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    84

    Last Page

    89

    WOS Identifier

    WOS:000331198500016

    ISSN

    0379-0738

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