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

NCJ Number
304190
Journal
Forensic Science International Volume: 236 Dated: 2014 Pages: 84-89
Author(s)
Jessica L. Frisch-Daiello ; Mary R. Williams ; Erin E. Waddell ; Michael E. Sigman
Date Published
2014
Length
6 pages
Annotation

In this project, the unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) was 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.

 

Abstract

The spectral data consisted 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 inter-laboratory 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 were 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. (publisher abstract modified)

Date Published: January 1, 2014