Citation

Frisch-Daiello JL, Williams MR, Waddell EE, Sigman ME. Forensic Sci. Int. 2014; 236: 84-89.

Affiliation

National Center for Forensic Science and Department of Chemistry, College of Science, University of Central Florida, P.O. Box 162367, Orlando, FL 32816-2367, United States. Electronic address: michael.sigman@ucf.edu.

Copyright

(Copyright © 2014, Elsevier Publishing)

DOI

10.1016/j.forsciint.2013.12.026

PMID

24529778

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 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 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.

Language: en