Multivariate analysis of statistically poor EDXRD spectra for the detection of concealed explosives

✍ Scribed by R. D. Luggar; M. J. Farquharson; J. A. Horrocks; R. J. Lacey

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 535 KB
• Volume: 27
• Category: Article
• ISSN: 0049-8246
• DOI: 10.1002/(sici)1097-4539(199803/04)27:2<87::aid-xrs256>3.0.co;2-0

✦ Synopsis

Energy dispersive x-ray di †raction (EDXRD) has been developed as a tool for the detection of explosives in passenger baggage. The measured spectra result from the combined di †raction from each of the materials within a scattering volume. Multivariate regression was used to identify known components within very noisy data, permitting the rapid detection of explosive materials in the presence of overlying media for security screening applications. Explosives can be positively identiÐed in spectra containing as few as several hundred counts and the error associated with the prediction is consistent from statistically reliable data (106 integrated counts) down to spectra containing in the region of 103 counts. This analysis can be employed in any situation where qualitative information is required from poor quality spectral data.