Author: Roy Lehmann
Lehmann, Roy, 2021 Comparison and discrimination of energetic materials via multiple analytical techniques and chemometrics, Flinders University, College of Science and Engineering
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Improved methods for extracting intelligence linking one sample of energetic material to another or identifying the location and method of manufacture are important. By investigating several analytical techniques with chemometric analysis, this research aims to assist in developing methodologies which may provide indications of such linkages.
Samples were prepared utilising methods observed in clandestine manufacturing, to replicate real-world variability due to differing starting materials or manufacturing procedures. These samples were subjected to a wide range of analytical techniques to investigate characteristic signatures within improvised energetic materials. The techniques included isotope ratio mass spectrometry (IR-MS), inductively coupled plasma mass spectrometry (ICP-MS), Raman and infrared (IR) spectroscopy.
The spectrometric and spectroscopic data collected was analysed through chemometric means to accomplish two goals. Firstly, to establish the quality of data obtained through each analytical technique. Secondly, to enhance each dataset by combining them to increase the discriminatory power of the data analysis, thereby capturing the unique traits and chemical ‘fingerprint’ or profile of the material. Principal component analysis (PCA) was the primary method of analysis used, as it is an unsupervised analysis better suited for the real-world application of extracting intelligence from sample data where the identity is unknown.
Combining these goals through the exploratory multivariate data analysis, PCA, there is the potential to condense data and extract the maximum value from it. The relative contributions of analysis techniques were also assessed, leading to method optimisation. For example, every additional element selected for ICP-MS analysis adds a significant amount of time, cost, and resources in regard to sample analysis and method validation. Any additional element further complicates the multivariate analysis so the number of elements should be optimised for to save time or reduce cost.
Chemical profiles enable the comparison of newly and previously acquired sample data with high fidelity and a measure of confidence that samples, which may have been collected at different locations and times, have a common origin. This process can be applied to large databases where discrimination between samples is desired.
This research project investigated each of these aspects and the results confirm the ability for the chemometric analysis of spectrometric and spectroscopic datasets to yield discriminatory information from both independent and combined datasets. The analysis also identifies where the discriminatory information comes from within each dataset. This allows a more targeted analysis and comparison of samples on a greatly reduced number of variables. Clear clustering of related samples was identified using an unsupervised multivariate analysis, rather than a supervised discriminatory analysis such as LDA, which would favour clustering. This is ideal in a real-world setting where the identities and relationships between samples are likely unknown prior to analysis.
Keywords: Energetic Material, Explosives, Chemometrics, Principal Component Analysis, Isotope Ratio Mass Spectrometry, Inductively Coupled Plasma Mass Spectrometry, Infrared Spectroscopy, Raman Spectroscopy, THz Spectroscopy, Far Infrared Spectroscopy, Potassium Chlorate, Erthyitol Tetranitrate, Ammonium Nitrate
Subject: Forensic & Analytical Chemistry thesis
Thesis type: Doctor of Philosophy
Completed: 2021
School: College of Science and Engineering
Supervisor: Stewart Walker