Improved methods for extracting intelligence linking one sample of energetic material to another or identifying the location and/or method of manufacture are important. By investigating several analytical techniques and chemometric analysis, this research aims to assist in developing methodology which may provide robust proof of such linkages. Samples were prepared utilising clandestine methods to replicate real-world variability as a result of differing starting materials or manufacturing procedures. These samples were subjected to a wide range of analytical techniques including isotope ratio mass spectrometry (IR-MS), inductively coupled plasma mass spectrometry (ICP-MS), Raman and infrared spectroscopy (IR) in order to investigate the characteristic signatures of improvised energetic materials. The spectroscopic and spectrometric 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 discrimination power of the statistical analysis, thereby capturing the unique traits or chemical ‘fingerprint’ of the material. Principal component analysis (PCA) was the primary statistical method used. Combining these two goals through a multivariate and exploratory analysis such as PCA, there is the potential to condense data and extract the maximal amount of value from it. The relative contributions of analysis techniques or aspects of the data collected towards the discriminatory capabilities of a dataset may also be assessed, leading to optimisation of the methodology. For example, every additional element selected for ICP-MS analysis adds a significant amount of time and cost in regard to sample preparation and method validation. The chemical ‘fingerprints’ 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 of samples where discrimination between samples is desired, not just energetic materials, e.g., identifying the provenance of food.

Chemometric analysis of spectroscopic and spectrometric data from energetic materials

2019 Fall National Meeting

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