Discrimination of non-explosive and explosive samples through nitrocellulose fingerprints obtained by capillary electrophoresis

摘要

This work is focused on a novel procedure to discriminate nitrocellulose-based samples with non-explosive and explosive properties. The nitrocellulose study has been scarcely approached in the literature due to its special polymeric properties such as its high molar mass and complex chemical and structural characteristics. These properties require the nitrocellulose analysis to be performed by using a few organic solvents and in consequence, they limit the number of adequate analytical techniques for its study. In terms of identification of pre-blast explosives, mass spectrometry is one of the most preferred technique because it allows to obtain structural information. However, it has never been used to analyze polymeric nitrocellulose. In this study, the differentiation of non-explosive and explosive samples through nitrocellulose fingerprints obtained by capillary electrophoresis was investigated. A batch of 30 different smokeless gunpowders and 23 different everyday products were pulverized, derivatized with a fluorescent agent and analyzed by capillary electrophoresis with laser-induced fluorescence detection. Since this methodology is specific to d-glucopyranose derivatives (cellulosic and related compounds), and paper samples could be easily found in explosion scenes, 11 different paper samples were also included in the study as potential interference samples. In order to discriminate among samples, multivariate analysis (principal component analysis and soft independent modeling of class analogy) was applied to the obtained electrophoretic profiles. To the best of our knowledge, this represents the first study that achieve a successful discrimination between non-explosive and explosive nitrocellulose-based samples, as well as potential cellulose interference samples, and posterior classification of unknown samples into their corresponding groups using CE-LIF and chemometric tools.