Rapid identification of cocrystal components of explosives based on Raman spectroscopy and principal component analysis

Energetic cocrystal materials are considered to be one of the important directions for the development of energetic materials, due to their high energy density and low sensitivity. However, there is still a lack of effective methods to carry out rapid structural and purity identification. Herein, we explored a method for rapid identification and identification of unknown components extracted from CL-20/MTNP and CL-20/HMX cocrystal processes based on Raman spectroscopy combined with principal component analysis (PCA). Thirty sets of cocrystal and 30 sets of mixed explosives were randomly selected as the training set and 10 sets each as the validation set. The principal components were extracted by dimensionality reduction of the collected Raman spectra using the principal component sub-featured clustering algorithm of chemometrics. The region identification structure formed by different principal components allows intelligent output of whether the sample was cocrystal or not. The results show that the cumulative contribution rate of the three principal components in the sample set was 98.7 %. The confidence ellipses of the validation set were all well distributed within the confidence ellipses of the training set. And the structure identification results of explosive cocrystals were output quickly, accurately and intelligently. Therefore, this method shows good potential application value in the rapid structural identification of other complex mixtures such as energetic even pharmaceutical cocrystals.