Classification of ANFO samples based on their fuel composition by GC-MS and FTIR combined with chemometrics

Ammonium nitrate fuel oil (ANFO) is one of the most favorite explosives used in terrorist attacks. This explosive is a complex mixture of 95-96% ammonium nitrate (AN) and 4-5% liquid hydrocarbons (fuel oil). In this study, we analyze a variety of ANFO explosive mixtures in order to classify their different sources of origin by observing the difference in fuel components. The study was performed by mixing ammonium nitrate with eight different diesel brands collected in Lincoln, UK in two seasons (winter and summer). The samples were extracted using appropriate solvent and extracts were subsequently analyzed in sextuplicate by gas chromatography-mass spectrometry (GC-MS) and Fourier transform infrared spectroscopy (FTIR). A classification model was performed using principal component analysis (PCA) and Lineal Discriminant Analysis (LDA). In this study, four fatty acid methyl ester (FAME) contents were observed by GC-MS in all summer samples but found lack in some winter sample resulting in seasonal variation effect. The classification of pre-blast ANFO samples was achieved using GC-MS and FTIR in a combination with PCA/LDA. The results significantly showed the variation of specific diesel components and providing different classification performance among ANFO samples with high classification performance. Therefore, this study can be beneficial in forensic investigation that the use of diesel components are able to classify among different ANFO samples.