###### Abstract  
Petrol is considered the most common fire accelerant. However, the identification and classification of petrol sources through the years has proven to be a challenging field in the investigation of fire debris analysis. This research explored the possibility of identifying petrol sources by high-field NMR methods accompanied by ML (machine learning). The automated identification and classification of petrol brands were achieved for first time based on the ML classification model developed in this research. A hierarchical classification model was constructed using local classifiers to categorize neat or weathered petrol into its sources. machine learning; petrol; fire investigation; NMR; MATLAB +
Footnote †: journal: Medical  
and no substantial clustering based on petrol type or brand was revealed. However, the HCA dendrogram demonstrated a linkage of the samples according to their degree of evaporation [3]. A method based on Gas Chromatography (GC)-Flame Ionization Detector (FID) analysis combined with an ANN (artificial neural network) algorithm was explored for the discrimination of petrol brands from five petrol stations in Spain based on the entire chromatogram [2]. It was concluded that despite there not being significant variations in the chromatogram, mathematically, the different petrol samples could be classified according to their brand. The authors suggested that the potential difference that contributed to the discrimination was the content of oxygenate and hydrocarbon groups such as aromatics and olefins. In that experiment, native petrol samples were only considered for identification purposes and no identification of specific compounds was made [2]. Research by [PERSON] and [PERSON] [4] offered promising results where unevaporated samples from different petrol sources were correctly grouped based on aromatic compounds. In addition, [PERSON] et al. [5] used Direct Analysis Real-Time Mass Spectroscopy (DART-MS) combined with the Partial Least Squares Discriminant Analysis (PLS-DA) model to classify petrol sources on different substrates; however, the petrol samples were grouped by the already identified class rather than unknown classes. Even though many spectroscopic and chromatography techniques have been considered, it can be concluded that the identity of the source of ILs recovered from a fire scene is still a challenging and ongoing research area. Therefore, there is a need for the individualization and classification of petrol sources to enhance ILs' evidential value. Nuclear Magnetic Resonance (NMR) is a non-destructive spectroscopic technique that studies the nuclei of atoms within a molecule and their chemical environment. NMR spectroscopy is sufficient to completely determine the structure of an unknown molecule and to differentiate between isomers or related compounds which can be difficult using GC-MS. Various NMR pulse sequences allow complex spectra to be dissected by focusing on individual small spectral regions and extracting the spectra of the coupled spin systems that have a resonance within that region, even when their spectra severely overlap. Therefore, NMR spectroscopy has the capability to extract the sub-spectra of an individual component without prior separation from highly complex spectra [6]. A simple \({}^{1}\)H NMR method has been successful in the determination of petrol composition and some individual compounds with rapid and accurate results. Further investigation of NMR applications in the petroleum industry displayed the capabilities of \({}^{1}\)H NMR coupled with PCA, k-NN (k-Nearest Neighbors), HCA and SIMCA (Soft Independent Modeling of Class Analogy), which proved to be a useful tool for categorizing petrol samples with adulteration (solvents), fuel additives and blends, petroleum mixtures (kerosene and diesel mixtures) and petrol samples with different octane numbers; NMR is a proven analytical tool for the identification and quantification of low-molecular-weight components [7; 8; 9; 10; 11].