###### Abstract
The primary application of high-field NMR spectroscopy in the petroleum industry is in the quality control of hydrocarbon classes in a sample rather than individual compounds of the overly crowded complex spectra. \({}^{1}\)H NMR methods coupled with clustering and multivariate classification techniques were used for the successful identification of adulteration in two types of samples. The potential of NMR spectroscopy for the structural elucidation of petrol components in a sample has been established. Considering the application of NMR in various scientific fields, forensic NMR is still in the early stages of development with a particular focus on the chemical compositions of single compounds. A \({}^{1}\)H NMR method was combined with statistical analysis to identify the chemical \"fingerprint\" of cocaine samples and to link cocaine samples based on this information. It was concluded that the NMR method could establish a link between seized samples obtained at different locations or in possession of different individuals. The relative ratios of the minor components in coca leaves are closely associated with plant varietal, cultivar and agronomic differences that can be exploited for the assignment of geographical origin, at least when suitable authentic databases are available [12]. One of the disadvantages of \({}^{1}\)H NMR is that it is generally used for nonselective analysis compared to the selectivity of MS. Peak overlaps from multiple detected compounds pose major challenges in the complex \({}^{1}\)H NMR spectrum of petrol. Therefore, band-selective sequences including selective (sel) TOCSY and pure shifts that use tailored pulses, which narrow the excitation bandwidth to the region of interest in a signal measurement to obtain information for a single spin system, are recommended. Machine learning has been proven to be beneficial in forensic science in its various fields such as public safety, image and video analysis, image recognition, gunshot detection, firearms identification, 3D crime scene reconstruction, huge digital data analysis, building statistical evidence, handwriting identification, time since death estimation, dental age estimation and personal identification through dental findings [13], sex determination of skeletal remains, 3D facial reconstruction from an unidentified skull, cybercrimes and digital evidence detection [14], bloodstain pattern analysis [15] and pattern recognition, which involves pattern evidence such as bite marks, lip prints, bullet marks, tool marks, shoe prints and fingerprint comparison and identification with more accuracy and ultimately higher speeds than human experts [16; 17]. NMR provides rapid and accurate data collection in high output forensic laboratories. The objective of this work, using high-field (600 Hz) NMR spectroscopy that delivers automated sample changing, was to uniquely individualize and discriminate aliquot petrol sources based on (1) source (origin of the crude oil); (2) refinery processes and procedures (blending agents); and (3) brand (additive package). Within forensic science, the identification and classification of petrol sources could help police forces in the investigation of various fuel offenses, including arson, motor vehicle incidents, environment spillage, fuel smuggling and petrol bomb-related incidents. Therefore, the objective of the study also included individualization and discrimination of evaporated and Ignitable Liquid Residue (ILR) samples (fire debris residues) to characterize the petrol sample collected at a fire scene. Petrol samples that are not hermetically sealed or are exposed to high temperatures undergo evaporation losses, which is called weathering. Therefore, evaporated samples and fire simulated debris samples are considered weathered. Several NMR pulse sequences were evaluated for this research.