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.

Petrol is considered the most common fire accelerant. However, the identification and classification of petrol sources through the years has been 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 clas-sification model developed in this research. A hierarchical classification model was constructed using local classifiers to categorize neat or weathered petrol into its sources.