## 4 Discussion and Conclusions  
The NMR method combined with ML was successfully applied for the individualization and classification of petrol samples from different sources. The NMR spectroscopy method had not been previously evaluated for fire debris analysis. The use of \({}^{1}\)H sel TOCSY NMR spectroscopy is also a new approach for fire debris analysis to identify distinctive compounds, background interference and its sources, and pyrolysis products in petrol sources. In addition, the \({}^{1}\)H selTOCSY method is innovative for the structural elucidation of petrol samples. Our study proves it can be used for the identification of trace amounts of \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Sample N**} & \multirow{2}{*}{**Class**} & **Native vs.** & **BP M** & **Jet** & **Esso** & **Shell/Texaco** & **Predicted Class by** **NMR Hierarchical** & **ATD-GC-MS** \\ \hline BLIND D & JET 25\% & \multirow{2}{*}{Weathered} & \multirow{2}{*}{Other} & \multirow{2}{*}{JET} & & & **JET** & & **No differentiation** \\  & evaporated & & & & & & & & achieved \\ \hline BLIND E & ESSO 25\% & \multirow{2}{*}{Weathered} & \multirow{2}{*}{Other} & \multirow{2}{*}{Other} & \multirow{2}{*}{BSSO} & & **ESSO** & **No differentiation** \\  & evaporated & & & & & & achieved \\ \hline BLIND F & SHELL on & \multirow{2}{*}{Weathered} & \multirow{2}{*}{Other} & \multirow{2}{*}{Other} & \multirow{2}{*}{Other} & **SHELL** & **SHELL** & **No differentiation** \\  & cardboard & & & & & & achieved \\ \hline BLIND G & SHELL burnt & \multirow{2}{*}{Weathered} & \multirow{2}{*}{Other} & \multirow{2}{*}{Other} & **TEXACO** & **TEXACO** & **No differentiation** \\  & & & & & & & achieved \\ \hline BLIND H & TEXACO & \multirow{2}{*}{Weathered} & \multirow{2}{*}{Other} & \multirow{2}{*}{Other} & **TEXACO** & **TEXACO** & **No differentiation** \\  & burnt & & & & & & achieved \\ \hline BLIND I & TEXACO 25\% & \multirow{2}{*}{Weathered} & \multirow{2}{*}{Other} & \multirow{2}{*}{Other} & **ESSO** & **ESSO** & **No differentiate** \\  & evaporated & & & & & & achieved \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{l l l} \hline \hline
**Classifier** & **Overall Accuracy (\%)** & **Classification** \\ \hline Linear Discriminant & 98.5 & Native vs. weathered \\ \hline Ensemble & 80 & BP S vs. BP M vs. Jet vs. Texaco vs. Shell vs. Esso \\ \hline k-NN & 84.4 & BP M vs. other petrol brands \\ \hline Logistic Regression & 82.4 & Jet vs. other petrol brands \\ \hline ANN & 82.1 & Esso vs. other petrol brands \\ \hline ANN & 60 & Texaco vs. Shell \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of machine learning model performance output using different classifiers and their accuracy in the classification of different petrol sources. specific compounds from the complex spectra of petrol and can be combined with ML for classification purposes. A hierarchal classification model based on a multi-class classifier for native petrol samples and a combination of binary classifiers for weathered petrol samples was constructed. The overall accuracy of the classification model was 80% for native petrol samples and 78% for weathered petrol samples, significantly outperforming the alternative method (ATD-GC-MS) that is currently used. The combination of \({}^{1}\)H combined with the NOESY NMR method and \({}^{1}\)H seITOCSY displayed potential in individual identification of fire debris samples and linking them to a source or suspect. The model has the potential to identify an unknown petrol sample and linking it to its source regardless of its evaporation rate (based on to 25% and 50% evaporated samples) and whether it was burned on a cardboard substrate. In conclusion, an automated hierarchical classification model was created for the successful discrimination and individualization of petrol samples based on their source using a machine learning classifier. This paper describes the first ML model that has the potential to be used for the classification of petrol sources of fire debris.