## 3 Results
### Hierarchical Classification Model for Individualization of Petrol Source  
Firstly, the petrol samples status native (unevaporated) or weathered (evaporated and simulated fire debris) needed to be determined. The evaluation of the blind dataset of native petrol brands showed that the classification model with the combination of all four olefs correctly predicted 80% of the petrol brands. On the other hand, it was found that classification model with the combination of 2-methyl-2-butene and the mixture of cis- and trans-2-pentene was not successful in discriminating between petrol brands of the native samples. The combined native and evaporated model displayed an accuracy of 44.4% on the blind dataset. The evaporated and simulated fire debris classification model displayed an accuracy rate of 33%. The multi-classifier classification models for the evaporated and simulated fire debris samples were not satisfactory when evaluated on the blind sets of samples. Therefore, a binary classifier for single sources of petrol vs. the rest of the petrol brands was needed. The binary models were created based on the discriminative potential of the minor compounds using \({}^{1}\)H selfTOCSY; the four sets of olefins displayed minor differences in the NMR spectra, which could contribute to the distinction between the petrol samples from different sources. The BP M and Jet samples displayed similar NMR spectral fingerprints compared to the other petrol brands based on 2-methyl-2-butene \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Dataset** & **Classifier** & **k-Folds** & **BP S** & **BP M** & **Jet** & **Esso** & **Shell** & **Texaco** \\ \hline Neat Combined & Linear Discriminant & 5 & 85.7\% & 88.9\% & & & & **76.9\%** \\ \hline Evaporated petrol samples & NN & 10 & & & & 75\% & & 60\% \\ \hline Neat and evaporated petrol samples & NN & 10 & & & 60\% & & 69.2\% & 70.6\% \\ \hline Neat, evaporated, burnt and burned on substrate petrol samples & NN & 5 & 100\% & 62.5\% & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A summary table of the classification models for neat, evaporated, burned on its own and on variety of substrates samples petrol samples with the most successful prediction of different petrol source with prediction rate 860% and 860%. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Dataset** & **Classifier** & **PCA** &
\begin{tabular}{l} **Feature** \\ **Selection** \\ \end{tabular} & **k-Folds** & **BP S** & **BP M** & **Jet** & **Esso** & **Shell** & **Texaco** \\ \hline Mixture of 3-methyl-2-butene and 1-pentene & Ensemble & \(\surd\) & & 5 & n/a & 100\% & & & & 100\% \\ \hline Mixture of 3-methyl-2-butene & & & & & & & & & 66.7\% & 76\% \\ \multicolumn{1}{l}{and 1-pentene} & kNN & & \(\surd\) & 10 & n/a & & & & & 66.7\% & 76\% \\ \multicolumn{1}{l}{2-methyl-2-butene} & SVM & \(\surd\) & & 10 & n/a & & 66.7\% & 85.7\% & & & 76\% \\ \hline
2-methyl-2-butene & Ensemble & & \(\surd\) & 5 & n/a & & 71.4\% & 71.4\% & 66.7\% & 69.2\% \\ \hline Cis- and trans-2-pentene & & Linear & \(\surd\) & & 10 & 85.7\% & 71.4\% & 60\% & & 60\% & 83.3\% \\ \hline Cis- and trans-2-pentene & Ensemble & & \(\surd\) & 10 & 100\% & 71.4\% & 60\% & & 60\% & 83.3\% \\ \hline Combined Olefins & Ensemble & \(\surd\) & & 10 & 100\% & 77.8\% & 71.4\% & 71.4\% & 77.8\% & 76.9\% \\ \hline Combined Olefins & Ensemble & & \(\surd\) & 10 & 100\% & 66.7\% & 71.4\% & 71.4\% & 88.9\% & 76.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._and the mixture of cis- and trans-2-pentene. In addition, Texaco and Shell shared similar NMR profiles; however, the 1H seITOCSY results for 2-methyl-2-butene and the mixture of cis- and trans-2-pentene could potentially assist with distinguishing these two brands from the rest of the petrol sources. For the weathered dataset, the combination of evaporated petrol samples (25% and 50% of sample weight), petrol samples burned on their own and petrol samples burned on a substrate (cardboard) was used for the training.