## 2 Materials and Methods
### Statistical Analysis
The SVM technique is one of the most widely applied classification methods in electronic nose technologies. It was developed for the linear classification of separable data, but can be applied to nonlinear data with the use of kernel functions. The principal idea is that separating classes by a particular hyperplane maximizes a quantity called the margin. The margin corresponds the distance from the closest point in the dataset to a hyperplane dividing the classes [16]. Four kernel types are available: linear, polynomial, radial basis function and sigmoid. Different kernel functions were tested to check the robustness of the classifier model [16]. The SVM analysis was performed using XLSTAT Premium software. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Dynamic Fit Options**} \\ \hline \multicolumn{4}{c}{Total Number of Fits} & 200 \\ \multicolumn{4}{c}{Maximum Number of Iterations} & 200 \\ \hline \multicolumn{4}{c}{Parameter Ranges for Initial Estimates} \\ \multicolumn{4}{c}{Minimum} & Maximum \\ \(\alpha\) & \(-1.0000\) & 3.0000 \\ \(\beta\) & 0.0000 & 3.0000 \\ \(\gamma\) & \(-1.0000\) & 3.0000 \\ \hline \multicolumn{4}{c}{Summary of Fit Results} \\ \multicolumn{4}{c}{Converged} & 87.0\% \\ \multicolumn{4}{c}{Singular Solutions} & 15.0\% \\ \multicolumn{4}{c}{Ill-Conditioned Solutions} & 14.5\% \\ \multicolumn{4}{c}{Iterations Exceeding 200} & 13.0\% \\ \hline \multicolumn{4}{c}{Results for the Overall Best-Fit Solution} \\ R & R\({}^{2}\) & Adj R\({}^{2}\) & Standard Error of Estimate \\
0.9978 & 0.9957 & 0.9956 & 0.0119 \\ \hline \multicolumn{4}{c}{Coefficient} & Std. Error & t & P \\ \(\alpha\) & 0.7282 & 0.0018 & 413.2867 & \textless{}0.0001 \\ \(\beta\) & 0.0055 & 2.9060 \(\times\) 10\({}^{-5}\) & 188.1148 & \textless{}0.0001 \\ \(\gamma\) & 1.0166 & 0.0026 & 387.0196 & \textless{}0.0001 \\ \hline \multicolumn{4}{c}{Analysis of Variance} \\ \multicolumn{4}{c}{Uncorrected for the mean of the observations} \\ \multicolumn{4}{c}{DF} & SS & MS \\ Regression & 3 & 341.3877 & 113.7959 & \\ Residual & 889 & 0.1268 & 0.0001 & \\ Total & 892 & 341.5145 & 0.3829 & \\ \hline \multicolumn{4}{c}{Corrected for the mean of the observations} \\ \multicolumn{4}{c}{DF} & SS & MS & F & P \\ Regression & 2 & 29.0691 & 14.5345 & 101,941.7917 & \textless{}0.0001 \\ Residual & 889 & 0.1268 & 0.0001 & \\ Total & 891 & 29.1958 & 0.0328 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical output of the nonlinear regression (dynamic fitting). The ANNs are programs conformed on the networks of the central nervous system (neural). These networks are compounded interconnection nodes (neurons) able of identify patterns and relationships in data [17]. ANN is made up of many artificial neurons, organized in layers, which form a network. An artificial neuron is a simple processing element that, as with biological neurons, receives signals from different inputs to produce an output [18]. JustNN software (version 4.0) was used for the analysis. JustNN is a free software package; it is easy to use, frequently updated, and performs well. The model is based on three layers of nodes: input, hidden and output. Based on the inputs in the first layer, and the outputs from the third layer, the model develops a number of hidden nodes that comprise the middle layer [19].