## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**GA Region**} &
\begin{tabular}{c} **Peak** \\ **Position** \\ \end{tabular} & **Vibrational Mode** & **Contributions [15; 20; 62; 63]** \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{861–936} & \multirow{4}{*}{886} & \({}^{\text{\tiny{\textbackslash}}}\)(C-O-H), \\  & & 886 & \({}^{\text{\tiny{\textbackslash}}}\)(C-C), \\  & & 933 & \({}^{\text{\tiny{\textbackslash}}}\)Ring vibration, \\  & & & (CH\({}_{2}\)), (C-O-C) \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{1124–1161} & \multirow{2}{*}{1134} & \({}^{\text{\tiny{\textbackslash}}}\)(C-N), \({}^{\text{\tiny{\textbackslash}}}\)(C-C) \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{1312–1386} & \multirow{4}{*}{1331} & \({}^{\text{\tiny{\textbackslash}}}\)(C-H), (C\({}_{3}\)-C), \\  & & (C\({}_{5}\)-C), (CH\({}_{\alpha}\)), \\  & & (CH\({}_{3}\)CH\({}_{2}\)), \\  & & & \({}^{\text{\tiny{\textbackslash}}}\)(CH\({}_{3}\)), \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{1613–1649} & \multirow{4}{*}{1612} & \({}^{\text{\tiny{\textbackslash}}}\)(C-H), (C\({}_{3}\)-C), \\  & & (C\({}_{5}\)-C), (CH\({}_{\alpha}\)), \\ \cline{1-1}  & & (CH\({}_{3}\)CH\({}_{2}\)), \\ \cline{1-1}  & & (CH\({}_{3}\)CH\({}_{2}\)), \\ \cline{1-1}  & & (CH\({}_{3}\)), \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{1613–1649} & \multirow{4}{*}{1612} & \({}^{\text{\tiny{\textbackslash}}}\)(C=O), (NH\({}_{2}\)), \\ \cline{1-1}  & & 1654 & \({}^{\text{\tiny{\textbackslash}}}\)(C=C) \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{1688–1724} & \multirow{2}{*}{1688–1724} & \multirow{2}{*}{\({}^{\text{\tiny{\textbackslash}}}\): stretching mode; \({}^{\text{\tiny{\textbackslash}}}\): bending mode.} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The neural network architectures that yielded the best results during the testing phase consisted of one hidden layer with 10 neurons for the binary model, of two hidden layers with 60 and 10 neurons each for the AD tertiary model, and of two hidden layers with 60 and 20 neurons each in the OD tertiary model. The performance of the optimal ANN model is assessed using the BLP cross-validation procedure in order to assess the trained ANNs ability to differentiate between classes of Raman spectra. Ten Latin partitions bootstrapped five times were used to measure the generalized prediction accuracy of the models [64]. BLP will preserve the class distribution in both the training and validation sets and ensure all samples are used for prediction only once. Here, 10 training-prediction subsets of complete samples (donors), ensuring that each sample is only used once for prediction, were furnished. For each bootstrap, the data was split into training and prediction sets so that nine Latin partitions were combined into a training set for model building, and the tenth partition was used for prediction. All results from the ten prediction sets and across the five bootstraps were pooled and averaged, and the standard deviation was calculated. This approach was used to measure the generalized prediction ability of the three optimal ANN models built. The final network results for each model are shown in Table 5. The performance parameters for all classes are listed as they were determined using BLP. The sensitivity (true-positive rate, i.e., percent of spectra belonging to a class that were correctly predicted as belonging to that class), specificity (true-negative rate, i.e., percent of spectra belonging to a class that were correctly predicted as not belonging to a different class), and accuracy (overall closeness of the predicted results to the known results) for each class prediction is reported. Table 5 represents the results of the ANN networks for HC vs AD differentiation in a binary model, for HC vs mild AD vs moderate AD in an AD tertiary model, and for HC vs AD vs OD in an OD tertiary model.