## 3 Results and Discussion
### Classification  
#### 3.2.1 Results of the Single-Mode Study  
Average cross-validated (4-fold) classification accuracies for the VNIR reflectance data are given in Table 3. The column labeled \"Benchmark\" gives the results for the case where all wavelengths are included. The set of columns under \"Selected Wavelengths\" list the resulting accuracies based on the spectral values at the \(k=3\), 4, 5, 6, 7 optimal wavelengths. Results for the fluorescence data are provided in a similar manner in Table 4 and for the SWIR reflectance data in Table 5. Values in bold denote the highest accuracy for each number of selected wavelengths. Figure 8: Average fluorescence spectrum for one of the red snapper fillets with the optimal \(k=3\), 4, 5, 6, 7 wavelength selections. Figure 9: Average SWIR reflectance spectrum for one of the red snapper fillets with the optimal \(k=3\), 4, 5, 6, 7 wavelength selections. Looking first at the accuracies for the benchmark cases, MLP yields the highest accuracy for the fluorescence data but comes in second for the SWIR data and third for the VNIR data. The superior performance of LD, a relatively simple classifier, for the VNIR and SWIR benchmark cases suggests that overfitting is a significant problem for these cases. Accuracies for the SWIR data are far lower, with LD yielding the highest accuracy at just \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Benchmark**} & \multicolumn{5}{c}{**Selected Wavelengths**} \\ \cline{2-7}  & **All Wavelengths** & \(k\) = 3 & \(k\) = 4 & \(k\) = 5 & \(k\) = 6 & \(k\) = 7 \\ \hline MLP & 87.7\% & 50.4\% & **60.1\%** & **72.7\%** & **79.7\%** & **82.7\%** \\ SVM & 89.8\% & **50.6\%** & 59.9\% & 68.7\% & 74.5\% & 77.6\% \\ WKNN & 69.8\% & 45.6\% & 56.0\% & 61.7\% & 65.1\% & 67.4\% \\ LD & **91.7\%** & 45.0\% & 51.2\% & 54.6\% & 58.4\% & 61.3\% \\ GNB & 33.1\% & 26.8\% & 31.2\% & 27.3\% & 28.6\% & 31.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Single-mode classification accuracies (4-fold cross-validation) for the fluorescence data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & \(k\) & **Simulated Annealing** & **ANOVA** & **RFE** & **Extra Trees** \\ \hline \multirow{4}{*}{VNIR} & 3 & 48.23\% & 31.42\% & 27.09\% & 14.09\% \\  & 4 & 57.90\% & 35.20\% & 28.00\% & 23.95\% \\  & 5 & 63.49\% & 36.28\% & 31.87\% & 25.93\% \\  & 6 & 67.08\% & 39.74\% & 37.04\% & 26.62\% \\  & 7 & 68.10\% & 41.21\% & 43.42\% & 29.58\% \\ \hline \multirow{4}{*}{Fluorescence} & 3 & 71.75\% & 59.71\% & 44.18\% & 54.96\% \\  & 4 & 75.90\% & 62.95\% & 48.21\% & 64.09\% \\  & 5 & 77.94\% & 65.83\% & 49.64\% & 63.51\% \\  & 6 & 78.08\% & 66.80\% & 51.95\% & 65.20\% \\  & 7 & 78.27\% & 68.05\% & 58.47\% & 66.30\% \\ \hline \multirow{4}{*}{SWIR} & 3 & 40.15\% & 20.30\% & 15.13\% & 11.56\% \\  & 4 & 46.55\% & 21.20\% & 19.81\% & 17.13\% \\ \cline{1-1}  & 5 & 51.21\% & 37.39\% & 20.15\% & 17.32\% \\ \cline{1-1}  & 6 & 51.77\% & 38.24\% & 30.75\% & 17.39\% \\ \cline{1-1}  & 7 & 52.01\% & 39.26\% & 32.28\% & 16.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of comparison between wavelength selection methods. Values represent 4-fold cross validation accuracies resulting from training the WKNN classifier with the selected wavelengths. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Benchmark**} & \multicolumn{5}{c}{**Selected Wavelengths**} \\ \cline{2-5}  & **All Wavelengths** & \(k\) = 3 & \(k\) = 4 & \(k\) = 5 & \(k\) = 6 & \(k\) = 7 \\ \hline MLP & **92.9\%** & **78.9\%** & **84.3\%** & **86.2\%** & **89.4\%** & **89.9\%** \\ SVM & 82.5\% & 66.7\% & 71.7\% & 70.8\% & 79.5\% & 79.5\% \\ WKNN & 79.2\% & 71.1\% & 75.2\% & 77.3\% & 77.1\% & 77.3\% \\ LD & 84.1\% & 59.0\% & 62.2\% & 65.4\% & 65.5\% & 68.5\% \\ GNB & 51.0\% & 40.2\% & 45.2\% & 44.0\% & 49.0\% & 49.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Single-mode classification accuracies (4-fold cross-validation) for the SWIR data. 80.7%.