## 3 Results and Discussion
### Classification
#### 3.2.1 Results of the Single-Mode Study
GNB yields the lowest accuracies for all three modes, reinforcing the notion that classification performance is not dependent upon the values from the selected wavelengths themselves but upon their values in relation to one another. The independence assumption of GNB results in low performance. Looking next at the \"Selected Wavelengths\" cases, MLP outperforms the other classifiers for all \(k\) values and spectral modes (except for the \(k=3\) case with the VNIR data). Accuracies >85% are possible given spectral values at just seven or fewer wavelengths for the fluorescence data and >80% for the VNIR reflectance data. Most importantly, with MLP trained on only seven spectral values, the resulting accuracies are within 10 percentage points of the benchmark case for all three spectral modes. The highest performance (89.91%) is seen for the fluorescence data. Figure 10, Figure 11, and Figure 12 show confusion matrices for the \(k=7\) MLP results from the single-mode VNIR, fluorescence, and SWIR data, respectively. The classification performance is clearly best with the fluorescence data with accuracies >95% for many species. However, the accuracies for some other species are much lower. For example, goosefish has the lowest accuracy at 62.5%, being misclassified as rockfish 28.2% of the time. This is an indication that nearly an entire goosefish fillet was misclassified as rockfish in one of the folds. The overall classification performance is a little lower with the VNIR data. Winter skate shows the lowest classification accuracy at 39.4% in this case, being misclassified as goosefish 26.0% of the time and as almaco jack 13.0% of the time. Much worse performance is seen with the SWIR data, where we find a larger variety of misclassifications. Rockfish has the lowest classification accuracy at just 15.7% with high percentages of misclassification (>14%) as Atlantic cod, haddock, Pacific halibut, and Pacific cod. Figure 10: Confusion matrix for the single-mode VNIR results with the \(k=7\) MLP (overall classification accuracy = 82.7%). The variability of these single-mode classification results with each of the four cross-validation folds can be seen in Figure 13. The lower and upper limits of the error bars in each plot represent the minimum and maximum accuracies, respectively, for the four-folds. The red dashed line in each plot represents the benchmark accuracy obtained by MLP using all wavelengths in the spectrum. Figure 11: Confusion matrix for the single-mode fluorescence results with the \(k=7\) MLP (overall classification accuracy = 89.9%). Figure 12: Confusion matrix for the single-mode SWIR results with the \(k=7\) MLP (overall classification accuracy = 67.6%). The results of the single-mode classification study prove that high accuracies can be obtained (especially with the MLP classifier) with just seven or fewer wavelengths. The best benchmark performance (92.9%) using all wavelengths was seen with the fluorescence mode with MLP followed by VNIR reflectance (91.7%) and then SWIR reflectance (80.7%), both with LD. The superior performance of LD in these cases suggests the inclusion of all wavelengths significantly increases the potential for overfitting. With seven wavelengths in the fluorescence case, the MLP accuracy came within \(\sim\)3% of the benchmark accuracy. Review of the confusion matrices from this study reveal that although high overall accuracies can result from these single-mode classifications, each spectroscopic mode has its own unique set of strengths and weaknesses. Furthermore, highly concentrated misclassification results were seen in a few cases, suggesting that entire fillets in the test sets were sometimes misclassified. This is likely a consequence of the somewhat small size of our current dataset. We believe these misclassifications can be alleviated in future studies as we increase the number of fillets per species to better represent the within-species variability of the spectra.