## 2 Materials and Methods
### Fish Fillet Data Collection  
Figure 3 shows an overview of the data acquisition and processing steps for the studies represented in this paper. The database for this study consisted of VNIR and SWIR reflectance and fluorescence spectra collected from 133 fish fillets representing a total of 25 different species groups (Table 1). The species for each fillet was verified using DNA barcoding [8]. Each fillet was placed in a 150 \(\times\) 100 \(\times\) 25 mm sample holder created with a 3D printer (Fortus 250 mc, Stratasys, Eden Prairie, MN, USA) using production-grade black thermoplastic. Image acquisition was conducted by the pushbroom method, where a linear motorized translation stage was used to move the sample holder incrementally across the scanning line of the imaging spectrograph. The length of the instantaneous field of view (IFOV) was made slightly longer than the length of the sample holder (150 mm) by adjusting the lens-to-sample distance. The resulting spatial resolution along this dimension was determined as 0.4 mm/pixel. Each fillet was sampled along the width direction (100 mm) of the holder with a step size of 0.4 mm to match the spatial resolution of the length direction [8]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Number of Fillets**} & \multicolumn{3}{c}{**Number of Valid Voxels**} \\ \cline{3-5}  & & **VNIR** & **Fluorescence** & **SWIR** \\ \hline Almaco Jack (_Seriola riroliana_) & 4 & 1157 & 1169 & 1992 \\ Atlantic Cod (_Gadus morhua_) & 4 & 1322 & 1391 & 1508 \\ Bigeye Tuna (_Thumus obesus_) & 4 & 831 & 572 & 2416 \\ California Flounder (_Paralichthys californicus_) & 4 & 1016 & 1113 & 2416 \\ Char (_Salvelinus_ sp.) & 4 & 1165 & 1156 & 1508 \\ Chinook Salmon (_Oncorhynchus tshavytscha_) & 4 & 1630 & 1570 & 2416 \\ Cobia (_Rachoscentron caud_) & 4 & 1235 & 1170 & 1508 \\ Coho Salmon (_Oncorhynchus kisutch_) & 4 & 894 & 887 & 2416 \\ Gilthehead Bream (_Sparus aurata_) & 4 & 1314 & 1275 & 1362 \\ Goosefish (_Lophitidae_ sp.) & 4 & 1304 & 1356 & 1508 \\ Haddock (_Melanogrammuna aeglefinus_) & 4 & 1193 & 1375 & 1508 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fish fillet database summary. Figure 3: Overview of the data acquisition and processing flow. Flat-field corrections were applied to the VNIR and SWIR reflectance images and the fluorescence images to convert the original absolute intensities in CCD counts to relative reflectance and fluorescence intensities [29]. An initial spatial mask was then created for each imaging mode to separate the fish fillets from the background. To filter out inaccurate measurements around the thinner edges and portions of the fillets near the bone structure, an outlier removal scheme was instituted. Outliers were handled by first calculating the mean (\(\upmu\)) and standard deviation (\(\sigma\)) of the fish pixel intensities over the entire fillet. Voxels of 10 \(\times\) 10 pixels were considered to mimic independent fish fillet spectral point measurements using the field of view of a fiber optic spectrometer. Exclusion occurred if \(\geq\)10% of the constituent pixels in a voxel exceeded \(\upmu\pm\) 2 \(\sigma\) to eliminate outliers. Figure 4 shows an example result of voxel processing where most of the excluded voxels are concentrated near the fillet edges. This approach produced a final set of spatial masks, one each for the VNIR and SWIR reflectance and fluorescence images, which determined the blocks to be used for analysis. Finally, the fluorescence spectra were scaled by a constant factor of 6000, the approximate maximum of fluorescence spectral values in the database. This was done to set the range of fluorescence values to between zero and one. Alternative normalization methods such as z-score and area under the curve (AUC) normalization were tried as well and produced similar results.