## 3 Results and Discussion
### Establishment of a Multi-Spectral Classification Model by PLS-DA  
Although spectra of full wavelengths performed well in high-CL and low-CL classification, the contiguous wavelengths made the data processing time-consuming and inefficient. The utilization of fewer wavelengths is preferable for easier implementation of the multispectral imaging system. In total, 18 and 25 wavelengths were selected using CARS and UVE, respectively, and the distribution of selected wavelengths is shown in Figure 6. It is clear that CARS identifies relevant wavelengths across the entire spectral range, whereas UVE tends to spread relevant wavelengths only in the spectral range low to 600 nm. Furthermore, simplified PLS-DA models were developed using the two sets of selected wavelengths, respectively, and the CARS-PLS-DA model revealed overall better performance than the UVE-PLS-DA model (Table 3). This finding may be due to the addition of more visible wavelengths when using CARS, including more effective color information for the classification. Additionally, compared to the original PLS-DA model using the spectra of full wavelengths within the VNIR range (also presented in Table 3), comparable predictive power (CCR = 0.86 vs. 0.90 and 0.79 vs. 0.79) of the CARS-PLS-DA model could be observed.  
To further evaluate the performance of the CARS-PLS-DA model, the actual categories against the predicted category of all samples predicted by the full-wavelength PLS-DA model and CARS-PLS-DA are compared in Figure 7. It could be observed that majority of predicted categories by CARS-PLS-DA model were the same as those by full-wavelength PLS-DA model, which indicated that the simplified model worked as effectively as the original model. Generally, the CARS-PLS-DA model performed acceptably regardless of considering the calibration set or prediction set (Figure 7c,d). As for the prediction set, 9 out of 12 high-CL fillets were correctly identified (sensitivity = 0.75), whilst only two low-CL fillets were misclassified as high-CL fillets (specificity = 0.83). This was mainly due to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Preprocessing** & **LVs** & **Calibration Set CCR** & **Prediction Set CCR** \\ \hline PLS-DA & Der1 & 11 & 0.90 & 0.79 \\ RBF-SVM & Der2 & - & 1.00 & 0.79 \\ \hline \hline \end{tabular}  
* PLS-DA, partial least square-discriminant analysis; RBF-SVM, radial basis function-support vector machine.  
\end{table}
Table 2: Performance of models based on visible and near-infrared (VNIR) full wavelengths with optimal preprocessing. LV: latent variable; CCR: correct classification rate 1.  
Figure 5: The optimal number of latent variables determination in the PLS-DA model.  
similarity in the membrane and structural integrity of cells of high-CL and low-CL samples when CL values were around 20%, which might lead to misclassification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Wavelengths (nm)** & **Number** & **LVs** & **Calibration CCR** & **Prediction CCR** \\ \hline \multirow{2}{*}{CARS} & 434, 435, 436, 469, 557, 576, 578, 654, 656, 679, 681, 686, 687, 747, 750, 960, 978, 979 & 18 & 13 & 0.86 & 0.79 \\ \hline \multirow{3}{*}{UVE} & 610, 611, 831, 832, 833, 835, 836, 837, 838, & & & & \\  & 866, 867, 868, 870, 871, 872, 886, 888, 929, & 25 & 12 & 0.78 & 0.71 \\ \cline{1-1}  & 930, 931, 974, 976, 977, 978, 979 & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison of full-wavelength and simplified PLS-DA models based on selected wavelengths by competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE).  
Figure 6: The indication of selected wavelengths by (**a**) CARS and (**b**) UVE. Blue lines: original Der1 spectral curve; Red lines: selected wavelengths.  
Figure 7: _Cont._