## 3 Results
### Prediction of Water Content Using Full Spectral Range  
In order to investigate which data are best suited for the prediction model, first the absorbance and reflectance data in the entire spectral range between 500 and 995 nm were considered (100 wavelengths). The performances of the PLSR models were evaluated using several variants of spectral preprocessing methods (normalization followed by scatter correction or spectral derivation), which influenced the final predictive power (shown in Table 3). The absorbance images of the kidney samples preprocessed by SG smoothing following area normalization and the reflectance images preprocessed by MSC following vector normalization exhibited the best results with R\({}_{\text{P}}\)2 values of 0.968 and 0.963, as well as RMSEP values of 2.016 and 2.155.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Normalization**} & \multirow{2}{*}{**Filter**} & \multirow{2}{*}{**LV’s**} & \multicolumn{2}{c}{**Validation Model**} & \multicolumn{2}{c}{**Prediction Model**} \\ \cline{3-6}  & & & **RMSECV** & \(\mathbf{R^{2}_{P}}\) & **RMSEP** \\ \hline \multicolumn{6}{c}{_Absorbance_} \\ \hline Min-Max & - & 17 & 3.570 & 0.912 & 3.339 \\  & SNV & 17 & 3.800 & 0.904 & 3.498 \\  & MSC & 26 & 6.220 & 0.939 & 2.774 \\  & SG & 17 & 3.557 & 0.905 & 3.463 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of PLSR for prediction TWC based on 100 wavelengths.  
Figure 2: (**a**) Absorbance and (**b**) reflectance average spectra of kidneys with different TWC.  
Figure 3 shows the reference versus predicted value plots for TWC using the optimal PLSR models. Both absorbance and reflectance data sets showed promising results.  
The Bland-Altman method was used to evaluate the differences between the measured and the predicted TWC values (see Figure 4). For both absorbance and reflectance, there was no average discrepancy between methods and no trend of the difference in relation to the mean. The limits of agreement were [-4.39; +3.32]% for absorbance and [-4.02; +4.49]% for reflectance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Normalization**} & \multirow{2}{*}{**Filter**} & \multirow{2}{*}{**LV’s**} & \multicolumn{2}{c}{**Validation Model**} & \multicolumn{2}{c}{**Prediction Model**} \\ \cline{3-6}  & & & **RMSECV** & \(\mathbf{R^{2}p}\) & **RMSEP** \\ \hline Area & - & 21 & 4.628 & 0.968 & 2.026 \\  & SNV & 17 & 3.961 & 0.901 & 3.542 \\  & MSC & 3 & 6.320 & 0.876 & 3.970 \\  & **SG** & **14** & **3.207** & **0.968** & **2.016** \\ \hline Vector & - & 15 & 3.285 & 0.960 & 2.263 \\  & SNV & 17 & 3.995 & 0.902 & 3.529 \\  & MSC & 25 & 5.949 & 0.926 & 3.068 \\  & SG & 15 & 3.085 & 0.957 & 2.343 \\ \hline \multicolumn{6}{c}{_Reflectance_} \\ \hline Min-Max & - & 17 & 3.048 & 0.961 & 2.225 \\  & SNV & 23 & 3.028 & 0.955 & 2.401 \\  & MSC & 8 & 5.620 & 0.795 & 5.102 \\  & SG & 17 & 2.795 & 0.963 & 2.162 \\ \hline Area & - & 16 & 3.026 & 0.958 & 2.318 \\  & SNV & 16 & 3.095 & 0.950 & 2.511 \\  & MSC & 21 & 8.965 & 0.957 & 2.325 \\  & SG & 15 & 2.928 & 0.942 & 2.704 \\ \hline Vector & - & 17 & 3.070 & 0.959 & 2.274 \\  & SNV & 16 & 3.095 & 0.950 & 2.511 \\  & **MSC** & **19** & **6.919** & **0.963** & **2.155** \\  & SG & 16 & 2.925 & 0.942 & 2.714 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 3: Measured versus predicted value of TWC for (**a**) absorbance and (**b**) reflectance data.