## 3 Results
### FSS-Based PLS Regression Models  
PLS-R models were developed to predict FSS scores from NIR spectra obtained at the four anatomical sites (Figure 10). Overall, the models displayed moderate to high predictive power, with performance varying across anatomical sites (Table 5). The cortical  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PLS Model** & **Biceps EDSS** & **Triceps EDSS** & **Brainstem EDSS** & **Cortical EDSS** \\ \hline
**LVs** & **2** & **2** & **5** & **7** \\ \hline
**RMSEC** & 0.34791 & 0.474 & 0.58775 & 0.28884 \\ \hline
**RMSECV** & 0.39863 & 0.475 & 0.59679 & 0.29334 \\ \hline
**Bias C** & 0 & 0 & 0 & 0 \\ \hline
**Bias CV** & 0.00015 & 0.000123 & 0.0016795 & 0.0007989 \\ \hline
**R\({}^{2}\)C** & 0.971 & 0.947 & 0.918 & 0.98 \\ \hline
**R\({}^{2}\)Cv** & 0.962 & 0.946 & 0.916 & 0.98 \\ \hline
**Cohen’s \(\hat{I}^{2}\)** & 25.316 & 17.519 & 10.905 & 49.000 \\ \hline \hline \end{tabular}  
* LVs: latent variables; RMSEC: root mean square error in calibration; RMSECV: root mean square error in cross-validation; Bias C: calibration bias; Bias CV: cross-validation bias; R\({}^{2}\)C: coefficient of determination in calibration; R\({}^{2}\)Cv: coefficient of determination in cross-validation.  
\end{table}
Table 4: Performance parameters for the EDSS-based PLS regression models of the spectra collected on biceps, triceps, brainstem, and cortical sites.  
Figure 9: EDSS-based PLS regression model results and corresponding VIP scores plot of biceps (**a**), triceps (**b**), brainstem (**c**), and cortical (**d**) sites.  
model yielded the best results, showing the lowest RMSECV (3.504) and the highest R\({}^{2}\)CV (0.939), indicating strong predictive ability despite the use of 7 LVs. Similarly, the brainstem model performed well with RMSECV = 4.675 and R\({}^{2}\)CV = 0.891, requiring only three LVs, which suggests a good balance between model complexity and performance. The biceps and triceps models showed lower predictive accuracy, with RMSECVs of 7.295 and 8.779, respectively, and R\({}^{2}\)CV values of 0.735 and 0.617, indicating limited ability to explain variability in FSS scores from spectra at these sites. The low bias across all models suggests minimal systematic error in predictions. Because all RMSECV values are substantially lower than the FSS standard deviation (15), these models capture meaningful spectral to clinical correlations despite differences in latent variable complexity. Moreover, the strong predictive power of the NIR spectral data for estimating FSS scores was confirmed by the Cohen's \(f^{2}\) values, particularly in the brainstem and cortical sites.  
Figure 10: _Cont._  
VIP score analysis revealed key wavelength regions relevant to FSS prediction. Notably, 1200 nm was consistently important across biceps, triceps, and cortical sites, while 1050 nm appeared across triceps, brainstem, and cortical regions. These wavelengths likely correspond to specific tissue components or metabolic features associated with fatigue in MS. The broader and more informative spectral features observed in the cortical and brainstem regions support their value for assessing fatigue severity using optical spectroscopy.  
Figure 10: FSS-based PLS regression model results and corresponding VIP score plots of biceps (**a**), triceps (**b**), brainstem (**c**), and cortical (**d**) sites.