## Results and Discussion
### PLSR Models for Protein and Total Glucan Contents  
The PLSR models based on the FT MIR, FT NIR and FT Raman data were developed for the prediction of the protein and total glucan contents in the basidiocarps of _P. ostreatus_ mushrooms. The obtained correlation and cross-validation plots are represented in Figure 4. The parameters of all calibration models, including number of factors and spectral regions, are summarized in Table 6. The regression coefficients \(R^{2}\) and the root mean square errors (RMSE) for the calibration (\(R^{2}_{\text{cal}}\), RMSE\({}_{\text{cal}}\)) were in the range of 0.908-0.996 and 0.269-2.98, respectively.  
Among the vibration spectroscopic methods used, the FT NIR regression models for both protein and total glucans gave the best calibration and cross-validation parameters. Unfortunately, in all cases, the cross-validation of PLSR models for total glucans showed worse results than for proteins, and no suitable RMSE models separately for \(\alpha\)- and \(\beta\)-glucans were obtained at all. Indeed, for proteins, the cross-validation parameters \(R^{2}_{\text{cv}}\) and RMSE\({}_{\text{cal}}\) were in the range of 0.901-0.970 and 0.608-1.11, respectively. These values are acceptable for PLSR models. By contrast, the corresponding values for total glucans significantly differed and were in the range of 0.370-0.804 and 4.66-8.50.  
Suitable PLSR models based on mean normalized FT MIR ATR spectra have already been successfully developed to predict total and \(\beta\)-glucans in oyster mushroom basidiocarps [27], but in this case, a larger set of 72 samples was used, including seven for external validation. Perhaps increasing the number of samples could improve the determination of glucans by vibration spectroscopic methods, and another way to normalize the spectra could also help. By normalizing the working spectral regions by the most intense peak, which in all cases had a large contribution from the vibrations of glucans and polysaccharides in general, it was possible to clearly distinguish the spectral differences associated  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Proteins**} & \multicolumn{3}{c}{**Total Glucans**} \\ \cline{2-7}  & **FT MIR** & **FT NIR** & **FT Raman** & **FT MIR** & **FT NIR** & **FT Raman** \\ \hline Spectroscopic & & & & & & \\ data & 1 st deriv. & 1 st deriv. & spectra & 1 st deriv. & 1 st deriv. & spectra \\ Region & & 6800–6550 & 1735–220 & 1800–845 & 6800–6550 & 1555–380 \\ (cm\({}^{-1}\)) & & 5150–4000 & 1735–220 & 1800–845 & 5150–4000 & 1555–380 \\ Number of & & & & & & \\ factors \({}^{1}\) & 10 & 5 & 5 & 7 & 12 & 8 \\ \(R^{2}_{\text{cal}}\) & 0.994 & 0.994 & 0.981 & 0.908 & 0.996 & 0.984 \\ \(R^{2}_{\text{cv}}\) & 0.910 & 0.970 & 0.901 & 0.370 & 0.804 & 0.599 \\ RMSE\({}_{\text{cal}}\) & 0.269 & 0.275 & 0.485 & 2.98 & 0.612 & 1.24 \\ RMSE\({}_{\text{cv}}\) & 1.08 & 0.608 & 1.11 & 8.50 & 4.66 & 6.61 \\ \hline \hline \end{tabular}  
* According to predicted residual error sum of squares (PRESS) diagnosis.  
\end{table}
Table 6: Parameters of the partial least squares regression (PLSR) models.