## 3 Results and Discussion
### Calibration Results and Modelling  
PLS Calibration of Pomace Oil and Moisture Content Model  
The 87 samples used for the PLS model had minimum, maximum, and average values of moisture and oil content in the pomace, which are specified in Table 1 below. The spectra of the training set and test set were mean centered before building the PLS model for oil content in pomace. The number of maximum components for building the model is set to 16. The cross-validation follows the scheme with four cancellation groups and the composition of the cancellation groups is randomly set. The results are shown in Figure 5.  
Figure 3: NIR spectra of the dataset in the region 850–1049 nm: (**a**) raw spectra; (**b**) spectra normalized with SNV. The optimal number of components for the PLS calibration of oil content is six. The spectra in red were identified and eliminated from the dataset considering that the difference between their actual and predicted values exceeded two times the RMSECV. The measured vs predicted results of the oil content in pomace after the removal of reference outliers are shown in Figure 6, and in Table 2, the calibration and validation results are summarized. A similar PLS model was built for humidity percentage in pomace, and the measured vs predicted results are shown in Figure 7. In Table 3, calibration and validation results are reported. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Calibration Performance**} \\ \hline Component & RMSECV & Bias & Slope & Intercept & R\({}^{2}\) \\ \hline Oil content & 0.155 & 0.000 & 1.000 & 0.000 & 0.700 \\  & & Validation performance & & & \\ \hline Component & RMSEP & Bias & Slope & Intercept & R\({}^{2}\) \\ Oil content & 0.184 & 0.068 & 1.283 & \(-\)0.406 & 0.773 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction results of the oil content in pomace for the calibration and validation model. Figure 4: GH value for the NIR spectra of olive pomace. Figure 5: Cont. Figure 5: PLS cross-validation for oil content: (**a**) RMSECV vs number of components, (**b**) measured vs predicted values, (**c**) reference outliers Blue dots: measured vs predicted values, for each sample. Red cross: reference outlier sample. Dashed line: trend line of PLS model. Figure 6: _Cont._  
Regarding the correlations of 0.700 and 0.829, these indicate a good relationship between the NIR spectra and oil and water contents, respectively, with a very low prediction error in both cases (0.155 and 0.653). Considering the validation performance, the correlations of 0.773 and 0.676 for oil and water content, respectively, are certainly significant for in-line application in an industrial process, also considering the low prediction error values of 0.184 and 0.698 for oil and water content, respectively. The PLS models obtained on the spectra provide an accurate estimation of both parameters. In addition, bias values were very close to zero, for both models, thus identifying the absence of systematic variations. Bias is a measure of the systematic error in prediction and is calculated as the average, with the corresponding sign, of the residuals. The prediction in terms of RMSEP both for oil and water content are coherent to the results obtained in previous research where NIR offline applications were used. Indeed, in both [33; 34], using bench NIR spectroscopy operating in the 1100-2500 nm range resulted in an RMSEP of 0.21 and 1.80 for oil and water content in the pome fruit and 0.2 for oil and 0.8 for water content, respectively, for the two different studies carried out, just like the values obtained in the present study. In both studies, however, an R\({}^{2}\) value higher than 0.9 was obtained. The R\({}^{2}\) values are slightly higher compared to the value obtained in the present study, probably because the study was carried out with NIR spectroscopy operating within a wider range (1100-2500 nm) than that used in the present study (800-1100 nm).