## 3 Results and Discussion
### PLS Regression: Carotenoid Content Prediction by IR Spectral Data  
Different spectral ranges and spectral pre-treatment were investigated to develop PLS models and predict TCC in _Cucurbita_ spp. samples. Models were developed for all the preparation protocols investigated (blender homogenization (A), freeze-drying (B), and hexane/acetone extraction (C)). Cross-validation was performed for each model, to confirm the calibration robustness, and to select the number of latent variables that minimized the calibration and cross-validation error (RMSEC and RMESECV). The details of the best models developed are reported in Table 1. For each sample preparation, the best model in the prediction stage was selected taking into account the lowest error (RMSEP) and the higher coefficient of determination (R\({}^{2}\)). The model obtained with fresh pulp presented low predictability (R\({}^{2}\)\({}_{\text{PRED}}\) of 0.66) compared to the R\({}^{2}\)\({}_{\text{CAL}}\) (0.72), demonstrating that the model is not stable when moving to the prediction dataset. Indeed, the RPD (residual predictive deviation) reached 1.72, marking an intermediate model performance, which will need some improvement to be further applied [29] This was probably due to the high water content, which entails a lower nutrient concentration, a lower absorbance of carotenoids in the spectra, and a higher effect of the absorbance linked to water content. The RMSEP obtained was 25.66 \(\upmu\)g/g on a wet basis, which is three times the mean standard deviation (S.D.) of the reference data on wet bases (6.6 \(\upmu\)g/g), and greater than twice the Standard Error of Laboratory (RMSEL), which was 8.47 \(\upmu\)g/g. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Treatment**} & **Spectral Range** & \multirow{2}{*}{**LV**} & \multicolumn{4}{c}{**TCC Range of**} & \multicolumn{4}{c}{**Calibration**} & \multicolumn{4}{c}{**Prediction**} \\ \cline{3-14}  & **Range (cm\({}^{-1}\))** & & & & & **\(min\)** & \(max\)** & S.D. & \(R^{2}\)\({}_{\text{CAL}}\) & _RMSEC_ & _bias_ & \(R^{2}\)\({}_{\text{PRED}}\) & _RMSEP_ & _bias_ & _RPD_ \\ \hline Fresh pulp & 1300–3000 & 4 & 21.0 & 230.1 & 57.1 & 0.72 & 23.26 & 0.05 & 0.66 & 25.66 & 0.81 & 1.72 \\ Freeze-dried pulp & 920–3000 & 8 & 182.8 & 2137.3 & 522.6 & 0.95 & 109.80 & \(-\)5.73 & 0.93 & 193.10 & \(-\)18.95 & 3.78 \\ \hline \hline \end{tabular}  
* LV, latent variables; TCC, total carotenoids content; \(min\), minimum value of the prediction set; \(max\), maximum value of the prediction set; \(S.D.\), standard deviation of the prediction set; \(R^{2}\), coefficient of determination; _RMSEC_, root mean square error of calibration; _RMSEP_, root mean square error of prediction; _RPD_, the residual predictive deviation. The error for fresh pulp is expressed as \(\upmu\)g/g on wet basis, whereas the error for freeze-dried samples is expressed as \(\upmu\)g/g on dry basis.  
\end{table} 
Table 1: Partial Least Squares (PLS) regression models for total carotenoid content prediction in pumpkin pulp from FTIR-ATR spectra. According to [PERSON] and [PERSON] [16], a prediction model can be considered for real implementation when RMSEP < 2 RMSEL, thus the obtained model is not reliable if not for screening purposes. On the other hand, the regression model obtained from the spectra of the freeze-dried samples improved considerably (R\({}^{2}\)PRED of 0.93, RMSEP of 193 \(\upmu\)g/g on dry basis), attributed to the increase of characteristic absorption bands from spectra not covered by water absorption bands. In this case, the RMSEL was of 91.07 \(\upmu\)g/g on a dry basis, half of the RMSEP, thus the model is within the limit of reliability for implementation of FTIR-ATR spectroscopy, coupled with PLS regression, for the determination of carotenoids in freeze-dried pumpkin samples. Moreover, the RPD was greater than three (Table 1), suggesting the model reliability for agriculture application [29].