## 3 Results and Discussion  
### Firmness and SSC Prediction Results  
#### 3.1.1 Partial Least Squares Regression (PLSR) Model Accuracy  
The results of the PLSR model under the different configurations analyzed, after the selection of relevant wavelengths and the removal of outliers, are summarized in Table 2, following a Monte Carlo simulation with 100 iterations. The regression models developed for predicting firmness generally demonstrated good predictive performance in cross-validation (CV), with R\({}^{2}\)CV values \(\geq\) 0.85. The best results in CV were obtained using the SG2 (R\({}^{2}\)CV = 0.880; RPDCV = 2.92) and MSC (R\({}^{2}\)CV = 0.872; RPDCV = 2.82) preprocessing methods. These were followed by the Raw and SNV models, which showed similar performances (R\({}^{2}\)CV = 0.856), with RPDCV values around 2.65. However, in external prediction, model performance decreased significantly, with R\({}^{2}\)P values ranging from 0.562 (SG1) to 0.749 (SNV). The SNV-PLS model achieved the best trade-off between accuracy and robustness (R\({}^{2}\)P = 0.749; RMSEP = 12.342; RPDP = 2.02),  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameter** & **Model** & **R2 CV** & **RMSECV** & **RPDCV** & **Outliers** & \ \begin{tabular}{c} **N\({}^{\circ}\)** \\ **Samples** \\ \end{tabular}  & \begin{tabular}{c} **Selected** \\ **Wave-** \\ **length** \\ \end{tabular}  & **R2P** & **RMSEP** & **RPDP** \\ \hline  
**SSC** & \begin{tabular}{c} **Raw-** \\ **PLS** \\ \end{tabular}  & \begin{tabular}{c} **0.967** \\ **(0.004)** \\ \end{tabular}  & \begin{tabular}{c} **0.753** \\ **(0.042)** \\ \end{tabular}  & \begin{tabular}{c} **5.57** \\ **(0.31)** \\ \end{tabular}  & **4** & **156** & **22** & \begin{tabular}{c} **0.935** \\ **(0.003)** \\ \end{tabular}  & \ \begin{tabular}{c} **1.142** \\ **(0.022)** \\ \end{tabular}  & \begin{tabular}{c} **3.98** \\ **(0.08)** \\ \end{tabular}  \\  
standing out for its stability across the tested configurations. In contrast, the SG1-PLS model yielded the poorest results (R\({}^{2}\)P = 0.562; RMSEP = 16.312; RPDP = 1.53). Regarding the soluble solids content (SSC), the models showed greater consistency between CV and external prediction. During training, all models achieved R\({}^{2}\)CV > 0.96 and RPDCV > 5, with RMSECV values ranging from 0.744 to 0.803\({}^{\circ}\)Brix, indicating strong model fitting and reliable performance during cross-validation. This trend was also confirmed in external prediction (P), where the models maintained excellent performance, with R\({}^{2}\)P > 0.91 and RPDP > 3, confirming their strong predictive reliability. Specifically, the Raw-PLS model achieved the best overall results (R\({}^{2}\)P = 0.935; RMSEP = 1.142; RPDP = 3.98), closely followed by the SG1-PLS model (R\({}^{2}\)P = 0.934; RMSEP = 1.151; RPDP = 3.95). These results indicate that SSC strongly correlates with spectral features, independently of the applied preprocessing method. These results are clearly illustrated in Figure 1, which shows the predictive performance of the best PLS models for estimating firmness (SNV-PLS) and soluble solids content (SSC, Raw-PLS), after the selection of relevant wavelengths and removal of outliers, along with the relative trend of the RPD metric during the model optimization cycle. Compared to the firmness model, the SSC model shows greater consistency between cross-validation results and external prediction outcomes, suggesting a lower risk of overfitting. This behavior can be partly attributed to the narrower range of the SSC target variable (from 5.2 to 18\({}^{\circ}\)Brix) compared to firmness (from 2.36 to 92.26 N), as well as to variability related to the presence of different maturation stages at the sampling times used in this study. Specifically, two ripening classes (related to the pre-harvest and postharvest periods) appear to contribute to this variability, highlighting a marked difference in postharvest (D60) kiwifruits.