## 3 Results and Discussion
### Firmness and SSC Prediction Results
#### 3.1.1 Partial Least Squares Regression (PLSR) Model Accuracy
The more diverse data distribution introduces complexity in establishing consistent predictive relationships, which may reduce the performance of external validation. In contrast, the distribution of SSC values across the three data acquisition periods (pre-harvest, harvest, and postharvest) is more uniform, resulting in better generalization and a lower risk of overfitting, together with a likely more linear spectral response to sugar content, as also highlighted by the RPD trend during wavelength selection. As is known, firmness is under the influence of several quality-related attributes of the fruit. According to [22], the spectral information acquired at harvest may not accurately reflect the firmness that the fruit will exhibit after the storage period. As a result, obtaining reliable quantitative predictions is particularly challenging. Kiwi firmness is a physical property linked to the internal cellular structure, whose variability can affect both the spectral features and surface scattering. [PERSON]. (1998) [37] hypothesized that pectin modifications, mainly responsible for changes in firmness, are more difficult to detect compared to other more abundant constituents, such as total soluble solids (TSS). Furthermore, cell turgor, which significantly contributes to perceived firmness, depends on the fruit's water content and the rate of water loss from the outer and inner pericarp tissues [38]. Finally, the intrinsic limitations of NIR, as pointed out by [22], clarify that near-infrared light penetrates only about 2 mm beneath the skin, whereas standard firmness measurements are based on penetration forces at approximately 8 mm depth 7. The results obtained in this study confirm that the quantitative prediction of firmness (FF) is more complex than estimating the sugar content, as previously highlighted by [19,22]. A comparison with the literature shows that, in the case of firmness, the SNV-PLS model developed achieved moderate predictive performance (R\({}^{2}\)P = 0.749; RMSEP = 12.342 N), which is lower than the performance reported by [19], who achieved R\({}^{2}\)P = 0.78 and RMSEP = 11.96 N using Vis/NIR data, but superior to the study by [22], which reported R\({}^{2}\)P = 0.50 and RMSEP = 4.41 N, despite considering a narrower range of firmness. In comparison, [PERSON] et al. (2019) [13] reported even higher performance, obtainingthe best predictive model for firmness using raw spectra (R\({}^{2}\)P = 0.90, RMSE = 28.8 N). Similarly, [PERSON] et al. (2022) [15] achieved significant predictive values, with R\({}^{2}\)P ranging between 0.82 (RMSE = 14.51 N) and 0.92 (RMSEP = 9.87 N). Regarding the sugar content, the Raw-PLS model developed in the present work (R\({}^{2}\)P = 0.935; RMSEP = 1.142\({}^{\circ}\)Brix) showed results comparable to those obtained by [19], with R\({}^{2}\)P = 0.859 and RMSE = 1.45\({}^{\circ}\)Brix, and superior to those reported by [21], whose models based on combinations of pretreatments (SG, SNV, MSC) and variable selection (SPA, CARS) achieved R\({}^{2}\)Pvalues between 0.22 and 0.69, with RMSEP between 1.07 and 1.81\({}^{\circ}\)Brix. An excellent performance was reported by [13], with SNV producing the best predictive model for SSC (R\({}^{2}\)P = 0.993, RMSEP = 0.40\({}^{\circ}\)Brix), while [15] obtained predictive R\({}^{2}\) values ranging from 0.85 (RMSEP 1.10\({}^{\circ}\)Brix) to 0.94 (RMSEP = 0.73\({}^{\circ}\)Brix), confirming the generally higher accuracy observed for SSC prediction compared to firmness. Moreover, portable NIR devices have shown promising results in predicting SSC and firmness in various fruits. SW-NIR spectroscopy (900-1650 nm), combined with Savitzky-Golay second-derivative preprocessing and SPA-PLS modeling, has been used to predict SSC in mango, achieving R\({}^{2}\)P = 0.78 and SEP = 0.67\({}^{\circ}\)Brix ([39]). Similarly, [PERSON] & [PERSON]. (2022) [40] applied a portable NIR spectrometer (900-1700 nm) to peers, obtaining excellent results using a Si-GA-PLS model, with R\({}^{2}\)P values of 0.9406 for SSC and 0.9119 for firmness.