## 4 Conclusions  
The study evaluated the application of a portable NIR spectrometer for the prediction of soluble solids content (SSC) and firmness (FF) in yellow-fleshed kiwifruit and for the classification of ripening stages.  
The various preprocessing methods tested in the PLSR models for FF prediction showed good performance in cross-validation, with the best results achieved using the SG2 and MSC methods. However, in external prediction, the performance decreased, with the SNV-PLS model offering the best balance between accuracy and robustness (R\({}^{2}\)P = 0.73). For soluble solids content (SSC), the models maintained excellent performance in both cross-validation and external prediction, with the Raw-PLS model achieving the best overall results (R\({}^{2}\)P = 0.93). Additionally, by selecting a minimum number of spectral features, 16 and 22 wavelengths for FF and SSC, respectively, the results highlight the importance of these choices for the applicability of portable instruments, as they allow reliable estimates using a more compact spectral dataset, ideal for use in low-cost portable measurement devices.  
Regarding the classification of ripening stage, the artificial neural network (ANN) model outperformed all other approaches evaluated, correctly classifying 97.8% of the samples in each class and showing excellent performance (R\({}^{2}\) = 0.95, RMSE = 0.08, MAE = 0.03). These results confirm the ANN's strong learning and generalization capabilities, thanks to its ability to model complex and nonlinear relationships and to handle high-dimensional data.  
Therefore, the portable NIR spectrometer, in combination with chemometric and artificial intelligence techniques, proved to be a promising tool for the non-destructive assessment of kiwifruit quality and the discrimination of ripening stages. This approach facilitates the separation of fruits suitable for long-term storage from those better suited for immediate market distribution, ultimately improving supply chain efficiency and reducing postharvest losses.  
However, analyzing the regression and classification components separately provides only a partial view of the decision-making potential associated with fruit quality. Integrating these two approaches paves the way for the development of a combined predictive system, in which the estimation of key quality parameters (such as Brix) feeds into a classification model capable of supporting more informed and automated operational decisions.  
Conceptualization, S.L., G.A. and M.R.; methodology, F.G., A.M., M.C. and A.N.M.; software, S.L. and M.R.; validation, S.L., M.R. and G.A.; formal analysis, S.L.; investigation, S.L.; resources, S.L.; data curation, S.L., A.M., A.N.M., M.C. and F.G.; writing--original draft preparation, S.L.; writing--review and editing, S.L., M.R. and G.A.; visualization, F.G., A.M., M.C. and A.N.M.; supervision, G.A.; project administration, G.A. and G.C.D.R.; funding acquisition, G.A. and G.C.D.R. All authors have read and agreed to the published version of the manuscript.  
This study was carried out within the Agricth National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILENZA (PNRR)--MISSIONE 4 COMPONENTE 2, INVESTMENTO 1.4--D.D. 1032 17/06/2022, CN00000022). This manuscript reflects only the authors' views and opinions; neither the European Union nor the European Commission can be considered responsible for them.  
Not applicable.  
Data availability statementData are available upon suitable request to the corresponding author.  
The authors are grateful to IIPhotonics S.r.l. Italia for making PolispecNIR available for this research and for the development of the method, and Ing. [PERSON] of Quantum Design S.r.l. for the technical support dedicated to this study.  
The authors declare no conflicts of interest.