## 1 Introduction
Vis/NIR spectroscopy (375-1050 nm) was used to monitor the ripening of 'Jintao' kiwifruit over a 13-week harvest period by [18]. The developed PLS models achieved good predictive performance, with R\({}^{2}\) values of 0.81 for SSC and 0.88 for Hue [18]. A similar approach is multi-range data fusion, as demonstrated by [PERSON] et al. (2024) [19], who integrated Vis/NIR HSI (400-1000 nm) and FT-NIR (800-2500 nm) spectra through both low-level (concatenation of pretreated spectra) and mid-level (fusion of features extracted via Principal Component Analysis (PCA) or PLS from each dataset) data fusion techniques. The best results were achieved with feature fusion (mid-level, PLS scores), showing R\({}^{2}\) values greater than 0.85 for SSC, DM, and FF, confirming the efficacy of an integrated approach across spectral bands. More recently, [PERSON] et al. (2024) [20] compared both diffuse reflectance and diffuse transmission, or predicting SSC in kiwifruit. Their study combined various preprocessing techniques (e.g., Savitzky-Golay smoothing and multiplicative scatter correction (MSC)) with competitive adaptive reweighted sampling (CARS) for wavelength selection and used PLS regression to build predictive models. The diffuse reflectance method yielded superior performance (R\({}^{2}\) = 0.98, RMSEP = 0.66) compared to the transmission method (R\({}^{2}\) = 0.95, RMSEP = 0.93), highlighting its higher potential in SSC prediction. Partial Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) are widely employed in the classification assessment of kiwifruit. For example, [PERSON] et al. (2022) [15] used Vis-NIR spectroscopy (400-1000 nm) to classify 'Hayward' kiwifruit into three maturity stages based on soluble solids content (SSC) and flesh firmness (FF), achieving classification sensitivities of 97% (for SSC) and 93% (for FF) using PLS-DA and soft PLS-DA models. Similarly, [PERSON] et al. (2025) [1] developed PLS-DA and Support Vector Machine Classification (SVMC) models to distinguish ripening stages, reporting classification accuracies of 91.46% and 91.55% for PLS-DA and SVMC, respectively. Vis-NIR reflectance has thus proven to be a reliable non-destructive technique for monitoring compositional changes and assessing quality in kiwifruit during both the harvest and postharvest periods [21]. However, given the dynamic nature of quality parameters over time, the application of more complex or nonlinear models may be required to capture these changes effectively. For instance, Support Vector Machines (SVMs) have outperformed linear models such as PLS in predicting SSC and firmness after storage [1; 22]. Similarly, Artificial Neural Networks (ANNs), as demonstrated by [23], have proven to be effective tools for predicting firmness and its interactions with mineral composition during kiwifruit storage. In addition, convolutional neural networks (CNNs) have shown great potential in multi-source data fusion scenarios, achieving an R\({}^{2}\) of 0.864 in SSC prediction [24]. Due to their ability to model complex, often nonlinear and previously unknown relationships between input variables and quality parameters during ripeness stages and postharvest storability, advanced machine learning (ML) models have become key tools for classification tasks in kiwifruit research. For instance, [PERSON] et al. (2022) [4] used spectral data acquired at harvest time to classify fruit based on firmness (with a threshold of 9.8 N) using Random Forest (RF), SVM, and Decision Stumps (DS) models. The best performance was achieved after 125 days of storage, with 79% of firm and 54% of soft fruits correctly classified. [PERSON] et al. (2023) [25] developed PLS-DA and simplified k-nearest neighbors (KNN) models to discriminate kiwifruit maturity stages, obtaining classification accuracies of 93.3% and 98.3%, respectively. More recently, [PERSON] et al. (2024) [26], explored the application of NIR sensors for real-time firmness evaluation during robotic fruit handling.