## 5 Conclusions  
This study proposes a robust and interpretable method for the nondestructive detection of cumulative mechanical damage in prunes by integrating Vis-NIR spectroscopy with machine learning techniques. A customized impact simulation system was constructed to replicate realistic postharvest handling conditions, enabling the controlled acquisition of spectral data under progressive damage scenarios. The proposed CWT-UVE-SVM pipeline achieved a high classification accuracy of 93.22%, demonstrating strong capability in capturing and modeling nonlinear reflectance responses caused by repeated low-energy impacts.  
The research successfully fulfilled the three objectives outlined in the Introduction:  
(1) To investigate the spectral response of prunes under cumulative mechanical damage. Progressive decreases in reflectance were observed at key wavelengths (e.g., 818 nm and 980 nm) corresponding to water absorption and tissue structural integrity. However, under severe and repeated impacts, reflectance tended to saturate or slightly rebound--likely due to extensive cellular collapse, water migration, and surface property alterations--highlighting the nonlinear spectral behavior of cumulative bruising.  
(2) To improve the classification accuracy of early-stage internal bruising. The proposed CWT-UVE-SVM model achieved a classification accuracy of 93.22%, with macro-averaged precision, recall, and F1-score all exceeding 0.90. More importantly, the model successfully distinguished between intact fruit and three distinct damage levels--mild, moderate, and severe--demonstrating strong discriminative power across a progressive bruising continuum. This capability is essential for fine-grained quality control in real-world postharvest applications.  
(3) To provide a methodological reference for real-world application. A complete detection pipeline was established, covering damage simulation, spectral acquisition, multistage preprocessing and feature selection, and robust modeling. This framework offers practical potential for integration into automated, high-throughput fruit sorting systems. While the current model demonstrated promising classification performance on a limited dataset, we acknowledge its exploratory nature. Future research should involve more extensive validation using larger, heterogeneous datasets under commercial logistics conditions to improve the system's robustness, generalizability, and practical deployment.  
Overall, this study provides a practical foundation for the industrial deployment of nondestructive bruise detection technologies and contributes new insights into the spectral modeling of cumulative damage in soft fruits.  
L.L.: conceptualization, methodology, validation, formal analysis, investigation, data curation, visualization, writing--original draft preparation, funding acquisition; H.Z.: conceptualization, resources, supervision, writing--review and editing; J.G.: methodology, validation, formal analysis, writing--review and editing; L.W.: validation, visualization. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region, China (Grant No. 2022D01C674, \"Research on Multi-domain Spectrum and NIR Transmission Fusion Detection Method for Apple Moldy Core Disease Based on Acoustic-Vibration Signals\"). The article processing charge (APC) was funded by Lisi Lai.