fruits like prunes, where low surface contrast and waxy bloom obscure damage cues [6]. Ultrasound and X-ray techniques offer better penetration into internal tissues [7; 8], but their high cost, operational complexity, and low throughput make them impractical for large-scale sorting. In contrast, near-infrared (NIR) spectroscopy provides a rapid, non-invasive alternative capable of capturing subtle chemical and structural changes within fruit tissues [9]. It has been extensively applied to assess mechanical injuries in fruits such as apples [10], pears [11], peaches [12], and avocados [13], demonstrating strong potential for nondestructive quality monitoring. However, the majority of existing studies primarily address bruises caused by a single, high-impact event or visible surface damage. Relatively few investigations have focused on cumulative low-energy impacts, which are more representative of real-world damage scenarios encountered during transportation and postharvest handling. Cumulative bruising results from repeated low-energy mechanical stresses--such as vibration, rolling, or compression--typically encountered during harvesting, packaging, or transport. Unlike single-impact injuries, cumulative damage often develops progressively and subtly, without immediate visible symptoms. Biologically, this process can cause gradual rupture of parenchyma cells, loss of cellular integrity, and redistribution of intercellular water. Chemically, it may trigger enzymatic browning, oxidation of phenolic compounds, and degradation of pigments such as anthocyanins or chlorophyll. These structural and compositional changes affect light absorption and scattering properties in plant tissues, particularly in the visible and near-infrared (Vis-NIR) spectral ranges. As a result, cumulative bruising can induce complex, nonlinear spectral variations, which serve as useful markers for nondestructive detection using spectroscopic techniques [14; 15; 16]. Although NIR spectroscopy has been widely applied to detect mechanical damage in fruits, most existing studies have focused on single-event bruising caused by isolated impacts. To our knowledge, no prior work has systematically modeled cumulative mechanical stress by simulating repeated low-energy impacts and explicitly incorporating bruise progression levels into model training. To complement the existing research, this study explores the feasibility of incorporating cumulative damage stages into the classification framework, with the aim of enhancing the generalizability of bruise detection models to cumulative damage scenarios encountered during postharvest handling. This approach may serve as a practical extension of existing NIR-based methods toward more robust, real-world applications. To address these challenges, this study presents a novel framework for detecting cumulative mechanical damage in prunes based on visible-near-infrared (Vis-NIR) reflectance spectroscopy integrated with advanced data analysis. A customized device was designed to simulate repeated impacts under controlled energy levels, generating prune samples with graded damage severity. Spectra in the 400-1050 nm range were collected from the equatorial zone of each fruit. Multiple preprocessing and feature selection methods were evaluated, and a classification model combining continuous wavelet transform (CWT), uninformative variable elimination (UVE), and support vector machine (SVM) was developed. This study is positioned as a pilot investigation to explore the feasibility of cumulative bruise detection using spectral and machine learning techniques, with future extensions planned on larger datasets and more robust frameworks. The specific objectives of this study are to (1) investigate the spectral response patterns of prunes under cumulative mechanical damage; (2) improve the classification accuracy of early-stage internal bruising; (3) provide a methodological reference for real-world deployment in automated, high-throughput fruit grading systems.