Intelligent fruit grading systems require automated sensing solutions capable of rapid and reliable non-destructive internal quality assessment. While near-infrared spectroscopy has been widely used for soluble solids content (SSC) prediction, most existing studies rely on manually acquired spectra, limiting scalability in smart agricultural environments. Although online Vis–NIR systems based on transmission configurations have been reported, automated interactance-based systems designed for deployment-oriented grading remain limited. This study presents the design and validation of an automated interactance near-infrared spectral acquisition system for mandarin SSC evaluation. The system integrates controlled clamping, rotational positioning, and automated probe actuation to ensure stable optical geometry and repeatable probe–fruit contact during measurement. Spectral consistency was assessed by comparing consecutive scans obtained using manual and automated acquisition modes. The automated system reduced spectral dispersion among consecutive acquisitions within a measurement session by approximately 65%relative to manual measurement, indicating improved acquisition stability. Chemometric models based on partial least squares regression, support vector regression, and extremely randomized trees were developed under multiple preprocessing strategies. Prediction performance under automated acquisition remained within the same range as manual measurement, with several preprocessing–model combinations (particularly PLS and SVR with smoothing-based preprocessing), showing slightly higher Rp values and lower RMSEp values under automated acquisition. The findings demonstrate the feasibility of the proposed auto-mated system for stable interactance spectral acquisition suitable for SSC prediction, supporting its potential future integration into automated fruit quality assessment systems.
Lam et al. (Thu,) studied this question.