Filter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp - Promnioy, Surasak; Phetpan, Kittisak; Riza, Dimas Firmanda Al; Sharma, Sneha; Sirisomboon, Panmanas; Terdwongworakul, Anupun; Posom, Jetsada; Hongwiangjan, Jeerayut (My University, 2026)
The development of affordable, real-time quality monitoring tools is essential for industrial applications involving high-value tropical fruits such as durian. This study presents a cost-effective short-wave infrared multispectral imaging (SWIR-MSI) system employing three discrete bandpass filters (880, 905, and 940 nm) integrated with machine learning algorithms for non-destructive evaluation of fresh-cut durian pulp. Compared with conventional point-based NIR spectroscopy and complex hyperspectral imaging systems, the proposed configuration markedly reduces system complexity and cost while maintaining sensitivity to key compositional variations. It accurately predicted dry matter content (DMC) and starch, demonstrating that limited spectral information within the 860–1100 nm range can effectively capture moisture- and carbohydrate-related features. These findings confirm the feasibility of implementing filter-based SWIR imaging as a practical and scalable alternative to hyperspectral systems for on-line fruit quality assessment. Practically, this approach enables rapid, non-destructive, and spatially adaptable evaluation of durian pulp quality, offering significant potential for on-site grading, ripeness classification, and process control in fresh-cut durian production and packaging operations, particularly for small- and medium-scale agro-processors.
Keywords: Eating quality; Fruit quality inspection; Machine vision; Multispectral imaging; Non-destructive technique