Filter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp

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dc.contributor.authorPromnioy, Surasak
dc.contributor.authorPhetpan, Kittisak
dc.contributor.authorRiza, Dimas Firmanda Al
dc.contributor.authorSharma, Sneha
dc.contributor.authorSirisomboon, Panmanas
dc.contributor.authorTerdwongworakul, Anupun
dc.contributor.authorPosom, Jetsada
dc.contributor.authorHongwiangjan, Jeerayut
dc.date.accessioned2026-08-06T10:56:21Z
dc.date.available2026-08-06T10:56:21Z
dc.date.issued2026-09-01
dc.description.abstractThe 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.
dc.identifier.citationPostharvest Biology and Technology, 239, 2026
dc.identifier.doi10.1016/j.postharvbio.2026.114365
dc.identifier.issn09255214
dc.identifier.other2-s2.0-105035233853
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18314
dc.sourcePostharvest Biology and Technology
dc.subjectEating quality
dc.subjectFruit quality inspection
dc.subjectMachine vision
dc.subjectMultispectral imaging
dc.subjectNon-destructive technique
dc.titleFilter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp
dc.typeArticle

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