Total soluble solids, dry matter content prediction and maturity stage classification of durian fruit using long-wavelength NIR reflectance

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dc.contributor.authorSaenphon, Chirawan
dc.contributor.authorDitcharoen, Sirirak
dc.contributor.authorMalai, Chayuttapong
dc.contributor.authorSaengprachatanarug, Khwantri
dc.contributor.authorWongpichet, Seree
dc.contributor.authorSirisomboon, Panmanas
dc.contributor.authorSaechua, Wanphut
dc.contributor.authorKhurnpoon, Lampan
dc.contributor.authorPhuphaphud, Arthit
dc.contributor.authorMaraphum, Kanvisit
dc.contributor.authorPosom, Jetsada
dc.date.accessioned2026-08-06T10:42:54Z
dc.date.available2026-08-06T10:42:54Z
dc.date.issued2023-12-01
dc.description.abstractThe DM and TSS of durian pulp moving on a conveyor belt were measured for their rapid and non-destructive qualities based on quantitative and qualitative measurements. The calibration set and prediction set equaled 209 and 69 pulps, respectively. The quantitative test compared the performance of PLS regression for DM and TSS prediction developed from full wavelength (860–1754 nm) and a few significant variables using SPA, GA, and VIP methods. The qualitative test identified the possibility of maturity stage classification by comparing three supervised machine learning classifiers, namely SVM, random forest (RF), and LDA. Effective models for DM and TSS prediction were developed from second derivatives spectra combined with the GA method, exhibiting r<sup>2</sup>, SEP, and RPD of 0.85, 4.50%, and 2.64, respectively for DM, and 0.66, 5.15%, and 1.60, respectively, for TSS. The model classifying samples into two distinct groups, namely “reject” and “pass,” utilizing the LDA algorithm, exhibited an impressive accuracy rate of 94.20%, making it a suitable choice for quality assurance purposes. This result indicates that the few effective variables were more efficient than full wavelength and improved model accuracy with greater model stability. Enhancing the classification model could involve data sample balancing in each group, leading to further improvements.
dc.identifier.citationJournal of Food Composition and Analysis, 124, 2023
dc.identifier.doi10.1016/j.jfca.2023.105667
dc.identifier.issn08891575
dc.identifier.other2-s2.0-85170715258
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14788
dc.sourceJournal of Food Composition and Analysis
dc.subjectClassification
dc.subjectDry matter
dc.subjectDurian fruit
dc.subjectMaturity grade
dc.subjectNoninvasive measurement
dc.subjectTotal soluble solids content
dc.titleTotal soluble solids, dry matter content prediction and maturity stage classification of durian fruit using long-wavelength NIR reflectance
dc.typeArticle

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