Understanding drying processes of steam-blanched Wolffia globosa under microwave vacuum drying through data-driven and semi-theoretical modeling

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dc.contributor.authorPambudi, Suluh
dc.contributor.authorChurat, Chutikan
dc.contributor.authorNitinarot, Manassanan
dc.contributor.authorBuntreekanok, Withitpong
dc.contributor.authorSaechua, Wanphut
dc.contributor.authorJongyingcharoen, Jiraporn Sripinyowanich
dc.date.accessioned2026-08-06T10:55:52Z
dc.date.available2026-08-06T10:55:52Z
dc.date.issued2026-06-15
dc.description.abstractSteam blanching and microwave vacuum drying (MVD) are increasingly applied to preserve heat-sensitive, protein-rich biomaterials such as Wolffia globosa . However, accurately modeling moisture removal under MVD remains challenging due to the highly nonlinear and stage-dependent nature of microwave-induced drying. This study investigates the combined effects of steam blanching pretreatment and microwave power (540, 720, and 900 W at 5 kPa) on the drying kinetics of W. globosa and evaluates the capability of machine-learning models to predict moisture evolution in comparison with conventional semi-theoretical models. The results indicated that steam-blanched samples consistently exhibited higher maximum drying rates across all power levels, increasing from 1.088 to 1.298 g<inf>w</inf>/(g<inf>dm</inf>·min) at 540 W and from 1.974 to 2.126 g<inf>w</inf>/(g<inf>dm</inf>·min) at 900 W. The pretreatment also reduced total drying time from 19.5 to 18.0 min at 540 W, while drying time remained unchanged at higher microwave powers. Among the evaluated semi-theoretical models, the Midilli equation provided the best fit to condition-specific experimental data (R<sup>2</sup> = 0.9981–0.9994). However, the generalized Midilli model showed systematic underestimation at low moisture ratios across operating conditions (R<sup>2</sup> = 0.9865, RMSE = 0.9990). In contrast, the k-nearest neighbors (k-NN) model demonstrated strong generalized predictive capability across multiple drying conditions, achieving R<sup>2</sup> = 0.9798 and RMSE = 0.0481 on the testing dataset. Beyond improved predictive accuracy, the machine-learning framework effectively captured stage-dependent drying behavior patterns that conventional generalized semi-theoretical models failed to represent. These findings highlight the potential of data-driven approaches for modeling complex drying behavior under microwave vacuum conditions. The accurate prediction of moisture ratio may further support the optimization of MVD processes for W. globosa -based food ingredients.
dc.identifier.citationLwt, 250, 2026
dc.identifier.doi10.1016/j.lwt.2026.119471
dc.identifier.issn00236438
dc.identifier.other2-s2.0-105038872029
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18194
dc.sourceLwt
dc.subjectDrying kinetics modeling
dc.subjectMachine learning
dc.subjectMicrowave vacuum drying
dc.subjectSteam blanching
dc.subjectWolffia globosa
dc.titleUnderstanding drying processes of steam-blanched Wolffia globosa under microwave vacuum drying through data-driven and semi-theoretical modeling
dc.typeArticle

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