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Record W4412441296 · doi:10.1016/j.jff.2025.106955

Synthesis and potential application of slowly digestible starch

2025· article· en· W4412441296 on OpenAlexaff
H. T. Doan, Tae-Ok Kim, Minseok Cha, Soo-Jung Kim

Bibliographic record

VenueJournal of Functional Foods · 2025
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsCarleton University
FundersKorea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and FisheriesKorea Institute of Planning and Evaluation for Technology in Food, Agriculture and ForestryRural Development AdministrationMinistry of Agriculture, Food and Rural Affairs
KeywordsStarchChemistryFood scienceChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Slowly digestible starch (SDS) has gained significant attention for its potential health benefits attributable to its extended glucose release. The gradual and steady release of glucose from SDS helps manage diabetes, cardiovascular disease, and obesity by stabilizing blood sugar levels, reducing insulin resistance, and supporting weight control. The formation of SDS is significantly influenced by starch sources and modification techniques used. Various approaches, including physical, chemical, enzymatic, and genetic modifications, have been developed to enhance SDS content. SDS has broad applications in the food, pharmaceutical, and other sectors, including the manufacture of low-glycemic-index foods, controlled-release medications, sports drinks, and energy bars. This review provides a comprehensive overview of SDS, focusing on the raw materials involved in its production, various modifications for its formation, and its applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.255
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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