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Record W4402378314 · doi:10.1002/star.202400063

Physiochemical, Pasting, and Morphological Properties of Native Oat Starch and Citrate‐Modified Oat Starch

2024· article· en· W4402378314 on OpenAlexafffund
Vanessa Alexander, Trust Beta, Lovemore Nkhata Malunga

Bibliographic record

VenueStarch - Stärke · 2024
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of ManitobaAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsStarchChemistryFood science

Abstract

fetched live from OpenAlex

Abstract Starch is widely utilized in the food industry, but its native form may have limitations in terms of functionality and nutrition. This study examines the characteristics of native oat starch and explores its potential for chemical modification by cross‐linking, with the aim of gaining a deeper understanding of its functionality. Citrate‐modified oat starch (COS) is generated by cross‐linking native oat starch (NOS) with citric acid. Chemical analysis reveals distinctions between NOS and COS, notably in terms of resistant starch content, which is elevated in COS. X‐ray diffraction (XRD) results reveal that NOS exhibits crystalline peaks characteristic of A‐type starch, distinguishing it from COS. In contrast, COS displays absent crystalline peaks, attributed to cross‐linking. Swelling factor, solubility, and paste clarity along with most of the pasting properties are found to be significantly lower for COS compared to NOS. The scanning electron microscopic images show how cross‐linking can alter the morphology of the NOS. The findings from this investigation highlight significant distinctions between NOS and COS. The cross‐linking process successfully enhances the resistant starch content and imparted distinctive properties absent in native oat starch. Consequently, there is potential for incorporating modified oat starch as a food ingredient.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.018
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.293
Teacher spread0.232 · 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 teacher head, 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

Citations9
Published2024
Admission routes2
Has abstractyes

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