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Record W2057674351 · doi:10.1002/star.200700623

Use of Iodine as a Tool to Understand Wheat Starch Pasting Properties

2008· article· en· W2057674351 on OpenAlexaff
Debora Saibene, Koushik Seetharaman

Bibliographic record

VenueStarch - Stärke · 2008
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAmyloseStarchIodineSwellingGranule (geology)SlurryChemistryLeaching (pedology)ViscosityPolymerPolysaccharideChemical engineeringFood sciencePolymer chemistryNuclear chemistryMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract The ability of iodine to bind amylose has been used to understand a variety of structural and functional aspects of starch in food systems. In this study, the changes in starch granule swelling, polymer leaching and their relationship to pasting properties was investigated by adding iodine to the starch slurry at either room temperature or 95°C. The objective of the study was to investigate the contribution of leached and solubilized starch polymers on the pasting behavior of wheat starch. The addition of very small amounts of iodine (0.1%, dry basis, db) at room temperature increased peak and final pasting viscosities as compared to when no iodine was added. Further increases in iodine concentration resulted in a decrease in peak and final pasting viscosities. The amount of iodine required to completely inhibit granule swelling was more than that required to minimize amylose leaching from the granule. Adding increasing amounts of iodine at 95°C to the starch slurry resulted in the gradual decrease in peak and final pasting viscosities. The contrasting effects of iodine on paste viscosity behavior when added at room temperature or at 95°C suggest that the contribution of extra‐granular amylose on the setback viscosity is minimal and a larger proportion of amylose is retained in the intragranular spaces.

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 score0.220
Threshold uncertainty score0.740

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.153
GPT teacher head0.291
Teacher spread0.138 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

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