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Record W4308494903 · doi:10.1002/star.202200189

Performance of Starch Gels on In Vitro Enzymatic Hydrolysis Assessed by Rheological Methodologies

2022· article· en· W4308494903 on OpenAlexaff
María Santamaría, Leticia Montes, Raquel Garzón, Ramón Moreira, Cristina M. Rosell

Bibliographic record

VenueStarch - Stärke · 2022
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
FundersEuropean Regional Development FundAgencia Estatal de InvestigaciónXunta de GaliciaGeneralitat ValencianaEuropean Commission
KeywordsStarchHydrolysisRheometerEnzymatic hydrolysisRheologyChemistryDigestion (alchemy)ChromatographyViscosityFood scienceMaterials scienceBiochemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Starch hydrolysis is attracting much attention due to its relationship to digestion and glucose release. The objective is to propose rapid and continuous analytical methods that allow measuring gels hydrolysis following apparent viscosity ( μ ). Three different starches (corn, wheat, and rice) are tested recording starch gelatinization followed by gels digestions (digestograms) using a rapid‐visco analyzer (RVA) or a rheometer. Results are compared with those obtained by measuring glucose release along hydrolysis. A modified first‐order kinetic model in the RVA ( R 2 > 0.99) and rheometer ( R 2 > 0.99) describes the gels digestograms. Wheat gel shows a higher hydrolysis rate ( k ), which indicates faster digestion followed by rice and corn gels. The proposed models allow rapid analysis of starch digestograms, allowing to discriminate among hydrolysis rate of different starches. These less time‐consuming methods can be an option to continuously analyze starch gelatinization followed by enzymatic digestion.

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)0.001
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 score0.058
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.329
Teacher spread0.255 · 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
Published2022
Admission routes1
Has abstractyes

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