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Record W2060917128 · doi:10.1002/star.201200253

Structure of starch hydrolysates following in vitro oral digestion: Effect of botanical source of starch and hydrothermal treatments

2013· article· en· W2060917128 on OpenAlexaff
Komeine Kotokeni Mekondjo Nantanga, Eric Bertoft, Koushik Seetharaman

Bibliographic record

VenueStarch - Stärke · 2013
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHydrolysateStarchFood scienceDigestion (alchemy)HydrolysisChemistryAmyloseResistant starchPolysaccharideAmylaseCarbohydrateBiochemistryChromatographyEnzyme

Abstract

fetched live from OpenAlex

Abstract Digestion of starch in humans occurs progressively in the gut. However, the literature is scant on the structures of the starch digestion products along the gut from the mouth to the small intestines – products that impact glucose homeostasis. This submission focuses on the first step of starch digestion, i.e., impact of human salivary amylase on the structure of hydrolysis products obtained from cooked starches from different botanical sources. Normal corn (NCS), normal wheat (NWS), and normal potato (NPS) starches were cooked at 1:0.7 (T0.7) or 1:2 (T2) starch:water ratios. Cooked starches were subjected to salivary amylase at conditions mimicking oral digestion. Extent of hydrolysis was lower at T0.7 compared to T2, but the amount of carbohydrates in different fractions and the MW profiles within each treatment showed no apparent differences between starches from different botanical sources. However, debranching of the hydrolysates revealed structural differences between the different starches with regards to extent of amylose hydrolysis and the amount and profile of lower MW fractions. Therefore, the structures of hydrolysates that are likely the substrate for subsequent hydrolysis in the gut are different based on cooking condition or botanical source of starch, with potential consequence for glucose homeostasis.

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 score0.038
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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