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Record W2769353963 · doi:10.1111/ijfs.13649

Physicochemical and digestion characteristics of flour and starch from eight Canadian red and green lentils

2017· article· en· W2769353963 on OpenAlexafffundabout
Zhan‐Hui Lu, Elizabeth Donner, Rong Tsao, D. Dan Ramdath, Qiang Liu

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2017
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsStarchFood scienceAmyloseDigestion (alchemy)Resistant starchChemistryHydrolysisCultivarAgronomyBiologyBiochemistryChromatography

Abstract

fetched live from OpenAlex

Summary Physicochemical and nutritional properties of flour and isolated starch from eight Canadian lentil cultivars were assessed to identify unique samples and key factors affecting starch digestion. The results showed that nearly half of apparent amylose in lentil flours was underestimated because it was complexed and embedded within the flour matrix, which led to slower starch digestion of cooked flour. Cooked red lentil flours showed significantly higher resistant starch content (11.0%) than flours from green lentils (6.8%) (P < 0.05). Among green lentils, Asterix and Greenland were unique for their high slowly digestible starch content after cooking, possibly owing to their high phenolic content and α‐glucosidase inhibitory activity. Long‐ and short‐range ordering in starch was more indicative of low starch digestion for raw or cooked lentil flour rather than for isolated starch. The results suggest the flour matrix protects the starch ordered structure from enzyme hydrolysis.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.621

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.273
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations28
Published2017
Admission routes3
Has abstractyes

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