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Record W4292183262 · doi:10.1002/cche.10594

Understanding structure, functionality, and digestibility of faba bean starch for potential industrial uses

2022· article· en· W4292183262 on OpenAlexafffund
Dongxing Li, Tommy Z. Yuan, Jiayi Li, Janitha P.D. Wanasundara, Mehmet Tülbek, Yongfeng Ai

Bibliographic record

VenueCereal Chemistry · 2022
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food Canada
KeywordsVicia fabaStarchChemistryTanninCultivarFood scienceAgronomyBiology

Abstract

fetched live from OpenAlex

Abstract Background and Objectives There is growing interest in fractionating faba bean to produce various food ingredients; however, the technological attributes of the leading co‐product, faba bean starch, are poorly understood. This study examined the structures, functional properties, and in vitro digestibility of starches isolated from five varieties of faba bean cultivated in two different years in comparison with commercial starches. Findings Some distinctive features were identified in the faba bean starches: no breakdown viscosity during pasting, remarkably stronger gelling ability, and greater enzymatic resistance in a raw state. The two growing years did not significantly influence the structures, physicochemical properties, and digestibility of the faba bean starches, except for the pasting profiles of certain cultivars. Conclusions Despite the obvious differences in the sizes, tannin levels, and vicine and convicine levels of their seeds, the faba bean starches generally exhibited technological characteristics similar to those of the pea starch, while being significantly different from those of the maize starch. Significance and Novelty This study presented the unique properties of faba bean starches from five representative cultivars grown in different years, which will be meaningful for industrial utilization of faba bean starch with value addition.

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 score0.437

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 head0.145
GPT teacher head0.280
Teacher spread0.136 · 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

Citations16
Published2022
Admission routes2
Has abstractyes

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