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Record W4399619096 · doi:10.1016/j.fochx.2024.101550

Tailoring soy protein/corn zein mixture by limited enzymatic hydrolysis to improve digestibility and functionality

2024· article· en· W4399619096 on OpenAlexaff
Dongjing Wu, Wei Wu, Na Zhang, Olugbenga P. Soladoye, Rotimi E. Aluko, Yuhao Zhang, Yu Fu

Bibliographic record

VenueFood Chemistry X · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of ManitobaGovernment of CanadaAgriculture and Agri-Food Canada
FundersCentre Scientifique et Technique du BâtimentSouthwest UniversityNatural Science Foundation of ChongqingNational Natural Science Foundation of China
KeywordsSoy proteinEnzymatic hydrolysisFood scienceHydrolysisProtein digestibilityChemistryEnzymeBiotechnologyAgronomyBiochemistryBiology

Abstract

fetched live from OpenAlex

This study aimed to modify plant protein mixture to improve their functionality and digestibility by limited hydrolysis. Soy protein isolate and corn zein were mixed at the ratio of 5:1 (w/w), followed by limited hydrolysis using papain from 15 to 30 min. The structural characteristics, in vitro digestibility, and functional properties were evaluated. Also, DPPH radical scavenging activity was determined. The results indicated that the molecular weight of different modified samples was largely reduced by limited hydrolysis, and the proportion of random coil was significantly increased. Furthermore, the solubility, foaming, emulsifying and water-holding capacity of hydrolyzed protein mixture were significantly improved, which were close to those of whey protein isolate. In vitro digestibility after 30-min limited hydrolysis was remarkably elevated. In addition, the hydrolyzed protein mixture exhibited a higher antioxidant activity than those of untreated proteins. Overall, limited hydrolysis of protein mixture led to improved digestibility, functionality and antioxidant activity.

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 score0.019
Threshold uncertainty score0.403

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 head0.015
GPT teacher head0.208
Teacher spread0.192 · 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

Citations21
Published2024
Admission routes1
Has abstractyes

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