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Record W2981998416 · doi:10.1111/ijfs.14428

Application of ultrasound treatment for modulating the structural, functional and rheological properties of black bean protein isolates

2019· article· en· W2981998416 on OpenAlexaff
Liang Li, Yan Zhou, Fei Teng, Shuang Zhang, Baokun Qi, Changling Wu, Tian Tian, Zhongjiang Wang, Yang Li

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsCAE (Canada)
FundersNational Natural Science Foundation of China
KeywordsRheologyAbsorption of waterUltrasoundSolubilityChemistryMacromoleculeChemical engineeringHydrogen bondMoleculeAbsorption (acoustics)BiophysicsMaterials scienceOrganic chemistryBiochemistryComposite materialMedicineBiology

Abstract

fetched live from OpenAlex

Summary The modulating effect of ultrasound treatments at varying powers and times on the structural and functional properties of black bean protein isolate (BBPI) was investigated. Compared with native BBPI, low‐power (150 W) and medium‐power (300 W) ultrasound treatments increased the solubility, foaming and emulsifying properties of BBPI, especially at 300 W, 24 min. This effect arises predominantly due to increased exposure of hydrophobic groups, which serve to increase the interactions between the protein and water molecules. Additionally, an increase in the protein surface activity improved the absorption of protein molecules at the oil–water and air–water interfaces. Rheology data showed that increased hydrophobic and hydrogen‐bonding interactions improved the water‐holding capacity of BBPI gels following ultrasound treatment. However, high‐power (450 W) ultrasound treatment weakened the functional properties of BBPI, and this was likely due to the formation of macromolecular BBPI aggregates. Overall, this study indicates that ultrasound treatment could be a promising approach for modulating other plant protein resources as well as expanding the application of black bean protein.

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 score0.014
Threshold uncertainty score0.288

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.246
Teacher spread0.218 · 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

Citations58
Published2019
Admission routes1
Has abstractyes

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