MétaCan
Menu
Back to cohort
Record W2211112966 · doi:10.1111/ijfs.13010

Calcium sulphate‐induced soya bean protein tofu‐type gels: influence of denaturation and particle size

2015· article· en· W2211112966 on OpenAlexfundno aff
Haibo Zhao, Weiwei Li, Fang Qin, Jie Chen

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNanjing UniversityNanjing Forestry UniversityUniversity of Guelph
KeywordsDenaturation (fissile materials)Particle sizeChemistryCalciumParticle (ecology)Water holding capacityChemical engineeringChromatographyFood scienceNuclear chemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Summary Soya bean protein isolate ( SPI ) dispersions (7.25%, w/v) were heated at 65, 75, 85 or 90 °C for different time periods to produce SPI aggregates with diverse degrees of denaturation and particle size to investigate the effects on calcium sulphate (Ca SO 4 )‐induced tofu‐type gel. The results revealed that gel hardness and water‐holding capacity correlated positively with the degree of denaturation of glycinin (11S) and the particle size of the SPI aggregates. The formed gels showed more uniform and denser network structures with increasing degrees of denaturation and particle size of SPI . Hydrophobic interaction was speculated to be the crucial factor for the retention of gels prepared by SPI whose degree of denaturation by 11S was lower than 4.35%. However, disulphide bonds probably played a more important role in the retention of gels generated by SPI with the 11S denaturation degree of >84.47%. Moreover, the bulk density of the protein aggregates might determine the gel structures to a certain extent.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.011
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.046
GPT teacher head0.290
Teacher spread0.243 · 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

Citations67
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Food Science & TechnologySame topicProteins in Food SystemsFrench-language works237,207