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Record W2902777116 · doi:10.1039/c8ra07912a

Effects of acylation and glycation treatments on physicochemical and gelation properties of rapeseed protein isolate

2018· article· en· W2902777116 on OpenAlexaff
Zhigao Wang, Cheng Zhang, Tian Zhang, Xingrong Ju, Rong He

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversity of TorontoToronto Public Health
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of China
KeywordsAcylationGlycationRapeseedChemistryLysineOrganic chemistryBiochemistryFood scienceAmino acidCatalysisReceptor

Abstract

fetched live from OpenAlex

The aim of this study was to improve the gelation property of rapeseed protein isolates (RPI) by means of acylation and glycation. The results showed that acylation and glycation within RPI occurred at Lys, and Lys, Met, Ile, Leu and Pro, respectively. Acylation and glycation both increased the surface hydrophobicity (So) and molecular weight of RPI, and decreased the free sulfhydryl (SH) content of RPI, while acylation resulted in a lower change of So and SH. The conformational structure of modified RPIs was changed, and acylated RPI (acylation degree, 38 ± 0.2%) possessed the highest ordered structure content among the modified RPIs. The thermal stability of the protein was improved after either acylation or glycation treatments. Furthermore, native RPI with moderate modification (low degree of acylation, 38 ± 0.2%) showed an overall improvement in the gelation and gel properties as evidenced by the reduced least gelation concentration and surface roughness, increased water-holding capacity, and better textural properties.

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 score0.052
Threshold uncertainty score0.106

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 head0.014
GPT teacher head0.221
Teacher spread0.208 · 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

Citations56
Published2018
Admission routes1
Has abstractyes

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