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Record W3092568371 · doi:10.1039/d0fo02089c

Fabrication and characterization of lentil protein gels from fibrillar aggregates and the gelling mechanism study

2020· article· en· W3092568371 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFood & Function · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
Fundersnot available
KeywordsMechanism (biology)FabricationCharacterization (materials science)ChemistryBiophysicsNanotechnologyChemical engineeringMaterials scienceBiologyMedicineEngineering

Abstract

fetched live from OpenAlex

Heat-induced aggregation and gelation in lentil protein isolate (LPI) were studied over pH levels (pH 2-9), protein concentration (1-13%, w/w), and heating time (0.5-16 h). The LPI gels were formed from both fibrillar and particulate aggregates at pH 2 and 7, respectively. The gels formed from fibrillar aggregates at pH 2 were translucent and showed homogeneous and highly interconnected networks. While lentil protein showed weak gelling capacity, the gels prepared from LPI aggregates possessed good mechanical properties, and the optimized gel demonstrated a compressive strength of 2.37 kPa and a water holding capacity of 80.62%. The gelling mechanism study suggests that the high aspect ratio allowed fibrillar aggregates to build a higher level of structures with positive characteristics along with other attractive interactions including hydrophobic interactions and disulfide bonds to build strong gels. Therefore, this research has developed a new strategy to prepare improved lentil protein gels for food texturization from LPI fibrillar aggregates.

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.

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 score0.455
Threshold uncertainty score0.127

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 head0.025
GPT teacher head0.181
Teacher spread0.156 · 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