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Record W4321374572 · doi:10.3390/fermentation9020194

Improving the Functionality of Lentil–Casein Protein Complexes through Structural Interactions and Water Kefir-Assisted Fermentation

2023· article· en· W4321374572 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

VenueFermentation · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsSolubilityChemistryFermentationCaseinFood scienceKefirFourier transform infrared spectroscopyChemical engineeringOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Highly nutritious lentil proteins (LP) have recently attracted interest in the food industry. However, due to their low solubility, extensive application of LP is severely limited. This study describes a new and successful method for overcoming this challenge by improving the nutritional–functional properties of LP, particularly their solubility and protein quality. By combining protein complexation with water kefir-assisted fermentation, the water solubility of native LP (~58%) increases to over 86% upon the formation of lentil–casein protein complexes (LCPC). Meanwhile, the surface charge increases to over −40 mV, accompanied by alterations in secondary and tertiary structures, as shown by Fourier-transform infrared and UV-vis spectra, respectively. In addition, subjecting the novel LCPC to fermentation increases the protein digestibility from 76% to over 86%, due to the reduction in micronutrients that have some degree of restriction with respect to protein digestibility. This approach could be an effective and practical way of altering plant-based proteins.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.281

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 head0.068
GPT teacher head0.291
Teacher spread0.224 · 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