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Record W4214558554 · doi:10.3390/membranes12030270

Functional Properties of Casein and Caseinate Produced by Electrodialysis with Bipolar Membrane Coupled to an Ultrafiltration Module

2022· article· en· W4214558554 on OpenAlexafffund
Rosie Deschênes Gagnon, Laurent Bazinet, Sergey Mikhaylin

Bibliographic record

VenueMembranes · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Laval
KeywordsElectrodialysisUltrafiltration (renal)ChemistryCaseinChromatographySolubilityMembraneChemical engineeringFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Electrodialysis with a bipolar membrane coupled to an ultrafiltration module (EDBM-UF) is a hybrid technology recently developed as an ecofriendly alternative to chemical acidification to produce casein and caseinate from skim milk. In this study, the composition and functional properties of casein and caseinate obtained by chemical acidification/basification and by the EDBM-UF method from winter and summer milks were analyzed and compared. Results show that the emulsifying properties, solubility, water holding, and gelling capacities are equivalent between casein and caseinate from both methods. However, the foaming properties of EDBM-UF ingredients were improved, and casein was less hygroscopic. Additionally, the season of milk influenced certain functional properties, such as water-holding capacity and hygroscopicity. Therefore, these results allow concluding that EDBM-UF ingredients have equivalent or higher functionality than chemically produced ingredients, and that the EDBM-UF process would be a more eco-efficient alternative to the chemical one.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.185
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2022
Admission routes2
Has abstractyes

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