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Record W2315246233 · doi:10.5539/jfr.v5n2p110

Viscoelastic Properties of Kefir as Affected by Milk Protein Addition and Starter Culture Type

2016· article· en· W2315246233 on OpenAlexvenueno aff
Stylianos Exarhopoulos, K.D. Antoniou, Stylianos N. Raphaelides, Georgia Dimitreli

Bibliographic record

VenueJournal of Food Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsnot available
FundersEuropean Social FundEuropean Commission
KeywordsStarterKefirFood scienceWhey proteinFermentationChemistryRheologyViscoelasticitySodium alginateSodiumMaterials scienceBiologyLactic acidOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of Sodium Caseinates (SCN) and Whey Proteins Concentrates (WPC) addition, as well as the starter culture type (kefir grains and commercial starter culture) on the viscoelastic properties of kefir samples was evaluated. The kefir samples were prepared from homogenized and pasteurized full fat bovine milk with or without the addition of SCN or WPC at 2% (w/w) concentration. According to the results, SCN increased the fermentation time of kefir samples when compared to control samples (samples without SCN or WPC addition), while WPC decreased it. The elasticity of the protein matrix was increased with SCN or WPC addition, however, the effect of SCN was more pronounced to than that of WPC. SCN contributed to the elasticity of the samples by the formation of strong as well as weak chemical bonds, while WPC participated to proteins interactions that were characterized as weak ones. The commercial starter culture resulted in lower fermentation time and increased viscoelastic properties of the kefir samples when compared to kefir grains.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.068
GPT teacher head0.285
Teacher spread0.218 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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