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Record W4313328276 · doi:10.3168/jds.2022-22483

Effect of reverse osmosis and ultra-high-pressure homogenization on the composition and microstructure of sweet buttermilk

2022· article· en· W4313328276 on OpenAlexafffund
Louise Krebs, Yves Pouliot, Alain Doyen, Koen Venema, G. J. Brisson

Bibliographic record

VenueJournal of Dairy Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaNovalaitUniversité Laval
KeywordsChemistrySyneresisChromatographyHomogenization (climate)MicrostructureSkimmed milkGlobules of fatCaseinReverse osmosisParticle sizeFood scienceMembraneBiochemistryCrystallography

Abstract

fetched live from OpenAlex

Buttermilk (BM), the by-product of butter making, is similar to skim milk (SM) composition. However, it is currently undervalued in dairy processing because it is responsible for texture defects (e.g., crumbliness, decreased firmness) in cheese and yogurt. One possible way of improving the incorporation of BM into dairy products is by the use of technological pretreatments such as membrane filtration and homogenization. The study aimed at characterizing the effect of preconcentration by reverse osmosis (RO) and single-pass ultra-high-pressure homogenization (UHPH) on the composition and microstructure of sweet BM to modify its techno-functional properties (e.g., protein gel formation, syneresis, firmness). The BM and RO BM were treated at 0, 15, 150, and 300 MPa. Pressure-treated and control BM and RO BM were ultracentrifuged to fractionate them into the following 3 fractions: a supernatant soluble fraction (top layer), a colloidal fraction consisting of a cloudy layer (middle layer), and a high-density pellet (bottom layer). Compositional changes in the soluble fraction [lipid, phospholipid (PL), protein, and salt], as well as its protein profile by PAGE analysis, were determined. Modifications in particle size distribution upon UHPH were monitored by laser diffraction in the presence and absence of sodium citrate to dissociate the casein (CN) micelles. Microstructural changes in pressure-treated and non-pressure-treated BM and RO BM particles were monitored by confocal laser scanning microscopy. Particle size analysis showed that UHPH treatment significantly decreased the size of the milk fat globule membrane fragments in BM and RO BM. Also, pressure treatment at 300 MPa led to a significant increase in the recovery of total lipids, CN, calcium, and phosphate in the BM soluble fraction (top layer) following ultracentrifugation. However, PL were primarily concentrated in the pellet cloud (middle layer), located above the pellet in BM concentrated by RO. In contrast, PL were evenly distributed between soluble and colloidal phases of BM. This study provides insight into the modifications of sweet BM constituents induced by RO and UHPH from a compositional and structural perspective.

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 score0.003

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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