Microfiltration of Buttermilk and Washed Cream Buttermilk for Concentration of Milk Fat Globule Membrane Components
Bibliographic record
Abstract
Buttermilk, the by-product from butter manufacture, has gained much attention lately because of the application potential of its milk fat globule membrane (MFGM) components as health ingredients. Microfiltration (MF) has been studied for buttermilk fractionation because of its ability to separate particles from dissolved solutes. However, the presence in this by-product of skim milk solids, especially casein micelles, restricts concentration of MFGM. The use of cream washed with skim milk ultrafiltrate to produce buttermilk with lower casein content was studied as well as fractionation of this buttermilk by MF. Results have shown that washing the cream prior to churning yields buttermilk with 74% less protein than normal cream buttermilk. Analysis of the protein profile of washed cream buttermilk revealed that caseins and whey proteins were the main classes of proteins removed. The MF of washed cream buttermilk resulted in permeation fluxes 2-fold higher than with normal cream buttermilk. The second separation of the cream induced high losses of phospholipids in the skim phase. However, retention of remaining phospholipids in washed cream buttermilk by the MF membrane was higher resulting in a phospholipids concentration factor 66% higher than that of normal cream buttermilk. The results presented in this study highlight the impact of casein micelles on the separation of MFGM components as well as their effect on permeation flux during MF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".