The affinity of milk fat globule membrane fragments and buttermilk proteins to hydroxyapatite
Bibliographic record
Abstract
Buttermilk differs from skim milk by the presence of milk fat globule membrane (MFGM) fragments that are released during cream churning. Milk fat globule membrane is rich in health-promoting components, such as phospholipids and membrane proteins, but these compounds have a negative impact on buttermilk techno-functional properties in dairy applications. The isolation of MFGM from buttermilk improved its functionality while also recovering the MFGM bioactive components. Hydroxyapatite (HA) can be used to extract MFGM by adsorption via charged site interactions. However, the affinity of HA to MFGM or the main buttermilk proteins (casein micelles [CM], β-LG, and α-LA) is not known. The influence of important physicochemical parameters such as pH and temperature on these interactions is also unclear. For each buttermilk component, a quartz crystal microbalance diffusion analysis was performed to determine the maximum adsorption time and the attached mass density on HA-coated gold sensors. The influence of pH, ionic strength (IS), and temperature (T) on the affinity of each buttermilk component for HA particles was assessed using a 3-levels and 3-factors Box-Behnken design. The absorption rate was highest for the CM, followed by β-LG and α-LA, and then by the MFGM. Nevertheless, the final maximal attached mass densities to the HA were similar for the MFGM and CM, and 2.5 times higher than for β-LG and α-LA. This difference can be explained by the higher number of binding sites found in CM and their heavier mass. The model obtained by the Box-Behnken design plan showed that the adsorption of the CM changed with T, pH, and IS. These results suggest that the techno-functional properties of buttermilk may be restored by specifically extracting MFGM with HA. Experiments are ongoing to determine conditions for fractionating MFGM directly from buttermilk.
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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 | 0.001 |
| 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".