Changes in the physical properties, solubility, and heat stability of milk protein concentrates prepared from partially acidified milk
Bibliographic record
Abstract
A limiting factor in using milk protein concentrates (MPC) as a high-quality protein source for different food applications is their poor reconstitutability. Solubilization of colloidal calcium phosphate (CCP) from casein micelles during membrane filtration (e.g., through acidification) may affect the structural organization of these protein particles and consequently the rehydration and functional properties of the resulting MPC powder. The main objective of this study was to investigate the effects of acidification of milk by glucono-δ-lactone (GDL) before ultrafiltration (UF) on the composition, physical properties, solubility, and thermal stability (after reconstitution) of MPC powders. The MPC samples were manufactured in duplicate, either by UF (65% protein, MPC65) or by UF followed by diafiltration (80% protein, MPC80), using pasteurized skim milk, at either the native milk pH (~pH 6.6) or at pH 6.0 after addition of GDL, followed by spray drying. Samples of different treatments were reconstituted at 5% (wt/wt) protein to compare their solubility and thermal stability. Powders were tested in duplicate for basic composition, calcium content, reconstitutability, particle size, particle density, and microstructure. Acidification of milk did not have any significant effect on the proximate composition, particle size, particle density, or surface morphology of the MPC powders; however, the total calcium content of MPC80 decreased significantly with acidification (from 1.84 ± 0.03 to 1.59 ± 0.03 g/100 g of powder). Calcium-depleted MPC80 powders were also more soluble than the control powders. Diafiltered dispersions were significantly less heat stable (at 120°C) than UF samples when dissolved at 5% solids. The present work contributes to a better understanding of the differences in MPC commonly observed during processing.
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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) | 0.000 | 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".