Impact of physicochemical changes in milk ultrafiltration permeate concentrated by reverse osmosis on calcium phosphate precipitation
Bibliographic record
Abstract
This study aimed to characterize and compare the mechanisms of calcium phosphate precipitation in skimmed milk ultrafiltration permeate (MP) and MP preconcentrated by reverse osmosis (ROMP). The effects of different physicochemical parameters such as the pH (8.0), the heating time (60 or 120 min at 60 °C) and the seeding of samples with dicalcium phosphate (DCP) were tested. The concentration of salts (K, Ca, Na, Mg, and P) in the freeze-dried precipitates was measured using inductively coupled plasma (ICP). The amount of remaining ionic calcium was also monitored. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis were used to characterize the type of calcium phosphate precipitates that formed. The morphological structure of particles was determined using scanning electron microscopy (SEM). The chemical analyses showed that RO increased the rate of precipitated ions, especially Ca and P in MP, while alkalinization to pH 8.0 and heating at 60 °C significantly increased the precipitation of salts, with the calcium phosphate structure changing into complex forms such as hydroxyapatite (HAP) and whitlockite. MP preconcentration by RO paves the way for improving the precipitation yield of milk salts in the form of HAP for Ca fortification in various foods. It offers an original way to valorize the milk salts contained in the high volumes of MP generated by the cheese industry.
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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".