Understanding the differences in cheese-making properties between reverse osmosis and ultrafiltration concentrates
Bibliographic record
Abstract
Concentrating milk by reverse osmosis (RO) has the potential to increase cheese yield but is known to impair cheese-making properties. The main compositional differences between ultrafiltration (UF) and RO concentrates are the high lactose and mineral contents of the latter. The objective of this work was to determine the distinct effects of high lactose and high minerals on the cheese-making properties of RO concentrate, by supplementing UF concentrate with lactose. The soluble colloidal equilibria of concentrates were studied as well as several other properties: rennet gelation behavior, cheese mass balance, composition, and microstructure. Rennet coagulation time was longer and gel firming rate was lower for RO concentrate than for UF concentrate. Lactose was mainly responsible for these differences. Lactose in RO concentrate was also responsible for the 7% increase of moisture-adjusted cheese yield, relative to UF concentrate. Compared with cheese made from UF concentrate, cheese made from RO concentrate showed higher moisture content, which could not be attributed to lactose but to the high mineral concentration. This study showed the potential of using RO instead of UF concentrate to maximize cheese yield. The approach is, however, limited to applications where post-acidification can be controlled, and will require appropriate strategies to reduce the negative effects of high mineral content in RO concentrate.
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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) | 0.000 | 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".