Effects of Genetic Variants of κ-casein and β-lactoglobulin and Heat Treatment of Milk on Cheese and Whey Compositions
Bibliographic record
Abstract
Milk samples with different phenotype combination of -casein and -lactoglobulin and different preheating temperatures of 30, 70, 75 and 80C were used for cheesemaking under laboratory conditions. For the 853 batches of cheese, mean composition was 59.64% total solids, 30.24% fat and 23.66% protein, and the whey contained 6.93% total solids, 0.30% fat and 0.87% protein. Least squares analysis of the data indicated that heating temperature of the milk and -CN/-LG phenotypes had significant effects on cheese and whey compositions. The total solids, fat and protein contents of cheese were negatively correlated with preheating temperatures of milk. Cheese from BB/BB phenotype milk had the highest and those from AA/AA phenotype milk had the lowest concentrations of total solids, fat and protein. Mean recoveries of milk components in the cheese were 53.71% of total solids, 87.15% of fat, and 80.32% of protein. For the 10 different types of milk, maximum recoveries of milk components in cheese occurred with preheating temperature of 70C or 75C and lowest recoveries occurred at 80C. The whey averaged 6.94% total solids, 0.30% fat and 0.87% protein. Losses of milk components in the whey were lowest for milk preheated at 80C and for milk containing the BB/BB phenotype. (
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".