Effects of Genetic Variants of κ-casein and β-lactoglobulin and Heat Treatment on Coagulating Properties of Milk
Bibliographic record
Abstract
Fifty-two Holstein cows with different phenotypes of κ-casein (κ-CN) and β-lactoglobulin (β-LG) were selected to provide weekly milk samples for heating at 30, 70, 75 and 80°C for 2 min.Coagulating properties of heated milk samples measured as rennet clotting time, rate of curd firming and curd firmness at cutting were determined by a Formagraph.Milk samples were analysed for fat and casein.Least squares analyses of data, after adjustments were made for effect of milk casein and fat contents, indicated that although an increase in heating temperatures resulted in less desirable coagulating properties, the effect of milk types was inherent irrespective of heating temperatures.The shortest rennet clotting time (6.06 min), fastest rate of curd firming (5.61 min) and firmest curd (38.05 mm) were obtained from milk with the B variant for κ-CN and B variant for β -LG when preheated at 30°C.It appears that milk bearing κ-CN B is more resistant to heat perturbation.All milk samples having the κ-casein AA (milk types AA/AA, AA/AB, AA/BB) did not have a measurable K20 value when preheated at 70°C.This effect was observed for κ-casein AB (milk types AB/AA, AB/AB, AB/BB) at 75°C and κ-casein BB (milk types BB/AA, BB/AB, BB/BB) at 80°C.(Asian-Aust.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".