Physical and Processing Properties of Milk, Butter, and Cheddar Cheese from Cows Fed Supplemental Fish Meal
Bibliographic record
Abstract
Physical, chemical, sensory and processing properties of milk produced by feeding a rumen-undegradable fish meal protein supplement to Holstein cows were investigated. The supplement contained (as fed basis) 25% soft-white wheat, 60% herring meal, and 15% feather meal. The total fat level in the milk decreased to 2.43%. For both pasteurized and ultra-high temperature processed drinking milk, no difference was found between fish meal (FM) milk and control milk in terms of color, flavor and flavor stability; in particular, no oxidized flavor was observed. Cheddar cheese made from FM milk ripened faster after 3 mo of ripening and developed a more desirable texture and stronger Cheddar flavor. The yield efficiencies for FM and control cheese, 94.4 (+/- 2.44 SE) and 96.4 (+/- 2.26 SE), respectively, were not different. Relative to controls, average fat globule size was smaller in FM milk and churning time of FM cream was longer. FM butter had softer texture and better cold spreadability, and butter oils from FM enriched milk had lower dropping points compared to control butter oil (average 32.89 versus 34.06 degrees C). These differences in physical properties of butter fat were greater than expected considering that iodine values were not different. This study demonstrates the feasibility of producing high quality products from milk naturally supplemented with FM, but the results also show that dietary changes affect processing properties.
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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".