Effect of feeding fresh forage and marine algae on the fatty acid composition and oxidation of milk and butter
Bibliographic record
Abstract
This study evaluated the effects of feeding fresh forage either as pasture plus a concentrate (PAS) or as a silage-based total mixed ration (TMR), combined with either a ruminally inert lipid supplement high in saturated fatty acids (-) or a ruminally protected microalgae containing 22 g of docosahexaenoic acid (DHA)/100 g of fatty acids (+) on the fatty acid (FA) composition and oxidation of milk and butter. For the 8 mid-lactation Holstein cows in this study, milk yield was not significantly affected by treatment, averaging 32.3 ± 1.28 kg/d. Milk fat content was higher for PAS⁻, averaging 5.05 compared with 4.10 ± 0.17% for the mean of other treatments, and was significantly depressed with microalgae supplementation (3.97 vs. 4.69 ± 0.17%). The saturated fatty acid level in the milk of cows fed TMR⁻ was significantly higher than that of the other treatments (66.9 vs. 61.2 g/100 g of FA). The level of monounsaturated FA was lowered by feeding TMR⁻ (27.4 vs. 32.0 g/100 g of FA), whereas levels of polyunsaturated FA were elevated by feeding PAS+ compared with the mean of the other treatments (6.54 vs. 5.07 g/100 g of FA). Feeding the rumen-protected microalgae increased the DHA content of milk more than 4-fold (0.06 to 0.26 g/100g of FA) with the PAS treatment. The conjugated linoleic acid content of milk was highest for PAS+ compared with the other treatments (4.18 vs. 3.41 g/100g of FA). In general, the fatty acid composition of butter followed that of milk. Overall, feeding the TMR supplemented with the rumen-protected microalgae increased the levels of volatile products of oxidation in milk and butter. No effect of forage type or microalgae supplementation was observed on the oxidative stability or antioxidant capacity of milk, although the oxidative stability of butter exposed to UV was reduced with microalgae supplementation, particularly with TMR, as assessed by using the ferric reducing ability of plasma assay.
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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) | 0.001 | 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 | 0.001 |
| 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".