Effect of feeding linseed oil in diets differing in forage to concentrate ratio: 2. Milk lactone profile
Bibliographic record
Abstract
Lactones are important contributors to the flavour and aroma of milk and dairy products. This study was conducted to evaluate the effects of dietary linseed oil (LO) and forage to concentrate ratio on milk lactone profile. Twenty four Holstein cows were used during a 4-week feeding trial in a randomised complete block design. Cows were fed diets containing 30% (LC) or 70% (HC) concentrate, and 0% (NLO) or 3% LO in a 2×2 factorial arrangement of treatments. Milk lactone profile was evaluated using the solid phase microextraction technique. The highest levels of δ-lactones (δ-6:0, δ-8:0, δ-10:0, and δ-12:0) were found with the LC/NLO diet. These concentrations were then decreased when cows received either a high level of concentrate or supplemental LO, but these effects were not additive (interaction of LO by concentrate, P<0·01). An interaction of LO by concentrate (P<0·01) was also noted on milk γ-12:0 for which the highest concentration was observed when supplementing LO in HC diet, while no effect was apparent when LO was added in LC diet. Moreover, feeding HC increased the level of γ-12:1 in milk as compared with LC, while LO had no effect on this γ-lactone. Finally, γ-12:2 was not detected in any of the milk samples studied. Organoleptic properties of milk were evaluated in a triangle test showing that a significant number of assessors perceived a difference between milk from cows fed LC/NLO as compared with milk from cows fed HC/LO. The sensory evaluation was completed by a ranking test where the intensities of fresh lactic, foreign and global flavours were not different between treatments. In conclusion, feeding LO in HC diet modified milk lactone profile with a shift toward more γ- and less δ-lactones as compared with LC diet not supplemented with LO. A difference was perceived in a triangle test between milk from these two treatments, but the sensory attributes responsible for this difference have not been identified in the current trial.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".