Mare milk nitrogen fractions during lactation and determination by reversed-phase HPLC of the major whey proteins
Bibliographic record
Abstract
The aim of this research was to describe the changes of the nitrogen fractions in milk of Haflinger nursing mares and in particular to determine the whey protein content and distribution, and their evolution throughout the first 6 mo of lactation. Samples were collected by hand-milking on days 4, 20, 40, 60, 80, 120, 150 and 180 post-partum. Nitrogen fractions were determined by Kjeldahl on 80 samples from 10 mares, while HPLC separation of whey proteins was conducted on 40 samples from 5 mares. The total N, casein N, and true whey protein N contents showed a statistically significant decrease from day 4 to day 40, and then remained unvaried. In general, the nitrogen distribution of mare’s milk significantly changed between day 4 and day 20 and then remained almost unchanged until day 180 (except for the day 150 value, which showed a statistically significant increase for CN × 100/TN). β-Lactoglobulin and serum albumin contents showed a marked reduction, of 27 and 45.5%, respectively, between day 4 and day 20, and then remained unchanged; α-lactalbumin and immunoglobulins contents had a reduction of 24.7 and 38.3%, respectively, between day 4 and day 20, and another decrease between day 20 and day 40, of 20 and 32.8%, respectively. All whey proteins, expressed as a percentage on the total sum of the four whey proteins considered, did not vary significantly during lactation. However, the sum on the two whey proteins of mammary gland origin (β-Lg + α-La) increased between day 4 and day 20 by 6.3%, and between day 20 and day 40 by 3.8%; the sum of the whey proteins of blood origin (SA + Ig) showed an opposite trend, with a decrease by 14.5% between day 4 and day 20 and by 11% between day 20 and day 40. Key words: Mares milk, lactation stage, whey protein distribution, reversed-phase HPLC
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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".