Variations of Raw Milk Components and Amino Acid Profiles in Different Dairy Buffalo Crossbreds
Bibliographic record
Abstract
This study aimed to assess the relationship between milk components and amino acid (AA) profiles among different buffalo crossbreds in Bangladesh. A total of thirty-six (36) lactating buffaloes were selected from Murrah, Nili-Ravi, and Mehsana crossbreds, and they were assigned to 03 groups, each with 12 buffaloes. The total experimental period was 10 weeks, including the initial 10 days of diet adjustment. The results from the experiment revealed that milk protein, fat, and total solids contents of Murrah crossbred was significantly (p<0.05) higher than Mehsana and Nili-Ravi crossbreds. In contrast, the lactose content of all buffalo crossbreds was statistically similar (p>0.05). In the case of milk amino acid contents, all the milk samples entailed a higher concentration of Glutamic acid (0.9-1.00 g/100 g of milk), whereas Cysteine had the lowest concentration (0.02-0.05 g/100 g). The most prevalent essential amino acids were Leucine, Lysine, and Phenylalanine, whereas the most prevalent non-essential amino acids were Glutamic acid, Proline, Asparagine, and Serine. Lysine, Isoleucine, Leucine, Phenylalanine, Cysteine, and Histidine concentrations differed significantly (p<0.05), and at the same time as the other 11 AAs concentrations were found non-significant (p>0.05). The highest Lysine: Methionine ratio observed in Murrah buffalo was about 3.20%, while in Mehsana and Nili-Ravi buffalo was about 2.80 and 2.50%, respectively. Thus, it was evident from the study that the raw milk components and amino acid composition vary considerably in the different lactating buffalo crossbreds. Finally, the compositional data of raw milk may create the way of sustainable use of milk from dairy buffalo crossbreds and improve food and nutrition security, particularly in developing countries.
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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) | 0.001 | 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".