Quantitative assessment and impact of thermal treatment on quality of Holstein dairy cattle colostrum immunoglobulin and viscosity
Bibliographic record
Abstract
Abstract BackgroundBovine postpartum colostrum delivers a vital source of immunoglobulins (Igs) and other non-specific immune factors for passive transfer of immunity to newborn calves. Heat treatment of colostrum is an effective practice to reduce potential pathogens that can be transmitted to the newborn calves. Therefore, there is a need to determine optimized temperature and time that can minimally change IgG concentration and the viscosity (consistency) of colostrum. In order to preserve the quality and value of bovine postpartum colostrum, this study aimed to determine the IgG concentration, influence of different thermal treatments on the quality of colostrum and other properties including viscosity, fat and color.ResultsA total of 40 German Holstein dairy cattle first colostrum samples, collected after birth, were evaluated for color gradation, fat (%), colostrum IgG (mg mL-1), IgG (%Brix) and refractive index (nD) concentration, visual and dynamic viscosity and impact of different treatments (60 °C/60 min; 63.5°C/30 min and 72.0°C/15 s) on the viscosity. The color was graded from white-pale yellow to yellow and dark-yellowish, fat (1.4 - 8.2%) and IgG concentration (4 - 116 mg mL-1), Brix (8.5% - 35.4% Brix) and nD (1.3454 - 1.3905), respectively. The visual viscosity of first colostrum was classified into watery, liquid and thick consistency, whereas the dynamic viscosity and treated colostrum ranged from <10 to 219 and <10 to 3066 centipoise (cP), respectively. The consistency of treated samples were classified into liquid, thick and solid with significant change in the dynamic viscosity.ConclusionData revealed significant variation in the IgG values where both measurement methods showed high congruence with IgG classification. Due to the fact that individual IgG values were disparate, this significantly affected the colostrum consistency with different treatments. The study showed that colostrum containing IgG ≤80 mg mL-1 and ≤68 mg mL-1 treated at 60°C for 60 min and 63.5°C for 30 min showed a slight to moderate change in the consistency which suggests that these two temperatures and lengths of time that had a minimal impact on the consistency can be used to pasteurize colostrum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".