Effect of Heat-treatment on Accuracy of Infrared Spectroscopy and Digital and Optical Brix Refractometers for Measuring Immunoglobulin G Concentration in Bovine Colostrum
Bibliographic record
Abstract
BACKGROUND: Heat-treatment of colostrum is a method developed to reduce calf exposure to pathogens. Infrared (IR) spectroscopy and Brix refractometers can be used for measuring colostral IgG concentration and assessing colostrum quality. OBJECTIVES: To determine the impact of heat-treatment on accuracy of IR spectroscopy and Brix refractometers for measuring colostral IgG concentration and assessing colostrum quality before and after heat-treatment. ANIMALS: A total of 60 Holstein dairy cows on 8 commercial dairy farms. METHODS: A cross-sectional study was designed to determine the effect of heat-treatment at 60°C and 63°C each for 30 and 60 minutes duration on colostral IgG concentration measured by the reference radial immunodiffusion (RID) assay, IR spectroscopy, and digital and optical refractometers. RESULTS: Colostrum IgG concentration significantly decreased after heat-treatment at 63°C for 30 or 60 minutes as measured by RID, but the IgG values remained unchanged when measured by IR spectroscopy and refractometers. The lowest correlation coefficient found between IR spectroscopy (r = 0.70) and RID results was in colostrum heat-treated at 63°C for 60 minutes. For digital (r = 0.48) and optical (r = 0.50) refractometers, the lowest correlation coefficient was at 63°C for 30 minutes when compared to RID. The accuracy of the IR spectroscopy, digital and optical Brix refractometers was decreased from 91.7 to 80%, 81.7 to 45%, and 80 to 45%, respectively, when colostrum heat-treated at 63°C for 60 minutes. CONCLUSIONS AND CLINICAL IMPORTANCE: Radial immunodiffusion, IR spectroscopy, and Brix refractometers exhibit utility for measuring IgG concentration when colostrum heat-treated at 60°C but does not detect decrease IgG concentrations when heat-treated at 63°C.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".