Prediction efficiency by near-infrared spectroscopy of immunoglobulin G in liquid and dried bovine colostrum samples
Bibliographic record
Abstract
The objective of this study was to compare the prediction efficiency of IgG concentration in bovine colostrum by NIRS, using liquid and dried (Dry-Extract Spectroscopy for Infrared Reflectance, DESIR) samples by transflectance and reflectance modes, respectively. Colostrum samples (157), obtained from 2 commercial Holstein dairy farms, were collected within the first hour after calving and kept at -20 °C until analysis. After thawing and homogenisation, a subsample of 500 mg of liquid colostrum was placed in an aluminium mirror transflectance cell (0·1 mm path length), in duplicate, to collect the spectrum. A glass fiber filter disc was infused with another subsample of 500 mg of colostrum, in duplicate, and dried in a forced-air oven at 60 °C for 20 min. The samples were placed in cells for dry samples to collect the spectra. The spectra in the VIS-NIR region (400-2500 nm) were obtained with a NIRSystems 6500 monochromator. Mathematical treatments, scatter correction treatments and number of cross-validation groups were tested to obtain prediction equations for both techniques. Reference analysis for IgG content was performed by radial immunodiffusion. The DESIR technique showed a higher variation in the spectral regions associated with water absorption bands, compared with liquid samples. The best equation for transflectance method (liquid samples) obtained a higher coefficient of determination for calibration (0·95 vs. 0·94, respectively) and cross validation (0·94 vs. 0·91, respectively), and a lower error of cross validation (9·03 vs. 11·5, respectively) than the best equation for reflectance method (DESIR samples). In final, both methods showed excellent capacity for quantitative analysis, with residual predictive deviations above 3. It is concluded that, regarding accuracy of prediction and time for obtaining results of IgG from bovine colostrum, NIRS analysis of liquid samples (transflectance) is recommended over dried samples (DESIR technique by reflectance).
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".