Immunoglobulin G Measurement in Blood Plasma Using Infrared Spectroscopy
Bibliographic record
Abstract
A rapid, simple, and inexpensive method to measure the immunoglobulin G (IgG) concentrations in blood samples in human and veterinary medicine is highly desired. Infrared spectroscopy (coupled with chemometric manipulation of spectral data) has the advantages of simple sample preparation, rapid implementation of analysis, and low cost. Here a method that exploits infrared spectroscopy as the basis to measure IgG concentration in animal plasma samples is reported, with radial immunodiffusion (RID) used as the reference test method for partial least squares (PLS) calibration model development. Smoothed non-derivative and the second-order derivative spectra were used to develop calibration models. Various additional spectral preprocessing steps were evaluated to optimize the calibration models, and the possible benefits of using an internal standard (potassium thiocyanate [KSCN]) were investigated. Monte Carlo cross-validation was used to determine the optimal number of PLS factors, and an independent prediction set was used to test the predictive performances of provisional models. The effects of various preprocessing options (spectral smoothing, derivation, normalization, region selection, mean-centering, and standard deviation scaling) on quantification accuracy were investigated. The root mean squared error of prediction (RMSEP) for different combinations of spectra preprocessing steps was 394 ± 36 mg/dL for the non-derivative spectra and 427 ± 101 mg/dL for the second-order derivative spectra. Immunoglobulin G concentrations produced by the optimized PLS model for the non-derivative spectra (RMSEP = 352 mg/dL) were found to be stable with respect to different splits of the samples among the calibration, validation, and prediction sets. The precision of the Fourier transform infrared (FT-IR) method is found to be slightly superior to that of the RID method. The results of this work indicate that infrared spectroscopy is a promising technique for economically and rapidly determining the IgG concentrations of plasma and plasma-derived samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".