Curve‐fitting regression: improving light element quantification with XRF
Bibliographic record
Abstract
Light elements are hard to quantify by X‐ray fluorescence (XRF) spectrometry because, after a photoelectric excitation, they predominantly relax emitting Auger electrons, greatly reducing the fluorescence count thus limiting the signal‐to‐noise ratios (SNR) observed. Low SNR values have deleterious outcomes in model building. Notable in ordinary least squares (OLS) regression based on peak height, they also affect more robust regression methods, such as partial least squares regression. While low SNR can also be observed with low concentrations of heavier elements, this paper focuses on boron. To overcome the low SNR hurdle, curve‐fitting regression (CFR), a novel method elaborated in this paper, seeks to fit full scans with summed Gaussian curves. The methodology was illustrated with pressed microcrystalline cellulose spiked with sodium tetraborate decahydrate (borax) powder samples. The calibration set ranged from 0% to 21.5% m / m boron, and a PANalytical Axios wavelength dispersive X‐ray fluorescence system with rhodium source was used to perform the tests. A calibration curve with determination coefficient ( R 2 ) = 0.990 and root mean square error of calibration (RMSEC) = 0.7% m / m was produced with CFR versus RMSEC = 1.2% m / m with OLS regression. Validity of the method was tested with a set of 17 pearled samples containing a mixture of different oxides. Root mean square error of prediction (RMSEP) of 0.1% m / m was obtained with the validation set, using CFR against RMSEP = 0.99% m / m with OLS regression, thus illustrating the proposed method's potential. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".