Smoothing Raman Spectra with Contiguous Single-Channel Fitting of Voigt Distributions: An Automated, High-Quality Procedure
Bibliographic record
Abstract
Theoretical probability distributions are often fitted to the individual peaks of Raman spectra to decompose them and facilitate further analyses. Fitting has the additional advantage of eliminating noise. We have exploited this noise-eliminating attribute of fitting procedures in an automated algorithm to smooth Raman spectra. An initial smoothing was performed by fitting Voigt distributions to every channel in a spectrum. The Voigt distribution characters used were strongly Gaussian, the distribution widths equal to the spectral resolution, and their initial amplitudes equal to the spectral intensities at the channels where they were located. The smoothed spectrum was then subtracted from the original noisy spectrum to obtain a residual. For channels where the residual exceeded a limit-of-detection threshold, the distribution width was decreased. The fitting was then repeated until a secondary, lower limit distribution width was reached. The residual was then smoothed repeatedly in the same manner until the minimum distribution width was reached. After each repetition, the smoothed residual was added to the smoothed spectrum. The process was continued until a combined limit of detection and chi-squared stopping criterion was reached. Although slower in comparison to spline- and Savitzky-Golay-based methods, the smoothing quality was significantly better allowing the majority of smoothed spectra, in contrast to these methods, to pass a stringent smoothing quality test.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".