Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples
Bibliographic record
Abstract
Spectral preprocessing is frequently required to render Raman spectra useful for further processing and analyses. The various preprocessing steps, individually and sequentially, are increasingly being automated to cope with large volumes of data from, for example, hyperspectral imaging studies. Full automation of preprocessing is especially desirable when it produces consistent results and requires minimal user input. It is therefore essential to evaluate the "quality" of such preprocessed spectra. However, relatively few methods exist to evaluate preprocessing quality, and fully automated methods for doing so are virtually non-existent. Here we provide a brief overview of fully automated spectral preprocessing and fully automated quality assessment of preprocessed spectra. We follow this with the introduction of fully automated methods to establish figures-of-merit that encapsulate preprocessing quality. By way of illustration, these quantitative methods are applied to simulated and real Raman spectra. Quality factor and quality parameter figures-of-merit resulting from individual preprocessing step quality tests, as well as overall figures-of-merit, were found to be consistent with the quality of preprocessed spectra.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".