Denoising of Raman spectroscopy for biological samples based on empirical mode decomposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Raman spectroscopy of biological samples presents undesirable noise and fluorescence generated by the biomolecular excitation. The reduction of these types of noise is a fundamental task to obtain the valuable information of the sample under analysis. This paper proposes the application of the empirical mode decomposition (EMD) for noise elimination. EMD is a parameter-free and adaptive signal processing method useful for the analysis of nonstationary signals. EMD performance was compared with the commonly used Vancouver algorithm (VRA) through artificial data (Teflon), synthetic (Vitamin E and paracetamol) and biological (Mouse brain and human nails) Raman spectra. The correlation coefficient ([Formula: see text]) was used as performance measure. Results on synthetic data showed a better performance of EMD ([Formula: see text]) at high noise levels compared with VRA ([Formula: see text]). The methods with simulated fluorescence added to artificial material exhibited a similar shape of fluorescence in both cases ([Formula: see text] for VRA and [Formula: see text] for EMD). For synthetic data, Raman spectra of vitamin E were used and the results showed a good performance comparing both methods ([Formula: see text] for EMD and [Formula: see text] for VRA). Finally, in biological data, EMD and VRA displayed a similar behavior ([Formula: see text] for EMD and [Formula: see text] for VRA), but with the advantage that EMD maintains small amplitude Raman peaks. The results suggest that EMD could be an effective method for denoising biological Raman spectra, EMD is able to retain information and correctly eliminates the fluorescence without parameter tuning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it