Integrating C–H Information to Improve Machine Learning Classification Models for Microplastic Identification from Raman Spectra
Bibliographic record
Abstract
Research has shown microplastic particles to be pervasive pollutants in the natural environment, but labor-intensive sample preparation, data acquisition, and analysis protocols continue to be necessary to navigate their diverse chemistry. Machine learning (ML) classification models have shown promise for identifying microplastics from their Raman spectra, but all attempts to date have focused on the lower energy “fingerprint” region of the spectrum. We explore strategies to improve ML classification models based on the k -nearest-neighbor algorithm by including other regions of the Raman spectra. The information content inherent in C–H bonds, which occur in the higher frequency region of 2500–3600 cm –1, is found to be particularly powerful in improving classification model performance. Variations in the relative intensity of peaks arising from C–H vibrations improve identification capabilities for plastics that the fingerprint region alone struggles with, such as resolving acrylonitrile butadiene styrene from polystyrene and identifying poly(vinyl chloride), polyurethane, and polyoxymethylene. Testing of strategies to both acquire and analyze data across the two regions is explored for their efficacy and their compatibility with real-world sampling restrictions. We find that localized normalization of spectra, independently acquired in the two regions, provides the most direct and effective route to improving the ML classification performance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".