μATR-FTIR Spectral Libraries of Plastic Particles (FLOPP and FLOPP-e) for the Analysis of Microplastics
Bibliographic record
Abstract
Raman spectral libraries specific to microplastics demonstrated improved spectral matching results when weathered plastics and a variety of particle colors and morphologies were included. Here, we explore if this is true for Fourier transform infrared (FTIR) spectroscopy as well. We present two novel databases specific to microplastics using attenuated total reflection (μATR-FTIR): (1) an FTIR library of plastic particles (FLOPP), containing 186 spectra from common plastic items, across 14 polymer types and (2) an FTIR library of plastic particles sourced from the environment (FLOPP-e), containing 195 spectra across 15 polymer types. Both libraries include particles from a variety of sources, morphologies, and colors. We demonstrate the applicability of these libraries for microplastics research by comparing spectral match results from two microplastic datasets. For this, we use different combinations of libraries including: commercially available reference libraries, an open-access polymer library, and FLOPP and FLOPP-e. Among tests, the greatest mean HQI result was achieved when the greatest number of libraries was included. This work demonstrates that spectral libraries specific to plastic particles found in the environment improve the accuracy of spectral matching and are best used in combination with commercial libraries containing chemical components that are commonly found within plastics and other anthropogenic particles. Multivariate principal component analyses of FLOPP and FLOPP-e spectra confirmed differences among polymer types and higher variation in principal component scores among weathered particles, but no patterns were observed among particle colors or morphologies. These results demonstrate that ATR-FTIR analyses are sensitive to weathering of plastics but not to particle color and morphology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".