An Advanced Gas Chromatography–Mass Spectrometry Workflow for High-Confidence Non-Targeted Screening of Non-Intentionally Added Substances in Recycled Plastics
Bibliographic record
Abstract
As circularity grows in the global economy, recycling has become more relevant in the plastic materials industry. Recycled plastics, sourced from various origins, can contain numerous non-intentionally added substances such as organic contaminants, polymer degradation products, and consumer residues. The confident identification of contaminants has become an important step in the quality assessment of the recycled material and the evaluation of cleaning processes. However, traditional one-dimensional gas chromatography often encounters challenges in reporting accurate results for these complex samples. In this work, we combine a cryogen-free comprehensive two-dimensional gas chromatographic separation coupled with high-resolution mass spectrometry and a new confidence-level-based data reporting workflow to achieve more rigorous and higher-confidence identification of nontargeted species in recycled plastics. We propose four confidence levels, and seven confidence descriptor classifications based on mass spectral matching, retention index matching, and mass accuracy from high-resolution mass spectral data. The workflow was applied to postconsumer recycled plastics before and after the cleaning process. Higher than 70% of identifications are made with medium-to-high confidence. About 50% more peaks are separated and identified by the workflow compared to traditional one-dimensional separation without significant increase in data collection and analysis time. The workflow was validated by recycled plastics spiked with 26 known compounds of environmental relevance covering a broad range of chemical structures.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".