A protocol for wide-scope non-target analysis of contaminants in small amounts of biota using bead beating tissuelyser extraction and LC-HRMS
Bibliographic record
Abstract
This work describes a robust and powerful method for wide-scope target and non-target analysis of xenobiotics in biota samples based on bead beating tissuelyser extraction, solid phase extraction (SPE) clean-up and further detection by liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). Unlike target methodologies, non-target methods usually aim at determining a wide range of still unknown substances with different physicochemical properties. Therefore, losses during the extraction process were minimised. Apart from that, the reduction of possible interferences showed to be necessary to expand the number of compounds that can be detected. This was achieved with an additional SPE clean-up step carried out with mixed-bed multi-layered cartridges. The method was validated with a set of 27 compounds covering a wide range of physicochemical properties, and further applied to the analysis of krill and fish samples.•The bead beating extraction was efficient for a wide range of organic pollutants in small quantities of biota samples.•Multi-layered solid phase extraction clean-up yield a wide xenobiotics coverage reducing matrix effects.•Method validation with 27 compounds led to a suitable method for non-target analysis of organic pollutants in biota.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".