Ultrafast Screening and Quantitation of Pesticides in Food and Environmental Matrices by Solid-Phase Microextraction–Transmission Mode (SPME-TM) and Direct Analysis in Real Time (DART)
Bibliographic record
Abstract
The direct interface of microextraction technologies to mass spectrometry (MS) has unquestionably revolutionized the speed and efficacy at which complex matrices are analyzed. Solid Phase Micro Extraction-Transmission Mode (SPME-TM) is a technology conceived as an effective synergy between sample preparation and ambient ionization. Succinctly, the device consists of a mesh coated with polymeric particles that extracts analytes of interest present in a given sample matrix. This coated mesh acts as a transmission-mode substrate for Direct Analysis in Real Time (DART), allowing for rapid and efficient thermal desorption/ionization of analytes previously concentrated on the coating, and dramatically lowering the limits of detection attained by sole DART analysis. In this study, we present SPME-TM as a novel tool for the ultrafast enrichment of pesticides present in food and environmental matrices and their quantitative determination by MS via DART ionization. Limits of quantitation in the subnanogram per milliliter range can be attained, while total analysis time does not exceed 2 min per sample. In addition to target information obtained via tandem MS, retrospective studies of the same sample via high-resolution mass spectrometry (HRMS) were accomplished by thermally desorbing a different segment of the microextraction device.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".