Issues in Mass Spectrometry Between Bench Chemists and Regulatory Laboratory Managers: Summary of the Roundtable on Mass Spectrometry Held at the 123rd AOAC INTERNATIONAL Annual Meeting
Bibliographic record
Abstract
At the 123rd AOAC International Annual Meeting in Philadelphia, PA, 45 residue chemists gathered for a roundtable discussion of mass spectrometry (MS) used for regulatory chemical residues analysis. The session was conceived to address current technical and communication issues about MS between "bench chemists and their bosses". The topics covered a range of practical, routine, and recurring issues on capabilities and limitations of MS techniques, and suggestions on how chemists may better communicate their MS results with customers. The customers in this sense include laboratory managers, quality assurance officers, laboratory clients, regulatory officials, policy-makers, lawyers, and others who have interest in the data. The stated goals devised by the roundtable panelists were to provide independent advice, describe limitations, give practical tips, help set realistic expectations, and answer questions from the attendees. The panelists divided the topics into three main themes: practical aspects in routine analysis using MS, choice of MS technique depending on the purpose for analysis, and qualitative identification and confirmation concepts. This report was written to summarize and expand upon the discussion, frame the current issues, and provide advice on handling common situations in MS analysis and reporting of results. Topics included LODs, data quality objectives, quantification and reporting results, matrix effects, calibration, terminology, differences in performance across MS platforms, proficiency testing, qualitative analysis, and laboratory accreditation. Conclusions are presented as a set of questions for structuring a dialog between bench chemists and laboratory managers.
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 | 0.052 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".