Extraction and cleansing of data for a non-targeted analysis of high-resolution mass spectrometry data of wastewater
Bibliographic record
Abstract
We provide a workflow to extract unidentified signals from chromatography-high resolution mass spectrometry (LC-HRMS) data of wastewater samples as a pre-step of a non-targeted analysis of dissolved organic matter (DOM). We provide detailed methodology on data processing and cleanup using MS processing software MZmine 2 and an own set of functions in R developed for wastewater analysis. The processing involves signal extraction, linear mass correction, reduction of noise, grouping of isotopologues, molecular formula assignment and merging of replicates. The article contains software settings and reasoning behind the choice of data extraction options. The supplementary information contains a script for the correction of signal masses using internal standards and templates of internal standard lists. We included a reproducible example as an R notebook with data cleansing workflow and data exported from MZmine. The data were used according to the described methodology in the article "A non-targeted high-resolution mass spectrometry data analysis of dissolved organic matter in wastewater treatment" by Verkh et al., 2018. •Includes a linear mass correction algorithm for LC-HRMS signals.•Describes a pipeline of non-targeted processing of LC-HRMS data of wastewater using free software.•Provides tests and reasons for parameter choice in non-targeted LC-HRMS wastewater data extraction.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.023 |
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".