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Rapid Communications in Mass Spectrometry
Article . 2026 . Peer-reviewed
License: CC BY
Data sources: Crossref
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PubMed Central
Article . 2026
License: CC BY
Data sources: PubMed Central
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Automated Near Real‐Time QC for LC‐HRMS

Authors: Michael J. Mohr; Linus Strähle; Tobias Bader; Pia Leurle; Jan H. Christensen; Wolfram Seitz; Rudi Winzenbacher;

Automated Near Real‐Time QC for LC‐HRMS

Abstract

ABSTRACT Rationale The quality of analytical measurements is typically evaluated after completion of the entire, or possibly multiple, measurement batch(es). Automated, near real‐time quality control (QC) during LC‐HRMS acquisition can prevent reruns and sample loss by flagging issues as they occur. Functionality was evaluated by retrospective application to 5 years of river‐water surveillance. Methods We present a modular MATLAB workflow that tracks isotopically labelled internal standards for peak height, retention time and mass error against rolling, method‐specific expectations; applies multivariate statistical process control (MSPC; PCA with Hotelling's T 2 and SPE on intensity/retention time ratios and mass error); issues immediate email alerts; and logs outcomes to a PostgreSQL database/Grafana dashboard for trend analysis. Also, qualitative target screening via cosine‐similarity MS 2 checks against a local library, retention time correction, robust peak‐height/noise estimation, configurable limits and automated vendor‐to‐open format conversion. Results In a high‐voltage power‐supply failure, 25/25 injections were flagged due to abnormal intensity patterns; during an organic‐pump malfunction, 17/25 were flagged for retention drift up to and beyond the extraction window; and during an air‐conditioning (AC) outage, MSPC detected mass error anomalies even when the ±10 ppm univariate limit was not breached. MSPC closely agreed with univariate thresholds: 95.7% of samples flagged by univariate rules were also flagged by MSPC (≈4.3% Type II), while 92.5% of MSPC‐flagged samples violated at least one univariate rule (≈7.5% Type I). Conclusion These capabilities enable immediate detection, triage and documentation of performance excursions, support proactive maintenance (e.g., column aging or pump delivery issues), minimise downtime and safeguard precious samples. Although showcased on a specific LC‐HRMS setup and matrix, the workflow is instrument‐agnostic and broadly applicable to internal‐standardised LC‐HRMS methods.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
hybrid