Reducing manual targeted LC-MS/MS peak integration using a supervised learning peak evaluation and automated review tool

Summary

Background: Our laboratory analyses over 100,000 samples for multiple analytes yearly. Correctly annotating and quantifying analytes using targeted MS/MS analysis is crucial for most laboratories. Therefore, mass spectrometric instrument manufacturers include software capable of automatic chromatographic peak detection and integration, which is used for most routine applications. Although this generally works well, mistakes such as accidentally selecting a nearby matrix peak or drawing an incorrect baseline are relatively common. Especially when results are to be used for enforcement, a time-consuming manual review of each integrated peak is still required to obtain reliable results. This work aims to provide a tool that can significantly reduce the manual workload of reviewing peak integration, thereby reducing the time used for manual review while ensuring that errors made by automatic integration can be corrected by human experts. Results: Peak Evaluation and Automated Review tool, or PEAR review, is a machine learning-type tool that can read automatic integrations from various brands of MS equipment and compare them with a set of examples stored in a database of correct peak integrations provided by analysts that are relevant for the type of analysis. Moreover, the automatic review process checks all available ion transitions for a target compound. With those ingredients, the tool can autonomously decide how a peak should be quantified or whether a human expert should review it. The developed tool was tested on routine data processed with a widely used vendor-specific software, and we found that 85% of all chromatograms were handled automatically by this tool. Only the remaining 15% needed a ‘conventional’ manual review. The qualitative and quantitative performance of the PEAR tool was found to be equivalent to that of expert human integration, underlining its reliability. Significance: Our findings indicate that 85% of all manual integration checks can be skipped using PEAR. This reduces the often tedious workload of reviewing all peaks in multiple chromatograms while offering the same quality as full human intervention.