Efficient algorithms for highly automated evaluation of liquid chromatography - mass spectrometry data

Sammanfattning: Liquid chromatography coupled to mass spectrometry (LC‐MS) has due to its superiorresolving capabilities become one of the most common analytical instruments fordetermining the constituents in an unknown sample. Each type of sample requires a specificset‐up of the instrument parameters, a procedure referred to as method development.During the requisite experiments, a huge amount of data is acquired which often need to bescrutinised in several different ways. This thesis elucidates data processing methods forhandling this type of data in an automated fashion.The properties of different commonly used digital filters were compared for LC‐MS datade‐noising, of which one was later selected as an essential data processing step during adeveloped peak detection step. Reconstructed data was further discriminated into clusterswith equal retention times into components by an adopted method. This enabled anunsupervised and accurate comparison and matching routine by which components fromthe same sample could be tracked during different chromatographic conditions.The results show that the characteristics of the noise have an impact on the performanceof the tested digital filters. Peak detection with the proposed method was robust to thetested noise and baseline variations but functioned optimally when the analytical peaks hada frequency band different from the uninformative parts of the signal. The algorithm couldeasily be tuned to handle adjacent peaks with lower resolution. It was possible to assignpeaks into components without typical rotational and intensity ambiguities associated tocommon curve resolution methods, which are an alternative approach. The underlyingfunctions for matching components between different experiments yielded satisfactoryresults. The methods have been tested on various experimental data with a high successrate.