Abstract: A method for characterization of a mass spectrometry instrument (100) comprising at least one mass analyzing cell (102, 104, 106) is proposed. The method comprising the steps of analyzing a sample (110) comprising at least one substance having a known molecular weight by means of the mass spectrometry instrument (100) so as to provide a mass spectrum (116, 118, 144, 146) of the sample (110), determining an outer envelope and an inner envelope of the mass spectrum (116, 118, 144, 146), calculating a squared difference between the outer envelope and the inner envelope, and determining a deviation of the calculated squared difference from a theoretical mass to charge ratio value of the substance.

Abstract: A computer implemented method (140) for determining lifetime of at least one chromatography column (116) of at least one chromatography device (110), wherein the method (140) comprises the following steps: i) receiving model input chromatography data via at least one communication interface (128); ii) determining at least one state variable indicative of lifetime of the chromatography column (116) using at least one data driven model based on the model input chromatography data using at least one processing device (130); iii) evaluating the determined state variable thereby determining information about lifetime by using the processing device (130), wherein the evaluation comprises comparing the determined state variable to at least one threshold. Further, a test system (112), a computer program and a method for operating a chromatography column (116) are disclosed.

Abstract: A computer implemented method for automated quality check of chromatographic and/or mass spectral data is disclosed. The method comprises the following steps: a) (110) providing processed chromatographic and/or mass spectral data obtained by at least one mass spectrometry device (112); b) (114) classifying quality of the chromatographic and/or mass spectral data by applying at least one trained machine learning model on the chromatographic and/or mass spectral data, wherein the trained machine learning model uses at least one regression model (116), wherein the trained machine learning model is trained on at least one training dataset comprising historical and/or semi-synthetic chromatographic and/or mass spectral data, wherein the trained machine learning model is an analyte-specific trained machine learning model.

Abstract: An analytical system including a mass spectrometer (MS) coupled to liquid chromatography (LC) via an ionization source (IS), and an automated method of maintaining the analytical system in a QC-compliant status are described. The method comprises determining a deviation of the analytical system from a QC-compliant status by determining a deviation of one or more predetermined parameters in an m/z spectrum above one or more predetermined thresholds, triggering an IS and/or MS maintenance procedure upon determining a deviation from the QC-compliant status, determining a return or a failed return to the QC-compliant status during and/or after the IS and/or MS maintenance procedure by determining a return or a failed return respectively of the one or more predetermined parameters in an m/z spectrum below the one or more predetermined thresholds, triggering another IS and/or MS maintenance procedure upon determining a failed return to the QC-compliant status.

Abstract: An analytical system including a mass spectrometer (MS) coupled to liquid chromatography (LC) via an ionization source (IS), and an automated method of maintaining the analytical system in a QC-compliant status are described. The method comprises determining a deviation of the analytical system from a QC-compliant status by determining a deviation of one or more predetermined parameters in an m/z spectrum above one or more predetermined thresholds, triggering an IS and/or MS maintenance procedure upon determining a deviation from the QC-compliant status, determining a return or a failed return to the QC-compliant status during and/or after the IS and/or MS maintenance procedure by determining a return or a failed return respectively of the one or more predetermined parameters in an m/z spectrum below the one or more predetermined thresholds, triggering another IS and/or MS maintenance procedure upon determining a failed return to the QC-compliant status.