Peaks are detected on a mass chromatogram at multiple m/z ratios characterizing a target component, and the detected peaks are classified into groups according to their occurrence time. The measured mass spectrum is acquired for each group, the measured mass spectrum and standard mass spectrum of the target component are matched for each m/z, and the standard mass spectrum is normalized by multiplying it by the same scale factor for all the m/z ratios such that it does not exceed the peak intensities on the measured mass spectrum. The quantitation ion m/z peak intensity on the normalized standard mass spectrum is then examined, and if this intensity exceeds a preset threshold and the confirmation ion ratio determined based on the measured mass spectrum obtained for the target component is outside a reference range, then that target component is taken as a narrowed result candidate.

Exemplary embodiments provide methods, mediums, and systems for visualization and advanced data science on information collected in an analytical data system. Embodiments identify correlations and patterns in chromatography metadata around areas of potential user error. Correlations between these data sources may point to compliance risk areas. Metadata from the analytical system may be combined with other data sources and/or analytical data to correlate an analytical outcome with compliance artifacts. Supervised and/or unsupervised machine learning techniques may be used to combine these data source and learn correlations between them and compliance risks. The results of these analyses may be displayed on a dashboard, allowing a user to visualize compliance risks across an entire enterprise or supply chain. Automatic notifications of compliance risks may be generated and presented on a user interface. A system may also use pattern recognition to provide insights around potential compliance risks that have not yet occurred.

One or more known compounds are separated from a mixture using a separation device that allows processor-controlled adjustment of a separation parameter. The separated compounds are ionized and, for each cycle of a plurality of cycles, a mass spectrometer executes on the ion beam a series of MRM transitions read from a list. Two or more contiguous groups of MRM transitions to be monitored separately are received. Each group includes at least one sentinel transition that identifies a next group that is to be monitored and identifies a value for the separation parameter for the next group. A first group is placed on the list. When a sentinel transition of the first group is detected, a next group identified by the sentinel transition is placed on the list and the separation parameter is adjusted to a value identified by the sentinel transition for the next group.

The operation efficiency and accuracy of the simultaneous analysis of phospholipids, including fatty acid compositions are increased. After a first-time LC/MS/MS analysis for determining the phospholipid classes of the phospholipid contained in a sample is performed (S2-S3), a second-time LC/MS/MS analysis for determining fatty acid compositions is performed only for the detected phospholipids (S4-S8). By associating a method list in which an MRM transition for phospholipid class determination is recorded for each compound of phospholipid classes with a method list in which an MRM transition for fatty acid composition determination is recorded for each phospholipid compound, it is possible to promptly select MRM transitions for fatty acid composition determination that correspond to compounds of the detected phospholipid classes, and to easily create an analysis method for the second-time analysis.

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.

A method of analyzing a multi-component polymer comprising: (a) dissolving an multi-component polymer having a primary monomer and primary comonomer to form a first volume (soluble portion of multi-component polymer); (b) injecting a portion of the first volume into a chromatographic column to get elution first slices, leaving a second volume behind; (c) filtering the second volume to isolate multi-component polymer solids; (d) dissolving solids to form solution third solution (insoluble portion of multi-component polymer); (e) injecting a portion of third solution into the chromatographic column to get elution second slices; (f) obtain infra-red spectra at wavelengths suitable for the primary monomer and the primary comonomer of first and second elution slices, separately; and (g) for each elution slice, separately calculate: (i) the different polymer components (soluble and insoluble); and (ii) the comonomer content of each component (soluble and insoluble).

Systems and methods are provided for improving the analysis of analytes by using electrophoresis apparatus. Exemplary methods provide an increase in the yield of useful results, e.g., quantity and quality of useable data, in automated peak detection, in connection with an electrophoretic separation, e.g., capillary electrophoresis. In various embodiments, the system virtualizes the raw data, transforming the migration time into virtual units thereby allowing the visual comparison of analyte electropherograms and the reliable measurement of unknown analytes. The analytes can be, for example, any organic or inorganic molecules, including but not limited to nucleic acids (DNA, RNA), proteins, peptides, glycans, metabolites, secondary metabolites, lipids, or any combination thereof. Analyte detection can be performed by any method including, but not limited to, fluorescence detection or UV absorption. The present teachings provide, among other things, for consistent comparisons of analyte peaks across samples, across instruments, across runs, and across migration times.

Systems and methods include a computer-implemented method for analyzing petroleum samples. Different boiling curves are received that are calculated for a petroleum sample using different analytical speciation techniques. The boiling curves include: 1) a detailed hydrocarbon analysis (DHA) is used for a speciation of light-end components of the petroleum sample; 2) a comprehensive 2-dimensional (2D) gas chromatography (GCxGC) is used for a speciation of a middle distillates range of the petroleum sample; and 3) a high-resolution mass spectrometry is used for a speciation of heavy-end components of the petroleum sample. A compositional coverage of the different analytical speciation techniques for the petroleum samples is determined using the different boiling curves. Each of the different analytical speciation techniques covers a different boiling range and produces a compositional model modeling a breakdown of components in the petroleum sample by carbon number, aromatic ring family, and heteroatom class.

A method for analyzing spectra comprises identifying a set of sample peaks in a sample spectrum, where the sample peaks are associated with fragments of a sample, each having a sample fragment mass. A reference spectrum is selected with one or more reference peaks corresponding to fragments of a reference, each having a reference fragment mass. A mass difference can be determined between selected sample and reference peaks, and a group exchange can be selected based on the mass difference; e.g., where the group exchange represents a change in the sample or reference fragment masses associated with the selected peaks. The selected peaks can be shifted by the mass difference, and a fit value can be determined with respect to the reference spectrum. The fit value characterizes similarity between the respective sets of sample and reference peaks, responsive to the group exchange and corresponding peak shift.

The data processing device includes a data acquisition unit that acquires detection data indicating signal intensity corresponding to a component in a sample detected by the detection device, and a computing unit that processes the detection data acquired by the data acquisition unit. The computing unit is configured to: generate a plurality of analysis data including a peak of the signal intensity based on the detection data; generate a plurality of clusters by grouping the peak included in each of the plurality of analysis data using hierarchical clustering based on peak information corresponding to the peak; and prohibit grouping a plurality of peaks included in the same analysis data, into the same cluster in the hierarchical clustering.