This invention provides methods and compositions, e.g., to reduce interference from non-specific binding sample constituents in a migration shift assay. Interference due to non-specific binding of sample constituents to an affinity substance (e.g., an affinity molecule or a conjugate of an affinity molecule and a charged carrier molecule) is prevented by, e.g., binding the constituents to charged polymers such as heparin sulfate. The present invention also provides methods to concentrate an analyte of interest with high concentration and to detect the analyte with high sensitivity, and further to optimize the reaction conditions for easily concentrating the analyte. Such objects of the present invention are attained, for example, by concentrating a complex of the analyte and a conjugate which is formed by contacting the analyte in a sample with an affinity molecule bound to a charged carrier molecule such as DNA.

The present invention aims at providing an analyzing apparatus control system which is capable of appropriately setting the conditions of measurement in an analyzing apparatus which is connected to a chromatograph, in which one more measurement events are performed based on a reference chromatogram. This system includes a measurement time range setting section for setting, for each of all peaks or previously selected peak or peaks included in a previously provided reference chromatogram which corresponds to the sample to be examined, the width of a peak as the measurement time range of the peak when the peak does not overlap another peak, or, when the peak overlaps another peak, the overall width of the peak and the overlapping peak as the measurement time range of the peak.

Disclosed are methods for improving compound detection and characterization. Methods for characterizing a sample are disclosed. The methods can include providing a sample to a liquid chromatography system capable of sample separation to generate sample components; analyzing sample components by multiplexed targeted selected ion monitoring (SIM) to generate an inclusion list; and performing iterative mass spectral data-dependent acquisition (DDA) from the inclusion list, to identify individual sample components thereby characterizing the sample. In one example, multiplexed targeted SIMs and iterative MS2 DDA acquisition is used to increase robust compound identification for cell culture medium analysis.

A method for operating a spectral data processing system. The method includes receiving a user input associated with processing of a spectral data of a chemical sample at least partly using a machine learning processing model. The machine learning processing model is arranged in a machine learning controller of the spectral data processing system. The method also includes training the machine learning processing model based on the received user input.

A component management system for an analysis device, includes a plurality of analysis devices, and a server connected to the plurality of devices via a network, wherein each analysis device includes an acquirer that acquires a behavior information piece associated with a use amount of a component attached to each analysis device, and a transferer that transfers the behavior information piece acquired by the acquirer to the server, and the server includes a registrar that registers the behavior information pieces received from the plurality of analysis devices in a database, and a comparison information provider that provides comparison information, of a same type of components that are used in the plurality of analysis devices, produced based on the behavior information pieces registered in the database.

A computer-implemented method for detecting at least one interference and/or at least one artefact in at least one chromatogram determined by at least one mass spectrometry device (110) is proposed. The chromatogram comprises a plurality of raw data points. The method comprises the following steps: a) retrieving the at least one chromatogram by at least one processing device (126); b) applying at least one peak fit modelling to the chromatogram by using the processing device (126); c) determining information about residuals of the raw data points by using the processing device (126); d) detecting the at least one interference and/or the at least one artefact by using the processing device (126) by comparing the determined information about the residuals with at least one pre-determined threshold, wherein, if the determined information about the residuals exceed the pre-determined threshold, the at least one interference and/or the at least one artefact is detected.

An object is to accurately detect peaks of various compositions, even in a case of unseparated peaks in which peaks of a plurality of compositions are superimposed. A computer acquires waveform data D1 having a peak P1 in a composition A measured by a data analysis device (S10). Next, the computer acquires waveform data D2 having a peak P2 in a composition B measured by the data analysis device (S20). Next, waveform data D12 including unseparated peaks by superimposing the waveform data D1 including the acquired peak P1 and the waveform data D2 including the acquired peak P2 (S30) is generated. Next, the generated waveform data D12 of the unseparated peaks is input as learning data, and the waveform data D1 and D2 corresponding to the waveform data D12 are input as training data in Step S40. Next, machine learning is performed using the waveform data D12, D1, and D2, and a learned model for estimating an accurate separation method of unseparated peaks is constructed based on the trained result (S50).

The present disclosure provides component analysis methods including a measurement process and an analysis process.

A method, mass spectrometer and computer readable medium for acquiring mass spectral data in raw profile; detecting presence of compounds and relevant time window; performing multivariate statistical analysis of raw profile data in a time window to determine compounds; obtaining separation time profiles for detected compounds containing respective time locations in a time window; and computing pure mass spectra for compounds based on separation time profiles or time locations. A method, mass spectrometer and computer readable medium for acquiring mass spectral data in raw profile of a known and unknown sample; combining mass spectral scans for a sample into a single mass spectrum across a separation time window; performing multivariate statistical analysis of the acquired mass spectral data and computing a distance measure between the known and unknown sample; and using the distance measure as an indication for an unknown sample belonging to a known sample or sample group.

A first trend chart and a second trend chart are displayed along with a chromatogram. The first trend chart is generated based on a plurality of first representative value arrays obtained from a plurality of mass spectra. The second trend chart is generated based on a plurality of second representative value arrays obtained from the plurality of mass spectra. A mass spectrum stable period is determined based on the first trend chart and the second trend chart.