A processor performs processing for generating training data by processing a plurality of pieces of peak information obtained by a data obtaining unit. The processor deletes data on a peak missing in any of the plurality of pieces of peak information from each piece of peak information. When a coefficient of correlation of data between peaks among remaining peaks is equal to or larger than a prescribed value, the processor further deletes data on one peak of the peaks from each piece of peak information, and defines peak information including data on the remaining peaks as input data for data for learning.

The invention provides a mass spectrometry analysis method and a mass spectrometry system. During implementation of the mass spectrometry analysis method, intensity data of the daughter ions, a first parameter of the daughter ions associated with the first physicochemical property, and a second parameter of the daughter ions associated with the second physicochemical property are all recorded to form a spectrogram data set. In a deconvolution step, the spectrogram data set is deconvoluted to categorize the daughter ions from the same parent ion according to two-dimensional features including the first parameter and the second parameter. In the above manner, the mass spectrometry analysis method and the mass spectrometry system provided by the invention can detect ions that partially overlap spectral peaks of other ions significantly, thereby improving the qualitative and quantitative ability of data analysis for data independent acquisition.

A plurality of measured product ion spectra is produced using a DIA tandem mass spectrometry method. One or more product ions are retrieved from a spectral library of known compounds or one or more theoretical product ions are calculated for the known compounds of a database. For each known or theoretical product ion, an XIC is calculated from the measured product ion spectra. Measured XIC peaks above a threshold intensity are grouped for the known compounds producing a subset of known compounds. Known or theoretical retention times are retrieved or calculated for the subset of known compounds. A regression function is calculated to correct the known or theoretical retention times using the known or theoretical retention times of the subset of known compounds as the independent variables and the measured retention times of the measured XIC peak groups of the subset of known compounds as the dependent variables.

A glycopeptide analyzer that performs a structural analysis on glycoforms of a glycoprotein, including: a spectrum creator creating an MS/MS spectrum for each elution time based on data acquired by an LC/MS analysis of a sample containing glycopeptides originating from a target glycoprotein; a peptide mass calculator selecting a glycopeptide-related spectrum from a plurality of MS/MS spectra and calculating the mass of a peptide from the selected spectrum; a similarity determiner determining a similarity between the glycopeptide-related spectrum and each of the other MS/MS spectra; an elution-time range estimator estimating an elution-time range based on a distribution of the frequency of occurrence of an MS/MS spectrum for which a high level of similarity has been determined on a time axis; and a glycan composition estimator selecting an ion peak corresponding to a mass equal to or greater than a peptide mass and estimating a glycan composition based on the peak.

Respective peaks detected on chromatograms created based on data obtained by conducting GC-MS analysis on a target sample are identified based on information stored in a compound database (S1, S2). A retention time list indicating relationships between compounds and measured peak retention times is created based on results of identification of the peak. Then a plurality of compounds in which the measured retention times are identical to each other or are within an allowable range are extracted and are combined into one group (S3, S4). A determination is made whether overlapping identifications exist by determining whether a single group includes a plurality of compounds (S5, S6). Retention times, mass spectrums, and confirmation ion ratio reference values stored in the compound database are used for the respective groups in which overlapping identifications are likely to exist so that a most likely compound candidate is selected (S7).

Respective peaks detected on chromatograms created based on data obtained by conducting GC-MS analysis on a target sample are identified based on information stored in a compound database (S1, S2). A retention time list indicating relationships between compounds and measured peak retention times is created based on results of identification of the peak. Then a plurality of compounds in which the measured retention times are identical to each other or are within an allowable range are extracted and are combined into one group (S3, S4). A determination is made whether overlapping identifications exist by determining whether a single group includes a plurality of compounds (S5, S6). Retention times, mass spectrums, and confirmation ion ratio reference values stored in the compound database are used for the respective groups in which overlapping identifications are likely to exist so that a most likely compound candidate is selected (S7).

Synthetic Cannabinoids are the most complex branch of designer drugs encountered in forensic chemistry. A screening method has been developed that can accurately identify the correct structural category of an unknown Synthetic Cannabinoid. Knowledge of this information is very important when no reference data or standards are available since certain sub-categories contain Schedule I Controlled Dangerous Substances. The Bridge portion of these molecules present a unique carbonyl band cluster within a small 200 wavenumber interval of the mid-infrared region that can only exist in vapor phase through GC/FTIR light-pipe technology or heated static vapor cell FTIR. This special relationship is not applicable to any other forms of solid phase vibrational spectroscopy (FTIR, RAMAN) including GC/FTIR solid-deposit techniques. The carbonyl frequency from the Bridge is used as the first step in the screening process which separates the entire forensically encountered class of Synthetic Cannabinoids into 35 sub-categories. Additional bands within the cluster from secondary functional groups, rotational isomerism, and fermi resonance add further refinement within these categories.

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.

A method for the identification of unknown compounds based on a novel Retention Index System comprising having a TAGs homologous series, wherein such identification is performed by means of liquid chromatography (LC), or liquid chromatography coupled with mass spectrometry (LC-MS) is disclosed.

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).