A computer implemented method for identifying at least one peak in a mass spectrometry response curve is provided comprising: a) providing at least one mass spectrometry response curve by using at least one mass spectrometry device; b) evaluating the mass spectrometry response curve by using at least one trained model thereby identifying a start point and an end point of at least one peak of the mass spectrometry response curve, wherein the model was trained using a deep learning regression architecture.

A known compound and at least one adduct, modified form, or peptide of the known compound are separated from a sample mixture and analyzed. An XIC is calculated for each of M product ions of the known compound and L product ions of the at least one adduct, modified form, or peptide. A first XIC peak group is calculated from the M XICs and a second XIC peak group is calculated from the L XICs using curve subtraction. Representative first and second XIC peaks are selected for the two XIC peak groups. The retention of the second XIC peak is shifted by an expected retention time difference found from a database. The retention time of the first XIC peak is verified as the retention time of the known compound if the difference of the retention times of the first and second XIC peaks is within a threshold.

A data processing method includes: a data preparing step of preparing actual data of a three-dimensional chromatogram including a chromatogram and a spectrum acquired by chromatography analysis for a sample containing a plurality of components, and spectral data for the plurality of components in the sample whose peaks overlap each other on the chromatogram of the actual data; a similarity calculating step of calculating, for each wavelength region, a similarity between wavelength regions corresponding to each other in the spectral data for the plurality of components prepared in the data preparing step while comprehensively changing the wavelength regions; a target range setting step of setting a target range by searching for a wavelength region having a similarity lower than an overall similarity between the spectral data for the plurality of components based on a calculation result in the similarity calculating step; and a peak separating step of creating chromatogram data for the plurality of components by performing, using the spectral data for the plurality of components, matrix decomposition of the actual data in the target range set in the target range setting step.

Disclosed is a method 100 for classifying a fluid sample. The method comprises the steps of: a. at least partially separating 110 one or more of the chemical constituents of the fluid sample; b. measuring and recording 120 the amount of separated chemical constituents of the sample during, or after, the chemical separation; c. measuring and recording 130 the spatial or time separation profile of sample constituents, during or after separation, and providing a data set of the same; d. comparing 140 the amount of said separated constituents to one or more reference samples; e. comparing 150 the spatial or time separation profile to the corresponding profile of the or each reference sample; f. assigning 160 a similarity score to the sample based on the similarity of the amount or the profile comparisons of the separated constituents, as performed under steps d 140 and e 150 above, or both, with the equivalent amount and/or profile of the or each reference sample respectively; g. providing 170 a classification of the sample based on the similarity score.

Provided is a method to initiate and analyze chemical derivatives formed from pyrotechnic smoke reactions. Milligram quantities of a lab-scale pyrotechnic smoke composition are reacted by encapsulation with a metal probe that is rapidly heated, which then sublimes the organic dye, allowing for the testing of all of the gas-phase products for identification by pyrolysis-gas chromatography-mass spectrometry. The thermally decomposed ingredients and new side product derivatives are identified at lower relative abundances compared to the intact organic dye. Any remaining residues within the thermal probe are optionally reconstituted into solution for further analysis by liquid chromatography-mass spectrometry if desired. The results are processed via a machine learning quantitative structure-activity relationship model that provides data related to health and environmental hazards.

Systems and methods are disclosed for identifying actual XIC peaks of compounds of interest from samples so that more accurate expected retention times and more accurate expected retention time windows can be calculated. In one system, an actual XIC peak is identified using standard samples. The ratio of the quantity of the compound of interest in any two different samples is known, so this ratios is compared to the intensities of the XIC peak calculated in the two samples to identify an actual XIC peak. In another system, an actual XIC peak is identified using information about other compounds of interest in a plurality of samples. It is known that the XIC peaks of compounds of interest in the same samples have a similar distribution of retention times across those samples, so the distributions of retention times of XIC peaks are compared to identify actual XIC peaks.

The invention relates to a method for mass analysing a sample by ionising the sample to first sample ions and to second sample ions and by obtaining mass spectra from the first sample ions and the second sample ions with a mass analyser (5). Thereby, repeatedly, a first assay is obtained from the sample and transferred past any chromatography column to a first ion source (2) and ionised by the first ion source (2) to the first sample ions, wherein the first sample ions obtained from the respective first assay are transferred to the mass analyser (5), wherein at least one first mass spectrum is obtained with the mass analyser (5) from the first sample ions obtained from the respective first assay and ionised by and transferred from the first ion source (2). Furthermore, at least once, a second assay is obtained from the sample within a time window being associated with the respective second assay and having a window width, wherein the respective second assay is transferred for chromatographic separation via a chromatography column (3) to at least one second ion source (4.1, 4.2) in that after being chromatographically separated, the respective second assay eluting from the chromatography column (3) is transferred to the at least one second ion source (4.1, 4.2) and ionised by the at least one second ion source (4.1, 4.2) to the second sample ions, wherein the second sample ions obtained from the respective second assay are transferred to the mass analyser (5), wherein at least one second mass spectrum is obtained with the mass analyser (5) from the second sample ions obtained from the respective second assay which has been ionised by and transferred from the at least one second ion source (4.1, 4.2). Thereby, each one of the at least one second mass spectrum is assigned to one or more of the at least one first mass spectrum from the first sample ions obtained from one of the first assays which has been obtained from the sample within the time window associated with the respective second assay which has been chromatographically separated and ionised by the at least one second ion source (4.1, 4.2) to the second sample ions from which the respective one of the at least one second mass spectrum has been obtained. Furthermore, the invention relates to an apparatus (1) for mass analysing a sample with the method according to the invention.

A chromatograph mass spectrometer including: an MS.sup.n−1 analysis setter for setting an analysis execution period for performing an MS.sup.n−1 analysis, an execution time for the analysis and a loop time; an analysis period divider for dividing the analysis period into segments according to a change in number or analysis condition of MS.sup.n−1 analyses to be performed within the same time window; an MS.sup.n analysis setter for performing MS.sup.n−1 analysis to obtain mass spectrum data and for scheduling MS.sup.n analysis, an ion corresponding to a peak satisfying a set condition being designated as a precursor ion; an MS.sup.n analysis execution time allotter for allotting, in each segment, a time period for execution of the MS.sup.n analysis, the time period being calculated by subtracting an event execution time from the loop time; and an analysis executer for repeatedly performing MS.sup.n−1 analysis and MS.sup.n analysis in each segment.

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.

An exemplary chromatographic alignment system accesses a target file including data representative of a plurality of chromatographic features detected from a first sample and a reference file including data representative of a plurality of chromatographic features detected from a second sample. The system identifies, based on the target and reference files, a distinct retention time offset value for each chromatographic feature included in a first subset of the plurality of chromatographic features detected from the first sample. The system determines, based on the identified distinct retention time offset values for the chromatographic features included in the first subset and on a machine learning model, a distinct predicted retention time offset value for each chromatographic feature included in a second subset of the plurality of chromatographic features detected from the first sample. The system assigns the distinct predicted retention time offset value for each chromatographic feature included in the second subset.