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.

Methods, compositions, kits, and apparatuses for MS-based quantification of a target analyte (e.g., a peptide, a hormone) in a sample are provided for increasing the productivity and/or throughput of samples by reducing the time and/or cost associated with conventional techniques for external instrument calibration that typically utilize a series of calibrators that are sequentially run through the same analytical process as a batch of samples. In various aspects, calibration mixtures are provided containing a known quantity of a plurality of calibrants for a target analyte of interest, with each calibrant being distinguishable in the calibration mixture by mass spectrometry such that a complete calibration curve can be generated from a single external calibration run.

Methods, compositions, kits, and apparatuses for MS-based quantification of a target analyte (e.g., a peptide, a hormone) in a sample are provided for increasing the productivity and/or throughput of samples by reducing the time and/or cost associated with conventional techniques for external instrument calibration that typically utilize a series of calibrators that are sequentially run through the same analytical process as a batch of samples. In various aspects, calibration mixtures are provided containing a known quantity of a plurality of calibrants for a target analyte of interest, with each calibrant being distinguishable in the calibration mixture by mass spectrometry such that a complete calibration curve can be generated from a single external calibration run.

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).

A confirmation ion ratio allowable value calculation unit calculates a confirmation ion ratio allowable value when a target ion and confirmation ions are interchanged based on a preset confirmation ion ratio allowable value, and a peak identification processing unit identifies mass peaks of the target ion and the confirmation ions based on the confirmation ion ratio allowable value. A peak waveform processing unit calculates peak areas of the target ion and the confirmation ions, and a calibration curve creation unit creates calibration curves for quantification based on the target ion and the confirmation ions from a peak area of a target compound included in a standard sample. A quantitative value calculation unit obtains quantitative values while referring to a calibration curve corresponding to a peak area for a target compound included in an unknown sample. A quantitative analysis result display processing unit displays the quantitative values and chromatogram peak waveforms.

A method for determining a grade of black tea by HPLC detection belongs to the field of tea grade determination. The specific steps are as follows: adding known black tea powder samples of different grades into boiling water of 95-100° C. for extraction, and filtering with a filter membrane with a pore size in a range of 0.20-0.25 μm to obtain black tea sample liquid; measuring contents of ten components by peak area normalization method; standardizing data of the contents of the ten components in a black tea sample solution; carrying out unsupervised principal component analysis; carrying out supervised partial least squares discriminant analysis; carrying out hierarchical clustering analysis on the basis of partial least squares discriminant analysis, and finally establishing a tea grade discrimination model based on HPLC. The method is simple, accurate and efficient, and whose effectiveness is not affected by the variety of black tea.

When a user inputs samples per group, a sample tree and a peak matrix are generated. Peak lists per group are shown in the sample tree, and m/z values and signal strength values from the peak lists are coordinates in the peak matrix. A multivariate analysis is applied to the generated peak matrix. The sample tree, peak matrix, score plot, and loading plot are displayed. When the user clicks a plotted point on the loading plot, a row indicating a corresponding peak on the peak matrix is discriminated. When the user deletes a checkmark corresponding to the discriminated row, the multivariate analysis is applied to the peak matrix from which the peak has been excluded. The score plot and other data are updated. When separation between groups is known from the score plot as failure, the excluded peak may be visually determined as a marker contributing to the group separation.

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.

An information processing apparatus includes information acquisition means configured to acquire quantitative information on a test substance, which is estimated by inputting spectrum information of a sample including the test substance into a learning model, and degree-of-contribution acquisition means configured to acquire a degree of contribution of the acquired quantitative information on the test substance.

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.