One mode of a chromatograph mass spectrometer according to the present invention includes: a measurement unit (1) that includes a chromatograph unit (1A) and a mass spectrometry unit (1B) capable of performing MS/MS analysis, and collects chromatograph mass spectrometry data having three dimensions of time, m/z, and a signal intensity by repeatedly performing the MS/MS analysis by data independent analysis in the mass spectrometry unit on a sample containing a compound separated by the chromatograph unit; a component detection unit (42) that detects a compound and a component corresponding to a partial structure of the compound by obtaining MS/MS spectra of a bar graph presentation based on chromatograph mass spectrometry data over a predetermined m/z range for a target sample, estimating precursor ion peaks in each of the MS/MS spectra, and selecting peaks based on a predetermined standard regarding an m/z direction in each of the MS/MS spectra and a predetermined standard regarding a time direction for peaks that can be considered to be identical or an identical group on the MS/MS spectra; a narrowing unit (43, 44) that narrows down components to be analyzed by performing screening using prior information on the detected component; and a composition estimation unit (45) that, by using m/z information corresponding to a narrowed down component, estimate a composition or a chemical formula of the component.

The EM algorithm for a Gaussian mixture model is applied to the separation of peaks that overlap one another on a chromatogram. However, the number of overlapping components is unknown. Thus, a suitable number of models is set, and the fitting of model parameters is performed while an actually measured signal is appropriately divided for each model by the EM algorithm. Then, when a solution converges, a determination is made as to whether a peak-like waveform is present in a residue signal that is not divided. When the peak-like waveform is present, a peak model is added. The EM algorithm is executed again. In the M step, optimization is performed using, not only a simple Gaussian function, but also a modified Gaussian function assuming a tailing. In the M step, the estimation of a spectrum assuming a chromatogram and the estimation of a chromatogram assuming a spectrum are repeatedly performed.

The present application relates to a method for determining process conditions to remove volatile organic compounds from a polymer product through blowing. According to the method of the present application, time and energy can be saved.

A method for deconvoluting the molecular weight characteristics of the copolymer or rubber in an impact copolymer (ICP) comprising at least one copolymer or rubber and at least one homopolymer such as polypropylene, as well as a method of determining the comonomer characteristics of the rubber in an ICP, the methods comprising in part eluting a solubilized ICP through a gel permeation chromatographic (GPC) column to form an eluate comprising the rubber and/or polypropylene, measuring the Infrared (IR) absorption of at least the primary monomer-derived unit stretch frequency and the comonomer-derived unit in elution volume slices to determine the amount of comonomer, measuring the concentration of ICP using any detector, and determining the mass concentration, comonomer composition, and the total comonomer content of the ICP through the appropriate mathematical transformations.

An automated method of calibrating a chromatography system and analyzing a sample is described. The method includes forming diluted standard solutions that are injected into a chromatography column. The detected peaks can be identified based on a first predetermined calibration ratio associated with the standard solution. Once the chromatography system is calibrated, samples can be chromatographically analyzed where the measured peaks are identified and quantified in an automated manner.

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 is provided for analyzing a sample and identifying species using chromatography and spectrometry. Possible candidate species to be used in a regression analysis are selected for consideration based on their retention indices in a chromatography column and peak locations in an infrared spectrum. By using such a selection process, the number of combinations of species to be used in the regression analysis can be significantly reduced. The species and respective concentrations in the sample are identified by using an iterative process with regression analysis and minimizing least squares errors between a sample spectrum and a computed spectrum associated with selected candidate species.

A cloud server platform end is used to construct a mass spectrum species classification model, extract a mass spectrum data feature, and construct a training model of the convolutional neural network; a user platform end is used to upload the mass spectrum, experiment condition and device data, directly screen and identify the type of the mass spectrum based on the mass spectrum species classification model or the mass spectrum information base, automatically compare and identify the species and name of the pesticides based on the neural network model trained by the cloud server platform end, and feedback the comparison result to the user. The disclosure solves the restriction on the purchase of standards for user, the use of the system is not limited by the location, and the pesticide residues could be detected automatically, quickly and accurately.

A drive module that includes an electric motor, a differential assembly with a differential input member and a pair of differential output members, a multi-speed transmission assembly, which transmits rotary power between the electric motor and the differential input member, and a pair of axle shafts that are each coupled to an associated one of the differential output members for rotation therewith. The multi-speed transmission assembly and includes a housing, a two-speed planetary transmission stage, a plurality of clutches and a transmission output member that provides rotary power to the differential input member. The two-speed planetary transmission stage has an input sun gear, which is driven by the motor, and employs a set of planet gears having compound planet gears that provide a gear reduction between the sun gear and the transmission output member. The clutches are employed to control an overall reduction ratio.

The present disclosure relates to methods and apparatus for identifying metabolic pathways and metabolites in complex biological samples. In particular, the present disclosure relates to a method and apparatus to increase the confidence of metabolite identification in metabolomics, such as in untargeted metabolomics data, using various statistical tools, such as over representation and enrichment analysis.