563 results about "Mass spectrum analysis" patented technology

The invention provides a method for identifying PFAS in an environment based on machine learning pseudo-targeted screening, which comprises the following steps: calling mass spectrum data containing PFAS compounds from a preset mass spectrum database, and performing interference peak elimination processing on the mass spectrum data to obtain a model training data set; extracting a feature data set for model training from the model training data set based on a feature extraction standard; training a plurality of machine learning classification models based on the feature data set, and performing performance evaluation on each machine learning classification model based on a training result to determine an optimal machine learning classification model; and analyzing the optimal machine learning classification model, determining key features when the PFAS is screened and identified, and carrying out PFAS screening identification verification on the optimal machine learning classification model according to the key features based on an actual environment sample. The method has the advantages of saving analysis cost, improving analysis efficiency and improving compound recognition accuracy.

The invention discloses a sewage high-risk pollutant screening and identification method based on fragmentation tree pre-training, and the method comprises the steps: constructing a fragmentation tree pre-training data set according to the second-level mass spectrum data of known high-risk pollutants, so as to carry out the self-supervision pre-training of a graph neural network encoder, and obtaining a fragmentation tree editor; the method comprises the following steps: acquiring secondary mass spectrum data of suspected high-risk pollutant related compounds in a to-be-detected sewage sample, constructing a fragmentation tree set, constructing a high-risk pollutant screening model based on a fragmentation tree encoder, performing high-risk pollutant signal screening on the fragmentation tree set to obtain a high-risk candidate fragmentation tree set, and performing high-risk pollutant signal screening on the high-risk candidate fragmentation tree set. And generating a candidate molecule set of each high-risk candidate fragmentation tree, and identifying a target molecular structure and a matching score of the high-risk candidate fragmentation tree from the candidate molecule set. According to the invention, rapid screening and priority ranking of high-risk pollutants in sewage are realized, and candidate output of structure identification is further provided on the basis of screening.

The invention relates to the technical field of vacuum measurement, in particular to a device for expanding the linear range of mass spectrometry measurement, which comprises a gas transmission pipeline, an electromagnetic valve, a small hole and a mass spectrometer, and is characterized in that the front end of the gas transmission pipeline is used for allowing a sample to be measured to enter, and the rear end of the gas transmission pipeline is connected with the mass spectrometer; the middle part of the gas transmission pipeline is divided into three channels, namely a first gas transmission pipeline, a second gas transmission pipeline and a third gas transmission pipeline; the first electromagnetic valve and the first small hole are sequentially arranged on the first gas transmission pipeline, the second electromagnetic valve and the second small hole are sequentially arranged on the second gas transmission pipeline, and the third electromagnetic valve and the third small hole are sequentially arranged on the third gas transmission pipeline. According to the method, manual dilution or segmented sample injection is not needed, a wide concentration range can be covered by single analysis, more than 60% of time cost is saved, and measurement in a dynamic range of 106 is realized.

The invention provides an insulating oil gas mass spectrum analysis method and system. The method comprises the following steps: firstly, circulating vacuum degassing for one time, then deoiling and dehydrating the removed gas, introducing the deoiled and dehydrated gas into a mass spectrometry detection unit, carrying out primary detection, calling a self-adaptive algorithm and selecting proper circulating vacuum degassing times based on a primary detection result in combination with degassing temperature and degassing pressure of circulating vacuum degassing, and entering second-stage vacuum degassing; and deoiling and dehydrating the deoiled gas, and introducing the deoiled gas into a mass spectrometric detection unit for secondary detection to obtain a final detection result. According to the method, effective fusion of efficient degassing and rapid detection is realized, and reliable technical support is provided for rapid and accurate online monitoring and real-time fault diagnosis of the insulating oil dissolved gas.

The invention relates to the technical field of monitoring and detection, and discloses an intelligent early warning method and system for gaseous pollutants in the semiconductor industry, and the method comprises the steps: analyzing the molecular weight of the gaseous pollutants according to the mass-to-charge ratio in an original mass spectrum signal, and analyzing the pollutant type and concentration space-time distribution matrix of the gaseous pollutants according to the molecular weight; carrying out pollution source analysis on the gaseous pollutants according to the pollutant types and the concentration space-time distribution matrix; analyzing the concentration contribution rate of the target pollution source by using the pollutant component characteristics, and analyzing the multi-stage dynamic early warning condition of the gaseous pollutants according to the concentration contribution rate and the concentration spatial-temporal distribution matrix; when the pollutant concentration data in the concentration space-time distribution matrix exceeds a multi-stage dynamic early warning condition, triggering a graded early warning signal of gaseous pollutants in the semiconductor industrial environment; and adaptively executing a control strategy corresponding to the gaseous pollutants in the semiconductor industrial environment based on the graded early warning signal. According to the invention, the accuracy of intelligent early warning of gaseous pollutants in the semiconductor industry can be improved.

Methods and systems are disclosed for predictive classification of a raw mass spectrum of a sample, the spectrum resulting from mass spectrometry analysis. The predictive classification is performed by a trained large-scale spectral model, including a self-supervised large-scale base spectral model trained with unidentified sample spectra from a plurality of sources and fine-tuned with a task-specific model trained with identified spectra.

The invention discloses a device and a detection method for online oxidation-enrichment-reduction determination of nanogram-level nitrogen isotopes in a solid, and belongs to the technical field of nitrogen isotope analysis. The device comprises an oxidation system, a nitrogen oxide enrichment system, a nitrogen reduction system, a mass spectrum interface system, a stable isotope ratio mass spectrometer and pipelines for connecting all parts of the device. The device provided by the invention greatly reduces the quantitation limit of the nitrogen isotope in the solid sample, can realize high-precision determination of the nanogram-level nitrogen isotope in the solid sample, and has the advantages of simplicity and convenience in operation, high accuracy and the like.

The invention discloses a distributed cooperative processing method, system and equipment for mass spectrum data analysis, and relates to the field of biological information distributed processing, and the method comprises the steps: receiving original mass spectrum data submitted by a user, and analyzing flow configuration and global parameter configuration; analyzing the analysis process configuration into a directed acyclic graph taking the mass spectrum data processing step as a node; splitting the directed acyclic graph into a plurality of parallel atomic tasks according to the quantity of the original mass spectrum data and the space occupation amount; each atomic task is allocated to a Worker node storing corresponding associated data, and the atomic tasks are executed in parallel; when it is detected that the atomic tasks fail to be executed, the Worker nodes are distributed to the atomic tasks which fail to be executed again; different data storage systems are adopted to store original mass spectrum data, intermediate results and final analysis results. According to the invention, efficient parallel processing of mass spectrum data is realized, and continuity and reliability of an analysis process are guaranteed.

The invention discloses a CARS-LA-ICP-MS combined multimode analysis system. The CARS-LA-ICP-MS combined multimode analysis system comprises a fusion light path module, a mass spectrometry gas path module, a three-dimensional mobile station, a CARS signal acquisition system and a computer control system, the fusion light path module comprises a laser source, an optical parameter amplifier, a light pulse time delay system, three dichroscopes, a light beam adjusting system, a galvanometer, a movable objective lens and a camera; the mass spectrometry gas circuit module comprises a carrier gas supply device, a multimode analysis sample cell and a mass spectrometry device; the multimode analysis sample cell comprises an upper cover, a lower cover and a side wall shell. On the other hand, the invention discloses an analysis method which comprises initialization setting, CARS analysis, mass spectrum analysis task parameter setting, laser ablation mass spectrum analysis and data synthesis. Through coaxial optical path design and an intelligent algorithm, deep fusion of chemical imaging and elemental analysis is realized, and a brand new research tool is provided for the fields of life science, material science and the like.

And re-sampling triggered by uncertain results in mass spectrum screening. The invention provides a system / technical scheme for promoting repeated sample injection triggered by uncertain results in mass spectrum screening. In various embodiments, the system may access a screening list specifying a plurality of target compounds. In aspects, the system can classify a plurality of target compounds as present or absent in a sample, respectively, based on causing a chromatograph and a mass spectrometer to perform an iterative injection scheme on the sample, with subsequent iterations of the iterative injection scheme having progressive compound selectivity, and is triggered by the uncertain presence or absence of the classification result in the previous iteration.

The invention discloses a mass spectrum data high-throughput alignment and parallel qualitative method based on deep characterization learning, and relates to the field of chromatography-mass spectrometry data processing and calculation mass spectrography. The method comprises the following steps: performing streaming, micro-batch and single traversal analysis on original mass spectrum data files of a plurality of samples to be detected; extracting metadata and main data by taking the fragmented spectrogram as a unit, inputting the metadata and the main data into a pre-trained depth representation learning model to generate a high-dimensional embedded vector, and incrementally writing the metadata and the main data into a column type storage database; constructing a sparse approximate nearest neighbor graph; executing a clustering algorithm on the graph to generate a consensus spectrogram set, and generating a consensus feature vector for each clustering cluster and a single example cluster; determining the nature of the consensus feature vector; and constructing a sample * feature matrix based on the cluster affiliation relationship and the qualitative result. According to the method, the technical problems of I / O bottleneck, memory overflow, combinatorial explosion, computing power waste and the like in large-scale mass spectrum data analysis are effectively solved.

The invention discloses a marine fungus fusarium keratinoides and application thereof. The fusarium keratinoides capable of efficiently producing the perindoprine is screened from marine sediments, and the content of the produced perindoprine can reach 561.7 mu g / L after the fusarium keratinoides is fermented in a GPY liquid culture medium, so that possibility and a foundation are provided for breeding strains suitable for industrial large-scale production of the perindoprine in the future; mass spectrum, hydrogen spectrum and carbon spectrum data of the separated perindoprine are completely consistent with perindoprine data reported in literatures.

The invention provides an atmospheric brown carbon aerosol light absorption characteristic determination method and electronic equipment, and the method comprises the steps: obtaining aerosol observation data and organic aerosol mass spectrum data; determining a brown carbon absorption coefficient corresponding to each wavelength according to the aerosol observation data; determining a plurality of organic aerosol sources and aerosol concentrations corresponding to the organic aerosol sources according to the organic aerosol mass spectrum data; according to the brown carbon absorption coefficient corresponding to each wavelength and the aerosol concentration corresponding to each organic aerosol source, training to obtain a plurality of brown carbon source decision models, and according to each brown carbon source decision model, determining at least one brown carbon source corresponding to each wavelength; and determining the absorption spectrum of each brown carbon source according to each brown carbon source corresponding to each wavelength. According to the method, accurate inversion of the absorption spectrum of each brown carbon source can be realized, cross-scale correlation from chemical components to climate effects is realized, and the accuracy and scientificity of brown carbon climate influence assessment are improved.

A method of determining a confidence measure of precursor inference in mass spectrum data. The method includes evaluating the response profile using response profile metrics and reporting the confidence measure of the precursor inference using the response profile and the evaluating of the response profile. Also a method of detecting conflicting relationships between precursors including filtering an intensity at at least one position across the scanning ion transmission window dimension. Another method a method of detecting conflicting relationships between precursors includes generating, based on an encoding, a trace of the scanning ion transmission window dimension wherein each sum is substituted with a binary metric for presence of the at least one unique product ion in each window. Further a method of identifying a precursor m / z including determining an intercept between the rising linear function and the falling linear function, and identifying the precursor m / z using the intercept.

The invention provides a traditional Chinese medicine preparation quality detection method and system based on multi-dimensional data analysis, and belongs to the technical field of traditional Chinese medicine preparation quality detection. The method comprises the following steps: acquiring mass spectrum data and nuclear magnetic resonance data of various active components in a preparation to generate a multi-dimensional chemical characteristic data set; synchronously collecting corresponding data of the medicinal raw materials, and constructing a process and environment association database; constructing a chemical and process coupling analysis model; and monitoring the production process in real time to obtain the dynamic chemical fingerprint spectrum. By constructing a full-component chemical map and associating the full-component chemical map with process and environmental parameters and combining clinical curative effect data to establish a drug effect and component response curved surface model, the limitation that only a few index components are concerned in traditional detection is broken through, so that a detection result can accurately reflect the multi-target and overall regulation characteristics of the traditional Chinese medicine preparation, and the detection accuracy is improved. The problem that the components are qualified but the curative effect is poor due to single detection dimension is avoided, and the relevance between quality detection and the clinical curative effect is remarkably improved.

The invention relates to the technical field of traditional Chinese medicine complex system component analysis, in particular to a rapid identification and analysis method and system for traditional Chinese medicine compound components. Performing step-by-step expansion on the spectral signals to construct a multi-level feature structure; meanwhile, trajectory tracking is conducted on mass spectrum fragments, and a fracture path network is established. And establishing an interactive verification channel between the two to transmit a matching degree parameter, and screening candidate compounds according to the matching degree parameter. And calculating the characteristic contribution degree of each candidate in the spectrum structure and the path integrity of each candidate in the mass spectrum network, merging to generate a confidence index, and finally completing component identification. According to the method, through deep coupling of spectrum multi-scale decomposition and a mass spectrum fracture network, cross validation of multi-dimensional information is achieved, and the accuracy and reliability of component identification of a complex traditional Chinese medicine system are improved.

The invention discloses a Baijiu blending big data analysis decision system and method, and belongs to the technical field of intelligent food manufacturing and flavor modeling. Physicochemical indexes, gas chromatography-mass spectrometry, sensory evaluation and historical proportioning schemes of multiple batches of raw wine are collected, and a heterogeneous flavor original data set is constructed; performing unified feature extraction and vectorization processing on the data to generate a flavor multi-modal feature matrix; constructing a flavor mapping map based on an attention mechanism, and extracting key flavor contribution features; using the target flavor label to guide a neural generation network to generate a wine base combination candidate set, and outputting a matching suggestion; performing subjective consistency scoring through a flavor simulation model and an expert scoring discrimination model, and screening an optimal matching scheme; the method has the advantages of being high in modeling precision, high in sensory consistency, high in blending efficiency and the like, and is suitable for intelligent matching design and personalized seasoning optimization of complex flavor products.

The application relates to a method for constructing a target proteome spectrum library and application in micro target proteome analysis, and the method comprises the following steps: performing multiple times of mass spectrum scanning on a target proteome in a data-dependent acquisition mode without dynamic exclusion through different scanning ranges, so as to obtain mass spectrum data of the target proteome, wherein the mass spectrum data of the target proteome comprises primary spectra of multiple scanning ranges of the target proteome and secondary spectra of parent ions in each scanning range, which meet a preset threshold; and performing library searching based on the mass spectrum data of the target proteome to construct the target proteome spectrum library. The application constructs a spectrum library through mass spectrum data in a data-dependent acquisition mode without dynamic exclusion in multiple scanning ranges, performs library searching analysis on DIA data of a micro proteome, improves the success rate of DIA data matching, and further improves the accuracy and sensitivity of DIA data analysis, and improves the accuracy and sensitivity of micro protein identification.

The embodiment of the specification provides a modification identification method of a thio-phosphorylated nucleic acid sequence, which comprises: performing enzymolysis on a to-be-identified nucleic acid sequence by using a nucleic acid mixed enzyme to obtain an enzymolysis product; performing secondary mass spectrum analysis on the enzymolysis product based on first mass spectrum information; and determining whether a target sequence fragment exists in the to-be-identified nucleic acid sequence based on the first mass spectrum information and the second mass spectrum information. At least the to-be-identified nucleic acid sequence modified by thio-phosphorylation is actually obtained based on a preset modification rule. The first mass spectrum information comprises a theoretical analysis result generated by analyzing a theoretical sequence fragment, and the theoretical sequence fragment is determined based on the preset modification rule, the to-be-identified nucleic acid sequence and the nucleic acid mixed enzyme. The modification identification method can accurately and efficiently identify the chemical modification of the thio-phosphorylated nucleic acid sequence.

Systems and methods are provided for predicting the mass spectrum of an unknown compound. An experimental mass spectrum of a known compound is obtained. One or more mass peaks of the experimental mass spectrum corresponding to a substructure of the known compound are annotated with at least one modification an unknown compound is predicted to include. An in-silico mass spectrum is created for the unknown compound from the experimental mass spectrum and the annotated one or more mass peaks. The unknown compound is then identified from a sample by mass analyzing the sample, producing an unknown experimental mass spectrum, and comparing the unknown experimental mass spectrum to the in-silico mass spectrum.

The application discloses a protein cleavage site identification method and system based on mass spectrum data and a storage medium. The original mass spectrum data is preprocessed to obtain mass spectrum-derived peptides corresponding to an input precursor protein sequence; the mass spectrum-derived peptides are compared to the corresponding precursor protein sequence to construct a protein site coverage matrix; based on the protein site coverage matrix, potential cleavage regions and potential cleavage sites in the protein are identified; statistical significance analysis is performed on the potential cleavage sites in the protein to extract cleavage site neighborhood sequences; and the statistical significance of the potential cleavage sites in the protein is used to filter the potential cleavage sites to obtain high-confidence protein cleavage sites. The application has low dependence on known protease cleavage rules, can identify cleavage events mediated by unknown or non-classical cleavage modes, and expands the application range of protein cleavage site identification.

The invention discloses a method for analyzing and identifying chemical components of Mingzhi astragalus decoction based on high-resolution mass spectrometry, and relates to the technical field of traditional Chinese medicine compound analysis. The method comprises the following steps: preparing a Mingzhi Radix Astragali decoction test solution; the method comprises the following steps: respectively acquiring mass spectrum data in positive and negative ion modes under specific chromatography and mass spectrum conditions by adopting an ultra-high performance liquid chromatography-time-of-flight mass spectrometry technology; and comparing the obtained data with a pre-constructed specific chemical component database of the Mingzhi Astragalus decoction, and realizing systematic identification of the chemical components in the compound and medicinal material attribution according to a principle that an accurate mass number error is in a range of + / -3 ppm. The method provided by the invention efficiently, accurately and successfully identifies 53 chemical components in the Mingzhi radix astragali decoction and clarifies the source medicinal materials of the 53 chemical components, and provides a reliable technical scheme for comprehensively illuminating the basis of the compound substance, quality control and subsequent research.

The invention provides an ion measurement device with high-resolution mass spectrum detection and charge state measurement functions, and the device comprises a deflection system which is used for introducing solar wind ions into a geometric factor control system; the geometric factor control system is used for introducing the solar wind ions into the hemispherical electrostatic analyzer and adjusting the flux measurement range by adjusting the voltage; the electrostatic analyzer is used for performing energy analysis on the solar wind ions through voltage; the flight time system is used for recording an initial charge signal and a termination charge signal of ion flight and measuring ion deposition energy; the pre-amplification system multiplies and amplifies weak signals of ions and electrons, identifies and processes the signals and outputs digital signals; and the electronic processing unit is used for processing a signal output by the pre-amplification system, obtaining information of components, charge states, directions, energy and flux of solar wind ions, and providing multiple paths of high voltage required by the device. The device has the advantage that mass spectrum detection and charge state measurement are simultaneously realized on one device.

The invention discloses a MALDI-TOF mass spectrum microbiological identification method based on the distance between spectrums, and the method comprises the following steps: 1, spectrum data preprocessing: carrying out denoising and baseline correction processing on original mass spectrum data, extracting and merging corrected data peaks, and carrying out normalization processing to form a data list; step 2, carrying out average reference spectrum construction on the data based on spectrum spacing dimension reduction, and collecting a plurality of representative spectrums for each microorganism; pairing the same spectrum by using a peak value matching algorithm; calculating the average position and intensity of each group of matching peaks to form an average reference spectrum; constructing an average reference spectrum, then carrying out inter-spectrum distance calculation, obtaining peaks corresponding to the sample spectrum and the average reference spectrum, then calculating an inter-peak distance, and generating a distance vector as a low-dimensional representation of the microorganism sample spectrum; and step 3, microorganism classification: performing binary classification and multi-classification on the microorganism sample to complete microorganism identification.

The invention relates to a method for detecting alkaloid compounds based on UPLC-MSMS (ultra performance liquid chromatography-mass spectrometry) and application. The method comprises the following steps: after a sample is extracted, detecting a sample extracting solution and a standard solution by adopting an ultra performance liquid chromatography-mass spectrometry combined method; a mobile phase adopted by the ultra-high performance liquid chromatography is as follows: a mobile phase A is an aqueous solution containing 0.1% of formic acid; the mobile phase B is an acetonitrile solution containing 0.1% of formic acid. According to the method disclosed by the invention, after the extracted tissue sample is subjected to liquid chromatography separation, the alkaloid metabolites are qualitatively and quantitatively detected in a targeted manner, and due to the high selectivity of the high-resolution mass spectrum, parameter optimization does not need to be performed on each metabolite.

The application provides a method for detecting psychotropic drugs in environmental sludge based on acid pretreatment, which comprises the following steps: S1: obtaining a sludge sample to be detected, performing pressurized fluid extraction on the sludge sample to be detected after freeze-drying to obtain an extraction solution, performing solid-phase extraction on the extraction solution to obtain an eluate, and redissolving the eluate to obtain a solution to be detected, wherein the pressurized fluid extraction is performed under the condition of 5 ‰ formic acid water without heating; and S2: performing liquid chromatography mass spectrometry on the solution to be detected to obtain a mass spectrum, wherein the psychotropic drug is one of eptazocine, codeine, AMP, MAMP, MDA, monoacetylmorphine, MDMA, PMMA, fentanyl, norfentanyl, ketamine, norfentanyl, benzoyl eptazocine, cocaine and tramadol, and the method realizes detection of multiple psychotropic drugs in environmental sludge.

The application belongs to the technical field of marine chemical analysis, and relates to an underwater in-situ mass spectrometer acquisition control system and method. The method comprises the following steps: a vacuum extraction step: the coarse extraction mode and the fine extraction mode are adaptively switched, the ion detection device is vacuum-extracted until the pressure is reduced to a preset threshold, and a required vacuum degree is maintained; a preheating preparation step: the membrane sampling device is initially sampled, and heating is performed at the same time until a preset working temperature is reached and the temperature is maintained constant; a dissolved gas formal detection step: the membrane sampling device is continuously supplied with water samples to be detected, and in-situ mass spectrum detection of dissolved gas in the water samples is completed; an instrument cabin gas discharge step: when it is detected that the underwater in-situ mass spectrometer floats to the water surface and the air pressure in the instrument cabin exceeds an upper limit threshold of gas discharge, the instrument cabin is discharged; a liquid leakage protection step: when liquid leakage occurs in the sampling cabin and the two core contacts are short-circuited, the high-pressure electromagnetic valve in the normally open state is closed to quickly block the liquid from entering the ion detection device.

Disclosed herein are scientific instrument support systems, related methods, computing devices, and computer-readable media. For example, in some embodiments, a scientific instrument support apparatus includes sample analysis logic to cause a sample to be injected into a gas chromatograph and to obtain a mass spectrum of the sample using a mass spectrometer coupled to the output of the gas chromatograph, the mass spectrometer utilizing a mass calibration or a lock mass correction while obtaining the mass spectrum of the sample; calibration gas logic to control a supply of a calibration gas to the mass spectrometer during the void time of the gas chromatograph; and lock mass calibration logic to obtain a mass spectrum of the calibration gas using the mass spectrometer during the void time of the gas chromatograph and to calculate the mass calibration or the lock mass correction to be used by the sample analysis logic.

This provides a method for identifying S. aureus and other bacterial species that make up the coagulase-negative staphylococcus group. [Solution] A method for determining the presence or absence of Staphylococcus species in a species-specific manner, wherein the Staphylococcus species is one or more species selected from the group consisting of S. aureus, S. epidermidis, S. capitis, S. caprae, S. hominis, and S. lugdunensis, and comprises a peak detection step of determining whether one or more peaks selected from a group consisting of peaks corresponding to specific m / z values ​​are detected in a mass spectrum obtained using matrix-assisted laser desorption / ionization mass spectrometry on a test sample with respect to S. aureus, and a bacterial species determination step of determining the presence or absence of S. aureus based on the peak detection result of the peak detection step.

The application discloses a sewage high-risk pollutant screening and identification method based on fragmentation tree pre-training, comprising the following steps: constructing a fragmentation tree pre-training data set according to secondary mass spectrum data of known high-risk pollutants, pre-training a graph neural network encoder in a self-supervised manner to obtain a fragmentation tree editor, obtaining secondary mass spectrum data of suspected high-risk pollutant-related compounds in a sewage sample to be tested, constructing a fragmentation tree set, constructing a high-risk pollutant screening model based on the fragmentation tree editor, screening high-risk pollutant signals from the fragmentation tree set to obtain a high-risk candidate fragmentation tree set, generating a candidate molecule set of each high-risk candidate fragmentation tree, and identifying a target molecular structure and a matching score of the high-risk candidate fragmentation tree from the candidate molecule set. The application realizes rapid screening and priority sorting of high-risk pollutants in sewage, and further provides a candidate output of structure identification on the basis of screening.