2210 results about "Mass spectrum analysis" patented technology

The invention relates to the technical field of industrial control, in particular to an intelligent centerless grinding machine cooling system and method for difficult-to-machine materials, and the system comprises a multi-source sensing unit, an edge calculation module, a decision center module, a precise execution module and a digital immune memory bank; compared with the defects that in the prior art, monitoring depends on a single temperature sensor, thermal field sensing is incomplete, thermal damage early warning lags behind and the like, according to the scheme, grinding power frequency spectrum, acoustic emission signals, triaxial vibration and coolant mass spectrum data are collected in real time through a multi-source sensing unit, and a micron-sized temperature field is reconstructed in combination with an edge end space-time diagram convolutional network; full-dimensional monitoring of the grinding thermal-mechanical coupling effect is achieved, and thermal anomaly recognition sensitivity and early warning timeliness are remarkably improved.

The invention provides a chromatography-mass spectrometry automatic integration method, system, equipment and medium for multiple sample items, and the automatic integration method comprises the following steps: converting an original spectrogram of a chromatography-mass spectrometry to generate a standardized spectrogram picture; performing peak recognition on the standardized spectrogram picture by using a trained deep learning peak recognition algorithm to obtain a candidate peak classification result; screening the candidate peak classification results based on the spectrogram classification result to obtain a preliminary integration result; performing peak clustering on the multi-sample items; carrying out peak recall on samples without peak reservation; and carrying out consistency correction on all reserved peaks. According to the method, deep learning model prediction is carried out on the chromatography-mass spectrometry image, peak clustering, peak recall and peak boundary inconsistency correction are carried out in combination with multi-sample data, manual judgment on the peak position and the peak shape of the chromatography-mass spectrometry can be replaced, and the method is high in accuracy, high in specificity and high in accuracy. And the false positive of the conventional automatic integration method is effectively reduced.

The invention relates to the technical field of mass spectrum analysis, in particular to an analysis method and system for improving the spectrum quality of a mass spectrum. The method comprises the following steps: scanning a sample by using a mass spectrometer, collecting original mass spectrogram data, and preprocessing the original mass spectrogram data; based on the preprocessed original mass spectrum data, identifying potential peak positions through a multi-scale Gaussian fitting technology, and screening out candidate peaks by calculating the signal-to-noise ratio of each peak; fitting each screened candidate peak by using an asymmetric Gaussian function model, and describing the shape of the candidate peak; and constructing a mass offset correction model to correct the mass spectrum data, and performing super-resolution processing on the corrected mass spectrum data by using a convolutional neural network model. According to the design, peak fitting is carried out by adopting a multi-scale Gaussian fitting technology, a signal-to-noise ratio screening mechanism and an asymmetric Gaussian function model.

The invention provides a method for determining hydrocarbon components in mixed aromatic hydrocarbons by using comprehensive two-dimensional gas chromatography-mass spectrometry. The method comprises the following steps: optimizing a target function to make a chromatographic peak signal obtained by a separation model accord with a chromatographic separation process mechanism, fully utilizing complementary information of chromatography and mass spectrometry, improving separation precision, and guiding analysis of complex aliasing peaks; analyzing overlapped chromatographic peaks determined by principal component analysis by adopting the established separation model to obtain a chromatographic peak signal representing a single component after separation, and matching the chromatographic peak signal with a corresponding mass spectrum; in combination with a single chromatographic peak signal and a mass spectrum obtained through separation, according to characteristic fragments and chromatographic retention time in the mass spectrum, component types corresponding to chromatographic peaks are judged, and characterization of the composition of the mixed aromatic hydrocarbon is achieved; a separated single chromatographic peak is subjected to quantitative analysis, the content of each component is calculated by adopting an external standard method in contrast with a standard curve, a quantitative result of the mixed aromatic hydrocarbon sample is obtained, and the measurement precision of the mass fraction of each hydrocarbon component is improved.

The invention provides a mass spectrum structure analysis method and device, a storage medium and a program product, and relates to the technical field of bioinformatics, and the method comprises the steps: calculating the similarity between each reference mass spectrum in a reference database and a to-be-analyzed target mass spectrum, screening at least one similar reference mass spectrum from the reference mass spectrums according to the calculated similarity; the reference database comprises a plurality of reference mass spectra and corresponding reference molecular structures; according to a preset mass spectrum structure analysis task instruction, the target mass spectrum, the similar reference mass spectrums and the corresponding reference molecular structures, generating prompt information; and inputting the prompt information into the large language model to determine a target molecular structure corresponding to the target mass spectrum. Therefore, a relatively good mass spectrum structure analysis effect is realized by combining the advantages of database retrieval and deep learning by utilizing the few-sample learning capability of the large language model.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention relates to the technical field of data processing, and discloses an incremental clustering method and system for an atmospheric aerosol particulate matter mass spectrum data set, a terminal and a storage medium, and the method comprises the steps: obtaining the atmospheric aerosol particulate matter mass spectrum data set, and carrying out the clustering initialization processing, and obtaining a plurality of clustering initialization parameters; determining a target cluster selection strategy, and performing first similarity calculation and cluster allocation processing on the plurality of cluster initialization parameters to obtain a plurality of cluster center matrixes; performing merging processing and de-noising processing on the plurality of cluster center matrixes to obtain an initial clustering result, and performing second similarity calculation to obtain an iterative similarity; and performing clustering convergence judgment and iterative loop clustering processing on the initial clustering result, and outputting a clustering result of the atmospheric aerosol particulate mass spectrum data set. According to the method, the inter-cluster mixing condition in the clustering analysis process of the atmospheric aerosol particulate matter mass spectrum data set can be remarkably reduced, and the accuracy of clustering result output is ensured.

The invention belongs to the field of mass spectrum imaging, and particularly provides an image segmentation method for a mass spectrum imaging image of an ESI source, and the method comprises the steps: (S1) dividing the mass spectrum imaging image into a plurality of mass spectrum imaging channels according to a mass-to-charge ratio; (S2) selecting a mass spectrum imaging channel where target ions are located, and preprocessing each pixel; (S3) carrying out preliminary segmentation on the spectrogram data; (S4) performing interval division on the mass spectrum data of the channel where the target ions of each pixel are located, calculating the lower area of each interval, selecting the central mass-to-charge ratio of each interval as the abscissa of the intensity value of each interval, and setting the intensity value of the interval with the ratio of the central mass-to-charge ratio to the highest intensity value lower than a set value as 0; (S5) analyzing, training and optimizing the transformed mass spectrum data; and (S6) the optimal combination model obtained in the step (S5) is used for classification of other pixel points of the mass spectrum imaging image, and an image segmentation result is obtained. The method has the advantages of accurate segmentation and the like, and is applied to mass spectrum imaging of the ESI source.

The invention discloses a method for simultaneously detecting ergothioneine, ergosterol, ergosterol peroxide and lentinine in lentinus edodes, and belongs to the technical field of measurement of active ingredients of the lentinus edodes. The method comprises the following steps: detecting a shiitake mushroom extracting solution by adopting an ultra-high performance liquid chromatography-tandem mass spectrometry mode, wherein a chromatographic column is an HILIC or C18 chromatographic column; the mobile phase A and the mobile phase B are an aqueous solution containing formic acid and a methanol or acetonitrile solution containing formic acid respectively; the mass spectrum conditions comprise an electrospray ionization source; a positive ion multiple reaction monitoring mode; the ion spraying temperature is 315-325 DEG C; the temperature of the ion transmission tube is 320-330 DEG C; and the ion spraying voltage is 3500 V to 3700 V. The method has the characteristics of high sensitivity, high selectivity and high accuracy, and is suitable for quantitative detection of ergothioneine, lentinine, ergosterol peroxide and ergosterol compounds in shiitake mushrooms.

The invention discloses a gas component detection method and device based on circulation analysis, and relates to the related field of gas detection.The method comprises the steps that sampling is conducted when dynamically monitored real-time environment conditions meet set environment constraints; activating a chromatographic detector to perform chromatographic separation treatment on a target sample obtained by pretreating the gas sample; activating a mass spectrum detector to perform mass spectrum analysis on a first component in the multiple components to obtain a first mass spectrum; calling a mass spectrum identification plan to analyze the first mass spectrum to obtain a first detection result, and forming a target detection result; performing multi-dimensional property collection on the target sample based on a predetermined sample property to obtain a target property parameter; and correcting the target detection result by taking the combined detection fitness obtained by analyzing the target property parameters as a weight coefficient. The technical problem that existing gas component detection is insufficient in accuracy, comprehensiveness and reliability is solved, and the technical effect of improving the detection accuracy, comprehensiveness and reliability is achieved.

The invention relates to the technical field of metabolite detection, in particular to a high-flux metabolite detection method and application thereof. According to the detection method, metabolites in a biological sample to be detected are detected by using a liquid chromatography-mass spectrometry technology, wherein mass spectrometry is quadrupole electrostatic field orbitrap high-resolution and triple quadrupole mass spectrometry; the mass spectrum conditions of the MRM ion pair are as follows: acquiring data in an NFSWI-DDA, NFSWI-DIA or DIA mode in a positive and negative ion mode, and acquiring and annotating the MRM ion pair; the MRM list is transited to triple quadrupole mass spectrometry, and the mass spectrometry conditions of the triple quadrupole mass spectrometry include verification and detection of MRM ion pairs in positive and negative ion modes. The detection method can meet the requirements of precise medical research on metabolite coverage rate, quantitative stability and detection flux, can be used for semi-quantitative and quantitative combined detection of metabolites in various biological samples, has applicability, and can provide technical support for disease diagnosis, drug development and biomarker discovery.

The invention discloses a dynamic tracking method for an air pollution source, which relates to the technical field of pollution source tracking and comprises the following steps of: acquiring a pollution concentration raster data set, a UAV sniffing set and a meteorological field data set, reconstructing a wind field and predicting pollution transportation, generating a candidate source hypothesis, matching fingerprints and calculating weights, adaptively scheduling sensors, and performing Bayesian inversion fusion and result output. Unified pollution concentration and meteorological data are generated through multi-source data collection and three-dimensional flow field reconstruction, second-level wind field calculation is achieved through a dimensionality reduction CFD model, candidate source hypotheses are generated in combination with Lagrange backtracking and Euler transport, and a weight matrix is formed through UAV mass spectrum fingerprint bidirectional matching. Secondary cruise of the unmanned aerial vehicle and densification sampling of ground nodes are driven through confidence ranking, and sampling density is adaptively optimized. Bayesian joint likelihood and Markov chain are adopted to fuse multi-source data, dynamic source coordinates and emission rate are output, and a closed-loop iterative air pollution source tracking system is realized.

The invention belongs to the technical field of aircraft oil leakage monitoring, and relates to an aircraft oil leakage detection method based on a high-sensitivity mass spectrometry technology, and the method comprises the steps: obtaining a time-resolved mass spectrogram of aviation kerosene molecules in a sample testing stage, and storing the time-resolved mass spectrogram in a database; then oil gas molecules are captured at leakage-prone points of an aircraft pipeline system through a gas collection probe, the captured oil gas molecules are transmitted to an ion source end of a proton transfer reaction mass spectrometer through a negative pressure transmission system to be ionized to generate ions, and the ions are analyzed by a mass analysis system to obtain a mass spectrum; comparing the mass spectrogram obtained by analysis with a time-resolved mass spectrogram in a database, and judging whether leakage occurs or not; based on the method, the problems of insufficient anti-interference capability, difficulty in sample collection and the like of a high-sensitivity mass spectrum oil gas leak detection method can be solved in a targeted manner, and the functions of environmental interference suppression, efficient collection of oil gas molecules, improvement of spatial scanning resolution and the like can be realized at the same time.

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 mass spectrometric detection method and system based on data twinning. The method comprises the following steps: acquiring original mass spectrometric data of a sample through mass spectrometric detection equipment; performing multi-stage intelligent mass spectrum data preprocessing on the original mass spectrum data; training the processed mass spectrum data by using a machine learning algorithm, and constructing a data twin model which is used for obtaining a mass spectrum analysis result; and inputting new mass spectrum data into the trained data twin model to obtain a mass spectrum analysis result. According to the invention, mass spectrum data are identified and classified, and specific chemical components in the sample are identified and quantitatively analyzed. By performing multi-stage intelligent mass spectrum data preprocessing on the original mass spectrum data, the quality and reliability of the data are improved, the accuracy of the data is improved, the mass spectrum data are analyzed through the data twin model, and the analysis efficiency of the mass spectrum data is improved.

The invention provides a mass spectrum peak identification method and device based on deep learning, computer equipment and a storage medium. The method comprises the following steps: inputting a mass spectrum, adjusting the channel number through a convolutional layer, then performing convolution and down-sampling operation through a multi-layer encoder to extract image features, extracting key information from a generated high-order feature map through a bottleneck module, and inputting the key information into a decoder; a decoder gradually recovers a feature map to an original size through up-sampling, cross-layer feature fusion is carried out on the decoder and a corresponding layer of the encoder through a jump connection channel, and a channel space attention module is embedded in a connection path to strengthen attention to important features while detail information is reserved; and finally, outputting a segmentation result through point convolution. According to the method, a feature fusion strategy of multi-level feature multiplexing and channel space attention enhancement of a coding and decoding structure and a lightweight network architecture considering precision and speed are adopted, so that the recognition effect is ensured, and meanwhile, the operation efficiency is optimized.

Methods for identifying a chemical structure of a compound based on mass spectrometry (MS) data using one or more computing devices are disclosed. The methods include receiving mass spectrometry (MS) data that includes a plurality of mass-to-charge values associated with fragments obtained from mass spectrometry performed on the compound, inputting the plurality of mass-to-charge values into a tokenizer trained to generate a plurality of tokens based on the plurality of mass-to-charge values, and determining one or more chemical structures of the compound based at least in part on the plurality of tokens.

The invention discloses an integrated circuit equipment data optimization monitoring system and method based on big data, and relates to the technical field of integrated circuit manufacturing. The method is used for solving the problems of insufficient multi-physical field monitoring, difficulty in abnormal traceability and lack of closed-loop control in plasma etching. A plasma sheath thickness inversion model and an etching selection ratio model are constructed by collecting radio frequency reflection phase, mass spectrum ion strength and wafer temperature data, and process parameter-plasma state dynamic coupling is established. Interference image distortion features and electron microscope size data are fused, micro-groove geometric parameters are analyzed, morphology instability risk indexes are generated, and nanoscale early warning is achieved. And designing a dual-channel fusion network, embedding a physical constraint attention mechanism, and generating an etching rate optimization instruction. On the basis of incremental learning, model parameters are updated online, a'monitoring-decision-feedback 'closed-loop system is formed, anomaly detection sensitivity and decision reliability are improved, and technical support is provided for intelligence of integrated circuit equipment.

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 greenhouse gas underway detection, and discloses a large-range greenhouse gas underway detection method suitable for leakage detection of a gas company, and the method comprises the following steps: S1, early-stage preparation; s2, performing field deployment; s3, underway monitoring is carried out; s4, data analysis; s5, confirming and repairing a leakage source; and S6, follow-up monitoring and rechecking are performed, rapid coverage of a large-range to-be-detected area is realized by adopting underway detection, so that a large amount of data is efficiently collected, the problem of non-uniform surface station distribution of a traditional detection method is solved, the concentration data of leaked gas is accurately detected through a mass spectrum underway monitoring system, and the detection accuracy is improved through continuous monitoring. The change trend of the leaked gas concentration along with time lapse can be displayed, and the accuracy of leaked gas detection is improved; and a repair scheme for the leakage source is formulated, the leakage source is repaired and registered in time, subsequent continuous monitoring is performed, and continuous and stable operation of the gas equipment is guaranteed.

The invention discloses a scorpion anti-inflammatory polypeptide as well as a screening method and application thereof, and belongs to the technical field of traditional Chinese medicinal material polypeptides. The scorpion anti-inflammatory polypeptide is one of G4, G5 and G17. The method comprises the following steps: detecting a scorpion buthus martensii polypeptide extract through nanoElute 2 nanoliter liquid phase separation, timsTOFPro2 mass spectrometry and BPS Novor search analysis, screening a scorpion buthus martensii polypeptide, and further synthesizing the screened candidate polypeptide by adopting a solid-phase synthesis method; the effect of the synthesized candidate polypeptide on inhibiting microglial cell inflammation is evaluated through a qPCR method, and then the scorpion buthus martensii anti-inflammatory polypeptide is screened out. The Buthus martensii Karsch anti-inflammatory polypeptides G4, G5 and G17 screened by the method, especially G5, can significantly inhibit microglial cell inflammation and present concentration dependence, a theoretical basis is provided for medicinal development of Buthus martensii Karsch polypeptides, and a potential anti-neuroinflammation mechanism of the Buthus martensii Karsch polypeptides is disclosed.

The invention relates to a mass spectrometer ion detector micro-current multistage amplification circuit, which comprises a primary amplification circuit, a voltage following circuit, a secondary amplification circuit, a gain adjustment control circuit and an analog-to-digital conversion circuit, the gain adjustment control circuit can dynamically adjust the gain gears of the first-stage amplification circuit and the second-stage amplification circuit, 16 groups of different amplification gains can be achieved in total, and the application requirement for simultaneous detection of nine orders of magnitude of substances with different concentrations is met. The circuit is simple and reliable, is flexible and convenient to use, greatly widens the detection range of the mass spectrometer, and improves the automatic control level and detection efficiency of the mass spectrometer.

The present invention provides a method for constructing a fingerprint of a Xin Su Ning capsule and a fingerprint. The method includes: S1: taking contents of Xin Su Ning capsules of different batches, adding a methanol aqueous solution, and performing ultrasonic extraction to obtain test article solutions; S2: injecting the test article solutions into a high performance liquid chromatograph, performing gradient elution, performing chromatographic analysis, and recording chromatograms from 0 to 140 min; S3: importing the chromatograms into a traditional Chinese medicine chromatographic fingerprint similarity evaluation system to obtain a fingerprint; and S4: performing mass spectral analysis on the test article solutions, and determining chemical constituents of chromatographic peaks in the fingerprint according to results of the mass spectral analysis. The method for constructing a fingerprint of a Xin Su Ning capsule provided in the present invention has advantages of simple, stable and high precision and good reproducibility.

The invention relates to the technical field of food detection, and discloses a food pesticide residue safety detection analysis method based on machine learning, which comprises the steps of food sample collection, extract pretreatment, two-dimensional liquid chromatography-high resolution mass spectrometry combined technology analysis, mass spectrum acquisition, mass spectrum numerical data conversion, data correction and machine learning model establishment. The method comprises the following steps: identifying and classifying pesticide residue components, extracting a pretreatment extract, analyzing a to-be-detected sample, acquiring a mass spectrum, converting the mass spectrum into to-be-detected mass spectrum numerical data, correcting the data, inputting a model, identifying and classifying pesticide residues, quantitatively analyzing, comparing food safety standards, judging conformity and generating a detection report. The invention aims to improve the accuracy, sensitivity and efficiency of pesticide residue detection. Meanwhile, through technical correction and optimization, the problems of environmental interference, data deviation and the like faced by an existing method are effectively solved.

The invention belongs to the technical field of detection, relates to a free radical detection method, particularly provides a water free radical cation detection method, and aims to realize water free radical cation detection which is efficient, rapid, free of sample pretreatment and convenient to analyze. According to the invention, an LTQ-XL linear ion trap mass spectrometer is used as a detection tool, and a dielectric barrier corona discharge method is adopted to provide a water free radical cation detection method. The method comprises the following steps of: filling argon (Ar) between two discharge electrodes as a working gas, applying a certain voltage to two electrodes of a dielectric barrier discharge ionization source (DBDI), detecting water free radical cations to obtain a corresponding mass spectrum to represent the water free radical cations, and optimizing a target signal to obtain corresponding mass spectrum data. The method can be used for efficiently and quickly detecting the water free radical cations without sample pretreatment and the like, is convenient to analyze and apply, and has remarkable innovativeness and wide application prospects.

The invention discloses a single-cell metabolite spectrum quantitative analysis platform and method based on droplet microfluidics, and relates to the technical field of mass spectrometry. The platform comprises a fluid pumping module, a cell oscillation module, a microfluidic mixed liquid drop generation module, a microfluidic mixed liquid drop reinjection and separation module and a mass spectrometry module. The fluid pumping module comprises a high-pressure nitrogen cylinder and a gas pressure driver; the cell oscillation module comprises a cell oscillator; the micro-fluidic mixed droplet generation module comprises a micro-fluidic droplet generation chip, a cell lysis solution, an internal standard, an oil phase, a cell suspension and a droplet collection and incubation centrifugal tube; the micro-fluidic mixed liquid drop reinjection and separation module comprises a micro-fluidic liquid drop reinjection and separation chip; and the mass spectrometry module comprises an electrospray needle and a mass spectrometer. According to the method, the single cell is captured and the internal standard substance is introduced at the same time by using droplet microfluidics, and mass spectrometry is carried out, so that high-flux single cell encapsulation and accurate quantification of metabolites are realized.

MS-based methods and systems are provided herein in which a desorption solvent desorbs one or more analyte species from an SPME device within a sampling interface that is fluidly coupled to an ion source for subsequent mass spectrometric analysis. In accordance with various aspects of the applicants teachings, the sampling interface includes an internal sampling conduit that provides increased interaction between the desorption solvent and the sampling substrate, thereby improving mass transfer (e.g., increased extraction or desorption speed).

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 utility model relates to the technical field of environmental quality detection, in particular to a high-precision water quality harmful substance detector based on a spectrum and mass spectrum coupling technology, which comprises a detection component, the detection component comprises a detector and a shunting component, the shunting component is arranged on one side of the detector and comprises a shunting block and two detection cavities, and the shunting block is arranged in the detection cavity. The shunting block is used for shunting water flow and discharging the water flow to the detection cavity; a detection opening of the detection cavity is communicated with the detector. When in use, the shunting assembly is arranged in the detection assembly, and a poured water source is shunted and flows in by utilizing the shunting assembly, so that the detector can be used for carrying out synchronous spectrum and mass spectrum detection on the same batch of water source samples, and the phenomenon that in step-by-step detection, after one detection is carried out, the detection time is shortened is avoided. Some substances contained in a water source sample can be influenced by a detection mechanism, so that the content of some substances in the water source sample is changed, and subsequent detection is influenced.

The invention relates to the technical field of biological detection, in particular to a detection primer for African horse pestivirus and west nile virus and a nucleic acid mass spectrometry detection method. On the basis of an MALDI-TOF nucleic acid mass spectrum detection method, two relatively conservative genes of VP7 and NS2 are selected as target genes for the African horse pestivirus; and for the West Nile virus, two same conservative genes NS2A and NS5 are selected. Based on the target genes, four pairs of amplification primers and four extension primers are designed. By optimizing selection of amplification sites and adding of modified bases, it is ensured that the molecular weight of the extension probe is effectively distinguished within the detection range, so that the multiple detection target is achieved, and meanwhile high accuracy, specificity and sensitivity of detection are ensured. The kit can efficiently, sensitively and accurately identify the infection conditions of the African horse pestivirus and the west nile virus, can be flexibly adjusted and optimized according to actual requirements, and is expected to play an important role in virus detection and differential diagnosis in the future.