1508 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 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 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 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 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 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 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.

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 discloses a double-pulse system and method for improving depth resolution of laser ionization mass spectrometry, and relates to the technical field of mass spectrometers, the system comprises a control system for controlling femtosecond laser parameters generated by a femtosecond laser and nanosecond laser parameters of a nanosecond laser; the laser energy regulation and control module is used for carrying out phase adjustment and detection on the input femtosecond laser and outputting the femtosecond laser meeting set parameters based on the femtosecond laser parameters; the light beam shaping and light beam morphology analysis module is used for shaping femtosecond laser and focusing the femtosecond laser on the surface of the sample for laser denudation; the nanosecond laser performs off-resonance multi-photon ionization on the denudated neutral particles to generate ionized ions for mass spectrometry. According to the invention, the depth resolution of the laser ionization mass spectrum can be improved to nanoscale, the ionization efficiency of ions is improved under the condition of reducing the sampling amount, and meanwhile, the sensitivity of an instrument and the accuracy of quantitative analysis are improved.

The invention relates to a detection method for determining isotope abundance of a 15N or 18O labeled nitrate sample by using an organic mass spectrum. The detection method comprises the following steps: (1) dissolving the 15N or 18O labeled nitrate sample in a methanol aqueous solution; (2) taking the supernatant, filtering with a filter membrane, and diluting with a methanol aqueous solution to obtain a solution to be detected; and (3) detecting the solution to be detected by using a high-resolution liquid chromatograph-mass spectrometer, selecting a mass spectrum in a peak appearance time window, recording relative mass spectrum signal intensities with mass-to-charge ratios of 61.988 and 62.985, and calculating to obtain the 15N isotope abundance. Or recording the relative mass spectrum signal intensities with the mass-to-charge ratios of 61.988, 63.993, 65.997 and 68.001, and calculating to obtain the abundance of the < 18 > O isotope. Compared with the prior art, the method has the advantages that the sample pretreatment time is greatly shortened, the economic cost is low, and the test result is accurate.

The invention discloses a protein mass spectrum coding method based on self-supervised learning, and the method comprises the steps: guaranteeing the consistency of model input through the preprocessing steps of data enhancement, normalization, sequence length standardization and the like; by constructing a self-supervised learning task and utilizing a multi-head attention mechanism of a Transform architecture, efficient extraction of global and local features of mass spectrum data is realized; through an autoregressive encoder training framework, the model can automatically learn the internal structure of mass spectrum data without a large amount of labeled data, and high-dimensional coding representation with robustness and generalization ability is generated. According to the method, the workload of manual feature design is remarkably reduced, and the generated coding representation can be applied to downstream tasks such as mass spectrum data quality evaluation, protein identification and quantification, post-translational modification identification and biomarker discovery, and has wide applicability and high efficiency; and an innovative intelligent solution is provided for proteomics research.

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.

Known mass spectral data of a library of spectra corresponding to known compounds or known mass spectral data determined from a database of known compounds are compressed using a neural network encoder, producing a group of corresponding compressed known representations of known mass spectral data. Experimental mass spectral data of an experimental mass spectrum is compressed using the neural network encoder, producing a compressed experimental representation of the experimental mass spectral data. The experimental representation is compared to the group of known representations and each comparison is scored. At least one comparison with a score above a predetermined score threshold is selected. A known compound is determined from the selected at least one comparison. The known compound is identified as a compound of the experimental spectrum.

The invention discloses a hybridoma cell strain 1D6 as well as a monoclonal antibody and application thereof. The invention also discloses an application of the monoclonal antibody for resisting the Tilletia foetida in the detection of teliospore of the Tilletia foetida. The invention also discloses a single base sequence DNA single chain modified Tilletia foetida monoclonal antibody. The invention also discloses a detection kit. The monoclonal antibody as well as the marker and the kit thereof show high specific recognition capability on Tilletia foetida. The method for detecting the single base by mass spectrometry has a signal amplification effect and is simple and convenient in detection operation, so that the sensitivity and the detection efficiency of the method are remarkably improved. With the progress and continuous popularization and application of the mass spectrum technology, the technology has wide application potential and prospect.

The invention discloses a mass spectrometric analysis-based paint component detection system and method, and the system comprises a data collection and tensor construction module which collects and processes original data to obtain a decomposable tensor; the tensor decomposition module is used for decomposing the sorted tensor through a PARAFAC model; the spectrum prediction model module is used for establishing candidate samples and predicting the candidate samples through a spectrum prediction model to obtain predicted spectrum vectors; the spectrum similarity calculation module is used for comparing the fragment spectrum vectors with the prediction spectrum vectors one by one and obtaining matching scores through calculation; the Bayesian fusion module is used for carrying out probabilistic fusion on the fragment spectrum vector and the prediction spectrum vector, and obtaining the existence probability and the concentration posteriori distribution through calculation; and the detection module is used for labeling and confirming the candidate samples of which the matching confidence is lower than a set threshold value. According to the method, the detection reliability is improved while the accuracy is ensured, and the requirement of being applied to a complex detection scene is met.

The invention relates to a brand-new microbial natural metabolite, namely Fcl-8, which is derived from a fermentation solution of Xenorhabdius budapestensis XBD8, and is characterized in that the Fcl-8 is a new microbial natural metabolite, namely Fcl-8, which is derived from the fermentation solution of the Xenorhabdius budapestensis XBD8. The purified Fcl-8 is a snow white flocculent crystal and is very easy to dissolve in water, and the solid state of the purified Fcl-8 is as shown in Figure 1. The chemical structural formula of the Fcl-8 is as shown in a figure 2, the chemical formula is C57H102N14O12, the molecular weight is 1175.78744, and the typical secondary mass spectrum information of the Fcl-8 is as shown in a figure 3. The Fcl-8 is derived from a microbial natural metabolite, is novel in structure and high in biological activity, and has very high potential to be developed into a novel biopesticide.

The invention provides a vacuum helium mass spectrum leak detection method and device for corrugated pipe products. The vacuum helium mass spectrum leak detection method comprises the following steps that S1, corrugated pipes are fixed through supporting rings; s2, the supporting ring and the supporting plate are assembled; s3, a first O-shaped ring is placed between the corrugated pipe and the upper cover for sealing, and the upper cover, the corrugated pipe and the supporting plate are fixed through studs; s4, the fixed corrugated pipe is placed on a base, a second O-shaped ring is placed between the base and an upper cover for sealing, and bolts are adopted for fixing; s5, the assembled corrugated pipe is vertically placed, a helium mass spectrometer leak detector is connected with a sampling inspection opening in the bottom, and the outside of the corrugated pipe is vacuumized; and S6, connecting the helium tank connected with the pressure reducing valve with a top helium filling port, and filling helium into the corrugated pipe for leak rate inspection. According to the invention, the corrugated pipe is ensured not to be stretched under the stamping condition and not to be distorted and deformed; the effectiveness of sealing between the corrugated pipe and the upper cover is ensured; the detection result is accurate and reliable.

The invention discloses a method for determining volatile organic compounds in surface water by purging and trapping and gas chromatography-mass spectrometry, which comprises the following steps: acquiring mass spectrometry data of volatile organic compounds in a surface water sample, and separating and detecting a multi-component mixture in a complex matrix by gas chromatography-mass spectrometry, obtaining an original data set containing retention time and mass spectrum information; according to an output result of the classification model, carrying out qualitative analysis on unknown compounds in a to-be-detected sample, and if the classification probability is greater than a preset threshold value, determining the types of the compounds to obtain an accurate qualitative recognition result; and establishing a concentration standard curve by adopting a corresponding quantitative ion peak area according to a target compound confirmed in the qualitative identification result, and calculating a linear relation parameter between the peak area and the concentration to obtain a regression equation for quantitative analysis.