11434results about "Component separation" patented technology

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

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 discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention discloses an application of an electronic waste pollution human body exposure fingerprint spectrum in exposure risk prediction, and the human body exposure fingerprint spectrum is composed of more than three of exposure markers of 2, 4, 6-tribromophenol, 2, 4, 5-trichlorophenol, 4-bromophenol, 1-hydroxypyrene and bisphenol A. The invention further discloses an application of the electronic waste pollution human body exposure fingerprint spectrum in exposure risk prediction. According to the method, a systematic method of urine pollutant exposure omics is adopted for the first time, comprehensive screening and characterization of pollutant exposure markers related to specific components and by-products are achieved in electronic waste exposure worker groups, and the method is the most systematic organic pollutant exposure research known in the field at present.

The invention discloses a wild jujube leaf characteristic fingerprint spectrum construction method and system, and belongs to the technical field of traditional Chinese medicine, and the construction method specifically comprises the following steps: I, collecting original spectrum data of each wild jujube leaf sample, and extracting and fusing deep nonlinear characteristics of each original spectrum data to form a unified spectrum fusion characteristic vector; iI, identifying chemical components in each wild jujube leaf sample, extracting chromatographic peak areas and mass-to-charge ratios of different chemical components, and constructing a chemical component association diagram; the method breaks through the limitation of constructing the wild jujube leaf sample characteristic fingerprint spectrum by traditional single data, can intuitively reflect the synergistic change rule of components of wild jujube leaf samples from different producing areas or in different batches, and enhances the interpretability of the wild jujube leaf sample characteristic fingerprint spectrum at the same time; the distinguishing precision of wild jujube leaf sample differences can be improved, a clear judgment basis is provided for wild jujube leaf sample quality evaluation and active ingredient traceability, and the integrity of the wild jujube leaf sample characteristic fingerprint spectrum is guaranteed.

The invention discloses an intelligent propolis component identification and traceability system and method, and relates to the technical field of computer vision and mode recognition, and the system comprises a data collection module which collects spectrum and chromatographic data and traceability information of a production place, processing and the like; the data preprocessing unit is used for eliminating overlapping peaks, normalizing data and synchronizing timestamps through filtering and baseline correction denoising; the component feature extraction module is used for screening 10 spectral feature peaks, calculating chromatographic features, and reducing the dimensions to 32 dimensions through principal component analysis after splicing; the intelligent identification module is used for judging the purity level and the confidence degree by using a CNN-LSTM model; according to the traceability management module, a block chain stores full-link information, and a distributed database manages query permissions; and the result output module is used for visually displaying the result and triggering an alarm when the confidence degree or the traceability integrity does not reach the standard. The multi-source fusion improves the identification precision, the model can be updated online to adapt to a new scene, and the propolis quality and the market order are efficiently guaranteed.

The invention relates to a metabolic marker for diagnosing diabetic secondary osteoporosis and application of the metabolic marker, and belongs to the technical field of biological medicine. The method comprises the following steps: firstly, screening out obviously different metabolites between osteoporosis (DOP) and osteoporosis-free groups (DM) in diabetic patients; and secondly, screening out significant difference metabolites between the primary osteoporosis patient and the healthy control group, and excluding the intersection of the two groups of difference metabolites to obtain metabolites which exclude the influence of the primary osteoporosis. Metabolic markers, namely cytosine nucleotide, ketoglutaric acid and triethylamine, are obtained through further screening, and when the three metabolic markers are independently used, AUCgt; the AUC during combined use is 0.99, so that the limitation that the sensitivity of the traditional bone mineral density detection on the osteoporosis recognition in the diabetic population is insufficient is effectively made up.

The invention discloses an on-load tap-changer oil dissolved gas monitoring device which comprises a variable capacity container, a two-way electromagnetic valve, a switching mechanism, a chromatographic column system, a high-sensitivity detector and a low-sensitivity detector which are sequentially connected, and the top and the bottom of the variable capacity container are connected through a circulating pump to form a gas flow loop. The variable-capacity container is connected with the on-load tap-changer through a connecting pipe, the switching mechanism is connected with at least two quantitative pipes with different capacities, and the switching mechanism is used for equally transferring gas in the variable-capacity container to the quantitative pipes and then sequentially transferring the gas in the quantitative pipes to the chromatographic column system. The invention further discloses a method for monitoring the dissolved gas in the on-load tap-changer oil. According to the monitoring device and the monitoring method for the dissolved gas in the oil of the on-load tap-changer, the detection range of the concentration is improved, a concentration blind area which is not detected and misjudgment of signal saturation can be avoided, and the detection accuracy is improved.

The invention discloses a liquid chromatography-mass spectrometry data peak shape recognition processing method and related equipment, and relates to the field of data processing. The method comprises the following steps: acquiring liquid chromatography-mass spectrometry data; extracting morphological characteristic data of a chromatographic peak based on the extracted ion current chromatogram time sequence; calculating and extracting a signal complexity index of the ion current chromatogram time sequence; adjusting peak shape identification parameters according to the signal complexity index, wherein the peak shape identification parameters comprise a noise threshold value and a peak width range; distinguishing the chromatographic peak into a normal peak or an abnormal peak by using the adjusted peak shape identification parameter; searching a liquid phase method optimization scheme matched with the morphological characteristic data of the abnormal peak in a preset optimization database, wherein the liquid phase method optimization scheme comprises gradient elution time adjustment, mobile phase proportion optimization or chromatographic column replacement suggestion; and sending the liquid phase method optimization scheme to a user. According to the invention, various abnormal peak shapes can be automatically and accurately identified, and the reliability and accuracy of identification are guaranteed.

The invention discloses a multielement multiphase fluid displacement coalbed methane 4D visual simulation experiment system and a multielement multiphase fluid displacement coalbed methane 4D visual simulation experiment method. The system comprises a gas-liquid mixed injection system, a Kr adsorption system, a visual triaxial core holder system, a CT scanning system and a tail gas analysis and treatment system. According to the experimental method, Kr similar to CH4 in dynamic diameter and other parameters is selected as a CT tracer to replace CH4, and 4D visualization of coal body internal pore and fracture structure evolution and Kr concentration field displacement migration process can be realized through three-dimensional reconstruction of CT images at different displacement moments and registration, gray correction and normalized concentration calculation of the images. The problem that the gas migration rule in the coal body in the displacement experiment is not clear is solved; the dynamic diffusion process inversion of the coal bed gas in the coal bed difficult to extract is realized by utilizing the distribution characteristic evolution of the Kr gas in the coal body, so that the in-situ coal body fissure structure in the coal sample and the dynamic migration law of methane in the displacement process are obtained, and the method has great significance for revealing the displacement development mechanism of unconventional natural gas such as the coal bed gas.

The invention relates to the technical field of intelligent control of HPLC (High Performance Liquid Chromatography) analytical instruments, in particular to an HPLC system fault early warning self-calibration method. Comprising the following steps: S1, acquiring a high-frequency pressure signal behind a pump and a chromatographic signal of a detector in real time; calculating a relative physical disturbance quantity based on the post-pumping high-frequency pressure signal; calculating a chemical state deviation degree based on the detector chromatographic signal; s2, fusing the relative physical disturbance quantity and the chemical state deviation degree, and constructing an instantaneous health index; and S3, comparing the instantaneous health index with a preset first early warning threshold value and a preset second early warning threshold value, and executing a closed-loop control strategy corresponding to a comparison result. According to the method, accurate fault early warning is realized by constructing a physical-chemical two-dimensional monitoring system, the defects of offline and lagging applicability test of a traditional system are overcome through real-time monitoring and fusion evaluation of the two dimensions, and the accuracy and timeliness of early warning are remarkably improved.

The invention discloses a liquid chromatography detection method and system for eye drops, and relates to the technical field of chromatographic detection.The liquid chromatography detection method comprises the steps that liquid chromatography analysis is conducted to obtain an original chromatographic signal; performing adaptive baseline correction on the original chromatographic signal to obtain a corrected chromatographic signal, synchronizing the corrected chromatographic signal to a chromatographic peak identification analysis model for identification to obtain a plurality of chromatographic peaks including principal component peaks and specific impurity peaks, and performing calculation to obtain a first integral area of the principal component peaks and a second integral area of the specific impurity peaks; the preset correction factor is combined with the first integral area and the second integral area to automatically carry out quantitative calculation on the eye drops, and a liquid chromatogram detection report of the eye drops is generated. The technical problems that in the prior art, main component peak and impurity peak recognition and quantification depend on manual processing in the eye drop liquid chromatogram detection process, and accuracy and consistency are insufficient are solved, and the technical effects that the accuracy and automation level of eye drop liquid chromatogram detection are improved, and it is guaranteed that main component and impurity quantification results are stable and consistent are achieved.

The invention discloses a transformer insulating oil chromatographic data abnormity identification method, and relates to the technical field of data identification, and the method comprises the following steps: carrying out dissolved gas analysis on an insulating oil sample through a gas chromatograph to obtain a chromatographic original response curve; performing baseline correction and de-noising processing on the chromatographic original response curve, positioning each gas peak based on a first-order derivative method and a second-order derivative method, and extracting chromatographic peak height and chromatographic peak area parameters; according to the extracted chromatographic peak height and chromatographic peak area, three Duval fault ratios are calculated, and the ratio of the total soluble gas concentration to each gas concentration threshold value is calculated; combining into a multi-dimensional diagnosis feature vector; according to the method, the chromatographic peak shape of the insulating oil sample is subjected to multi-stage baseline correction and de-noising treatment, and the gas peak position is accurately positioned based on the first-order and second-order derivative methods, so that the high precision of chromatographic peak height and chromatographic peak area extraction is ensured, the accuracy of dissolved gas concentration calculation is improved, and the misjudgment risk caused by noise or baseline drift is reduced.

The invention relates to the technical field of liquid chromatogram data processing, in particular to a liquid chromatograph state detection and maintenance method which comprises the following steps: establishing a multi-parameter online monitoring system which comprises a normal data set and an abnormal data set; learning the normal data set and the abnormal data set, and collecting real-time monitoring data to be matched with a known fault mode; according to the real-time monitoring data, the control unit draws a pressure curve and calculates a pressure fluctuation value; determining the test period of the chromatographic instrument based on the pressure fluctuation value and the baseline noise peak value, or sending an equipment fluctuation monitoring signal and calculating the difference value between the chromatographic column temperature and the environment temperature: determining the heating power of a constant-temperature box based on the difference value between the chromatographic column temperature and the environment temperature, or sending an equipment fluctuation monitoring signal; and determining the temperature correction coefficient of the chromatographic column according to the equipment vibration frequency. According to the invention, the operation stability and effectiveness of the liquid chromatograph are improved.

The invention provides a content detection method for a salvia miltiorrhiza medicinal material, a salvia miltiorrhiza clear paste and a musk heart-dredging dropping pill. According to the method, a high performance liquid chromatography method is adopted, octadecylsilane chemically bonded silica is used as a chromatographic column filler, acetonitrile is used as a mobile phase A, 0.02%-0.1% trifluoroacetic acid is used as a mobile phase B, gradient elution is carried out, and rosmarinic acid and salvianolic acid B in the salvia miltiorrhiza medicinal material are synchronously and quantitatively detected. And synchronously and quantitatively detecting tanshinol, protocatechuic aldehyde, caffeic acid, salvianolic acid E, rosmarinic acid, alkannic acid and salvianolic acid B in the salvia miltiorrhiza clear paste, and synchronously and quantitatively detecting tanshinol, protocatechuic aldehyde, caffeic acid, salvianolic acid E, rosmarinic acid, alkannic acid, salvianolic acid B, arenobufagin and bufalin in the musk Tongxin dropping pill. The method is used for monitoring the content of specific components, can also judge the transfer rate in the medicine production process, and facilitates traceability in the production link. In addition, the method is relatively low in equipment requirement and suitable for industrial popularization.

The invention provides a deep natural gas formation cause identification and quantitative evaluation method combined with methane cluster isotope composition, and belongs to the technical field of oil-gas exploration. Based on a method of combining methane cluster isotope values (delta13CH3D and delta12CH2D2) with stable hydrocarbon isotope values (delta13CCH4 and deltaDCH4), the relative contribution of each end member of an inorganic cause and a thermal cause to deep natural gas accumulation is quantitatively analyzed according to the cluster isotope values of two methane sources, namely Fischer-Tropsch inorganic synthesis and organic matter thermal cracking. According to the method, the multi-solution limitation caused by the fact that a traditional method only depends on the methane monomer isotope is overcome, the accuracy of deep natural gas cause type judgment is remarkably improved through the methane cluster isotope, and accurate quantitative evaluation of the proportion of different cause components in the mixed-source natural gas is achieved; and a more reliable basis is provided for exploration and development of deep natural gas resources.

The invention discloses an antler bone strengthening peptide for promoting bone repair as well as a screening method and application thereof, and belongs to the technical field of traditional Chinese medicinal material polypeptides. And the antler bone strengthening peptide is one of G40, G44 and G45. Grinding and crushing the antler, homogenizing the lysate, performing ultrasonic centrifugation, precipitating protein with methanol, centrifuging and discarding the supernatant; extracting, desalting, and freeze-drying to obtain a antler polypeptide extract; liquid chromatography-mass spectrometry detection, Peaks 8 search software and De Novo method analysis are adopted, according to the conditions that ALC is larger than or equal to 95%, local context is larger than or equal to 95%, Area is larger than or equal to 1000000 and an NCBI database, free polypeptide is obtained through comparison, the biological activity probability, the peptide fragment toxicity and the sensitization of the peptide fragment are predicted, and candidate polypeptide is obtained; synthesizing candidate polypeptides by a solid-phase synthesis method; the influence of the candidate polypeptide on osteoblast proliferation is evaluated, and the antler bone strengthening peptide is screened out. The antler bone strengthening peptide promotes osteoblast proliferation, and reveals a potential bone repair mechanism of the antler bone strengthening peptide.

The invention discloses an ion chromatography wave crest boundary extraction method based on fitting residual and tangent judgment, and belongs to the technical field of ion chromatography analysis. Comprising the following steps: S1, reading an original waveform data sequence, and executing frequency reduction sampling according to a set step length; s2, calculating the slope of the data after underclocking sampling by using a difference method, and searching positive and negative slope sections by performing first-order derivation on the waveform after underclocking sampling; s3, noise interference is eliminated, slope areas are grouped, then a chromatographic peak detection limit value is set, and an effective peak left section and an effective peak right section are screened out; s4, combining the effective wave crest left side section and the effective wave crest right side section to obtain an effective wave crest and a preliminary left boundary point and a preliminary right boundary point of the effective wave crest; s5, fitting a straight line through the preliminary left boundary point and the preliminary right boundary point to position an error integral region; and S6, updating the optimal boundary point by traversing the evaluation indexes of all points in the error integral region. The boundary search range is more accurate, and the calculation efficiency is higher.

The invention discloses a rapid identification and comprehensive evaluation method for a polygonatum cyrtonema provenance, and belongs to the field of improved variety breeding and quality control of traditional Chinese medicinal materials. The method systematically measures a plurality of specific biological traits covering three dimensions of growth, physiology and medicine. And carrying out standardization processing on the measured multi-dimensional data, carrying out dimensionality reduction by using a principal component analysis model, constructing an evaluation model capable of comprehensively reflecting the comprehensive quality of the provenance, and calculating a quantitative comprehensive evaluation score. The score can objectively and scientifically divide different provenances into four different quality grades including excellent, good, common and to-be-improved quality grades. The method has the beneficial effects that the strong positive correlation between the easily measured growth traits of the overground part and the medicinal component content of the underground rhizome for determining the final quality of the medicinal material is established, so that the rapid, predictive and non-destructive early screening of the provenance quality is realized.

The invention discloses a Raman spectrum-based online detection method for glucose and ethanol in a yeast fermentation process. The Raman spectrum-based online detection method specifically comprises the following steps: preparing a yeast seed solution, and inoculating the yeast seed solution into a fermentation tank for fermentation; in the fermentation process, an immersion type Raman optical fiber probe is used for collecting a Raman spectrum in real time, and meanwhile sampling is conducted at regular time to measure the concentration of glucose and ethanol; sequentially carrying out median filtering, adaptive iteration reweighted penalty least square method (airPLS) baseline correction, Savitz-golay (SG) smoothing processing and characteristic peak normalization on the acquired spectrum; respectively establishing concentration prediction models of glucose and ethanol by adopting a PLS1 algorithm; and applying the model to a subsequent batch fermentation process to realize online prediction of glucose and ethanol. According to the method, manual sampling is not needed, prediction is accurate, operation is easy and convenient, and in-situ online detection of glucose and ethyl alcohol in the yeast fermentation engineering can be achieved.

The invention discloses a method for enriching light metal ions in a pore channel based on carbon nanofiber / carbon fiber nano confinement, and belongs to the technical field of sample pretreatment, and the method comprises the following steps: S1, preparing a carbon nanofiber / carbon fiber confinement material; and S2, putting the carbon nanofiber / carbon fiber confinement material obtained in S1 into a light metal ion solution, and stirring. According to the method for enriching the light metal ions in the pore channel based on the carbon nanofiber / carbon fiber nano confinement, the carbon nanofiber / carbon fiber is used as a confinement fluid carrier, ultrapure water, methanol or n-hexane is used as an extracting agent, a chelating agent does not need to be introduced, the method is green and environmentally friendly, the mass transfer rate is increased, and the method is suitable for large-scale industrial production. Therefore, high enrichment of light metal ions in water is realized.

The invention belongs to the technical field of traditional Chinese medicine detection, and discloses a detection method for a characteristic spectrum of Uighur medicine Kspecific heat extracting tablets and application of the detection method. The detection method provided by the invention comprises the following steps: preparing a test sample solution by taking a 75% methanol solution as a solvent, and determining the test sample solutions prepared from different batches of test samples by adopting ultra-high performance liquid chromatography to obtain a characteristic spectrum; the chromatographic conditions of the ultra-high performance liquid chromatography are as follows: a chromatographic column is a C18 chromatographic column, a mobile phase A is acetonitrile, a mobile phase B is a 0.1 vol% phosphoric acid solution, and the flow velocity is 0.25-0.35 mL / min; the detection wavelength is 0 to 18 minutes, and the wavelength is 327 nm; the time is 18 to 28.5 min, and the wavelength is 254 nm; when the time is 28.5 to 37.6 minutes, the wavelength is 242 nm; after 37.6 to 70 minutes, the wavelength is 210 nm; the sample size is 1 microliter, the column temperature is 20-40 DEG C, and gradient elution is carried out. The invention provides a more scientific identification and quality control method for the Kespecific heat tablets.

The invention belongs to the technical field of liquid chromatography-mass spectrometry detection pretreatment, and particularly relates to a pretreatment method for liquid chromatography-tandem mass spectrometry analysis of pesticide and veterinary drug residues in serum, which comprises the following steps: (S1) carrying out heating reaction on Sc (III) salt, Cu (II) salt and 1H-pyrazole-4-formic acid (H2PyC) in a polar aprotic solvent, and cooling to obtain a green crystal, namely MOF-919; the chemical formula of the MOF-919 is [Sc < 3 > (mu3-O) (OH) < 3 >] [Cu3 (mu3-O) (mu-PyC) < 3 > (H2O) < 6 >] < 2 >; (S2) mixing the serum sample with an acetonitrile solution containing formic acid, carrying out ultrasonic treatment, vortex oscillation and centrifugation, taking supernate, and diluting the supernate with an aqueous solution containing formic acid to obtain a to-be-purified solution; and (S3) adding MOF-919 into the to-be-purified liquid, mixing, performing ultrasonic treatment, performing vortex oscillation, centrifuging, and taking supernate, thereby completing the pretreatment of the to-be-purified liquid. According to the method, the MOF-919 is used for adsorbing chemical pollutant residues in serum, especially phospholipids, so that the purpose of removing the chemical pollutant residues in blood is achieved. The method is simple and convenient to operate, small in organic solvent consumption and good in purification effect, and can be widely applied to residue analysis of chemical pollutants in serum.

The invention belongs to the technical field of honey analysis, and particularly relates to a method for analyzing characteristic chemical components of Qinling Mountain medium-peak honey. The analysis method comprises the following steps: obtaining different extraction layers, and carrying out mass spectrometry on each layer of sample; constructing a molecular network by mass spectrum data through a GNPS platform, and comparing the mass spectrum data with a database to obtain primary identification; unmatched nodes predict the skeleton by using an NAP tool to obtain a secondary result; then fractions are separated, and an unknown structure is deduced by combining an SMART technology with nuclear magnetism; and finally, synthesizing the three types of results to determine the characteristic chemical components of the honey. Compared with a single chromatography-mass spectrometry technology, the analysis method provided by the invention not only realizes data visualization, but also reduces the dependence of chemical component identification on a database to a certain extent, and provides reference for rapid characterization of chemical components in a honey crude extract or fraction.

The invention discloses a liquid chromatographic peak intelligent analysis method and system based on deep learning, and relates to the technical field of data processing. The method comprises the following steps: constructing a chromatographic peak intelligent analysis network based on deep learning, and training the network by using a sample chromatogram until convergence; inputting a to-be-analyzed chromatographic signal into the chromatographic peak intelligent analysis network, and evaluating the reliability of a prediction result based on the probability distribution of the key physical parameters output by the chromatographic peak intelligent analysis network to obtain a result confidence coefficient; dynamically adjusting the number and composition of the sample chromatograms based on the result confidence and a preset confidence threshold, and continuously updating the chromatographic peak intelligent analysis network; and recording an attention mechanism of the chromatographic peak intelligent analysis network as a weight fraction allocated to each data point, mapping the weight fractions back to a time sequence of an original chromatographic signal, carrying out visualization through a color gradient, and superposing the weight fractions on an original chromatographic curve in a semitransparent layer form to generate a chromatographic peak thermodynamic diagram. According to the invention, the efficiency and precision of liquid chromatography analysis are effectively improved.

The invention discloses a liquid chromatography determination method for a mixed solution of glyoxylic acid, glycollic acid and oxalic acid and application. The method comprises the following steps: (1) preparing standard solutions with different concentrations; (2) detecting the standard solutions with different concentrations by using high performance liquid chromatography; (3) establishing a linear regression equation of each acid according to the relationship between the concentration of the standard solution and the corresponding peak area; (4) pre-treating a sample to be detected, detecting under the same condition as that in the step (2) by using high performance liquid chromatography, and recording the peak area of each acid in the sample to be detected; and (5) substituting the peak area of each acid in the to-be-detected sample into the corresponding linear regression equation, and calculating to obtain the concentrations of glyoxylic acid, glycollic acid and oxalic acid in the to-be-detected sample. The method is a liquid chromatographic determination method which is simple and convenient to operate, excellent in separation effect, mild in chromatographic condition and compatible with various detectors, can accurately quantify three strong-polarity mixed acids of glyoxylic acid, glycollic acid and oxalic acid, and has important practical application value.

The invention relates to the field of pharmaceutical analytical chemistry, in particular to a method for determining tandospirone and salt intermediates thereof, high performance liquid chromatography is adopted, and chromatographic conditions include that a chromatographic column with octadecylsilane chemically bonded silica as a filler is adopted, and gradient elution is performed by a mobile phase A and a mobile phase B; wherein the mobile phase A consists of a first water phase and acetonitrile; the mobile phase B is composed of a second water phase and acetonitrile; the first water phase is a water solution containing monopotassium phosphate and sodium hexanesulfonate, and the pH value of the first water phase is 3.0-3.8; the second water phase is an aqueous solution containing monopotassium phosphate and sodium hexanesulfonate, and the pH value of the second water phase is 1.8-2.6. The testing method disclosed by the invention has good specificity, accuracy and repeatability, and can be used for monitoring the quality of the tandospirone raw material medicine.

The invention relates to the field of biological cells, and particularly discloses a method for quantitatively analyzing metabolites in biological cells based on a Raman spectrum technology, and the method comprises the following steps: S1, collecting a Raman spectrum data set; s2, metabolite with uniform distribution and metabolite with stable content in the cells to be compared are selected as internal standards; s3, extracting the Raman characteristic peak area of the target object, and performing Raman distribution imaging in the cells according to the Raman characteristic peak area of the target object to obtain Raman imaging pictures of a plurality of substances; s4, performing Raman imaging normalization on the target object; s5, converting the normalized Raman peak intensity of the target object into a thermal image, and presenting Raman signals with different intensities in different colors to obtain an intracellular distribution diagram of each component of the intracellular metabolite; and S6, spatial distribution and quantitative analysis of intracellular metabolites. The method disclosed by the invention has the advantages of in-situ, nondestructive, high speed, quick effect, capability of simultaneously and quantitatively analyzing various metabolites on the subcellular scale and the like.

The invention relates to the technical field of agricultural product detection, in particular to an agricultural product pesticide residue detection method and system. According to the method, the spatial orientation characteristics of molecular three-dimensional conformation parameters are introduced, a symmetry index set is established, matching calculation is carried out in combination with temperature distribution in chromatographic column operation parameters and the sample injection direction, accurate linkage of structural information and a chromatographic path is achieved, and through coupling analysis of response starting time and chromatographic regulation and control parameters, the accuracy of the chromatographic regulation and control parameters is improved. A detection time window taking a target protein signal as a reference is set, a response enhancement fragment with a significant rising rate is screened out in combination with a signal change trend in the window, and a target signal interval is obtained through weighted fitting, so that the specificity and pertinence of detection data are effectively improved, and the detection accuracy is improved through comparison of peak position time and response data. According to the scheme, accurate labeling of pesticide types in a complex sample is achieved, and the method has high stability and judgment accuracy under the scene that the sample heterogeneity is high and signal interference is complex.