2073 results about "Quantitative assay" patented technology

The invention discloses an organic sample analysis method based on micro gas chromatography and a chromatographic analyzer, particularly relates to the technical field of micro gas chromatography, and is used for solving the problems of unstable separation performance and low complex sample detection precision caused by insufficient uniformity of a stationary phase coating of an existing micro chromatographic column. The method comprises the following steps: constructing a stationary phase distribution initial profile by continuously pre-charging carrier gas, alternately applying segmented temperature gradient, micro-negative pressure and directional pulse flow to remodel a uniform distribution layer, generating a correction factor set of a temperature-resistance coupling relationship by combining a correction mixture and a tracer gas pulse, dynamically adjusting a carrier gas flow velocity and a temperature program to generate a matched operation curve, and finally, carrying out dynamic calibration. High-precision separation detection and quantitative analysis are achieved, stable separation performance is maintained in a miniaturized structure by cooperatively controlling stationary phase distribution optimization and real-time parameter feedback correction, and the detection precision and reproducibility of a wide-boiling-range organic mixture are improved.

The invention relates to the field of Raman spectrums, and discloses a Raman spectrum nonlinear generation method driven by mixed machine learning, which is used for meeting the requirements of a modern Raman spectrum analysis technology on mixture spectrum data with wide coverage, complete types and reliable quality. Comprising the following steps: performing weighted linear combination on pure component spectrums based on a concentration ratio to generate a primary mixture spectrum; the data diversity is enhanced; generating a prediction spectrum; generator parameters are optimized through multi-level feature comparison and adversarial training of the discriminator; matching and deleting false peaks, and reconstructing physical reasonable characteristic peaks; and dynamically adjusting the intensity of the characteristic peak, and outputting high-fidelity spectrum data. According to the method, by fusing physical constraint and deep learning, the peak position precision, intensity consistency and noise robustness of the generated spectrum are remarkably improved, quantitative analysis of a complex mixture in a wide concentration range is supported, and an efficient solution is provided for Raman spectrum database construction and chemical detection.

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 a spectrum pretreatment method for online detection of coal quality through multi-spectrum combination and related equipment. The method comprises the following steps: synchronously collecting LIBS and NIRS spectrums of raw coal by using a multi-spectrum combination online detection coal quality device; respectively carrying out effective screening on the synchronized LIBS and NIRS spectrums; identifying the defocusing amount of each group of synchronized LIBS and NIRS spectrums, and distributing a corresponding defocusing amount label for each group of spectrums; correcting the defocusing spectrum by adopting a spectrum correction method according to the defocusing amount label to obtain corrected LIBS and NIRS spectrums, and fusing the corrected LIBS and NIRS spectrums to obtain a corrected fused spectrum; and analyzing the LIBS spectrum, the NIRS spectrum and the fusion spectrum by adopting a quantitative analysis method, and calculating the fixed carbon content. The LIBS and NIRS spectrum fusion makes up the limitation that a single spectrum can only represent atomic information or molecular information, so that the chemical information of the coal sample is completely represented, and synchronous accurate analysis of multiple coal quality indexes is realized.

The invention discloses a liquid chromatography-tandem mass spectrometry detection method and system, and the method comprises the steps: designing a multi-dimensional gradient elution program according to the physicochemical properties of a target compound, and obtaining a chromatographic separation spectrogram; performing efficient ionization on the target compound according to the chromatographic separation spectrogram to obtain an ionization signal; according to the ionization signal, fragmenting the target ions, and obtaining a fragment ion spectrogram through fragment ion screening; according to the fragment ion spectrogram, a multivariable data analysis algorithm based on machine learning is adopted, a standard curve of a target compound and an interferent database are combined, quantitative analysis is carried out, and a quantitative result is generated through a dynamic interference correction technology in combination with principal component analysis and a partial least square method. By utilizing the embodiment of the invention, the sensitivity, the accuracy and the real-time performance of detection can be improved through multi-dimensional gradient elution, dynamic ionization and fragmentation parameter optimization in combination with an analysis means of machine learning.

The invention discloses a flotation reagent formula optimization method and system based on data analysis, and the method comprises the steps: monitoring the ore pulp of a flotation tank in real time through an online detection device, obtaining a mineral grade, reagent concentration and foam thickness data set, processing the data set based on a time-delay analysis algorithm, generating a reagent association feature library, and carrying out the optimization of the flotation reagent formula. And clustering the feature library according to medicament types to construct an effect association map, calculating the map by using a multi-objective optimization algorithm to generate a candidate formula set, screening and determining an optimal formula according to cost constraint, and controlling a medicament adding device to execute adjustment. According to the method, the problem that an existing flotation reagent formula optimization technology is lack of reagent synergistic effect quantitative analysis and time sequence correlation modeling is solved, and the scientificity and accuracy of reagent formula optimization are improved.

The invention relates to the technical field of chemical production management, in particular to a chemical production safety intelligent management system based on video monitoring, which comprises a video stream optimization module, a behavior analysis and evaluation module, a real-time risk management module, a monitoring process optimization module, a behavior mode updating module and an emergency response correction module. According to the invention, by optimizing the quality of the video stream, the high definition and the real-time performance of the monitoring content are ensured, so that the system can more effectively identify abnormal conditions in production, accurately identify the deviation between the operation and the safety standard through meticulous behavior tracking and quantitative analysis, and adjust the deviation in time, thereby improving the production efficiency. Therefore, the normalization of the operation behavior is accurately evaluated, the behavior deviating from the standard is corrected in time, the effectiveness of safety management is improved, and the early warning capability of potential risks is improved through automatic safety level classification and early warning threshold adjustment in real-time risk management, so that the safety response is more flexible, and the probability of accidents is greatly reduced.

The invention relates to the technical field of spectral analysis and concentration quantitative detection, and discloses an LIBS quantitative analysis method, device and system based on a thin plate spline regression algorithm. The method comprises the steps that multiple sets of sample data are obtained, and each set of sample data comprises corresponding target sample concentration, laser energy and spectral intensity; constructing a concentration quantitative model about the concentration, the laser energy and the spectral intensity based on a spline function; determining an optimal penalty parameter by adopting a cross validation method; based on the optimal penalty parameter, solving parameters of the concentration quantitative model through a minimization objective function to obtain a constructed concentration quantitative model; and performing quantitative analysis on a sample with unknown concentration by using the constructed concentration quantitative model. Thus, the output energy of the laser is incorporated into the core model, and an I-E-C nonlinear coupling relationship is constructed, so that the problem of calibration deviation caused by laser energy fluctuation is reduced, and the precision improvement of concentration quantification in a complex scene is realized.

The invention relates to the technical field of analysis and detection, in particular to a molecularly imprinted sensor for in-situ detection of epicatechin in plant leaves and a preparation and application method of the molecularly imprinted sensor. The molecular imprinting sensor for detecting epicatechin is prepared by assembling a zeolite imidazate skeleton-amide functionalized single-walled carbon nanotube composite material, amino-formyl imidazole modified reduced graphene and an MIP film on the surface of an electrode layer by layer. The high-selectivity and high-sensitivity detection on the target molecule epicatechin is realized. The technology can effectively reduce the detection cost, simplify the operation process and improve the performance of the sensor. Particularly, due to the introduction of the MIP, the sensor has the capabilities of in-situ detection and high-precision molecular recognition, can realize rapid and reliable quantitative analysis in a complex sample, and has a wide application prospect.

The invention relates to the technical field of spectrum detection, in particular to an element quantitative analysis method, system and equipment and a medium. The method comprises the following steps: acquiring LIBS spectral data of a sample; preprocessing the LIBS spectral data to obtain target spectral data; according to the target spectral data, on the basis of an element spectral line standard library, the analysis element and the matrix element are calibrated, and a calibrated analysis element spectral line and a calibrated matrix element spectral line are obtained; according to the spectral line of the analysis element and the spectral line of the matrix element, calculating to obtain the plasma temperature of the analysis element by using a Boltzmann planar graph method; and inputting the analysis element plasma temperature and the analysis element spectral line into the element quantitative analysis model to obtain the predicted content of the analysis element. The method effectively solves the problems of low detection efficiency, complex operation, sensitive matrix effect and the like in the prior art, and is suitable for rapid and accurate quantitative analysis of stainless steel elements on an industrial site.

The invention discloses a method for detecting organic carbon components in soil based on gas chromatography-mass spectrometry, and particularly relates to the technical field of soil detection. The method comprises the following steps: pretreating fresh soil to obtain a fresh soil sample and an air-dried soil sample, respectively extracting organic carbon components to obtain a DOC extracting solution and an LOC extracting solution, and sequentially and respectively carrying out purification treatment, concentration treatment and derivatization on the DOC extracting solution and the LOC extracting solution to obtain a DOC to-be-detected solution and an LOC to-be-detected solution; respectively detecting the DOC to-be-detected liquid and the LOC to-be-detected liquid in a GC-MS instrument to obtain a total ion flow chromatogram, and carrying out qualitative and quantitative analysis to obtain organic carbon component data. Through step-by-step extraction, selective derivatization and GC-MS accurate analysis, molecular level separation, identification and quantification of water-soluble organic carbon DOC and oxidizable organic carbon LOC in soil are achieved, the method is particularly suitable for evaluating contribution of carbohydrate, amino acid and organic acid to the carbon turnover rate, and the method is good in selectivity and high in sensitivity.

The invention discloses a fluorescent colorimetric sensor for detecting circulating tumor cells and a preparation method of the fluorescent colorimetric sensor. The preparation method comprises the following steps: S4, adding a prepared detection antibody modified with horse radish peroxidase and used for detecting the circulating tumor cells, and incubating; s5, adding a Tris buffer solution and a H2O2 solution, and incubating; s6, preparing an EuMOF nanoflower solution; and S7, adding the EuMOF nanoflower solution and 3, 3 ', 5, 5'-tetramethyl benzidine to react, detecting the fluorescence intensity in the presence of circulating tumor cells with different concentrations by using a luminoscope, detecting the ultraviolet absorbance value in the presence of circulating tumor cells with different concentrations by using a multifunctional microplate reader, and constructing a fluorescence intensity-concentration working curve and an ultraviolet absorbance value-concentration working curve. The method is used for qualitative and quantitative analysis and detection of the circulating tumor cells, realizes bimodal signal response to the circulating tumor cells, and has high sensitivity and simplicity and convenience in operation.

The invention provides a rapid quantitative analysis method for an outer vesicle proteome, and belongs to the technical field of quantitative analysis of the outer vesicle proteome. According to the rapid quantitative analysis method for the outer vesicle proteome, firstly, a high-quality outer vesicle sample is obtained through low-speed centrifugation, ultra-speed centrifugation and membrane filtration; and obtaining proteome data by utilizing two-dimensional electrophoresis and mass spectrometry. Then, a mathematical model equation set is constructed for preliminary analysis, a protein interaction network is optimized through an embedded game optimization index model, and feature extraction and quantitative analysis are conducted through a convolution parameter adaptive function and a residual network deep learning model; finally, a quantitative analysis result containing the protein expression level and the function importance is generated in combination with the multi-dimensional protein expression contribution index, and the technical problem that in the prior art, in the rapid quantitative analysis process of the outer vesicle proteome, the protein component complexity is high, and consequently the quantitative precision is low is solved.

The invention relates to the technical field of data compensation, and discloses an intelligent compensation method for dynamic response delay of a sensor, which comprises the following steps: judging whether a target sensor has dynamic response delay, collecting regional data, analyzing the distribution condition of the sensors in a regional set, judging whether the dynamic response delay is related to the environment, and if yes, judging whether the dynamic response delay is related to the environment. According to the intelligent compensation method for the dynamic response delay of the sensor, the efficiency of the whole process is improved through a batch processing and efficient data analysis method, analysis and compensation of a large number of sensors can be completed in a short time, and the intelligent compensation method for the dynamic response delay of the sensor is suitable for large-scale popularization and application. Through quantitative analysis and accurate environmental impact assessment, the accuracy and reliability of sensor measurement are improved, and the overall performance of the system is enhanced.

The invention relates to the technical field of agriculture, in particular to a Raman spectrum and deep learning-based pesticide residue detection method and electronic equipment. The method comprises the following steps: acquiring Raman spectrum data of a sample to be detected; inputting the Raman spectrum data into a pre-trained qualitative analysis model, and outputting the category probability of the pesticide components in the to-be-detected sample; inputting the Raman spectrum data into a pre-trained quantitative analysis model, and outputting the content proportion of the pesticide components in the to-be-detected sample; wherein the qualitative analysis model realizes conjoint analysis of spectral global features and local features based on a multi-modal fusion deep learning network; the quantitative analysis model realizes spectral decomposition and concentration regression based on a deep learning architecture of multi-scale feature fusion. The detection defects of an enzyme inhibition method and a chromatography method are overcome, the problem that the potential mode of the Raman spectrum cannot be accurately and stably found through machine learning is solved, and higher detection precision and higher detection speed are achieved.

The invention relates to an overlapped chromatographic peak positioning and splitting method and system based on a numerical stable function. The method comprises the following steps: de-noising chromatographic data and correcting a baseline; calculating a second derivative by using SG filtering, grouping continuous points according to a negative threshold value, and determining an initial position of each peak by using a second-order minimum value; establishing a peak function of bidirectional exponential correction by taking a peak position as an initial value, setting parameters and boundaries such as area, center, width, trailing and the like, constructing a residual error minimization target, and performing robust fitting by adopting a trust region reflection algorithm; and indexes such as suitability, output peak area, center, broadening, separation degree, theoretical plate number and the like are verified by residual errors. According to the method, valley point or prior component information is not needed, the method is insensitive to noise and baseline drift, serious overlapping and trailing / leading edge peaks of multiple components can be effectively processed, convergence is high, the method is insensitive to initial values, parameters can be expanded to different peak shapes, and the method is suitable for qualitative and quantitative analysis of automatic and high-flux chromatographic data.

The invention belongs to the technical field of medical diagnosis, and discloses a quantum multicolor immune digital pathological diagnosis analysis system, which comprises the following steps: acquiring high-resolution digital images of an immunohistochemical or immunofluorescence slice and a slide, and analyzing multicolor signal distribution in the images to obtain a quantum chromatographic image set; pixel-level color separation and intensity quantization are carried out on the quantum chromatographic image set, and a quantum intensity index set of each pixel is generated; performing multi-target color overlay analysis and post-translational modification protein specificity evaluation on the quantum intensity index set to construct a multi-color modification index set; identifying a single cell boundary and a multi-cell community based on the multicolor modification index set, and extracting a cell morphological parameter and a spatial adjacency relation to obtain a cell quantum analysis set; performing consistency correction and traceable recording by applying an automatic quality control mechanism, and generating a digital diagnosis report; the accuracy of accurate qualitative and quantitative analysis of immunohistochemistry and immunofluorescence is greatly improved.

The invention relates to the technical field of sewage / wastewater denitrification, in particular to a method for predicting and explaining denitrification performance and microbial abundance of a single-stage PNA system, which comprises the following steps: constructing an ML model of nitrogen removal rate and functional microbial abundance, and incorporating key water quality and operating parameters into the model to improve the interpretability and prediction accuracy of the model; and the influence of each input feature on the model and the optimal range of the environmental parameters are determined, so that targeted operation adjustment is realized. Quantitative analysis is carried out through the model, potential synergistic effects among the characteristics can be explored, and therefore the optimal characteristic range can be found, the wastewater treatment efficiency can be improved, and the optimal water environment can be obtained.

The invention constructs a universal visual virus molecularly imprinted sensor modified with an anti-protein coating, the sensor uses magnetic Fe3O4 as an imprinting carrier, HBV as a template and dopamine as a functional monomer for imprinting to obtain a molecularly imprinted polymer with a polydopamine coating (PDA), and before the template is removed, the molecular imprinting polymer with the polydopamine coating (PDA) is separated from the template, so that the molecular imprinting polymer with the polydopamine coating (PDA) is obtained. The surface of the PDA layer is covered with a slightly cross-linked PMEO2MA layer through a water precipitation polymerization method, and the polymer can effectively reduce non-specific protein adsorption; and finally, exposing an imprinting cavity of the PDA layer by eluting the template molecules. On the other hand, potassium permanganate (KMnO4) is used as a signal output molecule, and when no target object exists, the strong reducibility of PDA can enable KMnO4 entering the imprinting cavity to be subjected to a color fading reaction. Based on the principle, high-specificity colorimetric visual detection on the HBV is realized. The sensor can be combined with a smart phone through naked eyes to carry out visual semi-quantitative analysis and carry out quantitative detection through ultraviolet rays, and the universality of the sensor can be confirmed through a template replacement experiment. And meanwhile, the sensor shows an excellent cost advantage. The diagnosis which is high in sensitivity, low in cost and capable of achieving rapid visualization shows huge potential in practical application, and particularly has important application value in infectious disease screening and monitoring work in areas with limited resources.

The invention relates to the technical field of life science, in particular to a method for constructing an atrophic gastritis-based SPEM lesion model, which comprises the following steps: S1, constructing an Slc7a11 conditional knockout mouse strain: carrying out cage hybridization on an Slc7a11 flox / flox mouse and an ATP4b-Cre transgenic mouse, and knocking out a gastric wall cell specific Slc7a11 gene; s2, carrying out genotyping identification; the identification method based on the atrophic gastritis SPEM lesion model and the construction method based on the atrophic gastritis SPEM lesion model comprise the following steps: S1, pathological model verification: taking a mouse gastric mucosa tissue slice, quantitatively analyzing the proportion of an SPEM region by adopting an immunofluorescence double-standard technology, and confirming Slc7a11 knockout induced SPEM pathological characteristics in combination with the existence of SPEM in the gastric mucosa; and S2, long-term pathology evolution monitoring. Through gastric wall cell specific Slc7a11 gene knockout and multiple PCR identification technologies, the initial response SPEM state after gastric mucosa damage is successfully simulated, and the kit is used for researching atrophic gastritis pathological mechanisms and evaluating drug intervention.

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.

The invention relates to the technical field of plasma concentration monitoring methods, in particular to a method for rapidly detecting the content of tacrolimus in plasma through paper spray mass spectrometry. The method provided by the invention comprises the following steps: adding a to-be-detected plasma sample into a precipitation solution, vortex, taking supernate, and dropwise adding the supernate onto a paper base; spraying voltage is applied to the paper base, the paper base generates electrospray, and the electrospray enters a mass spectrometer for detection; preparing a gradient matrix matched tacrolimus standard solution, replacing the to-be-detected plasma sample with the gradient matrix matched tacrolimus standard solution, repeating the operation, recording a detection result, and drawing a standard curve by taking the matrix matched tacrolimus concentration as an abscissa and taking peak area data acquired by a mass spectrometer as an ordinate; and comparing the recorded detection result with the drawn standard curve, and quantitatively analyzing the content of tacrolimus in the to-be-detected plasma sample. The method disclosed by the invention is simple to operate and high in analysis speed, has relatively good precision and accuracy and also has high sensitivity and high selectivity.

The invention relates to the technical field of material analysis, and particularly discloses an ICP-AES (Inductively Coupled Plasma-Atomic Emission Spectrometry)-based automatic quantitative analysis method for primary and secondary elements in a high-temperature alloy, which comprises the following steps: dissolving a to-be-analyzed high-temperature alloy sample in a mixed acid of sulfuric acid and nitric acid to obtain a to-be-detected sample solution; feeding the sample solution to be detected into an atomizing chamber through a peristaltic pump, and collecting emission spectrum data of each element; establishing a standard curve of the concentration and emission intensity of each element by using a standard solution with known concentration; pre-processing the received emission spectrum data through a control system and a data processing module of the instrument; the data processing module compares the preprocessed emission spectrum data with a standard curve through a multiple linear regression method; and generating a detailed analysis report, and transmitting the generated analysis report to terminal equipment of a user through a printer or a network. Through an optimized sample dissolving method, a data preprocessing technology and a real-time correction algorithm, the accuracy and the stability of an analysis result can be effectively improved.

The invention provides a light-excited lateral flow immunosensor reagent strip, a detection analysis method and application of the light-excited lateral flow immunosensor reagent strip. The reagent strip comprises an electrode substrate, a combination pad, a sample pad layer, a chromatography membrane and a water absorption pad, and the sample pad layer, the combination pad, the chromatography membrane and the water absorption pad of the reagent strip are connected end to end and are sequentially overlapped and pasted on the electrode substrate. The light-excited lateral flow immunosensor reagent strip is applied to quantitative analysis of various disease markers in biological fluids such as blood, saliva, cerebrospinal fluid or urine.

This invention discloses a spectral preprocessing method and related equipment for online coal quality detection using multispectral coupling. The method includes: simultaneously acquiring LIBS and NIRS spectra of raw coal using an online coal quality detection device; screening the effectiveness of the synchronized LIBS and NIRS spectra; identifying the defocusing amount of each set of synchronized LIBS and NIRS spectra and assigning a corresponding defocusing amount label to each set of spectra; correcting the defocused spectra according to the defocusing amount label using a spectral correction method to obtain corrected LIBS and NIRS spectra, and then fusing them to obtain a corrected fused spectrum; analyzing the LIBS spectrum, NIRS spectrum, and fused spectrum using quantitative analysis methods, and calculating the fixed carbon content. This invention's LIBS and NIRS spectral fusion overcomes the limitation of a single spectrum only characterizing atomic or molecular information, enabling a complete characterization of the chemical information of the coal sample and achieving simultaneous and accurate analysis of multiple coal quality indicators.

The invention discloses an absolute quantitative correction method and device for medical imaging equipment, and relates to the technical field of nuclear medical imaging equipment correction. When the method is executed, the actual activity concentration of the calibration die body injected with the first known tracer activity serves as the first activity concentration; then, the calibration die body injected with the first known tracer activity is collected in medical imaging equipment, and the activity concentration in a first image domain obtained through calculation serves as second activity concentration; and finally, calculating a correction factor based on the first activity concentration and the second activity concentration, wherein the correction factor is used for correcting absolute quantification of the medical imaging equipment. Therefore, the correction factor is calculated by comparing the activity concentration calculated by the medical imaging equipment with the actual activity concentration of the calibration die body, so that the medical imaging equipment can accurately adjust the measurement result according to the correction factor during subsequent absolute quantitative analysis; the effect of improving the absolute quantification accuracy of the medical imaging equipment is achieved.

The invention discloses a substance-based quality detection method for Xiaohuang powder, and belongs to the technical field of quality detection of traditional Chinese medicines. According to the method, standard control of a classic famous prescription is realized by constructing a multi-dimensional quality control system. The method comprises the following steps: firstly, carrying out specific identification on gardenia, divaricate saposhnikovia root, liquorice and pogostemon cablin in the prescription by adopting a thin-layer chromatography; meanwhile, a multi-index component content determination method is established, and the contents of five components including geniposide, cimicifugin, 5-O-methylvisammioside, liquiritin and glycyrrhizic acid are synchronously determined by applying a high performance liquid chromatography (HPLC). Through a system quality control strategy combining qualitative identification and quantitative analysis, not only is a quality standard system of the substance reference of the Xiaohuang powder perfected, but also reliable technical guarantee is provided for ensuring the stability and consistency of product quality. The establishment of the method establishes a standardized method system for the substance-based quality control of the classical famous prescription Xiaohuang powder.

The invention discloses a method for rapidly determining the content of breast milk oligosaccharide in dairy products, which comprises the following steps: sampling and pretreating different types of dairy products according to sample states, carrying out ultrasonic extraction, refrigerated centrifugal degreasing and centrifugal ultrafiltration protein purification on the obtained sample, and reducing the purified sample solution by using sodium borohydride to obtain a sample solution to be detected; reducing the breast milk oligosaccharide standard substance by sodium borohydride to obtain a standard solution to be detected; determining the standard substance solution and the to-be-detected sample solution by adopting ultra-high performance liquid chromatography-tandem mass spectrometry, establishing a standard curve, and carrying out quantitative analysis on the to-be-detected sample solution; the chromatographic conditions are as follows: an ACQUITY UPLC BEH Amide hydrophilic interaction chromatographic column is adopted; the mobile phase A is 20 mmol / L ammonium formate; the mobile phase B is acetonitrile; and gradient elution.

The invention discloses a vitality-preserving decoction fingerprint spectrum and multi-component quantitative analysis method and application thereof, a DAD and evaporative light-scattering detector (ELSD) combined scheme is adopted, according to different properties of components, variable wavelengths are adopted for determination in different time periods, the detection process is simplified, single sample introduction is realized through series connection of detectors, and the detection accuracy is improved. Meanwhile, a plurality of medicinal components are represented, and a multi-component fingerprint spectrum can be directly generated, so that the quality control is more comprehensive and efficient, the detection efficiency is improved, and time and resources are saved.

The invention discloses a separation-free and signal-amplification-free quantitative analysis method for extracellular vesicles, which comprises the following steps: assembling a sulfydryl-modified aptamer on the surface of a gold nanoparticle through an Au-S bond to form a gold nanoparticle-aptamer compound; the aptamer is designed according to a specific membrane protein receptor on an extracellular vesicle; incubating the gold nanoparticle-aptamer compound and an extracellular vesicle to-be-detected solution, and after incubation, specifically binding the gold nanoparticle-aptamer compound with EVs to form a large-particle compound; under laser irradiation, recording the Tyndall effect of the to-be-detected liquid by using a camera, and reading the Tyndall effect intensity value by using image analysis software.