3203 results about "Quantitative assay" patented technology

The invention provides a rectal cancer neoadjuvant chemoradiotherapy curative effect prediction method and system based on magnetic resonance image analysis, and aims to improve accurate prediction of curative effects of patients with local advanced rectal cancer (LARC). According to the method, multi-modal magnetic resonance images (T1WI, T2WI, DWI and DCE-MRI) are obtained, qualitative and quantitative analysis is combined, tumor morphology, diffusion characteristics and microcirculation parameters are extracted, clinical and pathological data of a patient are integrated, and a multi-dimensional feature set is constructed. Curative effect prediction is performed through machine learning and a deep learning model, and the model can predict treatment results of complete remission, partial remission, ineffectiveness and the like in a classified manner. The system accurately predicts the reaction of a patient to neoadjuvant radiotherapy and chemotherapy through a non-invasive method, supports formulation of a personalized treatment scheme, reduces excessive treatment, optimizes the treatment effect, and has good clinical application value.

The invention belongs to the technical field of bridge detection, and discloses a point cloud data processing-based bridge defect automatic detection and quantitative analysis method, which comprises the following steps of: firstly, acquiring bridge point cloud data; secondly, preprocessing the acquired three-dimensional point cloud data; then, bridge component segmentation is carried out; secondly, performing defect detection on each component unit by using a region growing clustering segmentation algorithm; then, a Kriging interpolation method is adopted to carry out intelligent repair and reconstruction on the identified defect part, and missing information is filled; and finally, carrying out defect quantitative analysis. According to the method, automatic and refined processing is achieved in the whole process from data acquisition and preprocessing to defect recognition and reconstruction and quantitative analysis, the efficiency and accuracy of bridge detection are remarkably improved, and multiple key technical steps from data acquisition to quantitative analysis are effectively integrated; the bridge detection process is optimized, and the automation level of the work and the accuracy of the result are improved.

A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled / label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining and biomedical workflow.

The invention discloses an immunohistochemical digital pathological image processing method, and relates to the technical field of image processing, and the method comprises the steps: obtaining an immunohistochemical staining pathological image, and carrying out the preprocessing; carrying out region division on the preprocessed pathological image, carrying out feature extraction and fusion on each sub-region, and generating a multi-dimensional feature vector; based on the multi-dimensional feature vector, optimizing the pathological image by using a region growing and merging algorithm; carrying out dyeing intensity detection on the optimized pathological image, and classifying dyeing areas through a K-means clustering algorithm; and based on a classification result, performing quantitative analysis and generating an immunohistochemical image analysis report. According to the method, through preprocessing, feature extraction and fusion, and in combination with a region growing and merging algorithm, precise segmentation and classification analysis of the immunohistochemical staining region are realized, and the automation and recognition precision of image processing are improved.

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 discloses an acupuncture effect quantitative analysis and evaluation method and system, and relates to the technical field of quantitative analysis, and the method comprises the steps: collecting patient data, and carrying out the preprocessing of the collected patient data; feature extraction is carried out based on the preprocessed patient data; performing statistical analysis based on the patient data; constructing a multiple regression model, and generating an acupuncture effect quantitative analysis report in combination with a statistical analysis result; encrypting the patient data and the acupuncture effect quantitative analysis report; according to the method, the extracted feature data is subjected to statistical analysis, the multiple regression model is constructed to predict the quantized value of the acupuncture effect of the patient, key factors related to the acupuncture treatment effect are recognized, the acupuncture effect is quantitatively described, specific numerical values are provided for doctors, the doctors can conveniently evaluate and compare the acupuncture treatment effect, and the accuracy of acupuncture treatment is improved. And the scientificity and effectiveness of acupuncture treatment are improved.

The invention discloses a detection and analysis system for an oil-controlling and soothing extract, particularly relates to the field of extract detection, is used for solving the problem of accurate quantitative analysis of complex components, and is characterized in that omnibearing characteristics of a sample are synchronously acquired and captured through multiple spectrums, and the information is fused into a high-dimensional data framework, so that the limitation of a single spectrum is avoided; thirdly, noise is removed through accurate calibration, sparse decomposition and cross-modal verification are combined, and it is ensured that extracted component signals truly reflect chemical characteristics and are not covered by redundant interference; on the basis, a clear signal of a target active component is further separated by utilizing dynamic convolution and an attention mechanism, and irrelevant background influence is eliminated; finally, through segmented modeling and geometric constraint, absorbance change is accurately converted into concentration data, and the concentration data can be kept stable even under the background fluctuation or nonlinear condition, so that the quantitative inversion error of the active component is reduced under the scene of background signal fluctuation or non-uniform optical path, and the accuracy of a determination result is improved.

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 multi-element quantitative analysis method and system for induced breakdown spectroscopy, and the method comprises the steps: constructing a multi-element quantitative analysis model of laser-induced breakdown spectroscopy, and predicting the element content of the target laser-induced breakdown spectroscopy by using the trained multi-element quantitative analysis model; the multi-element quantitative analysis model comprises an input layer, a feature extraction layer and an output layer which are connected in sequence, the feature extraction layer comprises an attention fusion unit, and the attention fusion unit comprises a plurality of parallel feature extraction branches with different scales and feature splicing subunits; each feature extraction branch is connected with other feature extraction branches which are adjacent in sorting position and larger in convolution depth in a jumping manner; the feature splicing subunit is used for splicing the feature maps output by the plurality of feature extraction branches; through a multi-branch parallel structure and a feature fusion mechanism, efficient extraction and fusion of multi-scale features are realized, the expression ability of a network to complex features is improved, and meanwhile, the calculation complexity is reduced.

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 mixed fishery medicine Raman spectrum detection method combining a data enhancement method and an RA-CTNet model. Data is expanded by combining a stoichiometric method and an experimental random error simulation method, so that the database building cost is reduced, and the model robustness is improved. In the aspect of model building, the advantages of CNN on local feature extraction and the capability of Transform on global feature modeling are fully utilized, training strategies of pre-training and transfer learning are adopted, rich feature information of a single fishery medicine is effectively utilized, the accuracy of mixed fishery medicine prediction is improved, and the prediction accuracy of the mixed fishery medicine is improved. And quantitative detection of a nanomole-level concentration mixture under a complex mixed sample is realized. Compared with the prior art, the technical scheme provided by the invention aims to detect forbidden fishery drugs in aquaculture water and realize nanomole-level concentration forbidden drug quantitative detection under a low data sample, and aims to improve the accuracy and robustness of Raman spectrum detection and quickly qualitatively and quantitatively analyze mixed fishery drugs.

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 relates to the technical field of oil exploitation, and discloses a device for simulating hydraulic fracturing to reform a shale reservoir under limited temperature and pressure, the device comprises a bottom plate, an auxiliary feeding mechanism is arranged on the bottom plate, the auxiliary feeding mechanism is used for auxiliary feeding, the bottom plate is connected with a jacking mechanism, and the jacking mechanism is used for jacking the bottom plate. The jacking mechanism is used for jacking and applying external pressure, the auxiliary feeding mechanism is connected with a vibration diffusion mechanism, and transformation modes of the shale reservoir under different pressure conditions can be accurately simulated by accurately controlling confining pressure, axial pressure and fluid pressure; the shale core sample is filled with the gold tube, so that the integrity of the sample can be kept under the condition of effectively transmitting confining pressure, and quantitative analysis of the sample after fracturing transformation is facilitated; after set gas components are injected into the sample bin pipe, water injection fracturing is carried out, and quantitative characterization of the fracturing process is achieved by monitoring the content of fluid produced after fracturing.

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 pesticide detection, and discloses an analysis method and system of an all-in-one fluorescent quantitative test strip with a self-adaptive threshold adjustment function, the method comprises the following steps: firstly obtaining historical detection data of N fluorescent signal channels of a historical detection sample, and determining a first threshold parameter of each fluorescent signal channel; then acquiring real-time detection data of a to-be-detected sample, calculating first dynamic parameters of the N fluorescence signal channels, and dynamically adjusting the first threshold parameter to obtain a second threshold parameter; a fluorescence characteristic parameter, a sample matrix characteristic parameter and an environment interference parameter are obtained to correct the second threshold parameter, and self-adaptive thresholds of N fluorescence signal channels are obtained; and finally performing quantitative analysis and result evaluation on the to-be-detected sample according to the self-adaptive threshold value. According to the invention, the threshold value can be dynamically adjusted according to the change of the detection environment, and the accuracy and reliability of the detection result of the all-in-one fluorescent quantitative test strip are improved.

The invention belongs to the technical field of spectrum detection and analysis, and discloses an infrared spectrum-based rubber auxiliary component analysis and quality control method and a system thereof, the method comprises the following steps: using a Fourier transform infrared spectrometer to collect infrared spectrum data of a rubber auxiliary mixture in real time, and preprocessing the infrared spectrum data; based on the preprocessed infrared spectrum data, a partial least square regression method is adopted to construct a multi-component quantitative model for component identification and quantitative analysis; according to a result of component identification and quantitative analysis, generating a content report and quality control information of the rubber auxiliary component; according to the method, the influence of environmental interference on infrared spectrum data can be effectively reduced, and real-time and accurate quantitative analysis and quality control can be carried out on each component in the rubber additive production process.

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 discloses a magnetic nano-enzyme probe and a preparation method thereof, a kit for detecting helicobacter pylori and application of the magnetic nano-enzyme probe, the core of the magnetic nano-enzyme probe is Fe3O4 nano-particles, magnetic enrichment capacity is provided, and interference of a complex matrix in a sample is eliminated; a layer of Pt nano-particles is electrostatically adsorbed through the cationic polymer to serve as a colorimetric interlayer, and a strong colorimetric signal is provided; au (at) Pt for modifying 5, 5 '-dithiobis (2-nitrobenzoic acid) is arranged on the surface layer and is used for providing strong catalytic signals and sites coupled with a large number of specific antibodies; meanwhile, the inner and outer layers of nano enzyme particles can synergistically enhance the catalytic action. The magnetic nano-enzyme probe disclosed by the invention has strong magnetic responsiveness, high colorimetric signal, strong catalytic activity and specific bacterium capturing capacity, realizes rapid enrichment and quantitative analysis of helicobacter pylori in a sample to be detected, eliminates matrix interference in practical application, and has high sensitivity and high specificity. The method has great potential in the aspects of high sensitivity and clinical monitoring of bacterial infection or pollution.

The invention discloses a multi-mode LIBS mineral oxygen element analysis method. The method comprises the following steps: firstly, preprocessing an LIBS spectrum, and then fusing the spectrum and a mineral microscopic image by using a deep learning model to carry out feature extraction and oxygen analysis. The core of the method is model training: shielding a spectrum, pairing the spectrum with a corresponding mineral image (positive samples and negative samples), and generating a fused spectrum-image representation by using mask reconstruction and comparative learning. The element content is predicted based on this representation, and the model is optimized according to the oxygen prediction results. According to the method, accurate, robust and interpretable quantitative analysis of the oxygen element in the complex mineral matrix can be realized.

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

The invention discloses an auxiliary diagnosis method and system for bronchial asthma, and relates to the technical field of molecular diagnosis prediction.The auxiliary diagnosis method comprises the steps that an experimental group and a control group are defined, and RNA of the experimental group and RNA of the control group are extracted for low-temperature preservation; carrying out quantitative PCR (Polymerase Chain Reaction) analysis on the RNA stored at low temperature and extracting target cDNA; collecting clinical data of the experimental group and calculating the corresponding biomarker concentration according to the target cDNA; and constructing a bronchial asthma prediction model, outputting a predicted risk probability value of each patient suffering from asthma, and dynamically classifying and adjusting the treatment schemes of the patients. RNA samples of an experimental group and a control group are accurately extracted and stored, remarkably changed gene expression is obtained in combination with quantitative PCR analysis, the gene expression is further combined with biomarker concentration, dynamic monitoring of the asthma-related inflammatory response is successfully achieved, and the bronchial asthma risk of a patient can be accurately evaluated by constructing a bronchial asthma prediction model. And a personalized treatment scheme is provided according to comprehensive analysis of gene expression and biomarkers.

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 provides a medium-low maturity shale oil in-situ conversion mining pyrolysis kinetic model construction method, and belongs to the technical field of underground resource in-situ development, and the method comprises the following steps: firstly, preparing medium-low maturity shale samples composed of different organic matters, carrying out a pyrolysis experiment, and collecting and quantitatively analyzing pyrolysis products; based on original experimental data, a data augmentation model is constructed, an adversarial generative network model is established, and the generation effect of pyrolysis substance mass change data is optimized. And merging the generated substance mass change data with the experimental data to obtain merged data. Finally, on the basis of merged data, key parameters such as a reaction kinetic equation, reaction activation energy and a frequency factor are obtained through genetic algorithm optimization, a reliable pyrolysis kinetic model is provided for in-situ conversion mining of shale oil with medium and low maturity, and the problem that an existing model is difficult to accurately describe real kinetic behaviors of pyrolysis conversion of organic matter is solved.

The invention provides a method for simultaneously detecting various micro-plastics in water, and belongs to the technical field of micro-plastic detection. The method comprises the steps of sample pretreatment, thermal cracking-gas chromatography-mass spectrometer measurement, qualitative analysis and quantitative analysis. The method comprises the following steps: taking a water sample, uniformly shaking, filtering with a filter membrane, taking the filtered filter membrane, adding acetone, carrying out ultrasonic elution, washing the filter membrane, and collecting an eluent; adding acetone into the eluted filter membrane, carrying out ultrasonic elution again, washing the filter membrane, collecting the eluent, combining the two eluents, heating and concentrating in a water bath, adding acetone, ultrasonically mixing, transferring into a cracking cup, heating and volatilizing, and determining by a thermal cracking-gas chromatography-mass spectrometer. Carrying out qualitative analysis through analysis of cracking products with different types of micro-plastic characteristics, and then carrying out quantification through an external standard method; the method provided by the invention can accurately quantify the six microplastics in the effluent, and has the advantages of simple pretreatment, small sampling amount, high accuracy and lowest detection limit of 0.012 [mu] g / L.

The invention relates to the technical field of medical imaging, in particular to a nuclear medicine imaging method and system, and the method comprises the steps: patient preparation, tracer agent distribution detection, image enhancement, multi-target dynamic imaging, image quantitative analysis and data fusion, and result output and diagnosis suggestion. A preliminary image of tracer agent distribution in the body of a patient is obtained through a multi-modal imaging device, a high-resolution image is generated through a convolutional neural network noise suppression algorithm, a contrast ratio self-adaptive enhancement algorithm and a super-resolution reconstruction algorithm based on a generative adversarial network, and accurate dynamic analysis is carried out. In combination with a random forest algorithm and a time sequence analysis model, performing quantitative analysis on the radioactive intensity, the volume and the metabolic rate of the target spot, and generating personalized diagnosis suggestions; according to the invention, the precision and diagnosis effect of nuclear medicine imaging are greatly improved, and the method is suitable for early diagnosis, curative effect evaluation and disease progress monitoring of clinical diseases.

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

The invention 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.