54 results about "Sample purification" patented technology

The invention relates to a UPLC-MS / MS (Ultra Performance Liquid Chromatography-Mass Spectrometry / Mass Spectrometry) detection method for simultaneously determining 150 stimulant substances in cosmetics, and belongs to the technical field of stimulant detection. According to the detection method, a solid-phase extraction and QuECHERS combined pretreatment technology is adopted, firstly, an acetonitrile ultrasonic extraction mode in a QuECHERS method is adopted, secondly, a matrix is purified in a solid-phase extraction mode, an extraction tube adopts a filter type solid-phase extraction column, namely an Agilent Captiva EMR-Lipid filter column, different from a traditional HLB solid-phase extraction column, the filter column does not need to be activated and balanced, and the detection method is simple and convenient to operate. Gravity self-elution can be achieved in the sample purification process, and only 20% of pure water needs to be added into an extracting solution to achieve the purpose of activating a filtering column; and water can also be used as a dispersing agent and an impurity removing agent at the same time, so that impurities dissolved in water can be adsorbed while the sample is dispersed. The detection method provided by the invention can realize LC-MS / MS determination of 150 stimulant substances in cosmetics under the condition of simultaneously satisfying a pretreatment method for extracting 150 stimulant substances in cosmetics.

The butylene determination pretreatment device provided by the invention comprises: a sample introduction system, wherein the sample introduction system comprises a steel cylinder for containing a sample and a pretreatment sample introduction device communicated with the steel cylinder; the pretreatment sampling device is located at the front end of the waste gas treatment system, and the waste gas treatment system is connected with the pretreatment sampling device through a switching valve and a pipeline; the detection system comprises a gas chromatograph and a gas source which are connected, and the pretreatment sampling device is connected with the detection system; the pretreatment sampling device comprises a purification module, a heating module and a pressure reduction module which are connected, the purification module is connected with the steel cylinder, the heating module is arranged on the right side of the purification module, the pressure reduction module is arranged on the right side of the heating module, and the pressure reduction module is connected with the waste gas treatment system and the gas chromatograph; the waste gas treatment system comprises an adsorption device, a gas collection part and a flow control part; non-uniform sample gasification is avoided through the pretreatment sampling device, the obtained gas analysis result is good in repeatability, and a waste gas treatment device is added to complete sample purification.

A syringe-like sample preparation device is disclosed. The syringe-like sample preparation device comprises a syringe body having a proximate end, a distal end and a syringe body cavity between the proximate end and the distal end; a plunger located at the proximate end of the syringe body and movable along the syringe body cavity; and an overflow tube at the distal end of the syringe body and extending into the body cavity of the syringe body; and a sample purification tip at the distal end of the syringe body, wherein the sample purification tip comprises a sample binding matrix.

The application relates to the technical field of chemical analysis, and discloses a method for extracting, separating and detecting N-nitrosamine, which comprises the following steps: a sample obtaining step; a sample purification step; a preparation and extraction solution step; an extraction solution separation step; an extraction solution concentration step; a chromatographic detection and quantitative analysis step; the sample to be detected is crushed to a size of 0.01g of powder, and the powder is mixed with sodium chloride, barium hydroxide and water. In the application, the sample to be detected is subjected to a step-by-step sample treatment process before detection, so that the interference factors of the sample to be detected can be effectively reduced, the influence on the detection instrument is reduced, the detection result and detection data are more accurate, and when the application is detected, a large amount of sample does not need to be weighed like in the traditional detection mode, the overall operation is more convenient, and the detection demand of N-nitrosamine in smoked food is met.

This application discloses a sample purification method and apparatus, a device, and a medium, and belongs to the technical field of artificial intelligence. The method includes: acquiring an adversarial sample, the adversarial sample including an adversarial image and text; acquiring a noisy image obtained by adding first noise to the adversarial image; determining a first correlation and a second correlation; and adding second noise to the adversarial image based on the first correlation and the second correlation to purify the adversarial image to obtain a purified image.

The invention discloses a method for determining the content of recombinant human-like collagen, relates to the technical field of detection of the content of recombinant human-like collagen, and can be used for detecting and analyzing the content of human-like collagen in fermentation thallus samples, purification process intermediate products and stock solutions. For a zymophyte sample, content analysis is carried out through a flow mode of analysis method development-feasibility analysis (pretreatment and recovery rate analysis)-standard curve establishment-sample analysis and detection; for an intermediate product and a stock solution sample in the purification process, content analysis is carried out through a process mode of'method development-feasibility analysis (recovery rate analysis)-standard curve establishment-sample analysis and detection '. According to the method, the purity of the target protein in the fermentation thalli can be remarkably improved through pretreatment, the loss of a chromatographic column is reduced, the universality is high, a research thought and a technical means are provided for accurate quantitative analysis of the content of the target protein in large-scale production of recombinant protein, and the method has wide application value.

The utility model discloses a QuECHERs sample purification column, and relates to the technical field of QuECHERs sample purification, the QuECHERs sample purification column comprises a main column body and a supporting assembly, the upper end of the main column body is connected with a sealing column cover, the supporting assembly is arranged outside the main column body, and the supporting assembly comprises a vertical sliding groove, a transverse sliding groove, a limiting sliding block, a mounting ring and a supporting column rod, transverse sliding grooves are formed in the two ends of the exterior of each vertical sliding groove, limiting sliding blocks are connected into the vertical sliding grooves, mounting rings are arranged outside the limiting sliding blocks, supporting column rods are additionally arranged on the two sides of the exterior of each mounting ring, and a purification assembly is arranged in the main column body and comprises a copper catalyst layer, a molecular sieve layer, an activated carbon layer and a microfiltration membrane layer. A molecular sieve layer is arranged below the copper catalyst layer, and an activated carbon layer is arranged below the molecular sieve layer. According to the QuECHERs sample purification column, the purification column can be stably and vertically placed on a table top, and the flexibility and motility of the purification column in the using process are improved.

The present application relates to the technical field of artificial intelligence, and discloses a sample purification method and apparatus, a device, and a medium. The method comprises: acquiring an adversarial sample, wherein the adversarial sample comprises an adversarial image and a text; acquiring a noise-added image obtained by adding first noise to the adversarial image; determining a first correlation and a second correlation; and adding second noise to the adversarial image on the basis of the first correlation and the second correlation, so as to purify the adversarial image to obtain a purified image. The present application uses the first correlation, i.e., cross-modal attention distribution, as guidance to implement purification by adding noise to the adversarial image. The purification method not only processes the adversarial image, but also optimizes the cross-modal attention distribution mechanism itself. A visual language model can obtain a correct prediction result on the basis of the purified image, and the purification method enhances the robustness of the visual language model.

Sample purification systems include a particle extraction assembly having a mixing compartment and a settling compartment. A biological sample is mixed with two liquid phases formulated to effectuate transfer of a biological molecule into a first phase and particulate contaminants into a second phase. The first phase includes a solubilizing salt, the second phase includes an organic molecule, and the mixture can have little or no monoatomic salt or dextran. The molecule-containing first phase can be optionally concentrated without also concentrating the particulate contaminants and introduced into a multi-stage liquid-liquid extractor, by which the biological molecule or molecular contaminants are extracted from the first phase into a third phase, thereby purifying the molecule away from contaminants. The extracted sample can be further purified through a series of processing steps. The system can be run in continuously mode to maintain sterility of the sample.

The application provides a method for detecting biochemical oxygen demand in sewage, and belongs to the field of oxygen demand detection. The method comprises the following steps: forming a fluorine-containing organosilicon nano coating on the electrode surface of a dissolved oxygen sensor of a full-automatic BOD5 analyzer to obtain a fluorine-silicon modified dissolved oxygen sensor; pretreating the sewage to be detected in an adsorption column composed of modified activated carbon and porous silica gel to obtain a pretreated water sample; fixing bacillus subtilis on a porous carrier and then putting the carrier into the pretreated water sample to obtain an inoculated water sample; measuring the initial dissolved oxygen of the inoculated water sample and the end-point dissolved oxygen after 5-day culture; and obtaining the five-day biochemical oxygen demand in the sewage according to the initial dissolved oxygen and the end-point dissolved oxygen. Through the four-level collaborative system of "sensor anti-interference optimization, sample purification and enrichment, microbial reaction standardization and accurate signal acquisition", the application systematically eliminates the error sources in the traditional BOD5 detection, and realizes the accurate measurement of the organic matter pollution level in complex sewage.

The utility model belongs to the technical field of ion chromatographs, and particularly relates to an online sample purification device which comprises a four-way valve and a feeding pipe, the feeding pipe is fixedly connected to a valve port at the left end of the four-way valve, the four-way valve is in signal connection with a controller, and the outer end of the four-way valve is connected with a purification mechanism. According to the on-line sample purification device, when a six-way valve in an ion chromatograph conveys a to-be-detected sample to a chromatographic column, the to-be-detected sample firstly enters the four-way valve through the feeding pipe, then enters the communicated connecting pipe and then enters the purification cylinder to react with resin in the purification cylinder, ions which do not need to be detected in the sample are adsorbed, and then the to-be-detected sample is purified. The problems that when the ion chromatograph analyzes components to be detected, fine particulate matter in a sample blocks the interiors of pipelines and accessories, liquid leakage of a system is caused, parts are damaged, organic matter and heavy metal matter in the sample are prevented from being adsorbed on the chromatographic column and an ion exchange membrane, and the service life of consumables is affected are solved.

The application provides a kind of based on double guide diffusion model's countermeasure sample purification method and system, the purification method includes: to be purified countermeasure sample is standardized and is preprocessed with fixed structure anchor retention;With the standardized preprocessed countermeasure sample as input, explicitly construct the pseudo forward diffusion path in line with the pre-training model distribution law, then carry out reverse diffusion iteration denoising initialization and initial clean image estimation;Based on original sample, gradient field residual error is calculated, weighted injection and pixel-level structure boundary correction;Construct positive and negative sample pairs of adaptive diffusion inverse process timing characteristics, then based on the high-level semantic feature extraction of denoising U-Net encoder, construct contrast loss function calculation to obtain semantic guide item;Gradient field structure correction and contrast learning semantic constraint are optimized coordinately.The method of the application solves the problem that the countermeasure sample attack may cause a major safety accident, and improves the security.

The invention discloses a multi-stage chromatographic purification combined cryoelectron microscope sample preparation optimization method and system, and belongs to the technical field of cryoelectron microscope sample preparation. A sample purification time chain is constructed through biological information analysis, a time-purity coordinate system is established based on the time chain, a fluctuation curve is generated, the purification correlation degree between the overall trend index and two adjacent days is calculated, and purification conditions and sample preparation parameters are adjusted according to the positive and negative correlation degree. Data driving is used for replacing experience operation, time correlation of the purification process is captured, collaborative optimization of purification and sample preparation is achieved, the sample purity stability and sample preparation efficiency are effectively improved, and the problems that an existing method depends on experience, lacks dynamic adjustment and is poor in collaboration are solved. The method is suitable for efficient purification and optimization of biomacromolecule (such as protein and nucleic acid compounds) cryoelectron microscope sample preparation.

The present invention relates to a method for purifying labeled glycans by electroosmotic flow assisted capillary zone electrophoresis, or a method for on-line electrokinetic purification of a labeled reaction mixture of labeled glycans and free labeling agent, wherein the method is performed in a separation mode, wherein the free labeling agent and the labeled glycans migrate in opposite directions, thereby separating the free labeling agent from the glycans. The method according to the present invention allows for a purification-free capillary zone electrophoresis analysis of labeled glycans. Furthermore, the method allows for separation in a shorter analysis time without the need for a sample purification step.

The present application relates to a kind of detection methods of 7 kinds of alternaria toxins in edible vegetable oil, belong to food safety detection technical field.The method includes the following steps: S1: sample extraction: the mixed uniform edible oil sample is weighed, with the volume ratio of 1:1 ethyl acetate / cyclohexane solution is dissolved and constant volume, transfer to GPC sample bottle;S2: sample purification: using gel permeation chromatography (GPC) to purify sample, mobile phase is the volume ratio of 1:1 ethyl acetate / cyclohexane, the eluate of specific time period is collected;S3: sample concentration treatment: the collected eluate is transferred to rotary evaporation bottle, rotary evaporation is nearly dry, with acetonitrile-water solution is dissolved residue;S4: sample centrifugation: after dissolving, the sample is centrifuged under specific conditions, and the supernatant is taken;S5: instrument detection: using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) to detect 7 kinds of alternaria toxins in supernatant.

The invention relates to a generalized zero sample fault diagnosis method driven by a diffused Schrodinger bridge and sample purification, and belongs to the field of rotating machinery fault diagnosis. The method comprises the following steps: S1, collecting fault data; s2, carrying out signal analysis on the visible classes to obtain labels and distribution information, and constructing corresponding semantic prototypes at the same time; s3, the labels and the distribution information are input into a designed AGCDSB model, and unseen classes of different distributions are generated; s4, BRP is used for purifying the unseen species; s5, dividing the test samples into visible and non-visible classes through a domain separation mechanism; s6, using a visible class classifier to identify the fault which is predicted to be visible class; s7, aligning the fault semantics with the visible features; s8, extracting unseen class fault features, and correcting an unseen class fault prototype in combination with a prototype clustering matching technology; and S9, inferring by using nearest neighbor estimation and a correction prototype to obtain unseen class labels.

The application discloses a method for simultaneously detecting retinoids and lipid-soluble vitamin metabolic marker groups in biological samples and matching reagents. The application is based on magnetic bead solid-phase extraction technology, significantly improves sample purification effect, and reduces matrix interference; in combination with a high-aqueous-phase reversed-phase chromatography system and a positive-negative ion switching multi-reaction monitoring mode, simultaneous separation and detection of seven retinoid molecules and a full spectrum of lipid-soluble vitamins are realized. The method has high sensitivity, strong specificity and wide detection range, and for the first time realizes one-stop analysis across species and sample types, thereby providing reliable platform support for clinical and scientific research applications.

The application relates to a sample purification method, device and computer equipment. The method comprises the following steps: obtaining an adversarial sample in an edge device, adding random noise to the adversarial sample to obtain a to-be-de-noised sample; based on the to-be-de-noised sample, performing noise prediction and sample purification processing by using a noise prediction model to obtain a purified clean sample; the noise prediction model adopts a high-level feature map multiplexing mechanism, the high-level feature map multiplexing mechanism is used for multiplexing high-level feature maps sampled in de-noising steps t to de-noising steps t-n, so as to be spliced with shallow layer feature maps generated by a shallow layer network of the noise prediction model, and a spliced feature map is obtained, n is an integer in [1, 2,..., N-1], and N is a preset cache interval. By using the method, the purification efficiency can be improved, and the cost can be saved.

This application relates to the field of model training technology, specifically to a method and apparatus for extracting abnormal driving state samples. The method includes acquiring normal driving state samples and mixed driving state samples; extracting features from the normal driving state samples and mixed driving state samples respectively to obtain normal feature vectors and mixed feature vectors; determining a similarity score for each mixed driving state sample based on the distance between the mixed feature vector and the normal feature vector; determining a threshold at the boundary between normal and abnormal driving states based on the similarity score; and classifying the mixed driving state samples according to the threshold to obtain normal driving state samples and abnormal driving state samples. This application separates normal driving state samples and abnormal driving state samples from mixed driving state samples, achieving sample purification.

A technique is provided that shortens the setup time for analytical methods by pretreating samples using SPE columns before analysis in the analytical unit. In the analytical system, a first flow path (21) includes one or more solid-phase separation columns. Samples from an autosampler (10) are introduced into either the first flow path (21) or the second flow path (22). The samples are then conveyed from either the first flow path (21) or the second flow path (22) via a switching valve (30) to the analytical column and detector (50) within the column oven (40). Sample purification is performed in the first flow path (21), while it is not performed in the second flow path (22).

The invention discloses a filter membrane with an MOF derived porous structure, a preparation method, a mycotoxin separation and extraction device and a separation and extraction method, and relates to the technical field of mycotoxin pretreatment. A porous functional layer is constructed on the surface of a filter membrane substrate and in pore channels through a controlled conversion process, the porous functional layer has a porous structure containing a Zr-O-C network, microporous channels are reserved, polar functional groups are remained on the surface, and the MOF-derived porous structure filter membrane which does not contain a complete MOF crystal structure is formed. The membrane is used in a circulation type working mode, a target analyte can quickly pass through the membrane layer along with fluid, and the matrix effect can be effectively reduced. The membrane material can realize rapid and simplified sample purification on the premise of ensuring high recovery rate of a target object. The method has the advantages of simple preparation process, stable structure, low flow resistance, wide applicable solvent range and the like, and is particularly suitable for rapid pretreatment analysis of trace micromolecule pollutants in complex food and agricultural product matrixes.

The utility model relates to an efficient purification device based on a QuEChERS method, and belongs to the technical field of sample pretreatment, the efficient purification device comprises a purification pipe filled with an adsorbent, the bottom of the purification pipe is provided with a microfiltration membrane, and the purification pipe is detachably provided with a piston rod. The method has the advantage of improving the sample purification efficiency.

This invention discloses a method for detecting silkworm microparticle viruses. The invention relates to the field of detection technology. Using a controlled variable method, three experimental groups were conducted. By controlling the pH value and ion concentration under different environments, the final number of microparticles was detected, verifying the influence of different pH values ​​and ion concentrations on the accuracy of the experiment. Simultaneously, after sample purification, the impurity content was significantly reduced. The appearance characteristics of the microparticles were extracted and identified using a microscope. Color marking enabled the microparticles to be counted in a short time, resulting in high efficiency.

The invention belongs to the technical field related to computer vision, and discloses a self-adaption method and system during model testing based on malicious sample purification, and the method comprises the steps: dividing a current sample batch into benign samples and malicious samples; calculating a significance index of each sample; determining to-be-purified samples in the current sample batch, wherein the to-be-purified samples comprise malicious samples; for each to-be-purified sample, selecting a benign sample with the largest significance distance from the to-be-purified sample as a paired sample, fusing the to-be-purified sample and the paired sample by using an image fusion technology, and generating a purified sample and a pseudo label of a model prediction probability of the purified sample; and in combination with the current sample batch and the obtained purified sample, performing parameter optimization on the current to-be-optimized model by taking minimization of a total loss function as an optimization target. By means of the scheme, the utilization rate of the test data can be increased, and therefore a good model parameter optimization effect can be achieved by collecting less test data.

The invention discloses a multi-mode unlearnable sample purification method and device and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining a multi-modal unlearnable sample set, and performing data preprocessing on an image sample and a text sample to obtain a purified image sample and a purified text sample; and using the purified image samples and text samples to carry out comparative learning training on the multi-modal model, and adding intra-modal dispersion loss on the basis of comparative loss so as to improve the purification effect of the method. According to the method, through independent processing of the two modes, false connection between image noise and text trigger words is removed, and a model is forced to learn real connection between the modes; according to the method, the intra-modal dispersion loss is added after the original comparison loss, so that two feature vectors which are too close to each other in one modal are pushed away, the problem of modal collapse is relieved, and the purification effect of the unlearnable sample is improved.

This invention relates to the field of analytical instruments and automated control technology, and discloses an AI-driven real-time sample purification and delivery system. The system includes: an online purification module for providing a mobile phase and introducing the sample; a dynamic separation core unit connected to the output flow path of the online purification module, wherein the dynamic separation core unit contains a separation field generator for applying a spatially adjustable physical field along the axial direction of the dynamic separation core unit; an electrical control switching and interface module connected to the output flow path of the dynamic separation core unit; and a multimodal prediction and composite control AI module. The dynamic separation core unit also includes a linear scanning spectral probe configured to acquire spatiotemporal spectral data within the dynamic separation core unit in real time. This invention achieves high-purity, high-recovery adaptive online purification through real-time prediction by the AI ​​module and composite control of the mobile phase and spatial separation field.

The application relates to the technical field of purification filtration, and discloses a sample purification and filtration device and method for algal plant research, which comprises a filter box, a water filtering and drying mechanism is arranged in the filter box, the water filtering and drying mechanism comprises a water filtering cavity arranged in the filter box, a water filtering annular gear rack is rotationally connected to the end wall of the water filtering cavity, a water filtering electric push rod is fixedly connected to the inner side surface of the water filtering annular gear rack, centrifugal filtration can be realized on the algal plants, the water in the cleaned algal plants can be filtered out, the algal plants can be dried, sample preparation is facilitated, and research is facilitated; the algal plants can be cleaned, repeated cleaning can be realized, the cleaned water can be detected to check whether the cleaning is complete, and the cleaning efficiency is relatively high; other impurities in the algal plants can be removed, the purity of the filtration and purification is improved, and the generated wastewater can be collected.

The present application relates to the technical field of biological detection, and particularly relates to a natural steroidal estrogen extraction and detection method based on multi-mechanism synergistic adsorption; the method provided by the present application comprises steps of sample extraction, dispersion adsorption, magnetic separation, elution, elution and chromatographic detection; in the sample purification process, the adsorption material is dispersed in the sample solution, so that the adsorption material is fully contacted with the target, the synergistic enrichment of different steroidal estrogens is realized through multiple action mechanisms, and the influence of interfering substances in the complex matrix is reduced in the adsorption process; after the adsorption is completed, the rapid separation is realized through the external magnetic field, and after elution, the sample enters the chromatographic analysis stage; the present application can realize the synchronous extraction and detection of multiple natural steroidal estrogens, and has the advantages of good selectivity, strong anti-matrix interference ability and high detection stability.

This invention relates to the field of chemical analysis, specifically disclosing a method for detecting hydroxypolycyclic aromatic hydrocarbons (HPAs) in urine and its application. The method involves sampling and hydrolysis, followed by extraction of the crude extract using three-phase electrochemical membrane extraction (ECME). After extraction, high-performance liquid chromatography (HPLC) is used to detect the concentration of HPAs in the receiving phase. This invention simplifies the traditional pretreatment process for detecting HPAs in urine, simultaneously performing extraction separation, concentration enrichment, and purification using three-phase EME. The detection method provided by this invention features strong sample purification capabilities, fast analysis speed, high enrichment factor, high sensitivity, low detection limit, and environmental friendliness, making it more suitable for routine detection and analysis and showing broad application prospects in the detection field.

The invention discloses a remote sensing image classification training sample optimization method based on ensemble learning, and relates to the technical field of remote sensing. Reserving areas with completely consistent classification results as reserved areas; completing sample quality optimization processing; constructing a sample point by using the geometric center of the plaque of the reserved area, and mixing the sample point with the optimized sample to realize sample number multiplication and layout optimization; on the basis of all unsupervised classification results and optimized sample overlay analysis, different sub-category samples in the same category are identified, and sample type refinement processing is completed; and comprehensively checking whether the sample purification and optimization treatment is effectively completed or not based on the comparison of visual interpretation classification results, the class separation degree change between the samples and the change of comparison precision evaluation. According to the method, the problems of uneven quality and uneven distribution of traditional remote sensing samples are effectively solved through a multi-classifier collaborative supervision classification and grid superposition analysis technology.