38 results about "Drug Sample" patented technology

The present invention relates to the technical field of pharmaceutical analysis, and specifically provides a method and system for detecting the components of drugs based on basic pharmacy, including the following steps: Based on the detected drug sample, measure the weight of the drug sample, add a solvent for dissolution according to a preset ratio, combine dilution and mixing, record the color, viscosity, and pH value of the solution, and generate a standardized sample solvent. In the present invention, by standardizing the drug sample, the measurement baseline is ensured, the result deviation is reduced, the credibility of the data is improved, the changes in particle size and quantity are monitored in real time, the understanding of the dissolution process is enhanced, the impedance change of the drug solution is evaluated by monitoring the current response at multiple frequencies, the analysis of electrochemical properties is strengthened, impurities are accurately detected, the chemical properties data of the drug are provided by combining the determination of spectral absorption and fluorescence characteristics, the composition components of the target drug are identified, the drug formula is ensured to meet the specified standards, and the safety and effectiveness of the drug are guaranteed.

The invention discloses online detection equipment for a medicine material, relates to the technical field of medicine material detection, and solves the problem of detection distortion caused by slight inclination of a medicine sample. The equipment comprises an equipment shell, a console and a detection bin, wherein a state detection assembly and an infrared detection assembly are arranged in the detection bin. An integrated circuit board in the console is integrated with a sensing acquisition module, a data processing module and a control module, the data processing module comprises a tiny inclination identification unit, a spectrum offset correction unit and a component data correction unit, and the real component content is calculated after the inclination angle is identified through pressure data and spectrum distortion is corrected. And the control module judges the qualified state of the sample in combination with a preset standard, controls the equipment to operate and sends a regulation instruction to an external production system. According to the equipment, the detection accuracy and the operation convenience are improved, the linkage of detection and production is realized, the online quality control requirement is met, and reliable support is provided for medicine production quality control.

The application discloses a drug qualitative detection method based on multi-concentration mixed SERS data, comprising the following steps: constructing a data set of different concentrations of drugs based on SERS spectra of the same drug at high and low concentrations; mixing SERS spectrum data of low and high concentrations in the data set to construct a mixed data set; optimizing hyperparameters of a random forest model based on an improved NSGA-II algorithm to construct an optimized classification model; and classifying drugs based on the mixed data set and the optimized classification model. The application effectively improves the classification accuracy of machine learning models for low-concentration drug samples by mixing SERS data sets, and solves the problem of reliability decline of the prior art on low-concentration samples. In the hyperparameter optimization of the machine learning model, the improved NSGA-II algorithm is used for model hyperparameter optimization, which can simultaneously process multiple conflicting objectives to achieve a balance between model accuracy and efficiency.

The invention relates to the technical field of analytical chemistry detection, in particular to a method for determining trace nitrite in pharmaceutic adjuvants.The method comprises the steps that an acetonitrile solution, a potassium hydroxide solution, a phosphate buffer solution, a sulfuric acid solution, a 2, 3-diaminonaphthalene solution and a reference substance working solution are provided; the method comprises the following steps: preparing a standard curve solution and a test sample solution, and carrying out ultra-high performance liquid chromatography-tandem mass spectrometry analysis detection: respectively carrying out sample injection detection on the test sample solution and the prepared standard curve solutions with different concentrations under preset chromatographic conditions and mass spectrometry conditions, recording detection results of the standard curve solutions with different concentrations to make a standard curve, and calculating the content of nitrite in the test solution by adopting a standard addition method according to the standard curve and the detection result of the test solution. The method disclosed by the invention can be used for rapidly determining trace nitrite in a drug sample, and can be used for determining the trace nitrite in pharmaceutical adjuvants and detecting the nitrite in drugs.

The embodiment of the invention provides a medicine component identification method and related equipment, and belongs to the technical field of medicine identification. The method comprises: acquiring preset drug sample data including positive drug sample data and negative drug sample data, the positive drug sample data including a first thin-layer chromatography, and the negative drug sample data including a second thin-layer chromatography; constructing a model training map set through the first thin-layer chromatography map to train a preset twin neural network model to obtain a first twin neural network model; constructing a model test atlas set through the first thin-layer chromatography atlas and the second thin-layer chromatography atlas, and performing parameter optimization on the model in combination with the model training atlas set to obtain a target neural network model; and inputting the to-be-identified thin-layer chromatogram into the target neural network model for component identification analysis to obtain a component identification result. According to the embodiment of the invention, reference substances and reference medicinal materials are not used as much as possible in medicine identification, and the purposes of economy, greenness and environmental protection are achieved.

Aspects and embodiments relate to apparatus and methods to identify whether a drug is present in a provided sample. One aspect provides a drug detection method to identify whether a drug is present in a provided sample, the method comprising: determining a first fluorescence spectral matrix associated with the provided sample by: excitation of the provided sample at a first excitation wavelength; and receiving a fluorescence emission spectrum generated by the provided sample at the first excitation wavelength; repeating the excitation and receiving steps across a range of excitation wavelengths which differ from the first excitation wavelength; determining a wavelength for irradiation of the sample to induce a photochemical change in the provided sample and irradiating the provided sample with radiation having the determined wavelength; determining a second fluorescence spectral matrix associated with the provided sample by: excitation of the provided sample at a first excitation wavelength; and receiving a fluorescence emission spectrum generated by the provided sample at the first excitation wavelength; repeating the excitation and receiving steps across a range of excitation wavelengths which differ from the first excitation wavelength; comparing the determined first and second fluorescence spectral matrix associated with the provided sample with a library of first and second fluorescence spectral matrices obtained from a plurality of drug samples; and if a match within a predetermined threshold is revealed by said comparison, triggering a positive identification of a drug in the provided sample. Aspects recognise that the rapid detection of certain drugs can be enhanced by augmenting a fluorescence spectral fingerprint based detection methodology with photochemical reactivity tracking. The hybrid photochemical fingerprinting methodology in accordance with aspects allows for capture of information on both: new fluorescent species that emerge as a result of a specifically induced photochemical reaction(s) notable detectable drugs, but also the loss of fluorescent / absorptive species as they are photochemically degraded. Both such physical changes are reflective of the specific chemistry of the analyte (drug) which is being tested for.

The utility model discloses a multi-functional sampler for medicine quality detection, which comprises a cylinder body, the outer wall of one side of the cylinder body is fixedly connected with a handle, the top of the cylinder body is in threaded connection with a top cover, the top of the top cover is fixedly connected with a feeding channel, the inner wall of the cylinder body is provided with a material collecting box, and the inner wall of the material collecting box is provided with a crushing treatment mechanism. And a ventilation treatment mechanism is arranged on the inner wall of the material collecting box. Solid medicine entering from the feeding channel can be loaded by arranging the material collecting box, the medicine can be crushed by arranging the crushing treatment mechanism in the material collecting box, the medicine can be blown away by arranging the ventilation treatment mechanism in the material collecting box, the medicine crushing uniformity is guaranteed, and the medicine crushing efficiency is improved. The medicine can be filtered by arranging the filter plate, and further grinding and refining treatment can be performed on the medicine by arranging the grinding and refining mechanism in the material guide channel, so that the refining degree of the medicine sample finally collected in the bottom cover is ensured.

The invention discloses a drug stability analysis system, which relates to the technical field of drug analysis and comprises a sample data acquisition module, a data fusion and regulation module, an intelligent analysis and state judgment module and a result output and trend prediction module, through comprehensive application of the optical imaging technology, the fluorescence imaging technology and the infrared imaging technology, multi-dimensional and all-directional scanning of a drug sample in the stability testing period is achieved, the comprehensiveness and accuracy of data acquisition are remarkably improved, the optical imaging technology captures macroscopic surface morphology information of a drug, and the stability of the drug sample is improved. The fluorescence imaging technology is used for carrying out visual tracking on specific components in the medicine to obtain component distribution information, and the infrared imaging technology is used for analyzing chemical bond vibration conditions among medicine molecules to obtain chemical composition information, so that materials are provided for subsequent data analysis and processing.

The present application discloses an intelligent detection method for drug components and drug quality, and the present application belongs to the field of artificial intelligence. The method includes: obtaining a drug sample solution and a control drug solution, and injecting the drug sample solution and the control drug solution into a high performance liquid chromatography analysis system; determining whether it is necessary to adjust the operating parameters of the high performance liquid chromatography analysis system; determining the operating parameter adjustment information, and continuously updating the operating parameters of the high performance liquid chromatography analysis system until a complete drug sample chromatogram and a complete control drug chromatogram are obtained; determining the drug component information and impurity component information of the sample drug; determining whether the sample drug meets the preset drug quality evaluation standard according to the drug component information and the impurity component information. If the preset drug quality evaluation standard is met, the sample drug passes the quality inspection. This solution can reduce the manual operation time and human error, improve the detection efficiency and accuracy, and make the analysis results more accurate and stable.

The invention discloses an in-vitro unknown drug photoionization mass spectrometry rapid qualitative analysis method, which comprises: S1, dissolving a captured suspected drug sample in an acetone or methanol solution, taking out the supernatant, and transferring into a sample bottle; s2, selecting a polar or non-polar SPME probe to penetrate through the sample bottle isolation pad to be inserted into the sample bottle isolation pad, and extracting; and S3, inserting the probe tube into the sample inlet, pushing the handle rod, extending out of the probe extraction head, and performing thermal desorption to desorb the sample to be detected and enter a mass spectrum ionization area. According to the method, richer ionized ion fragment information and molecular ions are provided in the rapid qualitative analysis and accurate identification process of on-site investigation of drugs and precursor chemicals by using a miniaturized mass spectrum technology, and molecular structure information can be provided for analyzing chemical components of unknown novel drugs by using a compound mass spectrum library.

The utility model discloses sampling storage equipment for pharmaceutical analysis, which comprises a low-temperature storage box main body and an upper cover, the top of the low-temperature storage box main body is provided with the upper cover, the center of the top of the upper cover is provided with a control seat, and the top of the control seat is provided with a display screen for displaying the internal temperature of the low-temperature storage box main body. One side of the control seat is provided with a power supply socket used for being connected with an external power supply; and four groups of opposite mounting seats are fixedly mounted at two ends of the center of the bottom of the upper cover. According to the low-temperature preservation box, the low-temperature preservation box body, the upper cover, the control base, the display screen, the power socket, the mounting base, the ultraviolet disinfection lamp, the lens and other parts are arranged and used in cooperation, so that the surfaces of glass test tubes for storing medicine in the low-temperature preservation box body are conveniently disinfected and sterilized, bacterium breeding is reduced, and pollution to medicine samples is reduced; and meanwhile, through cooperation of a lens and an ultraviolet disinfection lamp, the interior of the low-temperature preservation box body can be disinfected and sterilized comprehensively.

The invention relates to the technical field of medicine sample reservation management, in particular to a medicine sample reservation management method based on artificial intelligence and an intelligent management platform. The method comprises the following steps: setting a sample reserving time limit: collecting sample information of a medicine, and setting the sample reserving time limit of a sample according to the sample information; the samples are subjected to storage position distribution through an artificial intelligence algorithm, environment parameters of the storage positions are monitored, and the positions of the samples are monitored; sample receiving management: carrying out identity authentication when the sample is received, and carrying out sampling control and weighing monitoring on the sample; and sample destruction management: carrying out residue detection on the sample, and carrying out evidence storage management through a block chain. According to the method, the whole-process closed-loop management from reserved sample setting to destroying is realized, the manual dependence degree is remarkably reduced, and the management efficiency is improved. In addition, real-time sample tracking and management are achieved, the problems that information transparency is insufficient, and the manual operation process is complex and tedious are effectively solved, and the safety and reliability of medicine management are improved.

The invention belongs to the technical field of bioinformatics, and particularly relates to a structure-based drug design model integrating a Bayesian flow network and a diffusion model. The drug design model integrates the advantages of a Bayesian flow network and a diffusion model through a stochastic differential equation. Atomic coordinates are sampled through a stochastic differential equation, and atomic types are sampled through Bayesian flow, so that the sampling time is shortened, and the attributes of molecules are improved. In addition, a continuous time loss function with an analytic solution is introduced, and the stability of the stochastic differential equation is improved by realizing smoother target distribution conversion. Compared with other models on a common data set CrossDocked 2020 in the field, the drug design model achieves better effects in the aspects of binding affinity of generated molecules, drug sample indexes and conformation stability, and the sampling efficiency is improved by 25%.

The embodiment of the application provides a drug component identification method and related equipment, and belongs to the technical field of drug identification. The method comprises the following steps: obtaining preset drug sample data, including positive drug sample data and negative drug sample data, wherein the positive drug sample data comprises a first thin layer chromatogram, and the negative drug sample data comprises a second thin layer chromatogram; constructing a model training atlas set through the first thin layer chromatogram, so as to train a preset twin neural network model, and obtain a first twin neural network model; constructing a model test atlas set through the first thin layer chromatogram and the second thin layer chromatogram, so as to optimize parameters of the model in combination with the model training atlas set, and obtain a target neural network model; and inputting a to-be-identified thin layer chromatogram into the target neural network model for component identification analysis, and obtaining a component identification result. The embodiment of the application can try not to use control samples and control medicinal materials in drug identification, so that the economic, green and environmentally-friendly purposes are achieved.

The invention discloses a small nucleic acid medicine purification process, and belongs to the technical field of nucleic acid purification. A small nucleic acid drug purification process comprises the following steps: S1, mixed mode chromatography: dissolving a small nucleic acid drug crude product in a buffer solution containing 20mM phosphate and 10% acetonitrile to obtain a solution A; loading the solution A to a mixed-mode chromatographic column with a mixed-mode chromatographic column medium comprising functionalized agarose gel, and carrying out gradient elution to remove impurities; eluting the target product with a buffer solution containing 2M NaCl and 10% acetonitrile; s2, size exclusion chromatography: diluting a small nucleic acid drug sample eluted by mixed mode chromatography with an equilibrium buffer solution, loading the sample into a size exclusion chromatography column, performing isocratic elution with the equilibrium buffer solution, monitoring an ultraviolet absorption signal at 260nm, collecting an elution peak, and freeze-drying to obtain a high-purity small nucleic acid drug; the functionalized agarose gel is agarose gel for simultaneously modifying a quaternary ammonium salt group and an alkyl group.

The utility model relates to the technical field of sampling devices, in particular to a powder sampling device for medicine production, which comprises a handle and a sampling assembly, and the sampling assembly comprises a sleeve, a moving block, a connecting rod and a plurality of shielding parts; the sleeve is provided with a plurality of sampling ports; when medicine powder is sampled, a worker holds the sleeve by hand, inserts the sleeve into a medicine sample, then pulls the handle to move upwards, the handle drives the connecting rod and the multiple shielding components to move upwards, the multiple shielding components and the multiple sampling openings are staggered to be exposed out of the sampling openings, then the sleeve is rotated, and the medicine powder is sampled. During sampling, medicine powder can enter the multiple sampling openings with different height positions, then the handle is pushed to move downwards, the multiple shielding components return to the side edges of the sampling openings again, the sampling openings are closed, and sampling is completed by moving the sleeve out of a medicine sample. By means of the mode, medicine powder at different depth positions of a medicine sample can be sampled at the same time, and the medicine powder sampling efficiency is improved.

The utility model relates to the technical field of dispensing barrel sampling devices, and particularly discloses an automatic dispensing barrel sampling device which comprises a circulating assembly, a sampling assembly is arranged on one side of the circulating assembly, a moving assembly is arranged on the other side of the circulating assembly, and a supporting table is arranged below the sampling assembly. The sampling assembly comprises a dispensing barrel located on the supporting table, a stirring motor is arranged on the dispensing barrel, a stirring shaft connected with the stirring motor is arranged in the dispensing barrel, and stirring blades are arranged on the stirring shaft; the automatic sampling device for the dispensing barrel is simple in overall structure and reasonable in design, and a worker can sample a medicine sample conveniently; due to the design of the circulating assembly, the automatic operation of the sampling process of the medicine in the medicine dispensing barrel can be realized, so that a relatively uniform sampling sample can be obtained; due to the design of the moving assembly, manpower time and manpower cost can be saved, and manual operation errors are reduced, so that the automation degree of operation is improved, and the working efficiency of sampling in the dispensing barrel is improved.

The utility model discloses a novel drug stability test box which comprises a tripod and a thermal stability box fixedly installed at the top end of the tripod, a sealing cover is hinged to the position of an opening in the top end of the thermal stability box, and a barrel type middle heat supply unit is arranged in the central axis area in the thermal stability box below the sealing cover. A plurality of column bases used for bearing medicine samples are installed at the bottom of the thermal stabilization box in an annular array mode, an annular base is fixed to the center of the bottom of the thermal stabilization box, and a multidirectional synchronous air blowing structure used for blowing air towards the periphery is installed in the annular base. According to the utility model, heat can uniformly cover each corner in the test box and the surface of a drug sample, so that the temperature of each position is approximately consistent, and the thermal stress difference possibly generated when the drug sample is heated can be obviously reduced due to uniform heating distribution; the stability performance of the medicine under different temperature storage conditions can be accurately evaluated.

The invention provides a method for determining related substances in a vitamin B6 drug, which comprises the following steps: dissolving a vitamin B6 drug sample in a prefabricated solution in a dark environment, filtering, and diluting to a target concentration interval to obtain a sample solution; placing the blank solvent, the contrast solution and the sample solution in a chromatographic column, introducing the mobile phase A and the mobile phase B, and sequentially carrying out gradient elution to obtain a chromatogram; and identifying and separating impurity chromatographic peaks in the chromatogram, performing quantitative or semi-quantitative analysis on each impurity chromatographic peak, and calculating the content proportion of each impurity chromatographic peak relative to the main chromatographic peak of the vitamin B6. Through collaborative design of the sample pretreatment state, the gradient elution path and the peak interpretation mode, known impurities and unknown impurities in the vitamin B6 medicine are in a separable state before entering a chromatographic system, and are fully expanded in the chromatographic separation process, so that accurate identification and effective separation of various impurities are realized.

The invention discloses a drug qualitative detection method based on multi-concentration mixed SERS (Surface Enhanced Raman Scattering) data, which comprises the following steps: constructing data sets of different concentrations of drugs based on SERS spectrums of the same drug under high concentration and low concentration; mixing the SERS spectrum data with low concentration and high concentration in the data set to construct a mixed data set; based on an improved NSGA-II algorithm, performing hyper-parameter optimization on the random forest model, and constructing an optimized classification model; and based on the mixed data set optimization classification model, classifying the drugs. According to the method, the SERS data sets are mixed, so that the classification accuracy of the machine learning model on the low-concentration drug samples is effectively improved, and the problem that the reliability of the low-concentration samples is reduced in the prior art is solved; on the aspect of hyper-parameter optimization of a machine learning model, an improved NSGA-II algorithm is used for model hyper-parameter optimization, and a plurality of mutually conflicting targets can be processed at the same time, so that the balance of model accuracy and efficiency is realized.

The present invention discloses a general drug target prediction method, device and medium based on self-supervised learning. The method includes: using a compound feature extraction module to extract drug feature vectors: splitting the drug molecular structure into a sub-structure sequence, converting each sub-structure into a vector encoding to obtain a sequence vector, and inputting it into an encoder for feature extraction; wherein, using masked language model prediction, molecular descriptor prediction and molecular functional group prediction, and performing self-supervised training on the compound feature extraction module and these three prediction models based on the feature vectors of drug samples to obtain the compound feature extraction module; extracting the feature vectors of the target; based on the feature vectors of the drug and the target, using an automated machine learning model to perform task prediction between the drug and the target. The present invention is applicable to prediction tasks including drug-target interaction, binding affinity and mechanism of action, etc., and the prediction accuracy in each task is better than that of the same type of prediction method.

The invention provides an AD drug affinity prediction method based on data enhancement and a graph neural network, and the method comprises the steps: obtaining a drug sample database by looking up a target protein of an AD target, hybridizing the drug sample database with target protein sequence information to form input data, and then arranging the input data into an input data table; the method comprises the following steps: preprocessing data, performing data enhancement on a drug sample, extracting expression of a drug and a target sequence, constructing a drug molecular map, calculating a protein contact matrix, constructing a protein residue sub-map, and splicing and enhancing the protein residue sub-map; constructing a joint graph, enhancing the connectivity of the joint graph, and constructing full connectivity and root node self-loop by using node index offset; carrying out characterization coding and node initialization; and inputting the joint graph feature vector and the node feature into a model for reasoning and regression prediction, and finally outputting affinity and writing the affinity into a collection table. According to the method, the prediction process is optimized, and the robustness and accuracy of the AD target drug affinity prediction process are improved.

The application discloses a kind of bicolour detection probe for zearalenone naked eye visual detection, comprising the following steps: step one: configuration solution 1;Step two: configuration solution 2;Step three: configuration solution 3;Step four: 3 μL of solution 2 is added to 37 μL of solution 1, then 10 μL of solution 3 is added to carry out reaction, and the absorption value of solution at 418 nm and 480 nm within 1-30 min is recorded using enzyme label instrument, the color change of solution is recorded by mobile phone photographing every minute, and the picture is digitized using RGB image conversion technology to improve the discernibility of naked eye.The application belongs to the technical field of ZEN detection, and is a kind of bicolour detection probe for zearalenone naked eye visual detection, which has high specificity, high sensitivity, good stability, simple operation, easy to carry, low cost, does not need professional technical personnel, fast detection time (10 minutes can be naked eye to see color change), and can be used as a useful tool for on-site analysis of ZEN in food and drug samples.

The present application relates to the technical field of drug identification, and particularly relates to a drug identification method, system, storage medium and electronic device, the method comprising: obtaining a Raman spectrum of each preset drug sample of a preset kind, preprocessing to obtain a plurality of first Raman spectra, and then calculating a threshold value; using an L1-KernelPCA method to perform non-multiplication kernel operation on all the first Raman spectra to obtain a matrix and a characteristic vector sorted in descending order of eigenvalue; selecting the first m characteristic vectors as principal component vectors, calculating the cumulative variance of each first Raman spectrum and the corresponding first reconstruction vector, and judging the quality of each preset drug sample according to the size relationship between the cumulative variance and the threshold value. The accuracy can be ensured, and the method is efficient and fast.

The utility model discloses a sample storage cabinet for laboratory drug research and development, which comprises a sample storage cabinet body capable of being applied to drug sample storage, a partition plate is welded in the middle of the inside of the sample storage cabinet body, and a plurality of storage plates are welded on one side of the partition plate close to the inner wall of the right side of the sample storage cabinet body. A plurality of moving plates are slidably connected to the sides, close to each other, of the partition plates and the inner wall of the left side of the sample storage cabinet body, inserting grooves are formed in the lower portions of the sides, close to each other, of the moving plates slidably connected to the partition plates and the moving plates slidably connected to the inner wall of the left side of the sample storage cabinet body, and placing plates are inserted into the inserting grooves. According to the sample storage cabinet for laboratory medicine research and development, by arranging the moving plates, the moving strip plates, the limiting columns and the like, medicine research and development sample containers with different heights can be placed on the placing plates, and the practicability of the sample storage cabinet body is improved.

Aspects and embodiments relate to apparatus and methods to identify whether a drug is present in a provided sample. One aspect provides a drug detection method to identify whether a drug is present in a provided sample, the method comprising: determining a first fluorescence spectral matrix associated with the provided sample by: excitation of the provided sample at a first excitation wavelength; and receiving a fluorescence emission spectrum generated by the provided sample at the first excitation wavelength; repeating the excitation and receiving steps across a range of excitation wavelengths which differ from the first excitation wavelength; determining a wavelength for irradiation of the sample to induce a photochemical change in the provided sample and irradiating the provided sample with radiation having the determined wavelength; determining a second fluorescence spectral matrix associated with the provided sample by: excitation of the provided sample at a first excitation wavelength; and receiving a fluorescence emission spectrum generated by the provided sample at the first excitation wavelength; repeating the excitation and receiving steps across a range of excitation wavelengths which differ from the first excitation wavelength; comparing the determined first and second fluorescence spectral matrix associated with the provided sample with a library of first and second fluorescence spectral matrices obtained from a plurality of drug samples; and if a match within a predetermined threshold is revealed by said comparison, triggering a positive identification of a drug in the provided sample. Aspects recognise that the rapid detection of certain drugs can be enhanced by augmenting a fluorescence spectral fingerprint based detection methodology with photochemical reactivity tracking. The hybrid photochemical fingerprinting methodology in accordance with aspects allows for capture of information on both: new fluorescent species that emerge as a result of a specifically induced photochemical reaction(s) notable detectable drugs, but also the loss of fluorescent / absorptive species as they are photochemically degraded. Both such physical changes are reflective of the specific chemistry of the analyte (drug) which is being tested for.

Smart Cartridge for Drug Sample Loading Without Cross-Contamination

The present invention provides a drug recognition method, device, equipment and medium based on spectral correlation dimension elevation, which relates to the field of drug recognition. The method includes: obtaining spectral data of a drug sample and preprocessing it using at least two preprocessing methods to obtain target spectral data, where the target spectral data includes near-infrared data and mid-infrared spectral data; using a spectral variable selection method to screen the target spectral data to obtain characteristic spectral data; constructing a spectral matrix based on the wavelengths of the characteristic spectral data to obtain a near-infrared matrix and a mid-infrared matrix, performing a correlation analysis on the near-infrared matrix and the mid-infrared matrix to obtain a correlation coefficient matrix, converting the correlation coefficient matrix into a heat map, and using a pre-trained recognition model to recognize the heat map to obtain a recognition result. By screening characteristic variables and performing a correlation analysis, the present invention elevates one-dimensional data to two-dimensional, highlights the spectral information of drugs, and the recognition accuracy of the established model reaches more than 94%.

The invention discloses an automatic medicine component content detector for medicine production, and belongs to the field of medicine treatment equipment.The automatic medicine component content detector comprises a support assembly, a crushing mechanism is installed on the surface of the support assembly, a stirring mechanism is installed on the lower surface of the support assembly, and a discharging mechanism is installed on the surface of the stirring mechanism; a detection mechanism is installed in the support assembly, the discharging mechanism comprises an isolation plate installed in the stirring mechanism, and connecting blocks are fixedly connected to the two sides of the surface of the isolation plate; through cooperative use of all the devices, a sample enters the sampling pipe through the discharging pipe and enters the spectrum detector through the sampling pipe, so that the sample is detected through the spectrum detector, the sample does not need to be manually added when the medicine component content is detected, the adding amount of the sample is more consistent, and the detection efficiency is improved. And the consistency among the samples is kept, so that the structure of drug sample detection is more accurate.

The invention discloses an AI-based one-stop unmanned medicament preparation dosing system and method. The method comprises the following steps: S1, collecting prescription information; s2, constructing a corresponding bionic organelle reaction cavity; s3, setting an initial process parameter group to obtain a first round of medicament sample; s4, collecting multi-channel response data; s5, generating a response track sequence and a response track diagram based on the multi-channel response data; s6, pairing the response track sequence with the target drug effect response track to construct a target matching subgraph; s7, executing an asynchronous reverse graph path traversal algorithm, reversely searching a response path meeting the minimum reverse cost, and outputting a new round of process parameter group; and S8, performing medicament sample preparation based on the new round of process parameter group, and repeating the steps S3 to S7 until the matching error between the target matching subgraph and the response trajectory graph is lower than a preset tolerance threshold. According to the invention, bionic reaction modeling and an asynchronous inverse graph path traversal algorithm are fused, and one-stop unmanned medicament intelligent preparation, dosing and control are realized.