1237 results about "Raman spectroscopy" patented technology

The invention discloses a switch cabinet discharge detection method and system, and relates to the technical field of power equipment state monitoring and fault diagnose.The light intensity attenuation rate and the vibration frequency are collected through a photonic crystal fiber, the concentration of decomposed gas is detected in combination with a Raman spectrum, and a space-time joint feature matrix is generated; performing space weight distribution on the space-time joint feature matrix based on the switch cabinet structure, and inputting the space weight to an RBF-SVM (Radial Basis Function-Support Vector Machine) to generate discharge type probability distribution; based on a high-frequency energy parameter and a gas decomposition concentration parameter in the spatio-temporal joint characteristic matrix, inputting the parameters into a multiple linear regression model, calculating an insulation aging index, and judging an insulation degradation grade according to a threshold value; the photonic crystal fiber is used for collecting the light intensity attenuation rate and the vibration frequency, the Raman spectrum is combined for detecting the decomposition gas concentration to generate the space-time joint characteristic matrix, synchronous monitoring of various physical and chemical parameters in the switch cabinet is achieved, and the accuracy and comprehensiveness of recognition of different types of discharge phenomena are improved.

The invention discloses an automatic alignment and integrated control system and method for an optical crystal array. The automatic alignment and integrated control system comprises a quantum entanglement enhanced ultra-precise sensing array, a multi-factor coupling error prediction module, a full-link adaptive compensation execution module, a central control module and a federal learning collaborative optimization module. The sensing array is used for acquiring multi-dimensional error data such as sub-nano displacement, Raman spectrum deformation, infrared temperature rise and friction stress in real time; the prediction module is used for mining coupling characteristics between error sources based on an attention mechanism and predicting an instant alignment error and a hysteresis deformation error; the compensation execution module realizes full-link compensation through quantum traceability calibration, mechanical arm dynamic composite compensation and process parameter cooperative adjustment according to the prediction error; the central control module performs synchronous scheduling and conflict coordination; and the federated learning module realizes collaborative optimization and privacy protection of models among multiple devices. According to the invention, ultrahigh-precision and adaptive intelligent integration of the optical crystal array is realized.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention belongs to the technical field of nondestructive testing, and discloses a gold ore and iron ore metal content detection method. The method comprises the following steps: acquiring an XRF energy spectrum by using an X-ray tube, acquiring an LIBS spectrum by using double-pulse Nd: YAG laser, and acquiring a Raman spectrum by using a semiconductor laser; performing baseline correction on the LIBS spectrum, performing Savitzky-Golay smoothing pretreatment on the Raman spectrum, and respectively extracting three types of spectrum characteristics; constructing a physical constraint network model, inputting XRF, LIBS and Raman features, and then outputting the correction content, fusion weight and LIBS dominant factors of Fe / Au elements; and finally, through a dynamic weight fusion strategy, combining XRF, LIBS fusion weights and network output parameters, calculating to obtain the content of the gold ore and the content of the iron ore. The technical breakthrough of multi-element cooperative detection and complex matrix interference suppression is realized, and the detection precision of Fe and Au is effectively improved.

The invention discloses a regional termite detection method and system based on multispectral fusion, and the method comprises the steps: carrying a multi-mode spectrum collection device through a movable detection platform, and synchronously collecting a visible light image, a near-infrared hyperspectral image and Raman spectrum data of a to-be-detected region; preprocessing and feature extraction are carried out on the modal data, and the modal data are converted to a frequency domain to obtain frequency features; based on the physical and biochemical characteristics of the termites and the nests thereof, calculating the matching degree and the credibility of each modal feature, and performing adaptive weighted feature fusion according to the matching degree and the credibility to generate comprehensive spectral features; inputting the fusion features into a pre-trained termite identification and risk assessment model to realize precise identification, positioning and threat level assessment of termite individuals, termite paths and nests; and carrying out trajectory analysis on termite activities by combining a multi-target tracking algorithm, and giving out early warning based on identification and tracking results. According to the invention, early-stage, lossless and accurate detection and active early warning of termites are realized, and the detection efficiency and reliability are significantly improved.

A platform for characterizing biomolecules, biological tissue sections, viruses, or other particles of interest in a non-invasive, high-throughput, cost-effective, and label-free manner. The platform includes a merged set of imaging modalities including and capable of interferometric microscopy and wide-field or confocal microscopy (such as, for example, a wide-field super-resolution surface enhanced Raman spectroscopy, SERS), although some aspects may be employed with data sets only received from wide-field or confocal microscopy. Related devices, systems, methods, techniques, and variations to the same are also provided.

The invention belongs to the technical field of biology, and particularly relates to a group of paired monoclonal antibodies for resisting H9N2 subtype avian influenza viruses and a surface enhanced Raman spectroscopy rapid detection test strip established based on the antibodies. The paired monoclonal antibody comprises a labeled antibody 1E9 and a capture antibody 3C10 which are prepared by taking hemagglutinin protein of the H9N2 subtype avian influenza virus as antigens and can be specifically combined with the H9N2 subtype avian influenza virus. According to the invention, an SERS (Surface Enhanced Raman Scattering) probe Au-coated Pt-4ATP is used as a marker and is coupled with a labeled antibody 1E9 to prepare a labeled probe, and the labeled probe is paired with a capture antibody 3C10 to establish an SERS immunochromatography test strip. The test strip has a colorimetric and Raman dual interpretation mode, can be used for qualitative and quantitative detection, has the advantages of strong specificity, high sensitivity, good repeatability and stability and the like, is simple and rapid in clinical detection operation, and provides reliable technical support for on-site rapid screening of subtype viruses.

The invention discloses a seawater corrosion environment biaxial test system and method based on Raman spectrum. The seawater corrosion environment biaxial test system comprises a biaxial in-situ loading device, a seawater corrosion environment simulation box and a Raman spectrum measuring instrument. According to the biaxial in-situ loading system coupled with the seawater corrosion environment and used for Raman spectrum measurement, simulation of a complex stress state and the seawater corrosion environment is achieved by coupling the biaxial in-situ loading equipment and the seawater corrosion environment simulation box, and meanwhile, based on the Raman spectrum measurement instrument, the measurement accuracy is further improved through data analysis. Key information such as chemical product evolution laws in the stress corrosion process is obtained in situ, and a technical foundation is laid for clarification of a corrosion failure mechanism of a material under a real deep-sea service condition.

The invention belongs to the technical field of spectral analysis, and discloses a Raman spectrum characteristic peak segmentation method based on distance vector and probability vector output. Spectral detail features are extracted through multi-scale wavelet decomposition, and a spectral multi-layer structure representation matrix is constructed; identifying a potential peak site and a hierarchical affiliation relationship thereof based on the local curvature change rate; generating an adaptive distance calculation kernel function in combination with the asymmetry index and the peak shape complexity coefficient; and calculating a distance vector of hierarchical perception and a probability vector extracted by deep learning, and constructing a joint segmentation decision function of a multi-layer peak structure. According to the method, the hierarchical relationship among the main peak, the shoulder peak and the sub-peak can be accurately distinguished, the complex conditions of peak overlapping, asymmetric peak shapes, low signal-to-noise ratio and the like are effectively processed, and the accuracy and the reliability of Raman spectrum analysis are improved.

The invention provides a tip Raman-based metal oxide nanoparticle in-situ detection method, which comprises: S1, dispersing metal oxide nanoparticles in a nanoparticle dispersion liquid, focusing laser to the tip of a nanometer tip through an objective lens, capturing a single nanoparticle by using an optical tweezers technology, and attaching the single nanoparticle to the nanometer tip; s2, focusing Raman laser on a nano needle tip-particle interface, generating a local high-temperature field through a photo-thermal near-field coupling effect, triggering a thermal chemical reaction of nano particles, and synchronously collecting a Raman spectrum; and S3, carrying out pretreatment and characteristic peak analysis on the collected Raman signal of the Raman spectrum, and determining the reaction critical temperature and the dynamic process in combination with temperature calibration data. According to the invention, capture and accurate heating of the nanoparticles are realized through photo-thermal coupling of the optical tweezers and the needle tip, and thermal chemical reaction monitoring of the nanoparticles is realized.

The invention belongs to the technical field of spectral analysis, and particularly discloses a Raman spectrum baseline correction method, device and system, a medium and equipment, and the method comprises the steps: obtaining a to-be-processed original Raman spectrum, recognizing trough points of the original Raman spectrum, and screening the recognized trough points to obtain baseline trough points; fitting the base line wave trough points to obtain a Raman spectrum base line; and correcting the Raman spectrum baseline to obtain a corrected Raman spectrum. Through multi-stage trough point screening and adaptive fitting, background interference can be effectively eliminated, and the signal-to-noise ratio and the characteristic peak fidelity of the Raman spectrum can be improved.

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

The invention relates to the technical field of spectrum detection, in particular to a Raman spectrum-based rice oil detection method, which comprises the following steps: extracting main and auxiliary peaks to construct a peak group structure, performing sliding comparison to output a conformity region, identifying jump to remove an abnormal signal, calibrating a peak position to generate a correction structure, and performing homing numbering to complete a detection scheme. According to the method, the feature matching rule is constructed by combining the peak position spacing, the intensity ratio and the wavenumber index, the recognition precision of the multi-peak structure in the spectrum is enhanced, non-continuous response signals are recognized and eliminated through derivative change trend and proportion deviation constraint, and the accuracy of stable response extraction is improved. Dynamic calibration and position adjustment of peak positions are realized by utilizing an offset trend sequence, the consistency of a peak group structure in an overall direction and the continuity of a local structure are ensured, a numbering system and structure attribution division is completed after homing sorting, and the integrity of feature expression in a complex sample and the orderliness and reliability of spectral data processing are improved.

The invention relates to the technical field of traditional Chinese medicine component detection and discloses a rapid detection method for traditional Chinese medicine compound components. The method comprises the following steps: performing Raman spectrum scanning on a traditional Chinese medicine compound sample to generate component spectrum distribution information, and performing fitting by adopting a partial least square algorithm to determine a local component anomaly risk area; meanwhile, atomic force microscopic imaging is carried out, a microstructure distribution map is generated, and a potential component mismatch feature region is marked through texture feature extraction and pattern recognition. And generating a detection optimization adjustment region based on the space coordinates of the region, analyzing the gradient change in the molecular arrangement direction of the region to evaluate the component distribution offset state, and analyzing the interface bonding state of the active component and the auxiliary material to evaluate the component interaction coupling strength. And finally, on the basis of an evaluation result, whether the real-time parameter calibration is started in the detection optimization adjustment area is judged through a dual-channel decision-making mechanism. According to the method, correlation analysis of component spectrums and microtopographies is realized, and the accuracy and pertinence of traditional Chinese medicine compound component detection are improved.

The invention belongs to the technical field of power equipment state monitoring, and discloses a transformer oil dissolved gas surface enhanced Raman spectrum in-situ monitoring method and a transformer oil dissolved gas surface enhanced Raman spectrum in-situ monitoring system. The system comprises a membrane degassing unit, a Raman sensing unit, a light path unit, a data processing unit and a calibration early warning unit, the method comprises the following steps: firstly preparing a gold nanostar array sensing substrate with bifunctional groups to realize selective adsorption and signal enhancement of characteristic gas, then completing in-situ oil-gas separation through the membrane degassing unit, collecting a Raman spectrum through the light path unit, and calculating the Raman spectrum of the gold nanostar array sensing substrate according to the Raman spectrum. Spectral analysis and quantification are completed based on multiple algorithms, and fault early warning is achieved in combination with temperature and pressure compensation and an industrial standard threshold value. According to the scheme, the problems of lagging, weak interference resistance and the like of traditional monitoring are solved, high sensitivity and high specificity are achieved, full-process automatic closed-loop monitoring can be achieved, and accurate data support is provided for operation and maintenance of the transformer.

The invention discloses a device and a method for measuring geometric parameters and performance parameters of a sample, the device comprises a white light interference measurement module, a Raman spectrum measurement module and an ellipsometry measurement module, and the white light interference measurement module and the Raman spectrum measurement module adopt a partial common-path design. Common-optical-path measurement of Raman spectrum data and interference fringe images is achieved, the surface appearance and material composition information of the same measurement point position can be obtained at the same time, and in-situ high-precision obtaining of geometric parameters and material composition information in ellipsometry modeling is achieved. The condition of analysis errors caused by inconsistent information carried by different measurement points of the heterogeneous film is avoided, and the precision and the reliability of an ellipsometry measurement result can be greatly improved. According to the method for measuring the geometric parameters and the performance parameters of the film layer on the surface of the wide bandgap semiconductor wafer, the morphological information and the performance parameters of the heterogeneous film can be comprehensively and accurately measured and represented by combining the ellipsometric spectrum, the white light interference, the Raman spectrum and other technologies.

The invention discloses an SERS (Surface Enhanced Raman Scattering) detection method for paclitaxel drug concentration in blood of a breast cancer patient, and belongs to the technical field of detection. The method comprises the following steps: performing acetonitrile two-step precipitation centrifugal treatment on a blood sample to obtain a detection sample; the method comprises the following steps: constructing a high-sensitivity SERS substrate (the volume ratio of gold nanoparticles to ethanol is 1: (90-100), and a film is formed on a water-cyclohexane interface) through self-assembly of gold nanoparticles on a liquid-liquid interface; the method comprises the following steps: dropwise adding a sample into a substrate, and detecting characteristic peaks of 1024cm <-1 > and 1053cm <-1 > by using a portable Raman spectrometer (785nm, 30mW, 0.5 s); and calculating the paclitaxel concentration in combination with a pre-established quantitative curve. The method is easy and convenient to operate and suitable for clinical bedside rapid monitoring.

The invention relates to the technical field of analysis and detection, in particular to a surface enhanced Raman spectroscopy (SERS)-based nano material detection method, which is characterized in that a heterojunction composite structure consisting of noble metal nano stars and semiconductor oxide nano particles is used as an SERS active substrate; and a specific recognition molecule carrying a Raman reporter molecule is used as a detection probe. During detection, the recognition molecules are firstly combined with the to-be-detected nano material, and then the compound is adsorbed on the surface of the substrate. The tip structure of the noble metal nanostar and a heterojunction interface jointly generate a synergistic effect of electromagnetic enhancement and chemical enhancement, and a reporter molecule signal is doubly amplified, so that high-sensitivity and high-selectivity detection on a to-be-detected nano material is realized. The method solves the problems of weak signal and poor selectivity of the traditional method, and is suitable for rapid and quantitative analysis of trace nano materials in the environment.

The invention relates to the technical field of physical property analysis, in particular to a rapeseed oil quality detection method and device and a storage medium. Determining Raman spectrum data of the rapeseed oil sample based on the oleic acid concentration and the erucic acid concentration; determining a peak intensity contribution coefficient based on the Raman spectrum data; based on the oleic acid concentration, the erucic acid concentration and the peak intensity contribution coefficient, constructing a rapeseed oil quality detection model; and performing quality detection on Raman peak characteristics of to-be-predicted rapeseed oil based on the rapeseed oil quality detection model to obtain a detection result of the to-be-predicted rapeseed oil. According to the method, the prediction precision of the rapeseed oil quality is improved.

The invention mainly relates to the technical field of spectral analysis, and provides a white spirit brewing spectral analysis method and system based on a multi-modal large language model in order to efficiently and accurately obtain white spirit brewing information according to multi-modal spectral data of all links of white spirit brewing. The method is characterized by comprising the following steps of: collecting multi-mode spectrum data including gas chromatography data, infrared spectrum data and Raman spectrum data in each link of white spirit brewing; converting the multi-modal spectral data and the natural language instruction into a feature embedding vector which can be recognized by a large language model to train the large language model which is finely adjusted based on professional knowledge in the field of white spirit, and receiving a question of a user in a natural language instruction form by the trained large language model; wine making analysis tasks such as flavor compound identification, key microorganism detection, fermentation state evaluation and base wine quality grading are carried out, and finally analysis results and wine making process optimization suggestions are output in a natural language form, so that the accuracy and efficiency of spectrum data interpretation in the white wine making process are improved.

The invention discloses a Raman spectrum joint classification and quantification method based on a prior fusion double-flow network, and belongs to the technical field of spectral analysis and chemometrics. The method comprises the following steps: acquiring Raman spectrum original data, and preprocessing to obtain standardized spectrum data; raman spectrum prior information is extracted, and a prior information double-flow network containing classification and quantitative flow sub-networks is constructed; standardized data and prior information are input, classification preheating-combined fine tuning training is adopted, conflict is relieved by combining dynamic weighting and PCGrad, and samples are expanded through a physical consistency strategy synchronously; processing unknown samples by using the trained network, and outputting categories and component contents; according to the method, weak peak information is mined through double-flow architecture and cross-flow attention, derivative noise is suppressed, classification-quantification performance is balanced through double-task optimization, and small sample overfitting is solved through physical expansion; on a polycyclic aromatic hydrocarbon data set, classification and regression indexes are superior to those of a traditional method, the low-concentration / weak-peak scene precision and robustness are better, and the engineering application prospect is good.

The invention discloses a method for detecting pollutant concentration based on deep ultraviolet Raman spectrum of deep learning, and belongs to the technical field of spectral analysis. According to the method, aiming at the problems of spectral line overlapping interference, weak trace signals, strong background noise, nonlinear response and the like when a traditional deep ultraviolet Raman spectrum technology is used for detecting pollutants, high-precision quantitative analysis is realized by constructing an SSA-CNN-LSTM-Attention fusion model and combining a deep ultraviolet Raman spectrum pretreatment technology. The model adopts 1D-CNN to extract spectral local features, LSTM to capture sequence long-range dependence, an Attention mechanism to enhance feature peak response weight, SSA to optimize hyper-parameters, and a concentration prediction value is output through a weighted loss function optimization model. And meanwhile, by combining a synthetic data expansion strategy and environment matrix standard sample library training, the adaptability of the model to a complex scene is improved. According to the method, the anti-interference capability and generalization of new pollutant detection are effectively improved, and the method has important application value in the fields of environmental monitoring, industrial safety and the like.

A negative electrode material, comprising composite particles. Each of the composite particles comprises a core and an amorphous carbon coating layer located on the surface of the core, wherein the core comprises a graphite framework and amorphous carbon in the graphite framework; and the amorphous carbon coating layer has a thickness of 10-50 nm. Raman spectral area scanning is performed on the negative electrode material, and there are 400 Raman spectral area scanning points; and the average ID / IG value of the Raman spectrum of the negative electrode material is 0.10-0.20, wherein the proportion of the number of scanning points having a single-point ID / IG value of less than 0.2 is greater than 60%. The negative electrode material has a high compaction density, a high capacity, a low expansion rate, good rate performance, and good high-temperature cycling performance.

The invention discloses a lithium supplement agent which comprises a lithium supplement inner core and a carbon coating layer coating the surface of the lithium supplement inner core, and the lithium supplement inner core is doped with an Al element; the lithium supplementing agent meets the condition that A / I is larger than or equal to 0.3 and smaller than or equal to 4, A is the mass ratio of the Al element in the lithium supplementing agent, and I is the ID / IG value of the lithium supplementing agent in a Raman spectrogram. By regulating and controlling the A / I ratio of the Al doping amount to the carbon coating layer structure, the lithium supplementing capacity, the conductivity and the air stability are improved, and meanwhile, the gas production phenomenon in the lithium removal process is effectively inhibited.

The invention discloses a deep learning wolfberry origin identification analysis model based on near infrared spectrum and Raman spectrum fusion. A wolfberry sample data set is acquired in a nondestructive manner through near infrared spectrum and Raman spectrum, spectral data is preprocessed, useful information is extracted, instrument noise is removed, and fluorescence background interference is reduced. The method comprises the following steps: firstly, constructing a cross-modal feature fusion model, namely, Multimodal Collastoration Attention Network (MCAN), and then, constructing a cross-modal feature fusion model, namely, the MCAN (Multimodal Collastoration Attention Network); according to the model, a spectrum branch network and a collaborative attention network are combined, information among different modes can be effectively integrated through feature level fusion, and the precision and robustness of Chinese wolfberry origin traceability identification are improved.

A system and method for determining charge variants in monoclonal antibodies using raman spectroscopy is disclosed.

The invention relates to the technical field of lithium ion batteries, in particular to a lithium iron manganese phosphate battery and a preparation method thereof, and the lithium iron manganese phosphate battery comprises a positive pole piece and a negative pole piece; the positive pole piece comprises a positive current collector and a positive active material layer arranged on the surface of the positive current collector; the positive electrode active material layer comprises lithium manganese iron phosphate; the lithium manganese iron phosphate comprises LiMnxFe1-xPO4, and x is more than or equal to 0.4 and less than or equal to 0.9; the surface density of the positive electrode active material layer is 493.4-584.3 g / m < 2 >; the negative electrode plate comprises a negative electrode current collector, a first negative electrode active material layer arranged on the surface of the negative electrode current collector and a second negative electrode active material layer arranged on the surface of the first negative electrode active material layer; the first negative electrode active material layer comprises first graphite; in a Raman spectrogram of the first graphite, ID / IG is 0.05 to 0.15; the second negative electrode active material layer comprises second graphite; and in the Raman spectrogram of the second graphite, ID / IG is 0.4-0.65. The lithium manganese iron phosphate battery provided by the invention has significantly improved energy density and charge.

The present application relates to the technical field of biological sensing, and discloses a composite functional porous metal membrane and detection system for monitoring and identifying airborne microorganisms, and a method for monitoring and identifying airborne microorganisms. The monitoring and identification method comprises: applying an electric field to the composite functional porous metal membrane, causing the membrane to freely switch charge and capture airborne microorganisms to be tested; adding a fluorescent probe to the composite functional porous metal membrane, which binds with the microorganisms to be tested to obtain a fluorescent sample, and collecting imaging of light emitted after irradiation of excitation light, to achieve preliminary determination and addressing of the microorganisms to be tested; performing Raman spectroscopy scanning on individual bacteria, and comparing with a standard Raman spectrum library to achieve species identification of the individual bacteria. Relying on the "composite functional porous metal membrane," an integrated process of "selective enrichment-total bacteria monitoring-species identification" is realized; the present invention takes selective enrichment of the microorganisms to be tested as an entry point, lays the foundation for preliminary judgment accuracy and species identification accuracy, and improves the efficiency and precision of monitoring and identifying airborne microorganisms.

The invention relates to a multi-machine calibration method and system for a Raman spectrometer, and a storage medium, and the method comprises the steps: outputting an adjustable substrate voltage, and setting an initial signal amplification gain; controlling a CCD sensor to collect first Raman spectrum data of a standard sample with a known standard wave number; establishing a mapping model from a pixel domain to a wave number domain based on a plurality of known standard wave numbers and second pixel point positions of the characteristic peaks corresponding to the known standard wave numbers in the first Raman spectrum data to obtain calibration parameters of the Raman spectrometer; verifying the accuracy of the mapping model by verifying the characteristic peak, and storing calibration parameters after the verification is passed; controlling the plurality of Raman spectrometers to respectively detect the same sample to be detected, acquiring second Raman spectrum data of the sample to be detected, extracting a third pixel point position corresponding to a characteristic peak of the sample to be detected, calling the calibration parameter, and converting the calibration parameter into an actually measured wave number; and feeding back and adjusting the signal amplification gain of each instrument based on a signal intensity measurement value corresponding to the second Raman spectrum data by each Raman spectrometer.

The invention relates to a method and device for recognizing antibiotics in water by combining Raman spectrum data enhancement with a multi-layer perceptron, and relates to the technical field of antibiotic detection and recognition, and the method comprises the following steps: collecting original Raman spectrums of different types of antibiotics and mixtures thereof by adopting a dendritic nano-silver Raman enhanced substrate; secondly, preprocessing an original Raman spectrum, expanding Raman spectrum data under different matrix conditions through a data enhancement method, generating an expanded spectrum data set, inputting the expanded spectrum data set into the constructed multi-layer perceptron model for model training, and forming sample key features through explanatory variables and feature selection of the SHAP processing model; and finally, inputting spectral data of to-be-identified antibiotics into the model, and identifying the antibiotics in combination with the key features of the sample. It can be seen that the sample data is expanded and enhanced through the data enhancement method, the multi-layer perceptron recognition model with high recognition precision is trained, and the method is used for recognizing antibiotics such as sulfonamide antibiotics in water and improving the recognition accuracy of antibiotic residues.