6484results about "Raman scattering" patented technology

The invention relates to a fermentation process abnormal state early warning method, system and device and a medium, and the early warning method comprises the steps: collecting fermentation process parameters in a fermentation tank in real time, and generating a physical parameter matrix; obtaining Raman spectrum data of the fermentation liquor and extracting metabolic feature vectors; fusing the physical parameter matrix with the metabolic feature vector to obtain a multi-modal data cube; constructing a metabolic network model, inputting the physical parameter matrix as a boundary condition, and generating calibrated metabolic flux distribution data; dynamically adjusting the monitoring weight coefficient of the fermentation process parameter, and generating a parameter sensitivity weight vector; inputting the multi-modal data cube and the parameter sensitivity weight vector into a space-time convolutional network, extracting time sequence features and spatial distribution features, and performing similarity matching with a preset abnormal mode library to obtain a matching result; and determining an abnormity early warning type according to a matching result with abnormity, and generating a corresponding compensation control instruction. The stability of the fermentation process is improved.

The invention discloses a water quality heavy metal pollution detection method and system based on Raman spectrum.The water quality heavy metal pollution detection method comprises the steps that water body Raman scattering light is collected in situ through a miniature optical fiber probe, and a continuous time sequence spectrum signal flow is generated; wavelet transform is combined with self-adaptive threshold setting, and high-frequency noise and effective spectral signals are separated; dynamically strengthening the characteristic peak of the target heavy metal through a frequency domain characteristic screening module, and inhibiting a water molecule interference peak at the same time; generating a cross-domain fusion feature vector; processing the fusion feature vector through a pre-trained heavy metal concentration prediction model, and outputting concentration prediction values of various heavy metals; a confidence score is dynamically calculated based on a deviation between a current predicted value and historical data distribution, and model parameter update and system calibration are automatically triggered. The method has the advantages that through acousto-optic signal cross-domain fusion and closed-loop self-calibration, the target peak is dynamically strengthened while water molecule interference is inhibited, and the real-time performance, the anti-interference performance and the prediction precision of heavy metal detection are improved.

The invention relates to the technical field of spectral analysis, in particular to a fabric fiber component high-resolution spectral rapid analysis method which comprises the following steps: S1, acquiring three-dimensional shape data of a fabric surface through laser scanning, and identifying a fiber arrangement direction and a surface curvature characteristic based on the three-dimensional shape data; s2, dynamically adjusting the incident angle and the focusing depth of a spectrum probe according to the fiber arrangement direction and the surface curvature characteristics identified in S1, and selecting a corresponding spectrum excitation mode; s3, performing feature extraction on the multi-mode spectral data acquired in the S2, and fusing feature vectors of different spectral modes through a deep learning algorithm; s4, analyzing the fused feature vector based on a transfer learning model, and outputting a fiber component type and a proportion thereof; and S5, associating the component data output in the step S4 with space coordinates, and generating a component distribution visualization map. By dynamically adjusting the spectrum probe, fusing multi-mode spectrum data and the like, the analysis precision and speed are remarkably improved.

An optical biomodule for detecting a disease specific biomarker(s) at an early stage, utilizing enhanced fluorescence emission (due to integration of three-dimensional (3-D) structures in a fluidic container) upon chemical binding / coupling of a disease specific biomarker(s) with its corresponding disease specific biomarker binder(s)-coupled with a fluorophore(s) positioned horizontally within a geometrical area (that includes a center region or a middle region) is disclosed. Furthermore, a corresponding disease specific biomarker binder(s) may include an amplifying chemical structure / sequence. The three-dimensional (3-D) structure(s) may be coupled with an optical resonator and / or a photonic crystal (PC) and / or a metamaterial and / or a metamaterial of Epsilon-Near-Zero (ENZ) of a suitable wavelength range.

The invention provides a water quality heavy metal pollution detection method and system based on Raman spectrum. The method comprises the following steps: collecting a water sample through a water area to be detected, and scanning the water sample by adopting a Raman spectrometer to generate an original Raman spectrogram; based on the original Raman spectrogram, using a wavelet transform algorithm to perform de-noising processing, reducing background interference, using a principal component analysis technology to extract characteristic peaks, and generating a heavy metal preliminary prediction result; based on the preliminary heavy metal prediction result, constructing a heavy metal concentration prediction model by applying a partial least squares regression algorithm, performing quantitative analysis, evaluating prediction accuracy by adopting a cross validation technology, and generating a heavy metal actual concentration result; and based on the actual heavy metal concentration result, comparing with a water quality safety standard, evaluating a pollution condition, and generating a water quality heavy metal pollution detection report. The technical scheme provided by the invention has high sensitivity, can efficiently denoise and accurately predict, and provides a scientific means for monitoring and treating heavy metal pollution of water.

The invention discloses a preparation method of PEDOT: PSS (poly (3, 4-ethylenedioxythiophene): poly (styrenesulfonate))-based composite fiber with silver nanoparticles loaded on the surface. The method comprises the following steps: uniformly mixing a silver nanowire solution with a PEDOT: PSS solution, or uniformly mixing the silver nanowire solution, the PEDOT: PSS solution and a polymer solution to obtain a spinning solution; extruding the spinning solution into a coagulating bath through a wet spinning process and soaking to obtain nascent composite fibers; and drying the nascent composite fiber to obtain the PEDOT: PSS-based composite fiber of which the surface is loaded with silver nanoparticles and the interior contains silver nanowires. The interior and surface structures of the PEDOT: PSS fiber are simultaneously modified, the interior of the fiber contains a silver nanowire structure, and the surface of the fiber has a silver nanoparticle structure. The composite fiber has the characteristics of ultra-sensitive SERS sensing capability, high conductivity, electric heating, molten drop resistance and antibacterial property.

The invention relates to the technical field of soil remediation, and discloses a remediation treatment method and system for simulating cadmium-contaminated soil and a storage medium. The method comprises the following steps: detecting and analyzing the adsorption performance of the remediation material according to soil cadmium content data to obtain remediation material performance data; based on the soil cadmium content data and the remediation material performance data, carrying out data processing on a pollutant migration rule to obtain pollutant migration characteristic data; carrying out data analysis on the repair material combination by utilizing the pollutant migration characteristic data to obtain material combination effect data; according to the material combination effect data, the repair process is dynamically detected, and repair effect data are obtained; and performing data evaluation on the remediation scheme by utilizing the remediation effect data to generate a cadmium-contaminated soil remediation scheme. According to the method, the performance of the repairing material, the pollutant migration rule and the repairing effect are accurately evaluated, and the scientificity and the reliability of the repairing scheme are improved.

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

The invention discloses a natural gas odorant concentration intelligent detection device suitable for remote monitoring, and belongs to the field of natural gas safety monitoring, and the natural gas odorant concentration intelligent detection device comprises an odorant concentration sensor array used for detecting original concentration data of a natural gas odorant; the cross validation module is used for carrying out fusion processing on the original concentration data corresponding to the various sensors respectively to obtain calibrated odorant concentration detection data; the pipeline monitoring module is used for obtaining pipeline operation data; the communication module is connected with the cross validation module, the pipeline monitoring module and the remote monitoring module; the remote monitoring module is used for determining an odorant concentration predicted value based on integrated multiple models according to the original concentration data and the pipeline operation data corresponding to the multiple sensors respectively; and obtaining a final odorant concentration confirmation result according to the calibrated odorant concentration detection data and the odorant concentration predicted value. According to the invention, high-precision, real-time and reliable remote intelligent monitoring of the concentration of the natural gas odorant is realized.

The invention belongs to the technical field of SERS (Surface Enhanced Raman Scattering) detection, and particularly relates to a preparation method and application of an Ag-coated NiFe2O4 composite SERS substrate. The preparation method comprises the following steps: firstly, preparing Ag nanoparticles which are uniformly distributed and have the same size through a sodium citrate reduction method; secondly, a nanometer flower type NiFe2O4 material is prepared through a hydrothermal method and a calcination method; and finally, the novel SERS substrate Ag-coated NiFe2O4 is prepared through a physical stirring method. The novel SERS substrate made of the Ag nano particles and the nano-flower type NiFe2O4 material is constructed, and compared with a conventional SERS substrate made of independent Ag nano particles, the SERS substrate can detect stronger intrinsic Raman signals of probe molecules. As the SERS substrate needs to be compounded with a nano-flower type NiFe2O4 material during preparation, a charge transfer phenomenon can occur at an interface where the SERS substrate is in contact with probe molecules, so that intrinsic Raman signals of the probe molecules can be enhanced, and the SERS substrate can be better used for detecting and analyzing chemical substances.

The invention discloses a method for analyzing mineral trace elements by utilizing a micro-Raman spectrum, which comprises the following steps of: fixing a mineral sample on a rotary sample table of a micro-Raman spectrometer, selecting an area to be detected by combining a micro-imaging technology, collecting a Raman background signal under the pre-illumination of low-power laser, and analyzing the trace elements in the area to be detected according to the Raman background signal. The laser focal length, the spot size and the incident angle are adjusted accordingly, the laser is focused on a target area, under the calibrated laser condition, through a multi-angle excitation micro-area, after an algorithm is applied to optimize signals, reduce impurity interference and integrate multi-angle Raman signals, the characteristic spectrum information of the microelements can be extracted, and the microelements can be accurately detected. High-resolution analysis of sample components is realized, and a three-dimensional distribution model of microelements in a microcell is constructed in combination with microscopic imaging data. The method has the characteristics of non-destructiveness, high resolution and rapid detection, and is suitable for mineral component analysis in the fields of geology, material science and the like.

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

The invention discloses a method for constructing a true and adulterated wine discrimination model, and particularly relates to the technical field of wine detection. The method comprises the following steps: extracting component data, a spectrum curve, smell fingerprints and texture information of wine liquid through nondestructive testing, carrying out standardization processing and feature fusion on multi-modal data, extracting distribution similarity features and domain offset robustness features, constructing a comprehensive feature vector, and evaluating a generalization ability value of a true and adulterated wine discrimination model by utilizing a machine learning model; aiming at a low generalization ability model, adopting a transfer learning method to carry out multi-stage training and optimization, and evaluating the detection accuracy of the model on unseen brands and batches in combination with cross validation; and finally, inputting the multi-modal characteristics of the to-be-detected wine on the optimized model, and outputting a true and false wine judgment result and a confidence score. According to the invention, the stability and adaptability of the true and adulterated wine discrimination model can be significantly improved, and it is ensured that the method has high accuracy, high generalization ability and wide market applicability in detection of wine of different brands and batches.

The invention relates to the technical field of intelligent detection and quality grading, in particular to an automatic fish maw detection and grading system which integrates a spectral imaging module, a trace component analysis module, a physical property detection module and the like to realize comprehensive detection of external characteristics and internal components of fish maw. Through a multi-band light source and a high-resolution camera, an image from visible light to near-infrared light is obtained, and key indexes such as collagen, mineral substances and nutritional ingredients are accurately analyzed by utilizing a nondestructive testing technology; the feature extraction and fusion module adopts weighted mutual information and a self-adaptive penalty factor model to optimize features, the intelligent grading decision module provides accurate grading based on a feature intelligent discrimination method, and the self-adaptive control module dynamically adjusts equipment parameters to ensure the process to be stable. The system has the advantages of high efficiency, accuracy and intellectualization, and provides technical support for standardized grading and industrial production of the fish maw.

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

In one embodiment, a system includes a pump light source configured to produce a pump beam of light at a pump frequency and a Stokes light source configured to produce a Stokes beam of light at a Stokes frequency, where the pump and Stokes frequencies are offset by a frequency offset Ω. The system further includes a photonic integrated circuit (PIC) that includes multiple optical waveguides configured to: direct the pump and Stokes beams of light to a sample; receive a Raman signal produced by coherent Raman scattering of the pump and Stokes beams of light at the sample; and direct the Raman signal to an optical receiver. The optical receiver is configured to detect the Raman signal. The optical receiver includes a probe light source configured to produce a probe beam of light at a probe frequency, an optical detector, and an electronic circuit.

The invention discloses a photoacoustic spectrum-confocal Raman spectrum multi-view-field high-dimensional imaging method and a photoacoustic spectrum-confocal Raman spectrum multi-view-field high-dimensional imaging device. Nanosecond pulse laser with adjustable wavelength is generated through a continuous spectrum nanosecond pulser and a narrow-band spectrum tuning module and cooperates with a microscopic detection module, a depth imaging module, a galvanometer system and an electric displacement table to obtain spatial three-dimensional data, a photoacoustic spectrum and a Raman spectrum under multiple view fields; shallow photoacoustic spectrum-Raman spectrum tomography and deep three-dimensional photoacoustic imaging are realized; correcting a photoacoustic spectrum quantitative error by using a spectrum decoupling algorithm, and fusing the multi-view-field data to generate multi-view-field high-dimensional photoacoustic spectrum-Raman spectrum data; high-dimensional data features are extracted based on a 3D convolution instance segmentation network, and quantitative analysis and spatial distribution visualization of blood oxygen, inflammatory factors and the like are achieved. According to the method, the limitation that substances are difficult to analyze in photoacoustic imaging is overcome, the contradiction between the imaging resolution and the penetration depth is solved, and an accurate analysis means is provided for diagnosis of diseases such as knee osteoarthritis and sepsis.

The invention discloses an SERS (Surface Enhanced Raman Scattering) sensing chip with stable reusability as well as a preparation method and application of the SERS sensing chip, and belongs to the technical field of photoelectric sensing devices. Comprising a substrate; the semiconductor photocatalyst is positioned on the surface of the substrate; the co-oxidant and the co-reducing agent are positioned on the surface of the semiconductor photocatalyst; the occupying molecule is covalently connected with an exposure site of the auxiliary reducing agent in space; the template molecule is in covalent linkage or coordination linkage with an exposure site of the pro-oxidant in space; wherein the template molecule can be subjected to photocatalytic degradation, so that the pro-oxidant obtains the exposure site again. The template molecules in the sensing chip can be quickly removed through space confinement photocatalytic degradation, and the accuracy of quantitative analysis is improved by using the inorganic imprinted polymer as internal standard molecules.

The invention provides a Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on an online Raman spectrum, belongs to the technical field of biological fermentation process control, and aims to solve the problems of unstable process and low efficiency caused by fermentation control lag and incapability of sensing the real metabolic state of cells in the prior art. The method comprises the following steps: acquiring the concentrations of a target product BDO and key byproducts such as acetic acid and ethanol in the fermentation liquor in real time through an online Raman spectrum; according to the method, a metabolic stress index is originally proposed and constructed, the index is obtained by performing weighted operation on the instantaneous generation rate of the by-product and the target product, and the index is used for quantitatively characterizing the intrinsic metabolic stress level of the cells in real time. The control strategy of maintaining the metabolic stress index in the preset optimal stable interval is taken as a core control strategy, the conversion from passive response to active prediction in the fermentation process is realized, and the yield, the stability and the batch repeatability of Bi-BDO production are remarkably improved.

The invention provides a super lens and a femtosecond laser manufacturing real-time monitoring system applying the super lens, the super lens comprises a substrate and a nano column array on the substrate, nano columns at different positions in the nano column array independently regulate and control a first wavelength light wave and a second wavelength light wave, so that the first wavelength light wave satisfies first phase distribution, and the second wavelength light wave satisfies second phase distribution. The second wavelength light wave meets the second phase distribution, so that the super lens not only can focus the first wavelength light wave and the second wavelength light wave on the same plane, but also can control the focal depth of the first wavelength light wave to be greater than the focal depth of the second wavelength light wave, so that the requirement of a femtosecond laser manufacturing real-time monitoring system is met; not only can the anti-interference capability in the machining process be improved, but also the axial position monitoring precision can be improved.

The invention discloses a method for detecting the biological enrichment microplastic degree of bivalve mollusks in a deep sea extreme environment. According to the method, biocenosis is sampled to acquire a community structure representing a biological bed layer, then morphological characterization statistics is carried out to classify age stage data of shellfish individuals, a multivariate factor analysis method is used to obtain the shellfish individuals with the largest contribution degree in each classification age stage, then tissue-divided micro-plastic extraction is carried out, and the classification age stage data of the shellfish individuals with the largest contribution degree is obtained. The method comprises the following steps: reducing the form of seabed micro-plastics, carrying out process identification on full-size micro-plastics, tracing the duration of adsorbing the micro-plastics by each shellfish monomer by virtue of a carbon-14 dating method, and constructing a seabed shellfish dynamic enrichment curve by splicing the life of each shellfish; and finally amplifying to the whole shellfish bed layer to deduce the microplastic enrichment rate and historical accumulation amount of the whole extreme ecosystem. The method provides a scientific basis and a technical support for formulating an effective marine environment protection strategy and clarifying a marine micro-plastic source-sink relationship.

In one embodiment, a system includes a first light source configured to produce a first beam of light at a first frequency and a second light source configured to produce a second beam of light at a second frequency, where the first and second frequencies are offset by a frequency offset Ω. The system also includes one or more optical elements configured to: direct the first and second beams of light to a sample, and collect a Raman signal produced by the sample in response to the first and second beams of light. The system further includes an optical receiver configured to detect the Raman signal. The optical receiver includes a third light source configured to produce a third beam of light at a third frequency, where the third light source includes a wavelength-tunable laser, where the third frequency is adjustable.

The invention discloses a single-pixel fluorescent Raman bimodal imaging system and method, and belongs to the technical field of optical imaging. Comprising a light source module, a coding structured light module, a microscopic imaging module, a sample control module and a signal collection module which are sequentially arranged along a light path, the light source module is used for generating laser with required wavelength; the encodable structured light module converts the received laser into structured light; the microscopic imaging module amplifies the structured light, projects the amplified structured light to a sample plane, and collects fluorescence, elastic scattering and inelastic scattering generated after the sample is excited; the signal collection module amplifies, separates and collects the received fluorescence signals and scattering signals, converts the light signals into electric signals, and transmits the electric signals to the control and calculation module for storage and subsequent processing. According to the invention, the problem that the existing imaging system cannot obtain time-space consistent multi-modal images under the same excitation source and cannot realize image fusion is solved.

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 SERS (Surface Enhanced Raman Scattering) detection, and particularly relates to an Ag-MoO2 composite SERS substrate as well as a preparation method and application thereof. The preparation method comprises the following steps: firstly, preparing Ag nanoparticles which are uniformly distributed and have the same size by a sodium citrate reduction method; secondly, a MoO2 material is prepared through a hydrothermal method; and finally, the novel SERS substrate Ag-MoO2 is prepared through a physical stirring method. According to the invention, the novel SERS substrate made of Ag nanoparticles and MoO2 materials is constructed, which is different from the conventional SERS substrate made of independent Ag nanoparticles, and can detect stronger intrinsic Raman signals of probe molecules. As the SERS substrate needs to be compounded with a MoO2 material during preparation, a charge transfer phenomenon can occur at an interface where the SERS substrate is in contact with probe molecules, so that intrinsic Raman signals of the probe molecules can be enhanced, and the SERS substrate can be better used for detecting and analyzing chemical substances.

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 relates to the field of ulcerative colitis, and discloses a multi-parameter monitoring system for evaluating the intestinal barrier function of ulcerative colitis, which is used for realizing the goal of automatically adjusting a treatment scheme according to a monitoring result by constructing a closed-loop feedback treatment system. According to the invention, by designing the nanoprobe of which the surface is modified with a specific binding element and combining with a swallowing capsule endoscopy, real-time imaging and molecular level dynamic tracking of the intestinal barrier are realized, genetically engineered bacteria are constructed to quantify intestinal permeability abnormality and local inflammation level, and an intestinal barrier function damage score and a disease progress prediction result are output; individual monitoring is achieved through transfer learning, ultrasonic elastography, optical coherence tomography, wearable equipment for measuring intestinal electric field impedance spectroscopy and other technologies are adopted, an intestinal wall mechanics-electrophysiology composite parameter set is obtained, and the evaluation accuracy is improved.

The invention discloses a heparin impurity detection method and system based on a Raman spectrum. Impurities in heparin production are efficiently detected through a Raman spectrum technology, and a production process is optimized. The method comprises the following steps: firstly, obtaining original Raman spectrum data of a heparin production sample, performing quality analysis, and optimizing data to obtain parameters; extracting spectral data features, and generating a three-dimensional feature matrix; detecting abnormal spectral characteristics by using a density clustering algorithm, identifying impurities, and obtaining impurity detection data; and the impurity change trend is analyzed in combination with production timestamp data, and production process parameters are optimized. According to the method, the impurities can be accurately detected in real time, the production process is dynamically optimized, and the quality and safety of a heparin product are ensured. The system realizes efficient quality control, and has a wide application prospect, especially in the field of biological pharmacy.

The invention discloses a Litopenaeus vannamei freshness nondestructive evaluation and prediction method based on a near infrared and Raman spectrum fusion machine learning strategy, and belongs to the field of food science, the method comprises the following steps: obtaining spectral data of a shrimp sample, the spectral data comprising near infrared spectral data and Raman spectral data; preprocessing the spectral data to obtain preprocessed spectral data; extracting a characteristic wavelength of the preprocessed spectral information based on a characteristic selection algorithm to obtain a multi-source spectral characteristic; fusing the preprocessed spectral data and the multi-source spectral features by adopting different levels of fusion strategies to obtain a fused spectrum; and performing nondestructive evaluation and prediction on the penaeus vannamei based on the fused spectrum to obtain the freshness of the penaeus vannamei.

The invention relates to the technical field of spectrum detection and optical imaging, and provides a time domain stimulated Raman scattering method and system.The method comprises the steps that pumping pulses or Stokes pulses are modulated, two stimulated Raman excitation pulse pairs with adjustable relative delay are introduced, a to-be-detected sample is excited to generate transient stimulated Raman scattering, and the time domain stimulated Raman scattering is obtained; transient stimulated Raman scattering signals can be obtained, acquisition of hyperspectral data covering hundreds of wave numbers can be achieved only through single excitation, and the method has the advantage of being high in time resolution. Meanwhile, the spectral resolution of the vibration level life broadening limit can be obtained through the method, and the main technical bottleneck faced by the traditional stimulated Raman scattering spectrum technology can be solved at a time. The method can be applied to the fields of Raman scattering imaging, spectrum detection, related Raman probes and the like.