6 results about "Raman imaging" patented technology

This invention belongs to the field of imaging analysis technology and discloses a rapid detection method for illegal food additives based on Raman imaging. The method includes: at the start of the detection task, performing sparse Raman imaging scanning on the boundary of the target region of the sample to be tested to obtain a target pixel spectrum set; constructing a reference spectrum set, and generating a detection wavenumber interval by analyzing the stability changes of the wavenumber highlight ratio in the reference spectrum set and the spatial thermal variation characteristics of different wavenumbers in the sample to be tested; obtaining an evidence heatmap by analyzing the similarity of the detection intensity of candidate gratings in the reference spectrum set and the target pixel spectrum set within the detection wavenumber interval; continuously updating the target pixel spectrum set by analyzing the correlation between the change attributes of evidence gain and the growth of connected regions in the evidence heatmap; and outputting suspicious regions by analyzing the convergence change attributes of the evidence coverage heat increment and the stability characteristics of the region to be matched. This invention generates spatial evidence and location results for suspicious regions more quickly.

This invention discloses a hardware-software co-processing method for real-time spectral signal processing in Raman microscopy, belonging to the field of spectral signal processing technology. The method includes: establishing strict temporal constraints between physical displacement and cloud inference, and initiating asynchronous communication using the physical gaps in stage movement; simultaneously, deploying a deep learning model (F2P) pre-trained with a self-supervised masking strategy in the cloud. This pre-trained model utilizes the structural correlation between the Raman broadband background and narrowband characteristic peaks to directly perform real-time forward inference on the input single-frame noisy spectrum, achieving millisecond-level high-fidelity reconstruction of a single frame without any complex parameter tuning or the need for a clean control spectrum. This invention completely eliminates the physical lag in multi-frame processing and the general technical bias of supervised learning algorithms, achieving true "synchronization between acquisition and display," and significantly improving the real-time processing performance and cross-device automated deployment capabilities of in-situ Raman imaging.

The application belongs to the technical field of biological probes, and relates to a PD-L1 guided specific targeted CTC detection combined with tumor boundary distinguishing biological probe and a preparation method thereof. The PD-L1 guided specific targeted CTC detection combined with tumor boundary distinguishing biological probe comprises a core, a covalent organic framework and a PD-L1 antibody, the core comprises a superparamagnetic nanoparticle, the covalent organic framework is coated on the surface of the core, and the PD-L1 antibody is combined on the surface of the covalent organic framework. The PD-L1 guided specific targeted CTC detection combined with tumor boundary distinguishing biological probe prepared by the application can be transported into the body across the membrane, smoothly enter a PD-L1 positive malignant tumor lesion, and perform Raman imaging to microscopically distinguish the tumor boundary.

This invention relates to a method, apparatus, device, and medium for single-cell feature analysis based on hyperspectral coherent Raman scattering imaging, used for single-cell feature analysis. The invention provides an apparatus for performing hyperspectral coherent Raman scattering imaging of samples at different Raman shifts; the method performs hyperspectral coherent Raman imaging on samples enriched with cells using medium A, acquiring images and Raman spectra of different substances at multiple Raman shifts; a multivariate curvature resolution method is used to obtain concentration distribution maps of different substances, and an image is obtained after removing medium A; deep learning technology is used to segment single-cell regions, extracting morphological and metabolic features of cells; simultaneously, a spectral phasor method combined with Lorentz fitting is used to extract morphological and metabolic features of single cells and their subcellular structures. This invention solves the problem of medium structure occlusion, resulting in richer extracted morphological and metabolic features of cells, facilitating more accurate differentiation of cell types.

This invention discloses a high-speed Raman imaging method based on physical dimensionality reduction and latent space restoration, comprising: S1, constructing a spectral physical encoder (SPE) module and training the SPE module; S2, constructing a spatial structure restoration (SSI) module and training the SSI module; S3, acquiring the original Raman hyperspectral image I based on sparse sampling. sparse S4. Use the trained SPE module to test I. sparse Compression is performed to output the latent vector map IZ. sparsei S5. Extract Z using the trained SSI module. sparsei Spatial context information in Z, and Z sparsei After repairing the zero-value region, the latent vector map II Z is output. dense_recon S6. Using the SPE decoder corresponding to the SPE module, the low-dimensional feature vector is nonlinearly mapped back to the high-dimensional space to obtain the initial reconstructed image Irecon_raw; S7. Linear calibration is performed on each pixel value in Irecon_raw to obtain the reconstructed hyperspectral image Irecon, completing high-speed Raman imaging. This invention can achieve high-speed, high-fidelity Raman imaging under highly sparse sampling conditions (e.g., 5%-10%).

The present application relates to a kind of PLS-based raman imaging method and the quantitative analysis method of polymer material, the imaging method includes (1) start raman testing instrument and calibration;(2) to the sample with single state change is carried out large spot raman test;(3) after using raman spectrum analysis technique to the raman spectrum data obtained in step (2) is preprocessed, using PLS is analyzed, and thus establish state-spectrum model;(4) using the wavelength range where characteristic peak is located carries out raman mapping scanning test;(5) using raman spectrum analysis technique to the mapping result obtained in step (4) is preprocessed, to make mapping scanning data and the model obtained in step (3) compatible;(6) according to the peak intensity of characteristic peak or the model to the mapping scanning data obtained in step (5) is plotted, to obtain distribution diagram.The present application patent broadens raman imaging application field, and it is important to obtain the sample topography and component distribution under microscale.