286 results about "Surface-enhanced Raman spectroscopy" patented technology

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 discloses a method and system for identifying perfluorooctanoic acid pollutants in tap water, and relates to the field of water environment new pollutant detection, and the method comprises the steps of SERS spectral signal collection, signal preprocessing, bimodal learning device construction and training verification, and concentration prediction result generation based on numerical value and image bimodal. The to-be-predicted data is put into the trained bimodal learning device for prediction and identification, and the concentration of the detection sample is output, so that batch rapid detection based on surface enhanced Raman spectroscopy and bimodal deep learning is realized, and high-precision identification and prediction based on small sample data are realized; the accurate detection result of the perfluorooctanoic acid pollutants in the tap water is obtained.

The invention relates to a data analysis technology, and discloses a spectral feature and artificial intelligence fused grain heavy metal detection method and system.The method comprises the steps that a near-infrared diffuse reflection spectrum, a laser-induced breakdown spectrum and a surface enhanced Raman spectrum of a grain sample are obtained, a matrix accumulation area thermodynamic diagram is constructed according to the near-infrared diffuse reflection spectrum, and the matrix accumulation area thermodynamic diagram is obtained; the method comprises the following steps: optimizing a laser-induced breakdown spectrum based on a thermodynamic diagram of a matrix accumulation area, carrying out dual-channel spatial-temporal feature extraction on a surface enhanced Raman spectrum and the optimized laser-induced breakdown spectrum to obtain a fusion feature vector, carrying out weighted fusion optimization on the fusion feature vector based on a signal-to-noise ratio of multispectral data to obtain an optimized fusion feature vector, and finally obtaining a fusion feature vector. And carrying out migration adaptation on a pre-trained grain detection model by using a pre-marked new grain sample, and carrying out heavy metal content identification on the grain sample by using the adaptive grain detection model according to the optimized fusion feature vector to obtain a heavy metal detection result. The precision of grain heavy metal detection can be improved.

The invention discloses a complex sample detection method based on surface enhanced Raman spectroscopy and application, and relates to the field of biomolecular detection.The method comprises the following steps that silver nanoparticles are prepared and serve as a surface enhanced Raman substrate; preparing a concentrated silver particle solution by using the silver nanoparticles; detecting surface enhanced Raman spectrum signals of the complex sample under excitation of different wavelengths; removing a background signal and noise from the obtained surface enhanced Raman spectrum signal; and averaging the surface enhanced Raman spectra obtained by processing each wavelength, and carrying out maximum and minimum normalization processing on the obtained average spectrum intensity. According to the method, richer and more comprehensive chemical information and component information of the complex samples can be obtained, more accurate recognition and classification of the complex samples can be achieved, the recognition accuracy is greatly improved, the method is convenient and rapid to operate, low in cost and high in generalization ability and practicability, and large-scale commercial application can be achieved.

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 belongs to the field of optics, and discloses a digital enhanced Raman spectroscopy method, which comprises the following steps of: establishing a nonlinear characteristic mapping relation between a spontaneous Raman spectrum signal and a surface enhanced Raman spectrum signal by using a deep learning method, and greatly enhancing the Raman signal in a numerical calculation mode. And a surface-enhanced Raman spectrum signal equivalent to a surface-enhanced Raman spectrum signal after the Raman-enhanced substrate is added can be obtained under the condition that the Raman-enhanced substrate is not needed. According to the digital enhanced Raman spectrum method, a Raman enhanced substrate does not need to be introduced, so that complex operation and professional experiment conditions required by sample preparation are effectively avoided, the time efficiency and the signal intensity of Raman spectrum measurement can be greatly improved, and the digital enhanced Raman spectrum method has important significance for quickly acquiring high-quality Raman spectrum signals.

The invention discloses a method for analyzing a complex system by using a surface enhanced Raman spectrum three-dimensional code and application of the method. The method specifically comprises the following steps: obtaining SERS sensing channels with different affinity characteristics by using an unmarked or functionally-marked SERS substrate; collecting an SERS spectrum data set by utilizing the constructed SERS sensing channel, and integrating the SERS spectrum data set into a'three-dimensional code 'image through data conversion and integration of three dimensions of Raman vibration band distribution, the SERS sensing channel and SERS intensity, so as to realize imaging of the molecular landscape; the feature information in the SERS three-dimensional code is screened and extracted through a convolutional neural network algorithm, and a mapping relation between the SERS three-dimensional code and a complex system label is established, so that a complex system classification / regression model is established, rapid qualitative or quantitative analysis of a complex system is realized, and the accuracy of the SERS three-dimensional code is improved. More applications can be expanded in the fields of molecular recognition, biological sample classification and identification, disease diagnosis, cell monitoring and the like.

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 provides a food additive identification method based on sparse features of a surface enhanced Raman spectrum. The method comprises the following steps: acquiring SERS spectrums of food additive samples of known types; according to the SERS spectrum, SERS spectrum sparse features are extracted, and the sparse features are optimized; dividing the optimized sparse features into a training set and a test set, establishing an identification model by using the training set, and verifying the identification accuracy of the identification model by using the test set; and performing type identification on the food additives in a to-be-detected sample by adopting the identification model. According to the method, the recognition model is established based on the SERS spectrum sparse features, the similar food additives can be accurately judged, the accuracy of additive type recognition is improved, and high-precision judgment of the food additives is achieved.

The invention discloses a surface-enhanced Raman scattering probe as well as a preparation method and application thereof. The surface enhanced Raman scattering probe comprises a conductive fiber, a bismuth-based nanosheet growing on the surface of the conductive fiber in situ and noble metal nanoparticles growing on the surface of the bismuth-based nanosheet in situ. The preparation method of the surface enhanced Raman scattering probe comprises the following steps: 1) preparing a conductive fiber grid loaded with a bismuth-based compound; 2) preparing a conductive fiber grid on which bismuth-based nanosheets grow; 3) preparing a conductive fiber grid on which precious metal nanoparticles grow; and 4) disassembling the conductive fiber grid on which the noble metal nanoparticles grow into single fibers. The surface-enhanced Raman scattering probe has the advantages of large specific surface area, high detection sensitivity, good biocompatibility, economy, environmental protection, simple preparation method and the like, and is suitable for manufacturing a surface-enhanced Raman scattering substrate for surface-enhanced Raman spectrum detection.

The invention provides a method for rapidly detecting various antibiotics in a water body. The preparation method comprises the following steps: firstly, preparing an enhanced substrate for surface enhanced Raman spectroscopy analysis, and enabling the substrate to have high SERS activity by a method for re-dispersing large aggregates through salt regulation and ultrasonic treatment; secondly, preparing a'glass-PDMS (Polydimethylsiloxane)-glass' micro-fluidic chip; the enhanced substrate and a sample to be detected are premixed and then pumped into the micro-fluidic chip for SERS (Surface Enhanced Raman Scattering) detection. According to the invention, ultralow-concentration detection of multiple antibiotics in multiple water bodies can be realized; the method has the advantages of no need of marking, short detection time and high sensitivity, and can be used for rapid and high-precision detection of various antibiotics in a water body.

The invention belongs to the technical field of surface enhanced Raman spectroscopy, and particularly relates to a high-activity surface enhanced Raman scattering substrate as well as a preparation method and application thereof. Comprising the following steps: by utilizing a three-phase liquid-liquid interface self-assembly method, adding dichloromethane into an AuNPs dispersion liquid, uniformly mixing, adding a halide salt solution, uniformly mixing to form a mixed solution, then adding normal hexane into the mixed solution, standing, performing interface self-assembly until a gold nano-film is formed on a liquid surface, fishing out the gold nano-film, and drying to obtain the gold nano-film. The high-activity surface enhanced Raman scattering substrate is obtained. According to the method, the halide salt solution is innovatively introduced in the self-assembly process of the AuNPs, so that the assembly arrangement mode of the AuNPs is remarkably changed, the AuNPs are arranged more tightly, local surface plasma resonance coupling is enhanced, more hot spot areas are generated in a unit area, and the sensitivity of the self-assembly nano array SERS substrate is effectively improved.

The invention discloses a system and a method for rapidly detecting trace active ingredients of a medicine, belongs to the technical field of medicine detection, and relates to cross application of surface enhanced Raman spectroscopy and artificial intelligence. The system comprises a sample preparation module, a micro-fluidic enrichment module, a Raman spectrum acquisition module, a self-adaptive spectrum analysis module, a multi-component identification module and an intelligent report generation module, and efficient enrichment and nanogram-level detection of drug molecules are realized through an enhanced surface Raman scattering micro-fluidic chip. Intelligent identification and accurate quantification of complex spectral characteristics are realized by adopting a deep learning algorithm and a self-adaptive weight calculation method, synchronous detection of 5-10 active pharmaceutical ingredients is supported, the detection sensitivity reaches nanogram / milliliter level, the quantification error is less than 5%, the detection time of the whole process is 5-10 minutes, and the detection accuracy is high. The sensitivity, the accuracy and the efficiency of medicine quality detection are obviously improved.

The invention discloses a Janus-MOF composite material as well as a preparation method and application thereof. The material is formed by heterogeneously integrating an adsorption module and a signal module through a lattice orientation growth method, wherein the adsorption module adopts a zirconium-oxygen cluster and a tetrafluoroterephthalic acid ligand to construct a molecular imprinting pore channel to specifically capture PFAS; the signal module comprises a spiropyrane modified ligand and an electrochemically deposited gold nano needle tip array, and the SERS detection limit reaches 0.05 ppt through photoresponse aperture regulation and three-mode (electromagnetic-chemical-mechanical) synergistic enhancement; the shell layer is doped with titanium dioxide quantum dots, PFAS can be degraded under ultraviolet irradiation, and the substrate can be recycled for more than or equal to 5 times. The invention further provides a surface enhanced Raman spectroscopy (SERS) and electrochemical impedance spectroscopy (EIS) dual-mode coupling technology, the cross validation detection error is less than or equal to 15%, the problems that the traditional method is poor in selectivity, low in sensitivity and incapable of in-situ regeneration are solved, and the method is suitable for rapid detection of trace PFAS in complex matrixes such as food, surface (undersurface) water and industrial wastewater.

The invention provides a method for detecting dichromate radicals based on surface enhanced Raman spectroscopy, and belongs to the technical field of analytical chemistry. Comprising the following steps: mixing a to-be-detected sample with a sulfydryl-containing compound solution, and adjusting to an acidic condition, so that dichromate Cr (VI) and the sulfydryl-containing compound are subjected to an oxidation-reduction reaction; constructing an SERS substrate, namely preparing a ZnO / Ag heterostructure substrate; mixing the suspension of the ZnO / Ag heterostructure substrate with a reaction system to obtain a mixed solution, so that the ZnO / Ag heterostructure substrate adsorbs a sulfydryl-containing compound and enhances a Raman signal; dropwise adding the mixed solution to the surface of a solid carrier, acquiring a Raman spectrum signal by using a Raman spectrometer, and quantifying the Cr (VI) concentration through the change of signal intensity. The method realizes rapid and high-sensitivity detection of Cr (VI), has strong selectivity and anti-interference capability, and is suitable for detection in the fields of environmental monitoring, food safety, pharmaceutical analysis and the like.

The invention belongs to the technical field of surface enhanced Raman spectroscopy, and relates to an AFB1 detection method based on SERS mapping and data fusion. The method comprises the following steps: firstly, acquiring an SERS (Surface Enhanced Raman Scattering) spectrum and a mapping image of an AFB1 standard sample solution; carrying out pretreatment on the SERS spectrum; then, carrying out feature variable extraction on the preprocessed SERS spectral data by using a variable screening algorithm, and obtaining RGB values from corresponding mapping images based on coordinates of screened feature variables; and selecting a variable combination with the lowest interactive verification root mean square error, fusing the variable combination with the RGB value of the mapping image at the feature variable, and constructing a prediction model, so that the rapid detection of the AFB1 concentration of the unknown sample can be realized. Compared with a traditional detection technology, the stability and precision of a detection result are remarkably improved through a spectrum-image multi-dimensional data fusion strategy, and the method has good application prospects in the technical fields of food safety, environment monitoring and the like.

The invention relates to the technical field of gas detection, and discloses a sulfur dioxide detection method and application thereof. The sulfur dioxide detection method comprises the following steps: S1, taking a sulfur dioxide standard sample and a reaction solution; s2, establishing a sulfur dioxide colorimetric standard curve and a surface enhanced Raman spectrum standard curve; s3, enriching sulfur dioxide gas in a to-be-detected sample in the reaction liquid by using a purge-trap method to obtain an absorption liquid; and S4, taking the absorption liquid, and respectively calculating the concentration of sulfur dioxide in the absorption liquid by utilizing the colorimetric standard curve and the surface enhanced Raman spectrum standard curve in the step S2 to obtain the content of sulfur dioxide in the sample to be detected. In the step S1, the reaction liquid comprises starch, an iodine compound and 4-mercaptopyridine. According to the detection method based on bimodal analysis of the colorimetric method and the surface enhanced Raman spectroscopy, provided by the invention, self-correction of a detection result can be realized, the accuracy of the detection result is improved, and the detection efficiency is improved.

The invention provides a method for detecting ultra-trace molecules to be detected and application. According to the method, a metal nano surface enhanced Raman scattering detection chip is installed in an in-situ electrochemical cell, a working electrode and a counter electrode are arranged in the in-situ electrochemical cell, and the metal nano surface enhanced Raman scattering detection chip and the working electrode are in a non-contact state; adding a to-be-detected molecular solution and an electrolyte solution into the in-situ electrochemical cell, applying a working potential to the working electrode, then taking out the metal nano surface enhanced Raman scattering detection chip, and carrying out surface enhanced Raman spectrum detection on the metal nano surface enhanced Raman scattering detection chip to obtain the metal nano surface enhanced Raman scattering detection chip. And obtaining the Raman spectrum of the molecule to be detected. The method is good in repeatability and high in detection efficiency, a higher positive spectrum count can be obtained on the premise that the total spectrum amount is detected in a limited mode, and the detection result is reliable. The invention further provides application of the detection method for the ultra-trace molecules to be detected.

The invention discloses a hydroxyl polycyclic aromatic hydrocarbon Raman spectrum detection method based on a dynamic weighted voting fusion network, relates to the technical field of hydroxyl polycyclic aromatic hydrocarbon detection, and provides an effective method which is rapid, accurate and free of chromatographic separation. Spectral information of OH-PAHs is obtained by using surface enhanced Raman spectroscopy SERS, and a Raman spectrum is obtained through density functional theory DFT calculation. A dynamic weighted voting fusion network DWVF-Net model is constructed, wherein the model comprises two core parts of cross-modal attention fusion (CMAF) and dynamic weighted voting (DWV). The CMAF dynamically weights key areas of SERS and DFT Raman spectrums through cosine similarity to realize feature fusion; and the DWV adaptively adjusts the weight of the model according to the real-time performance of the model, and optimizes an integration decision. Through the synergistic effect of CMAF and DWV, complementary information of SERS and DFT Raman spectrums is effectively fused, and the adaptability advantage of a dynamic weighting mechanism in the aspect of processing a complex data environment is verified.

The invention relates to the technical field of optical detection, in particular to a nano material detection method based on surface enhanced Raman spectroscopy, which comprises the following steps: acquiring spectral data at different acquisition moments based on a nano material, generating a spectral curve, and preprocessing; analyzing the preprocessed spectrum curve to obtain an initial shape feature as a crest factor, and constructing a particle set; calculating a fluctuation influence factor through the particle set, obtaining an overlapping parameter of the particle according to the preprocessed spectrum curve, and combining the fluctuation influence factor with the overlapping parameter to determine a final shape feature of the particle; carrying out clustering analysis on the ultimate shape features of the particles to obtain a plurality of clusters, calculating the feature change degree of each cluster, carrying out screening and redundant peak elimination according to the feature change degree, and correcting the spectrum curve after elimination; quantitative analysis is carried out based on the corrected spectrum curve, so that the method has higher adaptability to data noise, redundant information and external environment change, and the result has higher robustness.

A method for preparing a dual-mode immune structure based on a surface-enhanced Raman substrate and an up-conversion luminescent probe, and an application thereof. The dual-mode immune structure based on a surface-enhanced Raman substrate and an up-conversion luminescent probe comprises a copper / black phosphorus / silver nanoflower composite immunosubstrate and a NaGdF4:Yb3+ / Er3+ up-conversion nanoparticle immunoprobe, wherein the copper / black phosphorus / silver nanoflower composite immunosubstrate is prepared using an electrochemical reduction method, and the NaGdF4:Yb3+ / Er3+ up-conversion nanoparticle immunoprobe is prepared using a hydrothermal method. During the application to cancer detection, assembling an immunosubstrate and an immunoprobe realizes the combination of up-conversion luminescence and surface-enhanced Raman spectroscopy technology for realizing dual-mode detection of cancer biomarkers. Thus, simple operation and high detection sensitivity are achieved, thereby facilitating efficient and precise clinical screening and identification of cancer biomarkers.

The invention discloses an intelligent pesticide detection method and system for traceability, and the method comprises the steps: obtaining surface enhanced Raman spectrum data and hyperspectral data of an agricultural product sample containing a pesticide, screening spectrum characteristic variables from the two types of preprocessed spectrum data, and selecting an optimal spectrum characteristic combination; a pesticide detection model is constructed, and the trained pesticide detection model is used for detecting and recognizing the types of pesticide residues of agricultural products; according to the agricultural product representation information, the agricultural product geographic information and the pesticide residue type information, agricultural product residue detection data is obtained, a multi-dimensional database is constructed through cloud service, cross analysis is performed on the agricultural product residue detection data, and traceability information visualization is realized. According to the invention, pesticide residues in agricultural products are rapidly and accurately detected, multi-dimensional cross analysis is carried out on pesticide residue detection data, the traceability information visualization of pesticide residue detection is realized, and the pesticide detection and traceability efficiency is improved.

The invention discloses a method for online detection of hydrogen peroxide concentration based on surface enhanced Raman spectroscopy. The method comprises the following steps: preparing an Ag-COF-COOH substrate; a 4-MBA solution is mixed with the Ag-COF-COOH substrate, and a mixed detection solution is obtained; introducing hydrogen peroxide into the mixed detection solution to obtain a reaction mixed solution; and performing real-time Raman spectrum acquisition on the reaction mixed solution by using a Raman spectrometer to obtain a Raman characteristic peak. According to the invention, the problems of weak Raman signal of hydrogen peroxide, complexity of a traditional detection method and the like are solved through the surface enhanced Raman spectrum technology, the concentration change of hydrogen peroxide can be accurately monitored in real time, the detection efficiency is improved, and high sensitivity and high selectivity are especially suitable for application scenes with high requirements on hydrogen peroxide concentration.

The invention provides a silver substrate and a preparation method and application thereof.The preparation method comprises the steps that firstly, dispersed silver seeds are introduced to the surface of an aluminum-foil paper substrate through the reaction of silver nitrate and excessive alkali, and in-situ nucleation sites are provided for follow-up silver growth; and then, through in-situ reduction reaction of hydroquinone and silver nitrate, silver controllably grows on the basis of the silver seeds to form a structure rich in hot spots. Conventional raw materials such as aluminum foil, silver nitrate, alkali and hydroquinone are adopted in the whole process, complex equipment is not needed, and therefore the process is simple and low in cost; meanwhile, a step-by-step regulation and control mechanism ensures that a large number of'hot spots' formed by nano-structure gaps and tips are formed on the surface of the silver substrate, and the key structure can obviously enhance Raman signals, so that the high-sensitivity detection requirement is met. In conclusion, the method has the advantages of preparation convenience, cost and high performance of the product, a practical substrate which is easy to popularize and high in adaptability is provided for the field of surface enhanced Raman spectrum detection, and the method has remarkable application value.

The invention discloses a method for rapidly detecting a liquid phase diffusion coefficient of an analyte based on surface enhanced Raman spectroscopy of a microchannel, which ingeniously combines the characteristics of nondestructive, non-contact, label-free, rapid and sensitive laser confocal Raman spectroscopy with the characteristics of trace, high-throughput analysis and self-injection of a microfluidic technology. Wide application prospects are realized.

The invention discloses an immunodetection kit based on surface enhanced Raman spectroscopy as well as a preparation method and application of the immunodetection kit. The kit comprises a surface enhanced Raman scattering active substrate, wherein the active substrate comprises cellulose paper loaded with first metal nanoparticles, carboxyl-sulfydryl polyethylene glycol and a capture antibody, wherein the carboxyl-sulfydryl polyethylene glycol and the capture antibody are used for modifying the surfaces of the first metal nanoparticles; the surface enhanced Raman scattering immunodetection probe comprises a second metal nanoparticle, a Raman signal molecule, methoxy-sulfydryl polyethylene glycol and a detection antibody, wherein the Raman signal molecule, the methoxy-sulfydryl polyethylene glycol and the detection antibody are used for modifying the surface of the second metal nanoparticle. The immunodetection kit disclosed by the invention is high in sensitivity, rapid, accurate and low in false positive.

The invention discloses a prostate cancer bone metastasis identification method and system based on enhanced Raman spectroscopy, and the method comprises the steps: carrying out the preprocessing of one-dimensional surface enhanced Raman spectroscopy data obtained in advance, generating a multi-scale time-frequency characteristic pattern through continuous wavelet transform, and mapping an original signal to a time-frequency domain; performing frequency band division and conquer on a multi-scale time-frequency characteristic graph obtained after continuous wavelet transform, decomposing the multi-scale time-frequency characteristic graph into high-frequency, intermediate-frequency and low-frequency sub-bands, and extracting characteristics through a directional convolution kernel; segmenting the extracted feature map into block-level representations and injecting learnable position codes; generating complementary query and key vectors from the partitioned features through dual-path projection, and realizing local feature enhancement in combination with a local window mask and a dynamic gating parameter; and outputting a classification result through a Transform encoder and a full connection layer. The invention provides an efficient solution for high-sensitivity and real-time detection of prostatic cancer osseous metastasis, and can be widely applied to the field of biomedical spectrum analysis.

The invention provides a pathogenic bacterium distinguishing method and system based on surface enhanced Raman spectroscopy and AI assistance, and relates to the technical field of pathogenic bacterium analysis and detection.The pathogenic bacterium distinguishing method comprises the steps that original Raman spectrum data of a pathogenic bacterium sample capable of collecting Raman spectrum signals is obtained; preprocessing and dimensionality reduction processing are carried out on the original Raman spectrum data, characteristic peaks used for classification are screened out, and Raman shifts of the characteristic peaks comprise any one or more of 832 cm <-1 >, 1001 cm <-1 >, 1122 cm <-1 >, 1165 cm <-1 >, 1293 cm <-1 >, 1353 cm <-1 > and 1580 cm <-1 >; and inputting any one or more Raman shifts in the characteristic peaks into a pre-trained one-dimensional convolutional neural network model, analyzing the Raman spectrums of different pathogenic bacteria by the model, and outputting a classification result of the pathogenic bacteria. According to the method, the surface enhanced Raman spectroscopy technology is utilized, the AI auxiliary analysis method based on the convolutional neural network is introduced, different pathogenic bacteria are distinguished, and the method has the advantages of being easy and convenient to operate, short in consumed time, high in sensitivity and high in accuracy.

A diagnostic platform for detecting and analyzing brain tumors includes a portable fiber Surface Enhanced Raman spectroscopy (SERS) module, a removable fiber probe for intraoperative use, disposable nanoplasmonic cartridges for individual sample loading, and an updatable spectral library collected from healthy and diseased samples. The platform is designed to analyze blood and tissue signatures and employ artificial intelligence (Al) models to classify SERS spectra based on an updatable proteomics and SERS spectral library encompassing distinct brain tumor types. The platform is further configured to utilize artificial intelligence (Al) algorithms for the analysis and classification of the recorded SERS spectra.

The invention provides a Raman spectrum noise reduction method and device, computer equipment and a computer readable storage medium, and the method comprises the steps: obtaining Raman data which comprises a surface-enhanced Raman spectrum set; preprocessing the Raman data: removing the background of the Raman data, and performing spectrum baseline correction and standardization based on an asymmetric least square method to obtain preprocessed Raman data; and performing down-sampling and up-sampling on the preprocessed Raman data, performing feature extraction on the Raman data based on one-dimensional convolution in the down-sampling process to obtain a feature spectrum, and reconstructing the feature spectrum in the up-sampling process to obtain a noise-reduced spectrum.