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

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 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 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 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.

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 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 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.

A method of surface-enhanced Raman spectroscopy includes positioning an analyte in a field-enhancing region of a nanophotonic element, irradiating the analyte with a continuous wave excitation laser operating at a user-defined frequency in a range from near infrared (NIR) to short-wave infrared (SWIR) to generate a signal to cause photon scattering from the analyte and detecting the photons with one or more photodetectors in the SWIR. A surface-enhanced Raman spectroscopy system includes a laser operating at a frequency having a range from near-infrared (NIR) to short-wave infrared (SWIR) that emits light in a spectrum between 700 and 2500 nanometers, a nanophotonic element to receive the light from the laser, and one or more SWIR detector to detect photons scattered by an analyte on the nanophotonic element.

The present application belongs to the technical field of data analysis, and particularly relates to a high-throughput biosensing system for early warning of algal blooms and pathogens, which comprises multiple sampling units, a microfluidic chip with a multi-channel structure, and a high-throughput surface-enhanced Raman detection unit with an integrated metal nanostructure surface, and obtains the original surface-enhanced Raman spectrum of the water sample through a Raman spectrum acquisition mechanism. The system uses compressed spectrum sampling and sparse reconstruction methods to reduce the data volume and maintain the integrity of the spectrum information, constructs a pathogen Raman fingerprint dictionary through a dictionary learning method, and realizes the identification of different algal metabolites, algal toxins and pathogen-related molecular components and the concentration interval judgment thereof in combination with a sparse representation classification mechanism. Based on the type and concentration interval of the pathogens, the system further generates algal bloom risk warning and pathogen risk warning, and realizes early discovery of abnormal changes in the water body. The present application has significant advantages in stability, sensitivity, throughput and real-time performance.

The present invention relates to the technical field of security features comprising markers by means of surface enhanced Raman spectroscopy (SERS) and the detection thereof. Furthermore, the present invention relates to the field of UV-Vis curable security inks comprising SERS tags, said inks being used for printing security features on substrates, in particular security documents or articles, wherein the ink comprises i) one or more surface enhanced Raman spectroscopy (SERS) tags, ii) one or more vinyl ether-containing compounds, iii) one or more cationic radiation-curable compounds different from the one or more vinyl ether-containing compounds of ii), and iv) one or more cationic photoinitiators that are hexafluorophosphates.

The invention discloses a spiral chiral gold nanorod as well as a preparation method and application thereof, and the method comprises the following steps: (1) sequentially adding hexadecyl trimethyl ammonium bromide, cysteine and p-aminothiophenol into an anisotropic gold nanorod solution, uniformly mixing, and adsorbing; and (2) silver nitrate, chloroauric acid and ascorbic acid are added for a reaction, centrifugal dispersion is performed after the reaction is finished, and the spiral chiral gold nanorod is obtained. Dispersing the spiral chiral gold nanorod in an aqueous solution, adding phenylalanine enantiomer, dropwise adding on a clean glass slide after incubation is finished, and performing surface enhanced Raman spectroscopy test by using a Raman microscope after air drying. According to the invention, the broadband coverage of chiral response from visible light to near ultraviolet region is realized by accurately regulating and controlling the arrangement of interface molecules and the morphology of the nano structure.

The application discloses a new coronavirus strain XBB.1.5 identification method based on a surface-enhanced Raman spectrum chip, and belongs to the technical field of new coronavirus detection. The application identifies the new coronavirus strain XBB.1.5 through the detection method, avoids complex and expensive gene sequencing, can accurately determine the new coronavirus subtype, has low cost, high accuracy, short time and convenient operation.

The invention belongs to the technical field of antibiotic residue detection, and particularly relates to a rapid detection method for quinolone antibiotics in dairy products. The preparation method comprises the following steps: preparing gold nanoparticles as gold seeds by using a sodium citrate reduction method, epitaxially growing an ultrathin Cr-MIL-101-NH2 shell layer by using a layer-by-layer self-assembly method, and constructing an Au-coated Cr-MIL-101-NH2 NPs core-shell structure; a solid phase microextraction-surface enhanced Raman spectroscopy (SPME-SERS) combined method is established on the basis of the material, a shell layer is used for efficiently extracting and selectively enriching a target object to a gold nanoparticle local plasma resonance area, strong SERS signal enhancement is achieved, and the shell layer guarantees that the gold nanoparticles are stable in performance in a complex matrix at the same time. Methodology verification proves that the method is wide in linear range, good in precision, high in recovery rate, low in detection limit, convenient in pretreatment and suitable for rapid high-throughput detection of antibiotic residues in food.

The invention provides a water antibiotic pollution detection method and system based on spectral characteristics, and belongs to the technical field of water environment monitoring, analysis and detection.The method comprises the steps that firstly, a surface enhanced Raman spectrogram and oxidation-reduction potential time sequence data of a to-be-detected water sample are synchronously obtained; then, extracting a ratio of the characteristic peak intensity of the target antibiotic from the spectrogram to generate a first characteristic set, and meanwhile, performing wavelet packet decomposition on the potential data and calculating an energy entropy to construct a second characteristic set; then, splicing the two feature sets, and screening out a target feature subset highly related to the concentration through a mutual information maximization algorithm; finally, a support vector projection regression model trained based on a structural risk minimization mechanism is utilized, and accurate detection of the concentration of the target antibiotics in the water sample to be detected is achieved through kernel function mapping and hyperplane projection operation. According to the method, the anti-interference capability and the quantitative precision of antibiotic detection under the complex water body background are improved.

The invention relates to the technical field of biology, in particular to an SERS probe for Mfn2 detection, a preparation method of the SERS probe, an SERS sensor and application of the SERS sensor. Based on a surface enhanced Raman spectroscopy (SERS) technology, a gold nanoparticle SERS probe coated with an antibody 1 and a silver nanoparticle SERS probe coated with an antibody 2 are constructed, a sandwich structure is formed through combination of the antibody 1, an antigen and the antibody 2, the structure effectively improves SERS hot spot distribution and Mfn2 detection sensitivity, and traditional measurement steps are simplified. The constructed biologically coded SERS probe can be used for measuring Mfn2 with different concentrations. Based on the prepared SERS sensor, the dynamic measurement of the expression of the single living cell Mfn2 in the process of electrical stimulation of dental pulp stem cell odontogenic differentiation is realized, and the SERS sensor has wide clinical application prospect and transformation value in the aspects of measurement of protein molecules in a single cell organelle and diagnosis of Mfn2 related diseases.

The invention relates to a fabrication method for the substrate in which the sample to be tested in surface enhanced Raman spectroscopy is placed.

One aspect of the present disclosure provides a system for collecting a neutral gaseous fluid sample desorbed from a solid or liquid sample of a substance of interest, the system comprising: a sample collection substrate having an activated surface suitable for surface enhanced Raman spectroscopy (SERS), the activation surface is configured for collecting a sample of the substance of interest; a substrate receiving region in which the sample collection substrate is disposed; a SERS analyzer configured to perform SERS on the surface of the substrate in the substrate receiving area; and a desorber configured to desorb the substance of interest from the sample collection substrate to provide a neutral gaseous fluid sample suitable for further analysis.

The invention discloses an SERS (Surface Enhanced Raman Scattering) to-be-detected object drying system and a quick-drying SERS wafer. The SERS to-be-detected object drying system comprises a cavity, an SERS wafer and a drying device. The SERS wafer can be selectively located inside or outside the cavity, the SERS wafer comprises a base material, the base material comprises a first surface and a second surface which are located on the two opposite sides, the first surface defines a to-be-detected object area, and the to-be-detected object area is provided with a rough metal surface. The drying device is arranged in the cavity. The quick-drying SERS wafer comprises a base material and a drying unit. The base material comprises a first surface and a second surface which are positioned on two opposite sides, the first surface defines a to-be-detected object area, the to-be-detected object area is provided with a rough metal surface, and the drying unit is arranged at a position outside the to-be-detected object area of the base material.

The application discloses a Raman deep penetration imaging system based on a safe laser irradiation dose, which comprises a light source, a light spot amplifier, a sample table, a Raman optical fiber probe, a data processing module and a surface enhanced Raman spectrum probe contrast agent, the Raman optical fiber probe and the light spot amplifier are located on the two sides of the sample table, the Raman optical fiber probe is located on the side of the sample to be measured, the Raman optical fiber probe vertically faces the sample table, the Raman optical fiber probe is electrically connected with the data processing module, the outlet of the light source faces the light spot amplifier, the light spot amplifier is configured to expand the light spot emitted by the light source, the light spot output by the light spot amplifier vertically faces the sample table, and the surface enhanced Raman spectrum probe contrast agent comprises a surface enhanced Raman probe. Through the combination of the surface enhanced Raman spectrum probe contrast agent and the transmission type Raman detection device, accurate positioning and stable imaging of a tumor site are realized.

The invention is applicable to the technical field of nano material preparation, and provides a preparation method and application of a Mn < 2 + >-MoS2-Ag surface enhanced Raman spectrum substrate, the method comprises the following steps: synthesizing a Mn < 2 + >-doped MoS2 nano material through a hydrothermal method, and loading Ag nano particles on the surface of the Mn < 2 + >-doped MoS2 nano material by using an ultrasonic-assisted chemical reduction method to obtain the Mn < 2 + >-MoS2-Ag surface enhanced Raman spectrum substrate. The substrate combines a chemical enhancement effect brought by Mn < 2 + > doping and an electromagnetic enhancement effect provided by Ag nanoparticles to form a synergistic enhancement system. When being applied to SERS (Surface Enhanced Raman Scattering) detection of trace ofloxacin in a water environment, the sensor shows excellent sensitivity and specificity, and the detection limit can reach 10 <-11 > M. The method is simple in preparation process and mild in condition, the prepared substrate is good in stability and remarkable in enhancement effect, and an effective tool is provided for rapid and high-sensitivity detection of antibiotic pollutants in the environment.

The invention discloses a veterinary drug residue detection method and system based on a spectrum technology, and relates to the technical field of spectrum detection.The veterinary drug residue detection method comprises the steps that Raman spectrum data of a to-be-detected sample is collected, wavelength partition scanning is conducted, the position and intensity of a characteristic peak are obtained, and a detection priority is set according to the intensity change rate of the characteristic peak to generate a time sequence detection instruction; carrying out surface enhanced Raman spectrum collection at the position of the characteristic peak and calculating an enhancement factor; extracting the time-varying characteristics of the enhancement factors to judge the type and content of veterinary drug residues, and generating a residue distribution diagram; and calculating a velocity field and a concentration field of residue diffusion based on the residue distribution diagram, setting a dynamic monitoring point array tracking migration path along the direction of the velocity field, predicting a pollution diffusion range in combination with the concentration field, and outputting early warning information. The method can rapidly and accurately detect and early warn veterinary drug residue pollution, and has important practical application value.

The invention relates to the technical field of cosmetic component detection and analysis, and discloses a method for rapidly recognizing arbutin in cosmetics based on surface enhanced Raman spectroscopy, AgNPs sol is prepared by an ascorbic acid oxidation-reduction method, and an SERS sensing interface is constructed through aspartic acid electro-polymerization modification; for different dosage forms of cosmetics, a to-be-detected solution is obtained through methanol extraction and centrifugal filtration purification, the to-be-detected solution and AgNPs sol are mixed and adsorbed, then a Raman spectrum is collected, pretreatment is conducted through combination of multivariate scatter correction and standard orthogonal transformation, characteristic wavelengths are screened through a genetic algorithm-combined interval partial least square method, and detection is achieved through combination of qualitative and quantitative models. Through cooperation of sensing interface specific modification and a data processing technology, the arbutin signal recognition capability is enhanced, matrix interference is eliminated, rapid and accurate detection of arbutin in cosmetics is realized, and the effects of high sensitivity, high specificity and simple and convenient operation are achieved.

The invention provides a nitrite quantitative detection method based on surface enhanced Raman spectroscopy, relates to the technical field of surface enhanced Raman spectroscopy, and constructs a self-correcting SERS substrate through a three-phase liquid-liquid interface co-assembly technology. In the experiment process, a sample to be detected is mixed with a thiobarbituric acid solution, the mixture is adjusted to be acidic, NO2 <-> and thiobarbituric acid are subjected to an S-nitrosation reaction, and S-nitroso mercaptan is generated. And then, immersing the self-correcting SERS substrate into a reaction solution, so that the substrate adsorbs the generated S-nitroso mercaptan, and further, a Raman signal is enhanced. After soaking is completed, the substrate is taken out and dried, finally, a Raman signal is collected through a Raman spectrometer, and the concentration of NO2 <-> is accurately analyzed through the change of the signal intensity. According to the technical scheme, rapid and high-sensitivity detection of NO2 <-> is achieved, good selectivity and anti-interference capacity are achieved, and the method is widely suitable for detection requirements of multiple fields such as environment monitoring and food safety.

The invention relates to the technical field of Raman spectrum analysis and machine learning, in particular to a serum Raman spectrum noninvasive pathological detection device. Comprising an acquisition module for acquiring a surface enhanced Raman spectrum of a serum sample to be detected to obtain spectral data to be analyzed; the screening module is used for obtaining qualified spectral data based on a wavelet transform signal-to-noise ratio energy criterion; the storage module is used for storing the data to a database; the calculation module is used for reading qualified spectral data to obtain a characteristic signal; and the reasoning module is used for inputting the characteristic signal into a pre-trained model, outputting a reasoning process and a pathological judgment result, positioning a key interval and a characteristic peak corresponding to the result based on interpretability analysis, and outputting corresponding biomolecule attribution information. Therefore, the problems that in the prior art, spectrum quality is difficult to control, complex spectrums are difficult to analyze, and a model decision-making mechanism and interpretability are insufficient are solved, potential cancer marker characteristics in the spectrums are effectively mined, and efficient and credible early cancer accurate diagnosis is assisted to be achieved.

The invention discloses a surface enhanced Raman spectrum water pollution detection method and device based on machine learning, and the method comprises the steps: obtaining surface enhanced Raman spectrum data of a water sample to be detected, and preprocessing the surface enhanced Raman spectrum data of the water sample to be detected; determining a surface enhanced Raman spectrum feature vector of the polycyclic aromatic hydrocarbon from the preprocessed surface enhanced Raman spectrum data through a local average algorithm; determining a detection result of the to-be-detected water sample through a support vector machine model based on the feature vector; through multi-level feature fusion design, local average anti-noise extraction, a standardized preprocessing assembly line and a data-driven support vector machine optimization mechanism, the method systematically solves the problems that the surface enhanced Raman spectrum is insufficient in precision and poor in stability and cannot identify core pain points of specific pollutant types in complex water pollutant detection; and a high-sensitivity and low-cost automatic solution is provided for environment monitoring.

The multi-dimensional plasmonic coupling system based on Ag@PVP and Au@Ag NCs for surface-enhanced Raman spectroscopy (SERS) detection of gentamicin relates to the field of analytical detection, and adopts hydrothermal synthesis to prepare Ag@PVP core-shell nanoparticles and Au@Ag core-shell cubes (Au@Ag NCs). Ag@PVP is induced to agglomerate by gentamicin, and then Au@Ag NCs are introduced to change the electromagnetic field of the nanoparticle agglomeration, so as to change the Raman characteristic peaks of gentamicin and distinguish it from its homologues. Under the optimized experimental conditions, the linear detection range of gentamicin is 10 ‑5 ~10 ‑9 M, the detection limit is 6.23 × 10 ‑10 M, and the relative standard deviation of SERS intensity is less than 10%. The method has the advantages of simple operation, high accuracy and sensitivity, and does not need complex operation technology, and meets the requirements of large quantities and rapid analysis and detection.

The invention discloses a multi-mode pathogen identification method, device, equipment and medium, and relates to the technical field of microfluidics, the method comprises the following steps: obtaining pathogen sample data corresponding to each outlet in a plurality of outlets of a microfluidic chip; the pathogen sample data comprises Raman scattering spectrum information and outlet distribution information; the Raman scattering spectrum information is obtained by performing surface enhanced Raman spectrum processing on pathogen group data, and the pathogen group data is output after mixing a sample to be detected and Raman scattering enhanced particles in the micro-fluidic chip and performing physical separation based on particle size difference; the particle sizes of the pathogen population data output by the outlets are different; and inputting pathogen sample data into the trained multi-modal deep learning model to obtain a pathogen type prediction result. According to the invention, the identification accuracy of pathogen types in mixed samples or complex environments is improved.

The application discloses a method, system and device for detecting Tau protein based on machine learning of surface enhanced Raman spectroscopy, and the method comprises the following steps: S1, constructing a SERS signal probe with minimum initial Raman signal; S2, mixing a sample to be detected with the SERS signal probe, and collecting SERS spectra of the mixed system by using a Raman spectrometer; S3, pre-processing the collected SERS spectra, wherein the pre-processing comprises baseline correction, feature peak identification and minimum-maximum normalization processing in sequence, so as to obtain standardized spectral data; and S4, inputting the standardized spectral data into a machine learning regression model which is trained in advance, and automatically outputting a concentration value of Tau protein in the sample to be detected based on the input spectral characteristics by the machine learning regression model. The application can quantitatively detect phosphorylated Tau protein quickly, accurately and with low detection limit under the premise of keeping high specificity, and has high clinical conversion potential.

The invention relates to the technical field of food quality detection, in particular to a green tea variety and producing area identification method based on multi-solvent fusion surface enhanced Raman spectroscopy, which comprises the following steps: crushing and sieving a green tea sample, adding ultrapure water, absolute ethyl alcohol or hydrochloric acid solution, oscillating and extracting, centrifuging, and reserving supernate; mixing the supernate with the silver nanoparticle suspension, performing ultrasonic treatment, dripping on a silicon wafer, drying, acquiring spectrums at three positions by adopting a laser confocal microscopic Raman spectrometer, and performing baseline correction to obtain an average spectrum, thereby obtaining a surface enhanced Raman scattering spectrum; carrying out data fusion on the surface enhanced Raman scattering spectrum through matrix splicing, CARS, PCA, t-SNE and UMAP; according to the method, the fusion spectrum is divided into a training set and a test set, an identification model is constructed by adopting SVM, KNN and RF, green tea variety classification and origin traceability are realized, spectrum data are processed from the aspects of complete information retention, feature screening, linear dimensionality reduction, local structure retention and multi-scale structure retention, and the classification accuracy reaches 98.21%.