107 results about "Mid infrared spectroscopy" patented technology

The invention relates to a multi-mode-based pit mud quality detection method, device, equipment and medium, and the method comprises the following steps: acquiring near infrared spectrum data NIR and mid-infrared spectrum data MIR of a pit mud sample to obtain original spectrum data of the pit mud sample, and preprocessing the original spectrum data to obtain standard spectrum data; dynamically adjusting a data fusion mode of NIR and MIR by adopting a multivariate statistical analysis algorithm based on the standard spectral data, and further constructing an NIR-MIR fusion model; carrying out spectral analysis on the pit mud sample based on an NIR-MIR fusion model so as to extract key component information in the pit mud sample; and based on the key component information, according to a preset multi-dimensional evaluation strategy, carrying out comprehensive evaluation on the pit mud sample, and generating a quality evaluation result. The pit mud detection device has the effect of improving pit mud detection efficiency.

The invention relates to the technical field of component analysis, in particular to an intermediate infrared spectrometer sensor verification system which comprises the following steps: acquiring spatial distribution information of a target sample through an automatic sampling module, and generating a corresponding first feature group based on the spatial distribution information; adjusting an emission wave band and a modulation strategy corresponding to a mid-infrared light source, converting mid-infrared photons into visible light signals, and generating a corresponding second feature group; performing down-sampling processing on the original spectral data to generate a corresponding third feature group; inputting the sparse spectral coefficient into a pre-trained deep learning reconstruction model, and dynamically correcting the reconstruction process in combination with an environment temperature compensation parameter to generate reconstructed spectral data corresponding to high resolution; and analyzing the reconstructed spectrum data, matching a preset substance spectrum database, extracting a characteristic absorption peak position and an intensity ratio of the target substance, and generating a corresponding final analysis result. According to the invention, the intelligence of the sensor verification system can be improved.

The invention discloses a rice producing area identification method based on dynamic characteristic screening and a weighted integration model. Belongs to the technical field of agricultural product traceability. The prominent problems of large accuracy fluctuation, poor model mobility, low operation efficiency and the like of the existing mid-infrared spectrum identification method are solved. The method comprises the following steps: collecting a rice flour sample spectrum by using a mid-infrared spectrometer equipped with an ATR accessory, and carrying out spectrum standardization treatment; performing noise reduction and correction on the spectrum after standardization processing by using a wavelet denoising and improved SNV correction method to obtain a noise-reduced and corrected spectrum; performing dynamic feature screening on the noise reduction correction spectrum to obtain a low-redundancy and high-independence optimized feature subset; integrating a plurality of classification models into a multi-stage mixed classification model in a dynamic weight set manner by optimizing the feature subsets; and identifying the producing area of the rice flour sample by adopting a multi-stage mixed classification model.

Methods and systems for monitoring and / or controlling production units used at various stages of biological product development and manufacturing are presented herein. In particular, in certain embodiments, the biological production monitoring and control techniques described herein utilize a mid-infrared (mid-IR) analyzer that is capable of obtaining mid-infrared spectral data from an aqueous sample in substantially real time. The techniques described herein can utilize this mid-infrared spectroscopic data to measure sample quality metrics, such as protein content, titer, secondary structure, aggregation, and the like, as well as viral and / or nucleic acid characteristics. Thus, sample quality metrics can be measured substantially in real time and / or continuously to assess, for example, the production quality of protein and gene therapeutic agents, and can be used by a control system to adjust and / or control process parameters to improve product quality, etc.

The invention discloses a rice storage year classification method, system and equipment based on a mid-infrared spectrum detection technology and a storage medium, belongs to the technical field of agricultural product quality detection and testing, and solves the problem that a method capable of quickly, accurately and conveniently distinguishing the storage years of rice is lacked in the prior art. The method comprises the following steps: respectively treating rice with different storage years in six years and unknown storage years to respectively obtain rice flour with different storage times; respectively scanning the rice noodles in different storage years based on a mid-infrared spectrum detection technology to respectively obtain spectrums corresponding to the rice noodles in different storage years; sequentially carrying out standardization treatment and feature extraction on the spectrums corresponding to the rice flour in different storage years; parameters of the support vector machine are optimized based on a sparrow optimization algorithm, and the support vector machine after parameter optimization is obtained; and based on the support vector machine after parameter optimization, classifying the spectrums corresponding to the rice flour in different storage years after feature extraction, and completing classification of the rice in different storage years.

The invention relates to a noninvasive blood glucose detection technology, in particular to a blood glucose detection method for a noninvasive blood glucose meter and a blood glucose detector. The invention discloses a blood glucose detection method for a noninvasive blood glucose meter. The blood glucose detection method comprises the following steps: acquiring near-infrared light data and mid-infrared light data and calculating corresponding weight coefficients; acquiring a predicted blood glucose value by using the blood glucose detection model, calculating credibility, comparing the credibility with a preset credibility threshold, and executing corresponding operation; and acquiring a blood glucose health assessment result by using the blood glucose health assessment model. The near-infrared spectrum technology and the mid-infrared spectrum technology are combined, the influence of environmental factors is considered in real time, the weight coefficients of the near-infrared light data and the mid-infrared light data are calculated respectively, analysis of the spectrum data is adjusted and optimized, a more accurate blood glucose detection result is provided, and the accuracy of blood glucose detection is improved. And reliable blood glucose measurement values can be obtained under different environmental conditions.

The invention discloses an infrared spectrum quantitative model for the content of glycerol in gel as well as an establishment method and application of the infrared spectrum quantitative model. The establishment method of the infrared spectrum quantitative model of the glycerin content in the gel comprises the following steps: (1) collecting mid-infrared spectrum samples of a plurality of gels with different known glycerin contents, and removing abnormal samples in the collected mid-infrared spectrum samples to obtain a sample set; the sample set comprises a correction set; (2) preprocessing the mid-infrared spectrum of the correction set; and (3) selecting one or more of modeling wave bands of 4000-400cm <-1 >, 1150-820cm <-1 > and 1550-1150cm <-1 >, selecting the number of main factors to be 2-10, and establishing an infrared spectrum quantitative model of the glycerol content in the gel for the preprocessed mid-infrared spectrum by adopting a partial least square method. The infrared spectrum quantitative model is high in accuracy and precision and good in stability and repeatability, and the glycerin content in the gel can be rapidly and accurately measured on the basis of the model.

The invention relates to the technical field of multispectral detection, in particular to an online monitoring method and device for visible light and invisible light. The method comprises the following steps: in multispectral data at a single moment position in a monitoring time period, analyzing radiation change characteristics of spectral data on different wavebands to obtain a gas breakdown strength value; according to the change trend of the gas breakdown strength value in the continuous time period and the visible light radiation condition, a stage transition moment is obtained; determining a temperature response section according to the radiation intensity of the infrared wave band after the stage transition time, and determining a response effective section according to the change characteristics of the infrared spectrum data in the temperature response section; and monitoring model training is carried out based on the response effective segment. According to the method, an initial response stage is divided through analysis of radiation conditions in visible light and invisible light wavebands before a visible arc phenomenon, finer monitoring and recognition training are carried out, and the online monitoring sensitivity of an arc fault is improved.

The invention relates to the technical field of spectral measurement, in particular to a quantum correlation enhanced optical comb spectrum device and method, and the method comprises the steps: processing pump light and seed light to obtain idler frequency light and intrinsic light with quantum correlation; performing time domain matching adjustment on the idle frequency light, and reflecting the idle frequency light after the time domain matching adjustment to obtain a time-matched quantum correlation light beam; combining the intrinsic light and a continuous laser to obtain a first combined light; the mid-infrared light comb generates mid-infrared light carrying gas information after penetrating through the sample cell containing the gas to be detected; the light after the first beam combination and the mid-infrared light carrying the gas information are combined to obtain light after the second beam combination, frequency conversion processing is carried out, and then a near-infrared light comb is output; and processing the quantum correlation light beam and the near-infrared light comb which are matched in time, and outputting a double-light comb signal. Through combination of a quantum correlation mechanism and a frequency conversion technology, the intermediate infrared spectrum detection signal-to-noise ratio is remarkably improved, and high-sensitivity and high-speed parallel quantum enhanced spectrum is realized.

The invention belongs to the technical field of laser spectrum, and particularly relates to an intermediate infrared spectrum cavity ring-down system based on a quantum cascade laser. In order to overcome the wavelength drift of the laser and improve the accuracy of gas concentration measurement, a light beam emitted by the quantum cascade laser passes through two isolators and is divided into two light beams through a beam splitter, and the light with relatively low light intensity is reflected by a transmitting mirror, passes through a standard gas pool and a focusing lens and is emitted into a first detector to obtain a direct absorption signal. The light with higher light intensity is reflected by the gold film reflector, enters the optical resonant cavity and is driven into the second detector through the focusing lens, the signal is interrupted through DG535 to obtain a ring-down time signal, and the ring-down time signal is processed through a program to obtain a gas absorption signal. A direct absorption signal of the first detector is collected, a polynomial is used for fitting the absorption signal, each parameter of the fitting polynomial is obtained, and the fitting polynomial is used for fitting the signal obtained by the second detector.

The invention relates to the technical field of unprocessed grain analysis, and provides a sorghum glutinous property discrimination method and device based on spectrum and visual data coupling in order to improve the accuracy of sorghum glutinous property discrimination. A sorghum sample glutinous property database is constructed, and mid-infrared spectrum data is processed by adopting a one-dimensional residual convolutional neural network, so that the sorghum glutinous property discrimination accuracy is improved. Meanwhile, spatial structure features of visual images are introduced by adopting a convolutional neural network, so that collaborative learning and feature complementation of multi-modal information are realized; the method not only overcomes the limitation of spectrum or image single-mode identification, but also improves the complex feature extraction capability and discrimination precision through the deep learning model, improves the stability and robustness of the model, and has higher adaptability and generalization performance.

The invention discloses a method for rapidly determining the content of cannabidiol based on an intermediate infrared spectrum prediction model, and belongs to the technical field of detection. The method comprises the following steps: pretreating different industrial hemp products, collecting an infrared spectrum curve of a sample, actually measuring the content of cannabidiol in the sample by liquid chromatography, pretreating and analyzing infrared spectrum data, establishing a model, and verifying the model. The method is based on the mid-infrared spectrum technology and is combined with the predictive capacity of the PLS model and the distinguishing effect of principal component analysis, and the content of cannabidiol in industrial hemp extract products and floral leaves can be rapidly detected. The method is simple and rich in information content, simple to operate, green, environment-friendly, reliable in performance and suitable for popularization and application, adopts nondestructive identification, has lower related consumables and maintenance cost, reduces the operation cost and the environmental protection risk, and saves a large amount of manpower, material resources and financial resources. The invention provides a novel method for rapidly determining the cannabidiol content of different industrial hemp products.

The invention belongs to the technical field of biomass detection, and particularly relates to a device and a method for predicting three-component contents of biomass by combining FTIR (Fourier transform infrared spectroscopy) and machine learning. The device comprises a data acquisition module, a data preprocessing module, a model construction module and a result output module. The method comprises the following steps: adopting a normal form washing method to obtain the content of three components of the biomass and the FTIR spectrum data of the biomass, preprocessing the FTIR spectrum, extracting the characteristic wavelength of the FTIR spectrum, combining the spectrum characteristic wavelength and a machine learning algorithm to construct a plurality of prediction models, and comparing the performance indexes of the prediction models. Determining an optimal prediction model and performing evaluation; according to the method, the FTIR mid-infrared spectroscopic analysis technology and the machine learning technology are utilized, biomass samples with different sources and different composition characteristics are covered, the accurate and wide-applicability model is established, and accurate and universal prediction of the content of the three components of the biomass is achieved.

The invention provides a method and a device for identifying common plastics. The method comprises the following steps: measuring the mid-infrared spectrum of a sample to be identified by utilizing ATR-FTIR; intercepting a characteristic wave band of the intermediate infrared spectrum, and generating a two-dimensional data matrix according to the characteristic wave band; performing synchronous transformation processing and asynchronous transformation processing on the two-dimensional data matrix to obtain a synchronous two-dimensional correlation matrix and an asynchronous two-dimensional correlation matrix; splicing an upper triangle of the synchronous two-dimensional correlation matrix and a lower triangle of the asynchronous two-dimensional correlation matrix to obtain an integrated data matrix; and inputting the integrated data matrix into a pre-established two-dimensional convolutional neural network (CNN2D) model to obtain the plastic category of the sample to be identified. According to the invention, common plastic types can be quickly identified, secondary pollution is avoided, the efficiency is high, the speed is high, the identification accuracy rate is high, and the classification accuracy rate is high.

The invention discloses a chiral sample classification method for mid-infrared spectroscopy, which comprises the following steps: acquiring sample data by using a Fourier infrared spectroscopy system, calculating the transmittance and absorption coefficient of a sample in an infrared band, and recording the data as experimental data of a chiral isomer; constructing a sample molecular structural formula; electron density and energy of molecules are calculated, energy of different excited states and corresponding wave functions are calculated by using a time-dependent density functional theory, the energy of the excited states is converted into frequency, and the absorption peak frequency of the material is obtained and serves as theoretical data of a chiral isomer; extracting a chiral isomer frequency band based on a density functional theory; and carrying out feature classification by using a support vector machine. According to the method, characterization analysis of sample vibration spectrum information and detection results can be achieved, effective vibration frequency bands in the molecular structure are extracted based on the density functional theory, theoretical support is provided for chiral molecule identification, and the accuracy and generalization ability of classification results are further improved.

The application discloses a kind of multi-wavelength pumped mid-infrared upconversion spectral detection methods, its characteristics are that the method uses multi-wavelength narrowband laser as the pump source of nonlinear conversion, simultaneously adopt multi-period quasi-phase matching crystal as nonlinear upconversion medium, realize mid-infrared signal broadband and efficient conversion, and combine high-performance near-infrared spectral measurement and control element, finally obtain wide band, high resolution, ultra-sensitive mid-infrared spectral detection.The application has the characteristics of large bandwidth and high efficiency compared with existing upconversion spectral technology, simultaneously can high fidelity resolution mid-infrared spectral information, for realizing wide band, high resolution, ultra-sensitive mid-infrared spectral measurement provides effective means, is expected to be applied to molecular spectroscopy, trace substance detection, infrared remote sensing and atmospheric monitoring and other important fields.

An infrared spectrometer for operating in the mid-infrared spectral range is presented, wherein the spectrometer includes a spectral filter based on a Bragg reflector, the spectral filter being operable to provide an interrogation signal, the spectral content of the interrogation signal being dispersed along a first direction at a filter aperture. The filter aperture is imaged through a sample by a thermal imaging camera to create a focused image based on the interrogation signal and the absorption characteristics of the sample. Thus, embodiments according to the present disclosure may be smaller, less complex, and less expensive than infrared spectrometers known in the prior art.

In at least one example of the present disclosure, a device can include a sample stage configured to hold a sample, pulsed laser source configured to produce a pulsed light in the mid-infrared (IR) region, and a polarizer configured to receive the pulsed light. The device can further include a plurality of optical components, an active multiplexing device (AMD), a detector configured to receive the pulsed light, and a data processing unit configured to generate an image.

Mid-infrared spectroscopic analysis of tissue specimens can include training a learning model and using the trained learning model to classify tissue into at least two (or more than two) categories such as a tumor category, a non-tumor category, and a histology category. Resulting richer diagnostic indication can be provided and can optionally be mapped to a schema for RGB or other display. Efficient and accurate classification can employ an efficient linear SVM model representation. The model representation can include a β spectrum including the fuller wavelength set, a central tendency indicator (μ) of the training set, a spread indicator (σ) of the training set, and a scaling factor. Various types of illuminators and response detectors can include one or more Quantum Cascade Lasers, broadband light source configured to permit spectral separation, a thermal or other imaging array, among others.

The embodiment of the invention provides a soil bacterial community structure evaluation method and system based on an intermediate infrared spectrum transfer function and application, and relates to the technical field of soil microbial ecology. The method comprises the following steps: S1, collecting a soil sample; s2, acquiring a mid-infrared diffuse reflection spectrum of the soil sample to be detected; s3, acquiring an environment covariant corresponding to the soil sample, wherein the environment covariant comprises at least one of soil physicochemical properties, climate data and topographic parameters; s4, splicing the mid-infrared diffuse reflection spectrum obtained in the step S2 and the environment covariable obtained in the step S3 to form a fusion feature vector as an input feature set; and S5, inputting the input feature set into a pre-trained nonlinear machine learning regression model, and outputting the relative abundance and / or OTU richness of at least one bacteriophage in the soil sample. According to the method, a technical route of combining a mid-infrared (MIR) spectrum and a spectral transfer function (STF) is adopted and is used for predicting the soil bacterial community structure.

The present invention relates to an adsorption device capable of online monitoring. The adsorption device comprises an online mid-infrared spectrum detection system, a branch with a test sample pool is arranged on a material pipeline that needs to be monitored online, and an online mid-infrared spectrometer is used to detect the material in the sample pool. The adsorption device capable of online monitoring of the present invention can perform real-time online detection of the material concentration in the adsorption device.

The invention belongs to the technical field of gas detection and optical measurement, and discloses an intermediate infrared spectrometer and a detection method thereof. The mid-infrared spectrometer comprises a constant-temperature cavity and a measuring system connected with the constant-temperature cavity, wherein the constant-temperature cavity comprises a shell, a spiral flow channel formed in the shell, an annular base embedded and mounted in the spiral flow channel, and an optical fiber mounted in the annular base. The optical fiber is connected with the annular base through eutectic bonding, a through hole array is arranged on the inner wall of the annular base, a temperature-sensitive shape memory polymer is filled between the annular base and the spiral flow channel, and the optical fiber comprises a hollow base body, a metal film arranged on the inner surface of the hollow base body and a protective layer arranged on the inner surface of the metal film. The porous adsorption coating layer is arranged on the outer surface of the hollow substrate. The infrared spectrometer has the advantages of high sensitivity, high stability, strong environmental adaptability and compact structure, and can realize high-precision and reliable detection of trace gas isotopes in a complex field environment.

The invention discloses an experiment platform and an experiment method for testing corrosion behaviors of a cement sheath of a depleted oil and gas well based on early-stage vibration well cementation and later-stage hydrogen storage. Wherein the high-pressure reaction cabin body is fixed at the upper end of the vibration system, and the gas pressurization system, the hydrogen cylinder and the carbon dioxide container bottle are all introduced into the high-pressure reaction cabin body system; during working, the temperature sensor monitors and feeds back signals in real time, and the power of the electric resistance heater is regulated and controlled. The casing pipe is vertically installed in the high-pressure reaction cabin body, the casing pipe and a first cylindrical hinged door and a second cylindrical hinged door which are closed in the sealing ring hinged system form a cylindrical annular space area, and a bottom connecting frame in the detection system extends into the high-pressure reaction cabin body from the center of an upper cover of the high-pressure cabin. The infrared spectrometer, the X-ray emitter and the ultrasonic emitter are all mounted at the bottom end of the bottom connecting frame. According to the method, through various detection quantitative analysis and support process optimization, dual technical support is provided for the anticorrosion design and the construction scheme of the exhausted oil and gas well rebuilt hydrogen storage library.

The invention provides a method for evaluating the degree of association of liquor aging based on mid-infrared spectroscopy detection technology, and realizes rapid detection of the degree of association of liquor, belonging to the field of detection of the degree of association of liquor storage. The method comprises: configuring samples treated with different storage times and different temperatures; acquiring sample spectrum data through a Fourier infrared spectroscopy system; selecting a band of hydroxyl (4000cm ‑1 ~2600cm ‑1 ); normalization processing to eliminate the differences caused by different sampling amounts; multi-peak fitting to process samples of different storage times and classify the degree of association; define labels for all spectral data according to the classification and create a data set; pre-process the spectral data in different ways; train and predict through different models, compare the effects of different combinations, and provide a reference for the rapid detection of the degree of association of liquor.

The present invention discloses a method for determining the components of unconventional additives in gasoline, and relates to the field of testing and analyzing unconventional additives in gasoline, including: extracting a plurality of gasoline samples containing unconventional additives in batches using a pipette; placing one sample extracted each time on a separate window piece for infrared spectrometer experiment to obtain corresponding mid-infrared spectrum data; substituting the mid-infrared spectrum data into a multiple linear regression method to construct a quantitative analysis model; establishing a wavelength selection network for wavelength selection based on a neural network and an attention mechanism, and using the wavelength selection network to perform model tuning on the quantitative analysis model; and determining the unconventional additives in gasoline based on the tuned model. The present invention can quickly identify new unconventional additives and determine their concentrations.

The present invention provides a method, system, medium and device based on multiple spectral data fusion, the method comprising: collecting spectra: collecting near-infrared spectra and mid-infrared spectra of the substance to be tested, obtaining near-infrared spectrum matrices and mid-infrared spectrum matrices of the substance to be tested; constructing a detection model; training the detection model; predicting detection indicators. The present invention simultaneously ensures the comprehensiveness, accuracy and reliability of collecting the substance to be tested and the training of the detection model, thereby ensuring the accuracy of the prediction of the detection indicators.

The disclosure relates to a detection device (7) for detecting ultrasound waves, in particular optoacoustic waves, emanating from an object (1), in particular biological tissue, in response to mid-infrared (mid-IR) radiation (3) impinging on the object (1), the detection device (7) comprising an ultrasound-sensitive medium (4) which is configured to convert at least a part of the ultrasound waves emanating from the object (1) into at least one electrical signal (S), and at least one electrically conductive layer (5, 6) which is coupled to the ultrasound-sensitive medium (4) to enable the electrical signal (S) to be tapped, wherein the ultrasound-sensitive medium (4) and the at least one electrically conductive layer (5, 6) are at least partially transparent in the mid-infrared spectral range so that at least a part of the mid-infrared (mid-IR) radiation (3) can pass the detection device (7) prior to impinging on the object (1). The disclosure further relates to an according apparatus for optoacoustic analysis of an object (1), in particular biological tissue.

The invention provides a method and a device for identifying common plastics. The method comprises the following steps: measuring the mid-infrared spectrum of a sample to be identified by utilizing ATR-FTIR; the characteristic wave band of the mid-infrared spectrum is intercepted, the characteristic wave band is decomposed step by step through wavelet transformation, fine-scale and large-scale information in the characteristic wave band is separated into a wavelet detail coefficient and an approximation coefficient after wavelet transformation, the approximation coefficient comprises low-frequency components of signals, and the low-frequency components of the signals are converted into low-frequency components of the signals; the detail coefficient comprises the high-frequency change of the signal, and each level of decomposition can further refine the characteristics of the signal; selecting a middle layer of the multi-layer wavelet detail coefficient set obtained after step-by-step decomposition to carry out two-dimensional reconstruction to obtain a two-dimensional data matrix; and inputting the two-dimensional data matrix into a pre-established CNN2D model to obtain the plastic category of the sample to be identified. According to the invention, common plastic types can be quickly identified, secondary pollution is avoided, the efficiency is high, the speed is high, the identification accuracy rate is high, and the classification accuracy rate is high.

The present invention belongs to the field of dairy cow performance measurement and milk quality detection, and discloses a method for rapid batch detection of polyunsaturated fatty acids in milk by mid-infrared spectroscopy. In terms of the selection of characteristic bands, it breaks the commonly used method of using algorithms to screen features, and instead uses a method of manual selection + multiple traversals. Finally, characteristic bands for modeling are selected, especially the absorption region containing part of water is screened out, and it is proved that adding part of the water absorption band can improve the accuracy of the model. The optimal preprocessing and algorithm combination for the establishment of the polyunsaturated fatty acid model is selected, the optimal parameters are determined, and the accuracy of the model is improved. Rapid, accurate, and low-cost detection of the polyunsaturated fatty acid content in raw milk is achieved, and rapid batch detection is realized.

Disclosed herein is a system and method are provided for rapid, high-throughput phenotyping of nutritional traits in plants using mid-infrared (MIR) spectroscopy and chemometric modeling. One or more processors control MIR sensors to capture spectral images of plant material, which are analyzed using a chemometric model, such as partial least squares regression, to determine characteristics including protein, fatty acids, dietary fiber, starch, resistant starch, sulfur-containing amino acids, and digestibility. The method supports non-destructive analysis with minimal sample preparation and is applicable to a wide range of plant species and biological materials. Results can be stored in a local or cloud database and visualized through a graphical user interface. The chemometric models may be trained and validated using K-fold cross validation and can include spectral preprocessing and interpolation filters. This enables rapid, cost-effective nutritional analysis to support plant breeding, food quality assessment, and industrial applications.