40 results about "Mid infrared spectroscopy" patented technology

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 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 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 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.

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.

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 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 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.

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.

This invention belongs to the field of optical technology and spectral analysis technology, and particularly relates to a bandpass sampling infrared spectrometer based on a horizontal roof mirror and a mosaic grating. Along the beam propagation direction, it sequentially includes a cylindrical beam shaping system, a dispersion and compensation integrated system, a static interferometry system, a post-beam shrinking system, and an area array detector. The dispersion and compensation integrated system includes a differential blaze angle mosaic grating and a horizontal roof mirror. The static interferometry system is located at the output path of the differential blaze angle mosaic grating. The post-beam shrinking system is located at the output end of the static interferometry system, and the area array detector is located at the image plane of the post-beam shrinking system. This invention also discloses a staged spectral reconstruction method. This scheme achieves miniaturization of the mid-infrared spectrometer, high-efficiency detection across the entire spectrum, and effective suppression of spectral aliasing under fixed sampling.

The embodiment of the invention discloses a lubricating oil physical and chemical parameter detection method based on multi-model fusion. The method comprises the following steps: acquiring multi-channel mid-infrared spectrum data of an oil sample to be detected; performing preprocessing and feature extraction on the mid-infrared spectrum data to obtain feature data; selecting a pre-trained model according to the target parameter and the channel number; and inputting the characteristic data into a pre-trained model to obtain physical and chemical parameters of the oil sample to be detected.

The invention discloses a lubricating oil product viscosity index detection method, and relates to the technical field of lubricating oil viscosity detection.The method comprises the steps that a lubricating oil sample needing viscosity detection is selected; measuring the water content in the lubricating oil viscosity detection sample; detecting the impurity content in the lubricating oil viscosity detection sample; measuring the viscosity of the selected lubricating oil sample; comprehensively analyzing the viscosity index of the lubricating oil according to the measured viscosity; and recording the sample data of the viscosity index of the lubricating oil detected this time. According to the method, the water content and the impurity content in the lubricating oil are measured, lubricating oil samples of different classifications are collected, the water content in the lubricating oil is measured through mid-infrared spectroscopy, the impurity content in the samples is observed through a microscope, and the viscosity differences of the water-containing lubricating oil, the impurity lubricating oil and the pure lubricating oil are judged. And correction is carried out by establishing a lubricating oil viscosity model, so that the influence of moisture and impurities in the lubricating oil on the viscosity index can be accurately judged.

The embodiment of the invention discloses a lubricating oil product detection device based on an intermediate infrared spectrum technology. The lubricating oil product detection device comprises a data processing module, a photoelectric conversion module, a light source module and an oil sample cavity, the data processing module is used for sending a light source control signal to the light source module; the light source module is used for responding to the light source control signal and transmitting a first light signal to the oil sample cavity; the oil liquid sample cavity is used for accommodating oil liquid to be detected and outputting a second optical signal to the photoelectric conversion module in response to the first optical signal; the photoelectric conversion module is used for converting the second optical signal into a plurality of digital electric signals and sending the digital electric signals to the data processing module; and the data processing module is also used for obtaining the content of each index of the oil liquid to be detected according to the plurality of digital electric signals.

The invention relates to the technical field of spectral measurement, and provides a quantitative detection model construction and quantitative detection method and an A2 beta-casein quantitative detection model construction and quantitative detection method.The model construction method comprises the steps that samples to be detected are obtained, a sample set is constructed, mid-infrared spectrums of all the samples to be detected in the sample set are collected, and the mid-infrared spectrums of all the samples to be detected in the sample set are obtained; obtaining the real content of the target object in each sample to be detected; preprocessing the mid-infrared spectrum of each to-be-detected sample, wherein the preprocessing comprises scattering correction processing and standard transformation processing; screening wavelength variables in the mid-infrared spectrum of each preprocessed to-be-detected sample to obtain a characteristic wavelength variable combination related to a target object; and based on the characteristic wavelength variable combination and the real content of the target object in each sample to be detected, training an initial model to obtain a quantitative detection model. The method can realize quantitative detection of the target object in the to-be-detected sample, greatly reduces the detection time and cost, and improves the detection efficiency.

A smartphone application and mid-infrared spectrometric system for the analysis of honey. The system enables the determination of syrup and sugar adulterants, moisture content, and floral and geographical origin of honey using advanced machine learning and chemometric techniques. The state-of-the-art system may revolutionize honey purity testing by determining the quantity of the aforementioned parameters (e.g., quantity of syrup and sugar adulterants, percentage of moisture content, and floral and geographical origin of honey) in only 1-2 minutes. The system can be used in situ, thus offering the advantages of portability and convenience.

The present application relates to non-invasive blood glucose detection technology, and particularly relates to a blood glucose detection method and a blood glucose detector for a non-invasive blood glucose meter. The blood glucose detection method for the non-invasive blood glucose meter comprises the following steps: collecting near-infrared light data and mid-infrared light data and calculating corresponding weight coefficients; obtaining a predicted blood glucose value by using a blood glucose detection model, calculating a credibility, comparing the credibility with a preset credibility threshold, and performing corresponding operations; and obtaining a blood glucose health assessment result by using a blood glucose health assessment model. The present application combines near-infrared spectroscopy technology and mid-infrared spectroscopy technology, and considers the influence of environmental factors in real time, calculates the weight coefficients of the near-infrared light data and the mid-infrared light data respectively, adjusts and optimizes the analysis of the spectral data, provides more accurate blood glucose detection results, and ensures that reliable blood glucose measurement values can be obtained under different environmental conditions.

The present disclosure relates to a method for determining the category of oil, based on the rapid analysis technology of mid-infrared spectrum, without complex chemical experimental operation, by collecting the known oil spectrum data of different oil categories, using the extreme learning machine algorithm to establish an oil category identification model, realizing rapid, efficient and accurate oil category determination, suitable for large batch sample analysis, and can be widely applied in trade supervision, production control and quality detection fields, and provides technical support for relevant departments.

The present application belongs to the field of engineering technology, cow performance measurement and milk detection, and particularly relates to a method for segmenting and directly standardizing animal milk by using different brand instruments after condensation clustering of mid-infrared spectra. The method provided by the present application is more flexible in the selection of windows for the response of each spectral wave point between different instruments, and is superior to the single and limited fixed window selection of the past method. The model prediction result repeatability level based on the mid-infrared spectrum between different instruments is superior to the present technology, the determination coefficient (R 2 ) after standardization is higher, and the root mean square error (RMSE) is smaller.

This invention relates to the field of food origin traceability technology, and provides a method for tracing the origin of *Porphyra yezoensis* based on mid-infrared spectroscopy and CARS-GA, solving the problems of long processing time and low accuracy in modern food traceability technologies. The method includes the following steps: S1, collecting *Porphyra yezoensis* samples from different aquaculture areas and performing mid-infrared spectral acquisition; S2, preprocessing the spectral data to obtain the average spectrum of each *Porphyra yezoensis* sample; S3, using the CARS algorithm to perform preliminary screening on the average spectrum obtained in step S2 to obtain the first characteristic wavenumber variable; S4, using the GA algorithm to perform secondary feature selection on the first characteristic wavenumber variable obtained in step S3 to obtain the second characteristic wavenumber variable. Finally, 59 key characteristic wavenumbers are accurately extracted from 7468 original variables to obtain an origin traceability identification model. The LDA model achieves 100% identification accuracy.

The present application belongs to the field of dairy cow performance determination and greenhouse gas emission monitoring, and specifically discloses a dairy cow methane emission phenotype prediction method based on infrared spectrum in milk. By collecting basic information and milk samples of dairy cows, mid-infrared spectrum data are obtained and characteristic wave bands are screened, and spectrum characteristic processing values are extracted after pretreatment; the spectrum data are combined with parity, days in milk and daily milk yield in the DHI report to construct a prediction model for regression analysis. The present application solves the problems of high cost and low flux in traditional methane emission determination by fusing characteristic wave bands and basic information variables, and realizes low-cost and high-flux dairy cow individual level methane emission phenotype prediction. The present application is aimed at Chinese Holstein cow population, fills the blank of localized prediction model, reduces the breeding threshold of methane emission phenotype prediction, and provides a practical tool for low-carbon dairy cow breeding.

The present application belongs to the technical field of raw milk detection, and provides a device and method for infrared spectrum data collection and storage of raw milk, which comprises: taking a raw milk sample, homogenizing the raw milk sample by using a homogenizing device, evaluating the homogenization by using ultrasonic detection, determining whether to end the homogenization, if yes, obtaining the homogenized sample to be detected, deploying an infrared spectrum detection light path system, a multi-modal sensor group and a communication networking for infrared spectrum collection of the sample to be detected, collecting the infrared spectrum of the sample to be detected, marking the actual collection period and obtaining the actual sample infrared spectrum of the sample to be detected, collecting a multi-modal data group by using the multi-modal sensor group, analyzing the multi-modal data group, determining whether the sample to be detected has a high abnormal risk, if not, triggering multi-modal data compensation, and compensating the actual sample infrared spectrum.

The invention discloses a method for identifying plastics and additives thereof based on a bimodal multi-task neural network in the technical field of identification of plastics and additives thereof. The method comprises the following steps: collecting a plastic sample with known contents of a plastic base material and additives thereof; the method comprises the following steps: acquiring mid-infrared hyperspectral data and X-ray fluorescence spectral data of a sample based on a mid-infrared hyperspectral camera and a handheld X-ray fluorescence spectrometer; based on a sample formula or a chemical analysis result, obtaining a plastic base material type, an additive type and an additive dosage of the sample, and determining types and regression labels of the plastic base material type, the additive type and the additive dosage. Based on the natural complementarity of two modes of the mid-infrared spectrum and the X-ray fluorescence spectrum in the molecular structure information and element composition information level and oriented to the spectral characteristics of the plastic-additive system, the analysis capability of the complex waste plastic system carrying various additives can be improved by constructing the target recognition model; the method has outstanding uniqueness and engineering application value.

The application discloses a quick quantitative method for sulfates in saline soil based on mid-infrared spectrum combined with multivariate linear regression, and belongs to the technical field of saline soil detection, and comprises the following steps: S1, saline soil sample collection and pretreatment; S2, mid-infrared spectrum data collection; S3, joint pretreatment of spectrum data; S4, screening of sulfate-sensitive characteristic wave bands; S5, construction of a multivariate linear regression quantitative model; and S6, quick quantitative detection of a to-be-detected sample.The application adopts mid-infrared spectrum to quantitatively detect sulfates, utilizes the fundamental frequency characteristic absorption peak of sulfate ions in the mid-infrared wave band, and compared with the general frequency absorption of near-infrared, the characteristic peak is more specific, the absorption intensity is higher, and the anti-interference ability is stronger, so that the interference of soil organic matter, clay minerals and other salt can be effectively resisted, and the detection precision is greatly improved.

Driver assistance system (100) comprising a headlight (101) with an integrated transmitter unit (123) for emitting pulsed primary light (121) into a detection area (123) comprising a light source (104); a logic unit (107) configured to specify a pulse sequence; and a driver unit (106) configured to control the light source (104) to emit primary light (121) based on the specified pulse sequence; a receiver unit (102) for receiving secondary light (122) as image data from the detection area (123) comprising an image sensor (109) sensitive in the near and / or mid-infrared spectral range; a control unit (110) configured to switch the image sensor (109) between an active and an inactive state synchronized with the pulse sequence; an image signal processor (111) which is designed to preprocess received image data into 1 to n sub-images (114);and an interface (112) to a separate control unit (103) for transmitting the sub-images (114); a control unit (103) designed separately from the headlight (101) and the receiver unit (102), which is designed to create an environment model based on the sub-images (114) and to control functions of the driver assistance system (100) based on information about the environment model.

The invention relates to the technical field of mid-infrared spectrum analysis, and discloses a mid-infrared spectrum spectrogram high-precision splicing method and system.The method comprises the steps that an MCT detector is adopted to collect original spectrums according to preset parameters; generating a pre-spectrogram based on the original spectrum, and obtaining an adjusted overlapping region range and a characteristic anchor point peak according to the pre-spectrogram; sequentially carrying out dark current deduction, background and water vapor correction and self-adaptive noise reduction on the original spectrum according to the adjusted overlapping region range, and outputting a purified short-wave / long-wave spectrum containing the overlapping region; according to the purified short-wave / long-wave spectrum, performing baseline correction, peak position correction and intensity correction by using a characteristic anchor point peak, and outputting a corrected spectrum; and fusing the short-wave spectrum and the long-wave spectrum in the corrected spectrum according to the adjusted overlapping region range and a preset rule to generate a complete spliced spectrogram. According to the invention, the defects of non-uniform response of the double MCT detector and the traditional splicing technology are overcome, and efficient and stable support is provided for mid-infrared spectrum analysis.

A method and system based on the fusion of various spectral data, and a medium and an apparatus. The method comprises: step S1, collecting spectrums, involving collecting a near-infrared spectrum and a mid-infrared spectrum of a substance to be tested, so as to obtain a near-infrared spectrum matrix and a mid-infrared spectrum matrix of said substance; step S2, constructing a detection model; step S3, training the detection model; step S4, performing spectrum pre-processing; step S5, selecting a spectral variable; and step S6, predicting a detection indicator. The comprehensiveness, accuracy and reliability of both the collection of a substance to be tested and the training of a detection model are ensured, thereby ensuring the accuracy of detection indicator prediction.

The invention provides a method and a device for identifying common plastics. The method comprises the following steps: measuring an intermediate infrared spectrum of a sample to be identified by using an attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR); intercepting a characteristic wave band of the intermediate infrared spectrum, dividing the characteristic wave band into a plurality of equal-length sub-intervals, and performing two-dimensional reconstruction on the plurality of equal-length sub-intervals to obtain a two-dimensional data matrix; and inputting the two-dimensional 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 space coding filter chip, a spectrum recovery method and a miniaturized mid-infrared spectrometer, and belongs to the technical field of computational spectroscopy and micro-nano photonic devices. The space coding filter chip comprises a substrate, a cellular array, a phase change material active layer and an electrode structure. The cell is composed of different micro-nano structures, can generate specific response to different wavebands of the mid-infrared spectrum, and drives phase change under an external electric signal to realize dynamic switching of spectrum modulation states. Adjusting and controlling the on-off state of each cell according to a preset sequence, generating different spectrum modulation modes, and collecting output light intensity by a detector after each modulation to form a measurement vector; and in combination with a pre-calibrated system response matrix, reconstructing incident spectrum information through an inverse problem solving algorithm. High-speed spectrum scanning is achieved through all-solid-state electric control phase change, and the method is suitable for the fields of portable substance recognition, online process monitoring, biosensing, environment detection and the like.