58 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 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.

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 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 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 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 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 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 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.

The invention relates to a cigarette quality stability evaluation method based on tobacco shred mid-infrared spectroscopic analysis, and belongs to the technical field of tobacco analysis. The method comprises the following steps: collecting tobacco shred samples from different producing areas and batches, drying and crushing the tobacco shred samples, scanning the tobacco shred samples through a mid-infrared spectrometer (400-4000 cm <-1 >), and extracting characteristic wave bands such as 715-726 cm <-1 > and 833-910 cm <-1 > after pretreatment such as smoothing and vector normalization; two-dimensional coordinates are obtained through principal component analysis (PCA), a cut tobacco quality stability index (QSI) is calculated through a formula, and the larger the QSI value is, the poorer the stability is. According to the method, rapid, lossless and objective stability evaluation is achieved through the mid-infrared spectrum technology, and the defects that a traditional method is high in subjectivity, long in consumed time and the like are overcome. Embodiments show that the method has stable evaluation results on the same batch of samples, can effectively distinguish different brands or high-imitation cigarettes, and provides a scientific basis for tobacco processing quality control.

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 invention discloses a ship fuel oil detection method, device and equipment and a storage medium. The method comprises the following steps: collecting spectral data of a ship fuel oil sample through mid-infrared spectrum equipment; performing data preprocessing on the spectral data to obtain preprocessed spectral data; and performing multi-stage identification on the preprocessed spectral data by using a layered stripping iterative detection model to obtain a ship fuel detection result, the layered stripping iterative detection model comprising a plurality of detection sub-models, and each detection sub-model selecting different wavebands of the preprocessed spectral data for analysis. Compared with the prior art, the ship fuel oil detection method effectively improves the ship fuel oil detection efficiency and reduces the detection cost due to the fact that the ship fuel oil detection result is obtained by performing multi-stage identification on the preprocessed spectral data through the layered stripping iterative detection model.

The present invention provides a wavelength-tunable laser (WTL) delivering high-average power and / or high-pulse energy that is wavelength-tunable in a continuous manner over larger portions of the visible and / or near-infrared and / or mid-infrared spectra. The WTL uses a laser source at a given wavelength (1ω) generating short, high peak power laser pulses for injecting a photonic crystal fiber (PCF) and for pumping an optical parametric amplifier (OPA). The PCF generates a supercontinuum (SC) seed, which is delivered to the OPA. Phase-matched band of the SC seed is amplified by the OPA to a suitably high level. A delay line may be provided on the 1ω beam to synchronize the SC seed and pump pulses in the OPA. The OPA outputs include the amplified phase-matched band of the SC seed and an OPA idler. Wavelength tuning of the OPA outputs is preferably done by adjusting the angle of propagation in the crystal, which changes the phase matching condition. The 1ω wavelength may be selected from the range from about 1 μm to about 3 μm. In another embodiment, the 1ω wavelength is frequency doubled to 2ω prior to pumping the OPA. In yet another embodiment, the 1ω wavelength is frequency tripled to 3ω prior to pumping the OPA.

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.

A method for identifying the origin of rice based on dynamic feature screening and weighted integrated model. It belongs to the field of agricultural product traceability technology, and specifically relates to the field of intelligent identification technology of rice origin. It solves the prominent problems of existing mid-infrared spectral identification methods, such as large fluctuations in accuracy, poor model transferability, and low computational efficiency. The method comprises the following steps: using a mid-infrared spectrometer equipped with an ATR accessory to collect spectra of rice and rice flour samples, and performing spectral standardization processing; using wavelet denoising and improved SNV correction method to reduce noise and correct the standardized spectra to obtain reduced-noise corrected spectra; performing dynamic feature screening on the reduced-noise corrected spectra to obtain an optimized feature subset with low redundancy and high independence; by optimizing the feature subset, a number of classification models are integrated into a multi-level hybrid classification model by using a dynamic weight set; and the multi-level hybrid classification model is used to identify the origin of rice and rice flour samples.

The invention discloses a mid-infrared spectrum-based obese infant early diabetes risk screening method and system, and belongs to the technical field of combination of medical detection and infrared spectrum, the mid-infrared spectrum-based obese infant early diabetes risk screening system comprises a micro-fluidic chip used for performing serum separation and protein enrichment deposition on dropped tissue peripheral blood of a patient, serum samples rich in different proteins are obtained; the mid-infrared spectrometer is used for carrying out in-situ mid-infrared spectrum acquisition on the blood sample to obtain spectrum data; and the risk screening module is used for preprocessing the spectral data, inputting the preprocessed spectral data into a risk prediction model to carry out pre-diabetic risk prediction on the obese children, and carrying out pre-diabetic risk screening according to a risk prediction probability value. The technical scheme is clear in structure, standard in step and suitable for large-scale children health screening and basic-level chronic disease management.

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.

The invention provides a stable mid-infrared spectrometer convenient to disassemble and assemble, and relates to the technical field of spectrum analyzers, the stable mid-infrared spectrometer comprises a base, a protective frame is mounted above the base, a locking rod is mounted between the protective frame and the base, a vertical side plate is mounted on one side of the protective frame, a first locking frame is mounted on the outer side of the side plate, and a second locking frame is mounted on the outer side of the first locking frame. Two symmetrically-distributed first positioning plates are arranged on the inner side of the protection frame. After the side plates are installed, the two first pressing plates rapidly press and position the edge position of the top cover to achieve the effect of rapidly assembling the protection frame, the top cover and the side plates, and the second pressing plates rapidly press and position the clamping blocks to keep the stability of the connector lug. And through arrangement of a second pressing plate and a clamping block, the problem that a traditional power line needs to be locked through cooperation of a connector bar and a bolt is solved.

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.