47 results about "Calibration set" patented technology

The invention discloses a fire coal calorific value detection method based on a hyperspectral image technology, and relates to the field of coal calorific value detection. The problems that an existing coal quality evaluation method is complex in calculation process and low in detection efficiency are solved. The method comprises the following steps: taking coal samples with known coal calorific value parameters as correction set coal samples; a hyperspectral imaging system is used for collecting calibration set coal samples and corresponding black and white reference hyperspectral image data, and spectral data preprocessing is carried out; constructing a partial least square quantitative analysis model by taking hyperspectral data of the preprocessed calibration set as an input variable and coal calorific value parameters as dependent variables; the method comprises the following steps: acquiring a coal sample to be detected and corresponding black and white reference hyperspectral image data by using a hyperspectral imager, and preprocessing the spectral data; and detecting the calorific value of the to-be-detected coal sample by using the constructed partial least square quantitative analysis model by taking the hyperspectral data of the preprocessed to-be-detected coal sample as an input variable. The method is used for coal calorific value detection.

The invention relates to the field of pipeline defect measurement, in particular to a method for measuring the position from a pipeline defect to a pipe orifice based on binocular vision. Internal and external parameter calibration of the binocular camera is completed through the calibration module, the binocular vision acquisition module enters a pipeline to obtain a binocular image sequence containing pipe orifices and defects, and precise solving of internal parameters and external parameters of the camera is achieved by adopting a checkerboard and multi-attitude calibration set and combining a Zhang Zhengyou calibration method. The image is preprocessed, the image quality is improved, dust noise in a large pipe diameter is removed, and edge features are enhanced. On the basis of Canny edge detection, a least square method and a YOLOv8 algorithm are matched to complete pipe orifice reference and defect feature collaborative identification, and efficient stereo matching and three-dimensional reconstruction of defect points are achieved in combination with SGBM stereo matching and triangulation. Finally, the problems that traditional pipeline defect positioning depends on manual work and is low in precision and poor in adaptability are solved, and automatic and high-precision measurement from the defects in the pipeline to the pipe opening position is achieved.

The present invention relates to the technical field of near-infrared modeling data processing, and more specifically, to a near-infrared spectrum quantitative analysis dimensionality reduction method and system based on a random projection algorithm. The present invention includes: Step S1, obtaining near-infrared spectra x val samples and corresponding physical and chemical property values y val as a sample set; Step S2, dividing the sample set into a calibration set and a validation set, and calculating the average spectrum x avg ; Step S3, respectively preprocessing the near-infrared spectra x val and the average spectrum x avg to obtain a spectral matrix X val and an average spectral matrix X avg ; Step S4, performing random dimensionality reduction projection on the spectral matrix X val based on the Gaussian random projection algorithm to obtain a dimensionality-reduced spectral matrix X valRed ; Step S5, establishing an artificial neural network prediction model; Step S6, using the validation set to test the model; Step S7, performing quantitative analysis on the input near-infrared spectra and outputting corresponding predicted physical and chemical property values. The present invention does not require wavelength selection of the spectra, reduces the modeling difficulty, and shortens the modeling time.

This invention provides a method and system for rapid online detection of fertilizer nutrients based on near-infrared spectroscopy. The method includes: acquiring the online near-infrared spectrum and corresponding nutrient concentration data of the fertilizer to be tested, constructing and preprocessing a calibration set; screening characteristic wavelengths through multiple rounds of iteration: in each round, a sample subset is generated by randomly sampling from the calibration set, and a partial least squares regression model is established; determining the importance weight of each wavelength, and determining the number of wavelengths to retain by combining an exponential decay function, and screening high-weight wavelengths to obtain a candidate subset; evaluating the accuracy of the candidate subset using optimized k-fold cross-validation: calculating the leverage value and Mahalanobis distance of all samples, mapping them to a two-dimensional space and dividing them into grids; sampling and grouping by grid as a stratum, and calculating the root mean square error of cross-validation; comparing all iteration results, selecting the candidate wavelength with the smallest error as the optimal characteristic spectrum; and using the optimal characteristic spectrum and nutrient concentration data, training a quantitative prediction model for fertilizer nutrients to achieve rapid detection.

The invention relates to the technical field of quantitative analysis, in particular to a fly ash heavy metal element rapid quantitative analysis method and system.The fly ash heavy metal element rapid quantitative analysis method comprises the steps that a calibration set sample is analyzed through an LIBS spectrum, and corresponding spectrum data is obtained; constructing an XGBoost quantitative analysis model by taking the LIBS spectral data as an independent variable and the heavy metal element content as a dependent variable; performing intelligent optimization on hyper-parameters of the XGBoost model by adopting WOA, evaluating the influence of different spectrum preprocessing methods on the model prediction performance, and analyzing and screening key spectrum features based on feature importance to optimize the model performance; and the optimized XGBoost model is utilized to realize rapid quantitative detection of heavy metal elements in a test set sample. The developed fly ash heavy metal element on-line analysis system calculates and displays key indexes such as RMSE and the like in real time. The method realizes rapid and accurate detection of heavy metal elements in fly ash.

The invention relates to a substance classification method adopting single pulse excitation LIBS (Laser-induced Breakdown Spectroscopy) in combination with a K-nearest neighbor model. The method specifically comprises the following steps: 1, collecting spectral data; 2, performing internal standard processing on the spectral data; 3, K is set to be 1; 3.1, training the K-nearest neighbor model by using the training set of the K-nearest neighbor model and the K value, inputting the spectral variables in the calibration set into the K-nearest neighbor model to obtain an accuracy value of the calibration set, and recording the accuracy value; 3.2, enabling K to be equal to K + 1, and judging whether the K value is greater than the maximum K value or not; if not, turning to 3.1; if yes, the maximum accuracy value in all the accuracy values is found out, and the K value corresponding to the maximum accuracy value is the optimal K value; 4, obtaining a trained K neighbor model according to the optimal K value; 5, using the trained K neighbor model to classify and identify the to-be-classified substance; the method can quickly and accurately classify substances, and is very suitable for online quick automatic analysis.

The invention belongs to the technical field of vegetable quality detection, and particularly relates to an establishment method and a detection method of a tomato fructose content prediction model. The method comprises the following steps: firstly, collecting near infrared spectrums of tomato samples, dividing the tomato samples into a calibration set and a verification set, and removing abnormal values; secondly, comparing and screening the near infrared spectrums by using different pretreatment methods; carrying out dimension reduction treatment on the spectrum by adopting biPLS-CARS, and extracting a near infrared spectrum characteristic wavelength of the fructose content index; and finally, establishing a tomato fructose content prediction model by utilizing the selected characteristic wavelength through a partial least square method and combining with the absolute content of fructose. The established model can accurately predict the fructose content in the tomato, the operation process is simple, rapid and environment-friendly, and a new technical means is provided for detection of the fructose content in the tomato.

The invention discloses a method for detecting the content of bound acrylonitrile in butyronitrile degassed mucilage. The method comprises the following steps: step 1, collecting a nuclear magnetic resonance spectrum of a sample; 2, measuring the content of bound acrylonitrile in the sample; step 3, preprocessing the acquired nuclear magnetic resonance spectrum; step 4, dividing the data obtained in the step 3 into a correction set and a verification set, and then performing regression modeling; 5, evaluating the model to obtain a nuclear magnetic prediction model; and step 6, collecting the nuclear magnetic resonance spectrum of the to-be-detected mucilage and substituting the nuclear magnetic resonance spectrum into the nuclear magnetic prediction model to obtain the bound acrylonitrile content of the to-be-detected mucilage. The method for detecting the content of the binding acrylonitrile in the butyronitrile degassing mucilage solves the problems that in the prior art, measurement time is long, operation is tedious, sample pretreatment or sample preparation is needed, and operators make contact with chemical reagents for a long time.

The invention relates to the technical field of Beidou systems, and particularly discloses a radar detection method and system based on a Beidou system, and the method comprises the following steps: building a digital twin model of a ship, and determining a position A and a position B; controlling the attitude of the digital twin model to be converted from the angle combination i to the angle combination f; determining a compensation set when the attitude of the ship is converted from the angle combination i to the angle combination f based on the deviation condition of the position A and the position B; obtaining a calibration set according to the angle combination Z1 at the current moment and the angle combination Z2 at the previous moment; and correcting the Beidou positioning data and the radar positioning data according to the sub-angle combinations J1 and J2 in the calibration set. According to the invention, attitude deviation caused by installation position difference of the Beidou and the radar can be eliminated under the condition that the ship body swings by stormy waves, and the precision and stability of marine target positioning are obviously improved.

The invention discloses a method for rapidly detecting flavonoid components and moisture in persimmon leaves and products thereof. The method comprises the following steps: measuring the content of six flavonoid components and the content of moisture in a calibration set sample; scanning the persimmon leaf sample by using a near infrared spectrometer to obtain near infrared spectrum data; importing the obtained near infrared spectrum data of the sample into Brucker infrared spectrometer OPUS 6.5 software, analyzing the spectrum information, establishing a near infrared spectrum model by taking the spectrum information which is not preprocessed as a contrast and combining a partial least square method, and constructing a correction model of the relationship between the six flavonoid components and the moisture content of the persimmon leaf sample and the characteristic near infrared spectrum data; near infrared spectrum data of persimmon leaves to be detected are collected according to spectrum collection parameters the same as those of samples in the calibration set, the six flavonoid components and the moisture content in the persimmon leaves to be detected are obtained according to the constructed calibration model, the detection work efficiency is greatly improved, and comprehensive control over the product quality is facilitated.

The application provides a Dendrobium officinale powder release control method, relates to the technical field of drug quality control, and comprises the following steps: acquiring hyperspectral images of Dendrobium officinale powder under different temperature gradients and correcting; extracting average spectra and preprocessing by using an SNV algorithm; determining polysaccharide content; dividing a calibration set and a test set; extracting characteristic wavelengths by using a CARS algorithm; establishing a PLSR model under different temperature gradients; taking the characteristic wavelengths under all temperature gradients as input, and establishing a SO-PLS model; and determining whether to release or not by evaluating the probability risk of test set samples exceeding a control limit through bootstrap sampling. The application combines temperature control with hyperspectral technology, significantly improves prediction accuracy, introduces a probability risk evaluation mechanism, guarantees quality control, improves detection efficiency, and effectively solves the problems of long time consumption and low efficiency of traditional detection methods.

The present invention discloses a method for rapidly detecting the fatty acid content of paddy rice based on near-infrared spectroscopy, which relates to the technical field of rapid detection methods for the components of food crops. In the present invention, a batch of paddy rice samples with different fatty acid contents are prepared into powders as a calibration set. By collecting the near-infrared diffuse reflection and diffuse transmission spectra of the calibration set, performing first-order derivative preprocessing on them respectively, and then splicing the head and tail to obtain the diffuse reflection-diffuse transmission fusion spectrum of the samples, and measuring the fatty acid values of the calibration set by chemical methods, a regression model of the fusion spectrum and fatty acids is constructed using partial least squares method. Finally, when detecting unknown samples, the samples are prepared into powders, the near-infrared diffuse reflection and diffuse transmission spectra are collected, the fusion spectrum is obtained by the same treatment, and the fatty acid prediction result is obtained by calling the corresponding model. The beneficial effects of the present invention are as follows: it can improve the detection accuracy and reduce the detection limit, and this method does not consume reagents, is objective, rapid and simple.

The present application relates to the technical field of optical thickness measurement, in particular to a kind of ice thickness measurement method based on laser technology, comprising the following steps: first and second wave band laser beam coaxial superposition lock initial phase difference generation calibration set, analysis beam table bottom interface reflection transmission and utilize detector to capture waveform, separate time of flight difference and compare double wave band propagation time delay to generate characteristic matrix, extract propagation power attenuation and combine dynamic refractive index to correct light speed to obtain geometric thickness result numerical value.In the present application, by constructing double wave band interface reflection analysis mechanism, the non-uniform beam tracking denoising ability is strengthened, the distortion interference caused by multipath scattering is effectively solved, the inherent wave band delay jitter is separated, and the signal attenuation caused by medium loss is reduced, combined with refractive compensation attenuation, the physical space solving accuracy is corrected, the bubble and inclination optical path error are eliminated, the detection purity is ensured, and the ice layer physical thickness reconstruction stability and monitoring precision are greatly improved.

A system, method, and computer program product for predicting a characteristic value of a sample based on a NIR / MIR spectrum obtained from the sample of a particular sample type are disclosed. A wavelet transform is applied to the obtained spectrum to compute a sample set of wavelet coefficients in a plurality of wavelet bands. The system accesses a library of a plurality of calibration sets of wavelet coefficients that define a calibration coefficient space. The calibration sets are computed by a wavelet transform of respective NIR / MIR spectra obtained from calibration samples of at least the particular sample type. Each calibration set is associated with one or more reference characteristic values of the respective calibration samples. The system creates a local PLS model of the characteristic value to be determined for the sample by selecting a predetermined number k of nearest neighbors of the sample set in the calibration coefficient space and computes the local PLS model based on the selected k nearest neighbors and their associated reference characteristic values. The characteristic value of the sample is predicted by applying the local PLS model to the sample set.

The invention provides a quantitative analysis method for simultaneously determining essential oil and total flavonoids in illicium verum and fennel based on a near infrared spectrum quantitative analysis model, and relates to the technical field of illicium verum and fennel determination, and the quantitative analysis method comprises the following steps: S1, collecting illicium verum and fennel samples with different attribute information, and constructing a sample archive; s2, acquiring near infrared spectrums of illicium verum and fennel samples in the sample archive, and dividing the illicium verum and fennel samples into a calibration set and a verification set according to a Kennard-Stone method; s3, calculating the coverage degree from the correction set to the verification set, judging whether the coverage degree exceeds a threshold value or not, if so, turning to step S4, and if not, turning to step S3; if not, supplementing the sample according to error analysis; s4, acquiring a universal model which is established on the basis of the calibration set and can simultaneously predict the contents of the essential oil and the total flavonoids in the star anise and fennel samples, inputting the near infrared spectrum of the verification set into the universal model, calculating an error between a prediction result and a corresponding reference content, and performing sample supplementation according to error analysis.

A calibration method is disclosed for use in an analytical method for detecting at least one analyte in a sample of bodily fluid by using a mobile device (110) having at least one camera (112) and at least one ambient light sensor (114), the calibration method comprising: a. Providing a set of illumination conditions; and b. Determining a calibration set comprising a calibration function (152) for each of the lighting conditions of the set of lighting conditions, each calibration function (152) provides a relationship between at least one optical information item derived from at least one image captured with the camera (112) from at least a portion of at least one test field (120) of an optical test element (118) under respective illumination conditions and a concentration of the analyte in a sample applied to the test field (120). Further disclosed is a computer-implemented analysis method as well as a mobile device (110), a computer program and a computer-readable storage medium for performing the analysis method.

This invention relates to a method and apparatus for quantitative analysis of cement raw meal components based on feature screening and fusion, belonging to the field of cement component analysis technology. The invention acquires and preprocesses near-infrared spectral data and laser-induced breakdown spectral data to eliminate light scattering effects, noise interference, and differences in data dimensions. A concentration gradient method is used to divide the preprocessed dataset into a calibration set and a validation set. A predefined feature screening method is used to screen target feature points affecting the quantitative analysis of each target component from the calibration sets of the near-infrared spectral data and the laser-induced breakdown spectral data, respectively. An equal-weighted fusion method is used to perform feature-level fusion of the target feature points of the near-infrared spectral data and the laser-induced breakdown spectral data to construct a fused feature spectrum. Based on the fused feature spectrum, a partial least squares regression algorithm is used to establish quantitative analysis models for each target component to be measured. Through complementary spectral advantages, the accuracy of quantitative analysis is significantly improved.

The application discloses a discrimination method and system for sorghum varieties based on decision fusion and near-infrared spectroscopy, and the method comprises the following steps: collecting and processing samples of sorghum grains; collecting near-infrared spectra of each sample to construct a calibration set; determining the tannin content and amylopectin content of each sample; constructing a first quantitative detection model between the near-infrared spectra of the samples and the tannin content, and constructing a second quantitative detection model between the near-infrared spectra of the samples and the amylopectin content; constructing a qualitative discrimination model between the near-infrared spectra of the samples and the category attribution; predicting the calibration set respectively to obtain three groups of prediction values, taking the three groups of prediction values as independent variables, and taking the category labels of the samples as dependent variables to construct a decision fusion model; for unknown sorghum samples, the tannin content and amylopectin content of the samples are predicted respectively, the qualitative discrimination model is used to predict the category attribution, and finally, the category prediction value is obtained; the method has the advantages of high precision and strong robustness.

Systems and methods are disclosed for calibrating a multi-channel lighting system. The multi-channel lighting system may have a plurality of channels, each channel of the plurality of channels being configured to emit radiation at a respective control in a respective spectral band from a plurality of first spectral bands. The method comprises receiving or determining a set of calibration settings, each calibration setting defining a respective control value for each channel of the multi-channel lighting system. The set of calibration settings comprises a number of linearly independent calibration settings equal to a number of channels of the multi-channel lighting system. The method further comprises, for each calibration setting in the set of calibration settings: selecting a calibration setting from the set of calibration settings, configuring the multi-channel lighting system to emit radiation according to the selected calibration setting, and measuring, for each spectral band in a plurality of second spectral bands different from the plurality of first spectral bands, an irradiance value at a specified position relative to the multi-channel lighting system. Based on the set of calibration settings and the measured irradiance values, a calibration matrix is determined. The calibration matrix allows to convert a set control values for the plurality of channels into a set of irradiance values for the plurality of second spectral bands and / or vice versa.

The application relates to the field of Beidou system technology, and particularly discloses a radar detection method and system based on a Beidou system, which comprises the following steps: establishing a digital twin model of a ship and determining positions A and B; controlling the posture of the digital twin model to transform from an angle combination i to an angle combination f; determining a compensation set when the posture of the ship transforms from the angle combination i to the angle combination f based on the deviation of the positions A and B; obtaining a calibration set according to an angle combination Z1 of a current moment and an angle combination Z2 of a previous moment; and correcting Beidou positioning data and radar positioning data according to sub-angle combinations J1 and J2 in the calibration set. The application can eliminate the posture deviation of the Beidou and the radar caused by the installation position difference under the condition that the ship body is rocked by wind and waves, and significantly improves the precision and stability of offshore target positioning.

Some embodiments described herein relate to a method of calibrating a device sensor that includes inserting a reference sensor into a calibration system. In an embodiment, the calibration system includes a light source, and a photonic detector. In an embodiment, the method further includes measuring a reference transmission value of an amount of light from the light source that is transmitted through the reference sensor towards the photonic detector, and inserting the device sensor into the calibration system. In an embodiment, the method further includes measuring a transmission value of the amount of light from the light source that is transmitted through the device sensor towards the photonic detector, and calculating a scaling factor for the device sensor. In an embodiment, the scaling factor equalizes the transmission value of the device sensor to the reference transmission value of the reference sensor.

The invention discloses a sorghum variety discrimination method and system based on decision fusion and near infrared spectroscopy. The method comprises the following steps: collecting and processing samples of sorghum grains; collecting a near infrared spectrum of each sample, and constructing a calibration set; determining the tannin content and amylopectin content of each sample; constructing a first quantitative detection model between the near infrared spectrum of the sample and the tannin content, and constructing a second quantitative detection model between the near infrared spectrum of the sample and the amylopectin content; constructing a qualitative discrimination model between the near infrared spectrum of the sample and the category affiliation; respectively predicting the correction set to obtain three groups of predicted values, and constructing a decision fusion model by taking the three groups of predicted values as independent variables and the category labels of the samples as dependent variables; respectively predicting the tannin content and amylopectin content of an unknown sorghum sample, and predicting the category attribution of the unknown sorghum sample by adopting a qualitative discrimination model to obtain a final category prediction value; the method has the advantages of high precision and strong robustness.

A Coptis chinensis moisture content analysis method based on heterogeneous sensor data fusion. The analysis method comprises the following steps: respectively collecting spectra of Coptis chinensis samples by using two types of spectrometers; gathering the collected spectra of the Coptis chinensis samples to form a sample set, and removing outliers to obtain a calibration set and a validation set; preprocessing the spectra, and selecting an optimal processing algorithm of the spectrometers to reduce spectral noise and simplify background spectral information; and performing low-level data fusion on spectra data, stitching spectral bands of near-infrared spectra and infrared spectra, so as to obtain moisture content prediction results, establishing a heterogeneous data fusion model, and on the basis of RMSE values and RPD values, evaluating the model and the prediction results, so as to obtain accurate analysis results.

The wavelength selection method in near-infrared quantitative analysis provided by the application adopts a hyperspectral imaging system to obtain a hyperspectral image of a standard reference object, and adopts a micro spectral sensor to obtain a visible-near-infrared reflection spectrum of an analysis object. For the hyperspectral image of the standard reference object, second-order difference operation and smoothing denoising are performed on the spectral data to obtain a standard reference object data set; for the visible-near-infrared reflection spectrum of the analysis object, abnormal data is removed, a Monte Carlo sampling method is used for analysis, then a wavelet packet decomposition and reconstruction method is executed for data smoothing to obtain an analysis object data set. In the modeling process, the classification criteria of different data sets are fully mined, and the Euclidean distance similarity is judged, so that different classes of data sets are objectively obtained, and the largest and smallest data sets of the two classes are selected as the calibration set and the prediction set, respectively, so that better modeling effect can be achieved.

This invention relates to the field of optical thickness measurement technology, specifically a laser-based ice thickness measurement method, comprising the following steps: coaxially superimposing first and second band laser beams to lock the initial phase difference and generate a calibration set; analyzing the reflection and transmission at the beam surface and bottom interface and capturing the waveform using a detector; separating the time-of-flight difference and comparing the propagation delay of the two bands to generate a feature matrix; extracting the power attenuation after propagation and combining it with dynamic refractive index to correct the light speed and obtain the geometric thickness result. In this invention, by constructing a dual-band interface reflection analytical mechanism, the denoising capability of non-uniform beam tracking is enhanced, effectively solving the distortion interference caused by multipath scattering; separating inherent band time delay jitter and reducing signal attenuation caused by medium loss; and combining refraction compensation attenuation correction to correct the physical space solution accuracy, eliminating bubbles and tilt angle optical path errors, ensuring detection purity, and greatly improving the stability and monitoring accuracy of ice layer physical thickness reconstruction.

This invention discloses a calibration method for visual language models adapted during testing based on a single preparation. It utilizes a generative model to offline synthesize a small number of images for each category in the target domain, extracts features, and divides them into a pre-filled set and a calibration set. The pre-filled set is used to initialize positive and negative sample caches, achieving a "warm start." A lightweight probe network is trained based on the calibration set, which dynamically predicts the optimal fusion weights based on the original prediction scores and cached retrieval scores. During the testing phase, the network generates weights in real time and adaptively weights and fuses multiple predictions to obtain the calibrated prediction results. This invention requires only a one-time offline preparation, with extremely low online inference overhead. While maintaining efficient inference, it significantly reduces calibration errors, improving the reliability and security of model predictions, making it particularly suitable for real-time applications with strict confidence requirements.

The application discloses an anaerobic fermentation instability early warning method based on near-infrared transmission spectrum, which collects near-infrared transmission spectrum data of biogas slurry in an anaerobic fermentation impact test and corresponding biogas slurry samples online, determines the spectrum data corresponding anaerobic fermentation state according to the biogas slurry index determination results and the fermentation state threshold of the propionic acid / acetic acid ratio and the volatile fatty acid / total alkalinity ratio, performs wavelength variable primary selection on the preprocessed calibration set spectrum data by using linear classifier integrated wavelength selection fusion mutual information nonlinear feature supplement, simultaneously optimizes the characteristic wavelength and the extreme gradient boosting modeling hyperparameter by using a genetic annealing particle swarm algorithm, and constructs an anaerobic fermentation state intelligent diagnosis model, so that the anaerobic fermentation instability early warning can be completed in combination with host computer software and a 4G SMS cat. The method has unmanned online monitoring function, can realize more than 3-minute sampling interval full-automatic anaerobic fermentation state intelligent diagnosis and instability early warning, and further ensures efficient and stable operation of a biogas project.

The invention discloses a small sample near infrared spectrum calibration method based on partial least squares score, which comprises the following steps: acquiring a near infrared spectrum data set, and dividing the near infrared spectrum data set into a calibration set and a test set; constructing a partial least squares model; acquiring an optimal latent variable number in the data set by utilizing a partial least squares algorithm; constructing an integrated orthogonal extreme learning machine; constructing a relation model between a score matrix of a partial least square model and the concentration content by using a calibration set in the near infrared spectrum data set according to the partial least square model and an orthogonal extreme learning machine; and predicting the concentration in the test set by using the relation model to obtain concentration information. According to the small sample near infrared spectrum calibration method based on the partial least square score, potential concentration information is mined through the partial least square score matrix, the PLSELM quantitative model is constructed, and the quantitative analysis performance of the near infrared spectrum is remarkably improved; the near infrared spectrum quantitative model which is high in performance, high in robustness and suitable for small sample modeling can be provided.

The invention discloses a method for determining the content of bound styrene in styrene-butadiene rubber cement by a near infrared diffuse reflection method. The method comprises the following steps: step 1, collecting a near infrared diffuse reflection spectrum of a rubber cement sample; 2, measuring the content of bound styrene in the mucilage sample; step 3, preprocessing the near-infrared diffuse reflection spectrum information; step 4, dividing the near infrared spectrum data into a calibration set and a verification set, and performing regression modeling to obtain a near infrared analysis model; 5, evaluating the near-infrared analysis model to obtain a near-infrared prediction model; and 6, collecting the near-infrared diffuse reflection spectrum of the to-be-detected rubber cement, and substituting the near-infrared diffuse reflection spectrum into the near-infrared prediction model to obtain the bound styrene content of the to-be-detected styrene-butadiene rubber cement. The method for determining the content of bound styrene in the styrene-butadiene rubber cement by the near-infrared diffuse reflection method solves the problems of low measurement speed, long sample pretreatment time, influence of manual operation on a working curve and the like in the existing method.

This invention discloses a method for constructing a near-infrared identification model for Terminalia chebula medicinal materials. The method includes: collecting a first predetermined number of Terminalia chebula medicinal material samples from the original Terminalia chebula bud, Terminalia chebula velutipes bud, and Terminalia chebula fruit, and performing near-infrared spectral measurements to obtain an initial spectral set; establishing a calibration set and a validation set; preprocessing the sample spectra within the calibration set to obtain a preprocessed spectral set; selecting wavelengths from the preprocessed spectral set to obtain a first wavelength group or a second wavelength group, wherein the first wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages, and the second wavelength group is used to identify Terminalia chebula medicinal materials at different maturity stages and from different buds; and establishing a model within the spectral range of the first wavelength group and / or the second wavelength group. This invention utilizes near-infrared spectroscopy technology, and the sample preparation process is simple, non-destructive, and free from chemical reagent contamination.