47 results about "Infra red spectroscopy" patented technology

This invention relates to a method for identifying near infrared spectrum of rosewood including the following steps: applying rosewood and non-rosewood samples to collect several times of near infrared spectrums at different positions on the surface of the sample by a near infrared spectrum device and collect spectrums of 3-19 positions at a same sample to set up distinguishing models of true and false rosewood and its kind through spectrum pre-process such as smoothing, base line correction, a first stage derivative, a second stage derivative, multiple dispersion correction or pre-process of dimensionality reduction of data by a multivariable data analysis method of soft independent modeling sorting or deflection of the least square differential analysis so as to realize quick and harmless identification to true and false of rosewood and their kinds in several minutes.

The invention relates to an estimation method for the content of copper in soil on the basis of visible-light near-infrared spectrum technology. The estimation method is realized by the following six steps: (1) collecting a soil sample; (2) determining a visible-light near-infrared spectrum; (3) pretreating the spectrum; (4) determining the reference value of the content of copper in soil; (5) establishing an estimation model; and (6) estimating the content of copper in an unknown soil sample. The estimation model between the visible-light neared-infrared reflectivity spectrum and the content of the copper is established by utilizing a wavelet neural network method on the basis of the visible-light near-infrared spectrum technology, so that the visible-light neared-infrared reflectivity spectrum of the unknown soil sample is substituted into the estimation model and further the content of the copper in the unknown soil sample is determined. The estimation method has the advantages that the determination can be performed without direct contact with the sample, and is complete non-destructive measurement, the operation process and the calculation method for the content of the copper in the soil are simple, the determination speed is greatly enhanced, no other chemical reagents need to be added and the like, so that the estimation method is environmental-friendly and pollution-free.

The invention discloses a nondestructive testing method for Fushi apple quality based on a near infrared spectrum. The method comprises the following steps: 1) selecting a batch of intact Fushi apples as experiment samples; 2) collecting a near infrared spectrum of the apples; 3) utilizing a principal component analysis algorithm for performing dimension reduction treatment on near infrared spectrum data of the apple samples; 4) measuring the acidity and sugar degree values of the apple samples and recording; and 5) utilizing the pretreated near infrared spectrum data and acidity and sugar degree values, and combining with a neural network algorithm for establishing an apple acidity and sugar degree prediction model, thereby realizing the detection for the interior quality of the apples. According to the invention, the acidity and sugar degree values of the apples can be quickly forecast and the forecast value thereof can be directly displayed, the forecasting precision can meet the practical production requirement, the experiment system is simple in operation method, and the nondestructive testing method can be used for online detection and classification of the interior quality of the apples.

The invention provides a qualitative and quantitative combined near infrared quantitative model construction method which comprises the following steps: obtaining an actual sample of a modeling correction set, and detecting a basic chemical component of the actual sample; scanning a spectrum corresponding to the correction sample, and culling an abnormal sample; qualitatively projecting an available spectrum; classifying projection data; forecasting a verification set consisting of a near infrared spectrum of each type and a chemical value through a modeling set, and calculating a forecasting error; randomly selecting near infrared wavelength points; calculating a general correction set error corresponding to each generation of the wavelength points; determining near infrared wavelength selection points and characteristic information of the infrared spectrums according to the minimum general correction set error; rebuilding a regression model through the spectrums and the chemical values of the correction set; detecting the chemical values of verification samples, obtaining corresponding spectrums, and quantitatively evaluating the regression model. According to the method, the spectrums of the correction set are qualitatively projected and analyzed, so that the method is adaptive to changes of the spectrums, and the forecasting stability of the model can be kept.

The present invention relates to a method to detect microorganism in Chinese traditional medicine by using an AOTF approximate infrared spectrometer, and comprises the following steps: firstly, the spectral datum of a correcting set sample are collected by using the AOTF approximate infrared spectrometer. Secondly, a qualitative correcting model is founded according to the collected spectral datum. Thirdly, a quantitative correcting model is founded according to the correlation between the spectral datum and the microbial datum. Fourthly, the approximate infrared spectral datum of an unknown model are collected. Fifthly, the qualitative and quantitative analysis results about the microorganism are obtained through transferring the correcting model. The AOTF-NIR technology is used in the present invention to realize the fast detecting work of the lab microorganism. At the same time, the fast detection of microorganism can be realized online and the activity of the microorganism can be easily identified. The present invention has the advantages of no need of pretreatment of the sample, fast detecting speed (second grade speed), no consumption of reagent, green environmental protection analysis without pollution and high accuracy.

The invention relates to a wavelength similarity consensus regression-based infrared spectrum quantitative analysis method and a wavelength similarity consensus regression-based infrared spectrum quantitative analysis device. The device comprises a spectrometer, a preprocessor, a wavelength filter and a partial least-squares regression analyzer which are connected through a data signal line; and the wavelength of the near-infrared spectrum ranges from 780 to 50,000nm. By performing cluster analysis on spectra along the wavelength direction, a spectrum is divided into different information blocks, multiple models are constructed, and the difficulty for a single model method to extract information of a spectrum signal is overcome; and by singly determining factor number for different submodels, effective information is fully extracted, and the prediction accuracy and robustness of an infrared spectrum analysis model are improved.

The invention discloses an LPP (Local Preserving Projection)-based Infrared spectrometer calibration method. The LPP-based Infrared spectrometer calibration method comprises the following steps of: collecting infrared spectrograms of master and slave instrument guide sample sets; establishing a subspace LPP transformation matrix which can reflect the difference between the master and slave instrument infrared spectrograms according to the collected infrared spectrograms of the master and slave instrument guide sample sets; obtaining a corresponding transformation relation between the infrared spectrograms of the master and slave instrument guide sample sets by utilizing the subspace LPP transformation matrix, and transforming the infrared spectrogram on the salve instrument to the master instrument; and forecasting the transformed infrared spectrogram according to a multivariate calibration model established on a master instrument calibration set so that the master instrument model can be successfully applied to the infrared spectrogram of the slave instrument, thus achieving model transformation. The LPP-based Infrared spectrometer calibration method provided by the invention is simple in operation method, high in calibration forecasting accuracy and high in reliability, and the influence of nolinear factors, noise and other redundancy characteristics in the spectrogram can be effectively eliminated.

Provided is a device for testing normal spectral emissivities of infrared transparent materials. The device includes a sample heating and temperature-control subsystem, a vacuum sealing subsystem, a Fourier infrared spectrometer, a computer and a standard blackbody. According to the device, accurate measurement of the emissivities of medium-wave, short-wave and long-wave infrared transparent materials at the temperature from 300K to 800K can be achieved, the device has the advantages of being high in utilization rate and easy and convenient to operate, and the spectral emissivities of different kinds of infrared transparent materials at medium and high temperatures can be tested.

The invention discloses an infrared spectrum non-linear modeling quantitative analysis method based on the molecular vibration theory; the method makes use of Matlab software programming and Statistica software data processing to establish a non-linear model and obtain the final quantitative result through the model. Through the establishment of the non-linear model, the invention realizes quantitative analysis and reduces measurement error to improve the measurement accuracy, precision and feasibility.

Relating to the technical field of sorting equipment, the invention discloses near-infrared multispectral sorting equipment. The equipment includes a plurality of light sources; a material conveying mechanism; an optical signal receiving device located on both sides or one side of a material; and an image processing and displaying device for processing of signals received by the optical signal receiving device; wherein the light sources are symmetrically distributed on two sides of the optical signal receiving device. The near-infrared multispectral sorting equipment provided by the inventioncan acquire the near-infrared multispectral image of the material on line, utilize the near-infrared characteristic spectral information of multiple wave bands, and combine chemometrics method frequently used by near infrared spectrum analysis, can identify a variety of plastic materials simultaneously, and the multispectral combination also can improve the identification rate of plastic materialswith similar characteristics. The near-infrared multispectral sorting equipment provided by the invention can be widely applied in waste plastic recovery, food components, mildew and other sorting fields, and has important practical application significance.

The present disclosure generally relates to methods of using near infra-red radiation, such as from a near infra-red spectroscopy device, to monitor physical activity in a person. In one aspect, a method of measuring physiological parameters is provided. The method further includes determining a NIRS-derived measure of a tissue of a person using near infra-red spectroscopy over a time period, associating the NIRS-derived measure to the time period to determine a function-related change, and associating the function-related change to a biomechanical function.

The invention discloses a portable system NIR (near infrared spectrum) rapid food detection and modeling integration system and method. The system comprises a small-sized near-infrared spectrograph, an android smart phone and a driving module; and a Y-type USB cable is used for connection. According to the system, the smart phone is utilized to perform secondary software development on a small-sized NIR instrument so as to realize sample pretreatment analysis of a collected sample, establish a calibration model and perform detection analysis; the near-infrared spectrograph is small in size, light in weight and easy to carry; scanning and analyzing speed is fast; and a quantitative analysis can be conducted. By utilizing the technical scheme provided by the invention, a user cannot be restricted by a solidification model in supporting analysis software provided by an instrument; in practical detection, an appropriate model can be actively constructed according to the data characteristics of a detection object, and an optimal parameter is found and determined to establish an optimal model and improve the detection accuracy of the model. By the system and the method disclosed by the invention, the nondestructive detection analysis of flour can be realized.

The invention belongs to the technical field of quality detection of traditional Chinese medicine liquid preparations and particularly relates to a method for measuring tween-80 content in a compound musk injection by using a near-infrared spectrum. The method comprises the following steps of (1) collecting a calibration set and measuring the spectrum; (2) establishing and inspecting a calibration model; (3) forecasting the calibration model; and (4) measuring an unknown compound musk injection sample. The method provided by the invention has the advantages that input, calculation and output of spectrum data are realized by a computer and support software, the whole process time is short, rapid and exact, the method is used for rapid inspection in a laboratory, on-line production detection, the production efficiency is improved, manpower and material resources are greatly saved, big innovation is created fro the increase of the quality standard and rapid detection of the compound musk injection, and economical and social benefits are great.

The invention relates to the field of analytical chemistry, particularly to an analysis identification method of ophiocordyceps sinensis through an infrared spectrum, and aims to solve the technical problem that the authenticity of the existing ophiocordyceps sinensis is difficult to distinguish. In order to solve the above technical problem, the invention provides a technical scheme to rapidly and accurately distinguish ophiocordyceps sinensis. The analysis identification method through the infrared spectrum comprises the following steps: a, drawing a standard spectrum of ophiocordyceps sinensis; b, collecting an essential characteristic peak and a common characteristic peak of an infrared spectrum of ophiocordyceps sinensis; c, distinguishing the essential characteristic peak and similarity of the infrared spectrum of measured ophiocordyceps sinensis. The invention provides a novel method for rapidly and accurately distinguishing ophiocordyceps sinensis.

The invention discloses a method for measuring the natural rubber content in a plant and an LED near-infrared spectrometer for measurement. The method comprises the steps that (1) samples are prepared; (2) the prepared samples are measured to acquire a spectrum of a set of samples and establish a corresponding rubber content data model; and (3) the samples to be tested are measured, and accordingto the spectral peak, the model in the step 2 is used to acquire the rubber content. The near-infrared spectrometer comprises an LED array light source, a curved mirror and a detector. The LED array light source comprises 8 different single-wavelength LEDs arranged in a circle, and the LEDs are uniformly arranged in the curved mirror through a clamp slot. The curved mirror is a single-sided reflective mirror. The curved mirror and the detector are soldered on a circuit board respectively. The measuring method provided by the invention has the advantages of being convenient and quick and smallerror.

The invention relates to a laser near-infrared rapid detecting method for milk freshness. The method comprises the following steps: a plurality of milk samples are collected and divided into the calibration set milk samples and the test set milk samples, the acidity values of the calibration set milk samples and the test set milk samples are measured; near infrared spectrums of the calibration set milk samples and the test set milk samples are measured; a quantitative model of the acidity values of the calibration set milk samples and the fused spectrum of the calibration set milk samples is established; the acidity values of the test set milk samples are predicted through the quantitative model, and compared with those of the calibration set milk samples; the near infrared spectrum of the milk sample to be detected is collected, and the acidity value of the milk sample to be detected is predicted through the quantitative model. The laser near-infrared rapid detecting method for milk freshness is safe, reliable, fast and accurate, and has very high practical application value.

The invention discloses a method for distinguishing the infrared radiation absorption characteristics of cereals and pests. The method can be used to rapidly and conveniently acquire infrared radiation wavelength parameters of the cereals and the pests so as to determine an optimal band for infrared disinfestation. The method for measuring infrared characteristics of the cereals and the stored grain pests comprises the following steps: 1, preparing a sample; 2, measuring and scanning the cereals and the pests by adopting a Fourier transform attenuated total reflection infrared spectrometer, and collecting infrared spectrograms and data of the cereals and the pests; 3, carrying out qualitative analysis on the infrared spectrogram and data of the cereals and the pests, and drawing a fingerprint infrared spectrum; 4, acquiring a wavelength range with low infrared radiation temperature and small cereal quality change according to Wien displacement law and based on the acquired infrared spectrogram, and taking the acquired wavelength range as a working wavelength range of infrared Insecticidal equipment. When an infrared radiation device works under the wavelength range condition, the occurrence of pests can be effectively reduced during storage, the original quality and taste of the cereals are maintained, and the energy consumption is low.

The invention discloses a tea variety classification method based on GK identification clustering. According to the method, first, a near infrared spectrum of tea samples is collected, and infrared spectrum preprocessing and dimension reduction processing are performed on the tea samples; second, identification information of the near infrared spectrum of tea training samples is extracted, and fuzzy C-means clustering is performed on test samples; and last, tea variety classification is performed by use of GK identification clustering. The method has the advantages of being high in detection speed, high in classification accuracy, environmentally friendly and the like. Through the method, nondestructive, quick and accurate classification of tea varieties can be realized.

The invention discloses a method for detecting the total nitrogen in a manure of a large-scale cattle farm based on a near-infrared transmission-diffuse reflection spectrum. The method comprises the following steps: (1) preparing manure samples of the large-scale cattle farm with different concentrations used for experiments; (2) detecting the total nitrogen content of each sample to obtain a total nitrogen concentration matrix of the manure samples; (3) scanning a near infrared transmission spectrum and a diffuse reflection spectrum of each sample to obtain spectrum matrixes A and B; (4) putting A and B in a column for a fusion to obtain a transmission-diffuse reflection near infrared spectrum matrix of the manure samples; (5) establishing a quantitative analysis model on the transmission-diffuse reflection near infrared spectrum matrix and the total nitrogen concentration matrix; and (6) performing a near infrared transmission spectrum scanning and a diffuse reflection near infraredspectrum scanning on an unknown manure sample to obtain spectrum matrixes M and N; fusing M and N to obtain a transmission-diffuse reflection near infrared spectrum matrix of the unknown manure sample; and substituting the transmission-diffuse reflection near infrared spectrum matrix into the quantitative analysis model to obtain the total nitrogen content in the unknown manure sample.

The invention relates to a method for identifying deer blood powder. The method comprises the following steps: (1) taking known genuine deer blood powder and sample powder, using KBr pellets to respectively measure infrared spectral diagrams of the known genuine deer blood powder and the sample powder, and performing peak position, peak intensity, peak form and wave number contrastive analysis onthe two infrared spectral diagrams; and determining that the sample powder is a counterfeit product if the two infrared spectral diagrams are different completely, and performing the following step for analysis if the two infrared spectral diagrams are similar or the same; and (2) taking the known genuine deer blood powder and the sample powder, respectively performing solvent extraction on the known genuine deer blood powder and the sample powder by using n-hexane, petroleum ether (60-90 DEG C), methanol, ethanol and a mixture of ethanol and water (v/v), respectively determining the infraredspectral diagrams of extracts of the known genuine deer blood powder and the sample powder, and performing two-two peak position, peak intensity, peak form and wave number contrastive analysis on theinfrared spectral diagrams of the extracts of the genuine product and the sample obtained by extraction with the same solvent; and determining that the sample is a counterfeit product if main data inthe contrastive analysis of three and more infrared spectral diagrams with respect to the five solvent extracts is different. By adopting the method, ingredients in the original medicinal material donot need to be separated one by one, and the authenticity of the deer blood powder can be quickly, simply and directly judged without losing the nature of the original medicinal material.

The invention discloses a tea infrared spectrum classification method for fuzzy uncorrelated C means clustering. By adopting the method, fuzzy uncorrelated authentication information of tea infrared spectrum data can be dynamically extracted in the fuzzy C means clustering process, so that the accuracy of tea variety authentication can be improved. The method comprises the following steps of firstly, collecting an infrared spectrum of a tea sample with a Fourier infrared spectrometric analyzer; then, performing multiplicative scatter correction pretreatment on the infrared spectrum; then, performing dimensionality reduction on spectrum data to 20 dimensions with a main component analysis method; then, utilizing linear discriminant analysis to extract the authentication information in the spectrum data; and finally, performing tea variety classification with a fuzzy uncorrelated C means clustering method. The fuzzy uncorrelated C means clustering method is designed on the basis of a fuzzy C means clustering method, and the tea infrared spectrum classification method for fuzzy uncorrelated C means clustering has the advantages of rapidness in detection speed, rapidness in classification speed, high classification accuracy and the like and can realize correct classification of tea varieties.

The invention discloses an NIRS (near infra red spectrum) model construction method for predicting saccharose content in sweet corn. The method comprises the following steps: (1) collection of near infrared spectra of a sample: obtaining near infrared spectrum information of the sample; (2) preparation of an enzyme method sample solution; (3) determination of sample saccharose: obtaining a chemical value of the sample; and (4) NIRS modeling: carrying out scatter correction and mathematical treatment of the spectrum on a saccharose calibration model. Specific to the saccharose content in the sweet corn, the chemical value determined by an infrared spectrum analysis combined with enzyme method is adopted, by the scatter correction and mathematical treatment of the spectrum, the analytic statistics of chemometrics are carried out on a full-spectrum wave band, an NIRS model of the saccharose content is constructed, the NIRS model can be used for quickly predicting the saccharose content inthe sweet corn, and then valuable references are provided for accelerating superior seed quality resource screening and quality breeding of the sweet corn.