1005 results about "Near infrared spectra" patented technology

Methods and apparatus for measuring cerebral O₂ saturation and detecting cerebral hypoxia-ischemia using multi-wavelength near infrared spectroscopy (NIRS). Near-infrared light produced by an emitter is directed through brain tissue. The intensity of the light that passes through the brain tissue is measured using photodiode detectors positioned at distinct distances from the emitter. This process is conducted for at least three wavelengths of near-infrared light. One of the wavelengths used is substantially at an isobestic point for oxy-hemoglobin and deoxy-hemoglobin, but the other two may be any wavelengths within the near-infrared spectrum (700 nm to 900 nm), so long as one of the additional wavelengths is greater than the isobestic point and the other is less than the isobestic point. Tissue oxygenation is calculated using an algorithm derived from the Beer-Lambert law. Cerebral hypoxia-ischemia may be diagnosed using the calculated tissue oxygenation value.

A method for the evaluation of target media parameters in the visible and near infrared is disclosed. The apparatus comprises a light source, an illuminator/collector, optional illumination wavelength selector, an optional light gating processor, an imager, detected wavelength selector, controller, analyzer and a display unit. The apparatus illuminates an in situ sample of the target media in the visible through near infrared spectral region using multiple wavelengths and gated light. The sample absorbs some of the light while a large portion of the light is diffusely scattered within the sample. Scattering disperses the light in all directions. A fraction of the deeply penetrating scattered light exits the sample and may be detected in an imaging fashion using wavelength selection and an optical imaging system. The method extends the dynamic range of the optical imager by extracting additional information from the detected light that is used to provide reconstructed contrast of smaller concentrations of chromophores. The light detected from tissue contains unique spectral information related to various components of the tissue. Using a reiterative calibration method, the acquired spectra and images are analyzed and displayed in near real time in such a manner as to characterize functional and structural information of the target tissue.

The invention discloses an online nondestructive testing (NDT) method and an online nondestructive testing (NDT) device for comprehensive internal/external qualities of fruits. The online NDT method of the invention comprises the following steps: a comprehensive quality evaluation model for the fruits is established firstly by a detection device consisting of a conveying system, a machine vision system, an Near Infrared Spectrum (NIR) system and a grading system, and the fruits performs online uniform motion through the conveying system; the machine vision system acquires the image information of the fruits and extracts the external characteristics of the fruits; the NIR system acquires the spectral information of the fruits; the grading system analyses the spectral information by a pre-established mathematical model, and extracts the internal characteristics of the fruits; and the grading system fuses the internal characteristics and the external characteristics of the fruits by a pre-established information fusion model so as to obtain the comprehensive quality level of the fruits. The method and the device of the invention can detect the internal characteristics and the external characteristics of the fruits simultaneously; a DSP high-speed image processing system is used to handle complex image information, which greatly improves the real-time characteristic of the system; and an information fusion technology is used to carry out online real-time detection on the comprehensive qualities of the fruits.

The invention discloses a near infrared spectrum analyzing method based on the isolated component analysis and genetic neural network, which comprises the following steps for the acquired near infrared spectrum: firstly, effectively compressing spectrum data by using wavelet transform; secondly, extracting an independent component and a corresponding mixed coefficient matrix of a near infrared spectrum data matrix by using an isolated component analysis method; thirdly, building a three-layer BP neutral network, using the mixed coefficient matrix of a training sample as the input and correspondingly measured component concentration matrix as the output, and optimizing a neutral network structure by adopting a genetic algorithm, and obtaining a GA-BP neutral network by the training of the training sample; fourthly, predicting and analyzing the measured component concentration of the predicted set sample by using the GA-BA neutral network. The method enriches the chemical measurement method, widens the application range of the isolated component analysis and has favorable application prospect.

Apparatus and methods to enhance light intensity within useful red to near-infrared spectral ranges, using direct or indirect sunlight, or from other ambient white light, are described. The disclosed devices provide high quantum yield photoluminescent ambient light spectrum conversion to increase the supplied energy primarily in the 590 nm-850 nm spectral range. These devices also pass much of the incident light in the spectral range in which the device's photoluminescent materials emit light, thereby greatly increasing the effective intensity of light available in the targeted 590-850 nm wavelength range. The ambient light conversion devices of the disclosure may be incorporated in apparel, bandage-like patches, converting reflectors, large area converters, awnings, window covers, and other articles, materials, and products. The converted light may be used in therapeutic treatments, horticultural and biotechnological applications, and other applications in which the converted light outputs of the present disclosure are beneficial.

The invention relates to a fruit internal quality on-line detection method and an apparatus thereof. The detection method comprises: implementing the spectral scanning to the fruit to be detected and collecting a near infrared spectrum of the fruit to be detected; and putting acquired spectrum signals into a pre-established model and getting the internal quality index of the fruit to be detected. The detection apparatus includes a spectrum collection device and a computer; wherein, the spectrum collection device is used in spectral scanning to the fruit to be detected and collection of near infrared spectrum signals of the fruit to be detected so as to transmit the signals to the computer; and the computer is used for putting the received spectrum signals into the pre-established model for data analysis so as to obtain the internal quality index of the fruit to be detected. The method applies the optical detection means based on near infrared to the detection process of fruit internal quality, and can release the labor force, and the method also has advantages of high detection precision, good consistency of results and high degree of automation, and creates the conditions for the standardized classification of internal quality of fruit products.

A method for multivariate adulteration detection on an edible oil includes (1) construction of a model: S1, acquiring near-infrared spectra of edible oils; S2, establishing a near-infrared spectral database of the edible oils; S3, establishing a multivariate adulteration detection model for a type of edible oil; and (2) application of the model: acquiring spectra of a sample to be tested according to the near-infrared spectral signal acquisition method in step S1, preprocessing the obtained near-infrared spectra by using the method in step S2 to obtain near-infrared spectral data of the sample, and determining the authenticity of the sample to be tested by using the multivariate adulteration detection model for the edible oil established in step S3. The method is simple and rapid in operation, can effectively and rapidly screen the authenticity of an edible vegetable oil, and has strong practicability.

The invention discloses a qualitative analysis method for improving an identification result on the basis of the near-infrared mode. The qualitative analysis method comprises the steps of (1) acquiring near-infrared spectral data of a sample and determining a modeling set and testing sets; (2) carrying out pretreatment, partial-least-square feature extraction and orthogonal-linear-identification feature extraction on the modeling set and the testing sets in sequence; (3) calculating a spectral transformation matrix between the modeling set and the testing sets by adopting a direct model transferring method, and correcting the remaining testing sets; (4) establishing a quantitative analysis model; and (5) carrying out quantitative identification on the remaining testing sets by utilizing the established quantitative analysis model. The qualitative analysis method disclosed by the invention is established on the basis of near-infrared quantitative analysis, and the orthogonal-linear-identification method which is used in multi-classification and two-classification problems is used in the feature extraction step; in addition, the testing sets can be corrected by the direct model transferring method, so that the model applicability caused by long-time spectral shift of the same instrument can be realized and the result of quantitative identification is improved.

The present invention relates to a similar tobacco leaves search method based on the near infrared spectrum of tobacco leaves. The near infrared spectrum of tobacco leaves is used as basic data by the present invention. Distributed sampling is first carried out to the target tobacco leaves of each species; samples are pre-treated; the near infrared spectrum of the samples is obtained by scanning the samples on a near infrared spectrometer; principal component analysis (PCA) operation is carried out to a plurality of near infrared spectra of the target tobacco leaves of each species, obtaining loading matrixes, characteristic values and standardized residual errors, so as to generate a data model of the target tobacco leaves of each species; the near infrared spectrum of an unknown tobacco leaf, and the loading matrixes in the target tobacco leaf data models are used to carry out principal component decomposition calculation to the near infrared spectrum of the unknown tobacco leaf, so as to obtain the principal component score and decomposition residual error of the unknown tobacco leaf; the principal component score of the unknown tobacco leaf and the principal component space distance of the target tobacco leaf data models are calculated, and the residual error distance between the decomposition residual error of the unknown tobacco leaf and the standardized residual errors in the target tobacco leaf data models is also calculated; the distance between the unknown tobacco leaf and the target tobacco leaves is measured through the sum square root of the principal component space distance and the residual errors; the smaller the distance is, the higher the similarity is; finally, the distances between the unknown tobacco leaf and each target tobacco leaf is compared and sorted according to the size of the distances, so as to obtain a similar tobacco leaves search result.

Methods are disclosed for identifying and treating oil sand ores having degraded bitumen. This can be done by considering the location from which the ore is mined or its history, or by microscopic examination of a bitumen froth made from the ore, or by near-infrared spectroscopy. Ore which contains degraded bitumen is treated with at least 0.05 wt/wt % alkaline material, preferably 0.1 wt./wt % of such alkaline material, in the water addition step of a hot or warm water extraction process for the making of bitumen froth, such as the Clark process.

The invention provides a method for using near infrared spectra to identify the reduction milk-like liquid of fresh milk and product milk, which comprises the following steps: first doing front process to the sample with known reduction milk-like liquid content, collecting the sample's near infrared spectra, establishing the qualitative or quantitative identifying model, collecting the near infrared spectra of the unknown sample to do data process and doing qualitative or quantitative identifying.

The invention relates to a visible/near infrared spectroscopy-based poultry infertile egg detecting method and device. The device is a poultry egg detecting device based on visible/near infrared spectroscopy. The device comprises a power source (1), a heat sink (2), an object carrying table (3), a light probe (4), a support (5), an optical fiber (6), a temperature sensor (7), a light source (8) and a detection dark box (9) and also comprises an optical signal sensor (10). The device can monitor environment temperatures in a poultry egg detection process and prevents influences of overhigh temperatures on normal incubation of poultry eggs to be detected. The adjustable probe is adopted, and the position of the light probe can be fixed without adjustment when samples of a same kind are detected, thus reducing detection time. The device adopts a non-contact detection process, overcomes disadvantages that traditional egg candling or other detecting methods need contact with poultry eggs to be detected, and avoids the problem of outside contamination caused by contact type detecting methods. The device and the method (through building an optimum mathematic model from poultry egg spectrum data and fertilization results) can detect, in high accuracy, infertile eggs and fertile eggs before incubation and can increase the production efficiency.

The invention discloses a near infrared spectrum based detection method for rapid discrimination of oil-tea camellia seed oil real property. The method comprises the following steps: first, a near infrared spectrometer is used to acquire spectrum data of an oil-tea camellia seed oil sample to be detected, and a near infrared spectrum scan wave number, a near infrared spectrum scan frequency and a resolution are controlled during acquisition; then data with spectral band within 5750-6000cm<-1> are selected from an acquired standard spectrum and treated with a smoothing, a first derivative processing and a self-normalization; at last the data is input into an oil-tea camellia seed oil discrimination and analysis model, and a mahalanobis distance discrimination method is employed to determine whether a sample point falls in a delimited real sample area in the oil-tea camellia seed oil discrimination and analysis model, so as to determine true or false of the sample to be detected. The detection method of the invention has advantages of simple operation, rapid detection, safety, environmental protection, and high precision.

The invention discloses a method for measuring effective component content in kuhuang injection on line by near-infrared spectroscopy quickly. Effective components in kuhuang injection mainly comprise aloe-emodin, rhein, emodin, chrysophanol, physcion, sophocarpine and matrine so on. Near-infrared absorption spectroscopy collecting kuhuang injection make data of effective component content relate with near-infrared spectroscopy by HPLC method. Calibration model is built by partial least-squares regression method. Quick on-line measurement for effective component in injection can be accomplished. Studying result shows that near-infrared spectroscopy analyzing method can measure effective component in kuhuang injection effectively. The method is provided with simple pretreatment for sample, quick measurement and measuring multiple components at the same time. It can be used in on-line analysis and on-line quality control in tcm manufacture process.

The invention relates to a method for detecting urea-doped milk based on synchronous-asynchronous two-dimensional near-infrared related spectra. The method comprises the following steps: 1, preparing pure milk for experiments and urea-doped milk; 2, respectively scanning the near-infrared spectra of the pure milk for experiments and the urea-doped milk; 3, calculating to obtain the normalization synchronous-asynchronous two-dimensional near-infrared related spectrum matrix of the pure milk for experiments and the normalization synchronous-asynchronous two-dimensional near-infrared related spectrum matrix of the urea-doped milk; 4, building a discrimination model with a categorical variable matrix by a multi-dimensional partial least squares; 5, scanning and calculating unknown sample milk to obtain the synchronous-asynchronous two-dimensional near-infrared related spectrum matrix of the unknown sample milk, and substituting into the discrimination model to obtain whether urea is doped or not. Similarity and difference information of a to-be-analyzed system, changing with external interference, are fully utilized, and the influence of the single adoption of synchronous spectrum or asynchronous spectrum matrix redundant information on the model is overcome. The method is simple and scientific, and the analysis efficiency and the discriminating accuracy are high.

The invention discloses a quick edible oil variety identification method based on multisource spectral feature fusion. The method comprises the steps of selecting an unknown variety of edible oil sample to be identified, acquiring a Raman spectrum chart and a near infrared spectrum chart, preprocessing the Raman spectrum chart and the near infrared spectrum chart to obtain a preprocessed Raman spectrum chart and a preprocessed near infrared spectrum chart, conducting feature extraction on the preprocessed Raman spectrum chart and the preprocessed near infrared spectrum chart to obtain a Raman characteristic variable and a near infrared characteristic variable, conducting spectral characteristic fusion on the Raman characteristic variable and the near infrared characteristic variable to obtain a characteristic fusion spectrum chart, and conducting variety identification on the unknown variety of edible oil sample by means of an optimized qualitative model. The method is safe and quick, detection is convenient, identification accuracy is high, and practical value and popularization value are high.

The invention discloses a soil total nitrogen real-time detection method based on a soil visible-near infrared spectrum library. The soil total nitrogen real-time detection method comprises the following steps: measuring data of visible-near infrared spectrums and data of total nitrogen contents of soil samples across the country to establish a soil visible near infrared spectrum-total nitrogen database; collecting the data of the visible-near infrared spectrums of a plurality of soil samples to be detected; selecting model establishing sample from the spectrum library for each sample to be detected to form a calibration subset by using a local weighted regression algorithm to establish a total nitrogen linear regression model based on the soil visible-near infrared spectrum database to obtain the total nitrogen contents of the samples to be detected, and evaluating accuracy of a prediction model. Compared with the conventional method for establishing a prediction model by merely using all the soil sample spectrums in the region, the prediction model established by the method is excellent in stability and universality, so that the prediction capability is significantly improved and the defects that the soil spectrums are repeatedly collected, the data format is non-uniform and is incapable of being shared, and the established models are incapable of being universally used can be avoided.

This invention relates to a method for testing density of alive stumpage wood with near infrared spectrum including: selecting not less than 120 bug-free wood samples, controlling the temperature of the samples at 20-25deg.C, dividing them into five regions of moisture rate of 3-30%, 30-80%, 80-30%, 130-200%, 200-300% and collecting the diffuse reflection near infrared spectrums of the woods, applying a water discharge method to test the volume when the sample is full of water, applying a drying method to test the absolute weight of the sample to compute the water ratio and density of the woods, setting up a pretest model of moisture, setting up density correction models separately for five regions of water rate and utilizing the set-up models to analyze the density of unknown samples.

The invention discloses a fruit near infrared spectrum sorting method based on fuzzy clustering. The fruit near infrared spectrum sorting method based on the fuzzy clustering comprises the steps of carrying out dimension reduction processing of characteristic sequencing on a near infrared spectrum of a fruit sample, and extracting identification information of the near infrared spectrum data processed by the dimensionality reduction with a characteristic extraction method, and sorting the quality of the fruits by a rapid fuzzy clustering method. The fruit near infrared spectrum sorting method has the advantages of high detection speed, high sorting accuracy, high sorting efficiency, no damages on fruits, and the like, and can realize the assortment of one kind of fruits with different inner quality and the assortment of different kinds of fruits in the same category.

The invention belongs to the technical field of agricultural product quality analysis, and particularly relates to a near-infrared detection method for the peanut quality and application. The method comprises the following steps that peanut samples are collected, physical and chemical testing is performed on the peanut samples, near-infrared scanning is performed on the peanut samples, denoising processing and preprocessing are performed on obtained light absorption values, obtained preprocessed light absorption values are analyzed, near-infrared spectrum characteristic wavelengths are obtained through screening, and a prediction model of the peanut quality is built through a stepwise regression method. According to the near-infrared detection method for the peanut quality and the application, the obtained information is intuitive and reliable, the characteristic wavelengths of the peanut quality are determined and are few in number, and the analytical method that the model is built through the characteristic wavelengths is applied, so that the model precision is improved; on the condition of the same prediction precision, the prediction speed is high; meanwhile, through the built near-infrared prediction model method for the moisture, protein, fat, total sugar and ash content of peanuts, the peanut quality can be analyzed more comprehensively, and usage and popularization are easy.

The invention relates to a method for quick and non-destructive determination of content of amylase in corn single grains and application thereof. The method mainly comprises a step (1) of collecting corn single grain materials; a step (2) of collecting near infrared spectra of a sample; a step (3) of using a conventional chemical method to determine the content of amylase of the sample; a step (4) of preprocessing the obtained near infrared spectra and eliminating interference factors; a step (5) of establishing a calibration model between a chemical value of the content of amylase in the corn single grains and the near infrared spectra, and checking the model; a step (6) of externally verifying the model; and a step (7) of collecting near infrared spectra of a to-be-measured sample, and using the established near infrared model to perform quick and non-destructive determination of the content of amylase in the to-be-measured sample.

The invention provides a textile qualitative classification method, and belongs to the field of textile identification. The textile qualitative classification method comprises the following steps: (1)establishing a qualitative classification prediction model by using a convolutional network; (2) collecting a near infrared spectrum of a textile sample to be detected, and processing the collected near infrared spectrum to obtain a processed near infrared spectrum; and (3) inputting the processed near infrared spectrum into a qualitative classification prediction model, wherein the qualitative classification prediction model outputs the category of the textile sample to be detected. According to the invention, the normalized and pixelated near infrared spectrum is adopted, so that the acquisition difficulty and time are saved, and the method is an environment-friendly and rapid detection method; according to the method, the network weight and the offset value are automatically obtained through convolution kernel training, the spectral characteristics can be automatically extracted, the adaptability is high, the automatic qualitative classification problem of cotton, polyester and other textiles is effectively solved, and the detection level and speed of textile components are effectively improved.