50 results about "Nir spectra" patented technology

In a method for determining the dry mechanical strength for a green wood, the improvement comprising: (a) illuminating a surface of the wood to be determined with a reduced range of wavelengths in the VIS-NIR spectra 400 to 1150 nm, said wood having a green moisture content; (b) analyzing the surface of the wood using a spectrometric method, the method generating a first spectral data of a reduced range of wavelengths in VIS-NIR spectra; and (c) using a multivariate analysis technique to predict the mechanical strength of green wood when dry by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data of a reduced range of wavelengths in VIS-NIR spectra obtained from a reference wood having a green moisture content, the second spectral being correlated with a known mechanical strength analytical result obtained from the reference wood when dried and a having a dry moisture content.

NIR spectra are often pre-processed in order to remove systematic noise such as base-line varation and multiplicative scauer effects. This is done by differentiating the spectra to first or second derivatives, by Mtltiplicative Siga Correction (MSC), or by sirnilar filtering methods. This pre-processinp may, however, also remove infosmaion fror The spectra regarding Y (the measured response variable in multivarate calibration applications). Provided is a variant ofPLS that can be used to achieve a signal correction that is as close to orthogonal as possible to a given Y-vector or Y-matrix (430). Thus, ensung that the signal correction removes as little information as possible regarding Y. In the case when the number of X-variables, in an X matrix, K exceeds the number of observations N, strict orthogonality is obtined. A filter (470) according to the present invention is named Orthogonal Signal Correction (OSC).

Disclosed is a method for determining properties of hydrocarbonaceous samples including a component prepared from: 1) the thermo-catalytic conversion of biomass, or 2) the pyrolytic conversion of biomass with subsequent upgrading. The determination of the property(ies) is by use of a near-infrared spectra based correlation.

A method and system for detection and identification of concealed materials, is provided, wherein a dark image and two or more NIR sample images are taken at two or more key wavelengths or bands of wavelengths corresponding to peaks and / or valleys in the NIR spectra of known materials, and differential wavelength imaging processes are used to produce a a differential wavelength image based on therein. The differential wavelength image is then analyzed / processed so as to detect any materials concealed on the target of interest, such as a human or piece of baggage, by calculation of pixel intensity values in the image and identification of distinctive pixel values. Then, via various methods, the distinctive pixel values of the detected materials are compared to a data set of known wavelengths related to known materials, such as explosives and other contraband. Correspondence thereof results in an accurate identification of the concealed material(s).

A method for determining the mechanical properties of decayed wood that has been exposed to wood decay microorganisms, comprising: a) illuminating a surface of decayed wood that has been exposed to wood decay microorganisms with wavelengths from visible and near infrared (VIS-NIR) spectra; b) analyzing the surface of the decayed wood using a spectrometric method, the method generating a first spectral data of wavelengths in VIS-NIR spectra region; and c) using a multivariate analysis to predict mechanical properties of decayed wood by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data of wavelengths in VIS-NIR spectra obtained from a reference decay wood, the second spectral data being correlated with a known mechanical property analytical result obtained from the reference decayed wood.

The invention discloses a NIR method for fatty acid content of oilseeds, including: selection of oilseed samples, and analyzing the oilseed samples by an near infrared spectrometer to obtain NIR spectra; preprocessing of NIR spectra and establishment of a NIR spectral database of oilseeds; establishment of a fatty acid database of oilseeds based on gas chromatography; establishment of prediction model of fatty acids in oilseeds; acquiring NIR spectrum of a sample to be tested by the near infrared spectrometer, and importing the preprocessed NIR spectrum into the prediction model of fatty acids to obtain the predicted fatty acid content of the tested sample. The method is simple and rapid to operate and is non-destructive, and the detection time of the sample is greatly shortened and the detection cost is reduced.

A personal liquid analysis system includes a portable liquid analyzer device, a mobile application as a user interface, and a cloud server for data analysis. The compact and portable liquid analyzer device is integrated by miniaturized near-infrared (NIR) optical sensors setup for spectroscopy measurement from a sample cell, control circuits, I / O user interface modules and wireless communication modules. The analyzer can be used to collect NIR spectra data from a liquid sample in the sample cell, and to transmit the data to a cloud server where compositional information is calculated, and presented to the user on the device and / or on a mobile application. The personal liquid analysis system enables the user to rapidly acquire qualitative and / or quantitative compositional information from liquid samples for a wide range of applications.

A new rapid Fourier transform near infrared (FT-NIR) spectroscopic method is described to screen for the authenticity of extra virgin olive oils (EVOO) and to determine the kind and amount of an adulterant in EVOO. To screen EVOO, a partial least squares (PLS1) calibration model was developed to estimate a newly created FT-NIR Index based on the carbonyl overtone absorptions in the FT-NIR spectra of EVOO and other mixtures attributed to volatile (5200 cm−1) and non-volatile (5180 cm″1) components. Spectra were also used to predict the fatty acid (FA) composition of EVOO or samples spiked with an adulterant using previously developed PLS1 calibration models and modified for this purpose. To identify the type and determine the quantity of an adulterant, gravimetric mixtures were prepared by spiking an EVOO with different concentrations of each adulterant. Based on FT-NIR spectra, four PLS1 calibration models were developed for four specific groups of adulterants each with a characteristic FA composition. These different PLS1 calibration models were used for prediction of type and amount of adulterant. A more comprehensive double or triple adulterant PLS1 calibration models were also developed and tested which provided means of analyzing commercial virgin olive oils which had been adulterated.

A method of estimating the relative concentration of at least two components contained in a mixture of the components is disclosed. At least two mixtures are produced by combining the at least two components, each of the at least two mixtures having different concentrations of the at least two components. NIR mixture spectra are acquired from each of the at least two mixtures. The NIR component spectra and the NIR mixture spectra are input into a computer utilizing chemometrics software and the spectra are analyzed to produce a calibration model for each component and each of the mixture NIR spectra. NIR monitored spectra for a monitored mixture of the components having an unknown concentration of the components is acquired. The calibration models are applied to the NIR monitored spectra to thereby estimate the concentration of at least one of the components in the monitored mixture. The method can be applied to monitor fluid mixtures produced from one or multiple hydrocarbon reservoirs and surface facilities including pipelines and tanks.

A method for selection of a calibration set and a validation set based on spectral similarity and modeling. The method includes: performing NIR spectrometry on original samples to obtain a spectral matrix of the original samples; randomly selecting m samples as an independent test set; calculating spectral similarity between each of the samples in the independent test set and each of the remaining samples in the original samples respectively to obtain g samples having the highest similarity to be written into the validation set; and calculating spectral similarity between each of the samples in the validation set and each of the remaining samples in the original samples respectively to obtain n samples having the highest similarity to be written into the calibration set. Based on the validation set and the calibration set selected through the method, an obtained model can predict unknown samples more accurately.

A method of estimating the relative concentration of at least two components contained in a mixture of the components is disclosed. At least two mixtures are produced by combining the at least two components, each of the at least two mixtures having different concentrations of the at least two components. NIR mixture spectra are acquired from each of the at least two mixtures. The NIR component spectra and the NIR mixture spectra are input into a computer utilizing chemometrics software and the spectra are analyzed to produce a calibration model for each component and each of the mixture NIR spectra. NIR monitored spectra for a monitored mixture of the components having an unknown concentration of the components is acquired. The calibration models are applied to the NIR monitored spectra to thereby estimate the concentration of at least one of the components in the monitored mixture.

This invention discloses a spectral preprocessing method and related equipment for online coal quality detection using multispectral coupling. The method includes: simultaneously acquiring LIBS and NIRS spectra of raw coal using an online coal quality detection device; screening the effectiveness of the synchronized LIBS and NIRS spectra; identifying the defocusing amount of each set of synchronized LIBS and NIRS spectra and assigning a corresponding defocusing amount label to each set of spectra; correcting the defocused spectra according to the defocusing amount label using a spectral correction method to obtain corrected LIBS and NIRS spectra, and then fusing them to obtain a corrected fused spectrum; analyzing the LIBS spectrum, NIRS spectrum, and fused spectrum using quantitative analysis methods, and calculating the fixed carbon content. This invention's LIBS and NIRS spectral fusion overcomes the limitation of a single spectrum only characterizing atomic or molecular information, enabling a complete characterization of the chemical information of the coal sample and achieving simultaneous and accurate analysis of multiple coal quality indicators.

There is provided a PDP filter for absorbing near infrared ray capable of significantly reducing the kind and amount of dyes that are used to block a near infrared ray (NIR) emitted from PDP, the filters having physical properties that are improved when compared to conventional PDP filters for absorbing near infrared ray in which a large amount of 2, 3 or more dyes are used to block wide NIR spectra. The PDP filter includes a dye whose maximum absorption wavelength to a near infrared ray ranges from 880 to 1000 nm, and preferably from 900 to 960 nm. The PDP filter can be useful to improve its productivity, reduce the manufacturing cost and prevent the decrease in the light transmittance by unnecessary dyes.

A method and system for detection and identification of concealed materials, is provided, wherein a dark image and two or more NIR sample images are taken at two or more key wavelengths or bands of wavelengths corresponding to peaks and / or valleys in the NIR spectra of known materials, and differential wavelength imaging processes are used to produce a differential wavelength image based on therein. The differential wavelength image is then analyzed / processed so as to detect any materials concealed on the target of interest, such as a human or piece of baggage, by calculation of pixel intensity values in the image and identification of distinctive pixel values. Then, via various methods, the distinctive pixel values of the detected materials are compared to a data set of known wavelengths related to known materials, such as explosives and other contraband. Correspondence thereof results in an accurate identification of the concealed material(s).

Methods for sorting haploid maize kernels in haploid induction crosses are provided. A method of sorting haploid kernels can include capturing near-infrared (NIR) spectra and applying a general multivariate statistical model to the acquired NIR spectra to discriminate haploid kernels from diploid-hybrid kernels mixed therein. NIR spectra can be collected on a single kernel using a high-throughput apparatus such that the amount of time required to analyze individual kernels can be significantly reduced in comparison to existing NIR technology and in comparison to traditional manual sorting.

A fiber identification method includes collecting near-infrared spectrum data of a fiber to be tested, using a chemometric method to analyze the correlation between the near-infrared spectrum data of the fiber to be tested and the near-infrared spectrum of a fiber standard sample, and judging the fiber to be tested according to the correlation coefficient. Whether it belongs to the same type of fiber as the fiber standard sample. The fiber identification method provided by the present invention only needs to collect the near-infrared spectrum data of the fiber to be tested, and then use the chemometric method to analyze the correlation between the near-infrared spectrum data of the fiber to be tested and the near-infrared spectrum data of the fiber standard sample. Whether the fiber to be tested and the fiber standard sample belong to the same type of fiber, the identification can be completed in a few minutes, and the identification result is accurate.

Provided is a method for the simultaneous determination of parameters, in particular of at least two, three or four parameters, of at least one resin layer applied to at least one carrier material by recording and evaluating at least one NIR spectrum in a wavelength range between 500 nm and 2500 nm, preferably between 700 nm and 2000 nm, more preferably between 900 nm and 1700 nm, and particularly advantageously between 1450 nm and 1550 nm, using at least one NIR measuring head, in particular at least one NIR multimeter head.

A medical imaging device configured to spatially resolve recording of multispectral video data of an examination area of a patient including a light source having multiple optical emitters with different wavelengths in the visible and NIR spectral range. The light source has an emitter whose wavelength lies in the range of ±50% of its half-width around the intersection of the blue and green filter curves or the green and red filter curves, and the exposure control and the data processing means are arranged to separately detect the affected two of the red and green or the green and blue colour signals in an exposure pattern with activation of the emitter at the intersection point and to evaluate them in the multispectral analysis with mutually different wavelengths shifted by the two affected filter curves as two supporting point wavelengths.

The present invention relates to a measurement method for determining the amount of organic constituents in a wood ash, the method comprising the steps of: - providing ash samples, each having different quantitatively defined amounts of organic material, as reference samples; - recording at least one NIR spectrum of each of the reference samples using at least one NIR measurement head in a wavelength range between 1300 nm and 1800 nm, preferably between 1400 nm and 1650 nm; - assigning the different quantitatively defined amounts of organic material in the reference samples to the recorded NIR spectra of said reference samples; and - generating a calibration model for the correlation between the spectral data of the NIR spectra and the corresponding quantitatively defined amounts of organic material in the reference samples by means of multivariate data analysis; - providing at least one ash sample to be measured having an unknown amount of organic material; - recording at least one NIR spectrum of the provided ash sample using the at least one NIR measurement head in a wavelength range between 1300 nm and 1800 nm, preferably between 1400 nm and 1650 nm; and - determining the quantitative amount of organic material in the ash sample to be measured by comparing the NIR spectrum recorded for the ash sample to be measured with the calibration model generated from the reference samples.

The invention provides a method for establishing a qualitative analysis model of near-infrared spectra and spectral images of seeds. The method includes: collecting sample data of each category in different environments, and determining a model training data set and a test data set. The sample data is Near-infrared spectral data and / or spectral images; use different data preprocessing methods and / or feature extraction methods to process the sample data in the model training data set to obtain the separability and classification accuracy between different types of sample data and deviation degree; select the best data preprocessing method, the best feature extraction method and the optimal data dimension of the best feature extraction method according to the separability, classification accuracy and deviation degree, and extract the sample data in the model training data set The characteristics; the qualitative analysis model is established by using the improved bionic pattern recognition method. The invention improves the efficiency of establishing the qualitative analysis model and the robustness of the qualitative analysis model, and has broad application prospects.

The present disclosure relates to a method and system for preprocessing near infrared (NIR) spectroscopy data for non-invasive monitoring of blood glucose. In accordance with an embodiment, the method receiving the NIR spectroscopy data from a subject; performing a scatter correction on the NIR spectroscopy data to obtain scatter corrected NIR spectra; removing interference from the scatter corrected NIR spectra to obtain glucose spectra; removing noise from the glucose spectra to obtain noise removed glucose spectra; obtaining noise removed NIR glucose data as a set of noise removed glucose spectra corresponding to a plurality of reference glucose values; removing drift from the noise removed NIR glucose data to obtain preprocessed NIR glucose data; and obtaining a set of global features from the preprocessed NIR glucose data for non-invasive monitoring of blood glucose of the subject.

The invention discloses a method for establishing a Vis-NIR spectral depth characteristic model based on SAE-LSSVR crop cadmium content, initializes the weight matrix W and the offset b of the stacked autoencoder, and uses the spectral data set S as the input of the i layer; from the spectral The training set, prediction set and cross-validation set are respectively extracted from the data set S and the cadmium content label set V, and the coefficient of determination R corresponding to the training set, prediction set and cross-validation set is calculated using the partial least squares support vector machine regression algorithm. 2 and the root mean square error RMSE, and assign m‑1 to m. When m=0, the prediction set SP i Maximum coefficient of determination R p 2 Corresponding node number m c is the optimal number of nodes in the i+1 layer; if the i-th layer and the i+1-th layer stack the autoencoder’s optimal node number corresponding to the prediction set determination coefficient RB i and RB i+1 Satisfied conditions: ||RB i+1 -RB i ||<ε, ε is the error, and i is greater than or equal to 2. Find the optimal number of stacked autoencoder layers to complete the model. This method provides a fast and high-precision method for establishing a Vis‑NIR spectral depth model with cadmium content.

The present invention relates to a non-destructive measurement method for the online determination of the amount of binder applied to wood particles in a production plant for the manufacture of wood-based panels, comprising the steps of: applying at least one binder in different quantitatively defined quantities to respective samples of wood particles as reference samples and scattering the reference samples onto a conveyor belt; recording at least one NIR spectrum of each of the reference samples scattered onto the conveyor belt using at least one NIR measuring head in a wavelength range between 1000 nm and 2500 nm, preferably between 1350 nm and 2500 nm; and assigning the different quantitative quantities of binder of the reference samples scattered onto the conveyor belt to the recorded NIR spectra of said reference samples.and creation of a calibration model for the relationship between the spectral data of the NIR spectra and the corresponding quantitative amounts of binder of the reference samples scattered on the conveyor belt by means of a multivariate data analysis; application of at least one binder to wood particles (as samples to be measured) and scattering of the wood particles coated with the binder onto a conveyor belt, recording of at least one NIR spectrum of the wood particles coated with the binder and scattered on the conveyor belt using the at least one NIR measuring head in a wavelength range between 1000 and 2500 nm, preferably between 1350 nm and 2500 nm; and determination of the quantitative amount of binder applied to the wood particles scattered on the conveyor belt by comparing the NIR spectrum recorded for the wood particles coated with the binder with the created calibration model.

Use of Near Infrared (NIR) spectroscopy, preferably Fourier Transform NIR- spectroscopy, for obtaining a NIR spectrum or NIR spectra for determination of one or more chemical species in one or more process stream(s) of a urea process, said urea process comprising at least one from among a synthesis process, an evaporation process, a crystallisation process, a waste water treatment process, and / or a finishing process. In particular, the urea process comprises a urea synthesis process by reacting carbon dioxide and ammonia. The chemicals involved in the synthesis, such as ammonia, carbon dioxide, urea and biuret, can be quantitatively determined by NIR spectroscopy and the synthesis process can be adjusted based on the determination. The invention relates also to a urea synthesis plant involving NIR spectroscopy.

Disclosed is a method for determining properties of hydrocarbonaceous samples including a component prepared from: 1) the thermo-catalytic conversion of biomass, or 2) the pyrolytic conversion of biomass with subsequent upgrading. The determination of the property(ies) is by use of a near-infrared spectra based correlation.

The present invention relates to the detection of the germination rate of fungal spores, and specifically provides a method for measuring the germination rate of spores of wheat stripe rust by using near-infrared spectroscopy. Based on the germination rate of spores and the near-infrared spectral data of corresponding samples, a discriminant model of spore germination rate was established, and the automatic non-destructive detection of uredia spore germination rate of wheat stripe rust was realized.

The invention discloses a NIR method for fatty acid content of oilseeds, including: selection of oilseed samples, and analyzing the oilseed samples by an near infrared spectrometer to obtain NIR spectra; preprocessing of NIR spectra and establishment of a NIR spectral database of oilseeds; establishment of a fatty acid database of oilseeds based on gas chromatography; establishment of prediction model of fatty acids in oilseeds; acquiring NIR spectrum of a sample to be tested by the near infrared spectrometer, and importing the preprocessed NIR spectrum into the prediction model of fatty acids to obtain the predicted fatty acid content of the tested sample. The method is simple and rapid to operate and is non-destructive, and the detection time of the sample is greatly shortened and the detection cost is reduced.

A method for selection of a calibration set and a validation set based on spectral similarity and modeling. The method includes: performing NIR spectrometry on original samples to obtain a spectral matrix of the original samples; randomly selecting m samples as an independent test set; calculating spectral similarity between each of the samples in the independent test set and each of the remaining samples in the original samples respectively to obtain g samples having the highest similarity to be written into the validation set; and calculating spectral similarity between each of the samples in the validation set and each of the remaining samples in the original samples respectively to obtain n samples having the highest similarity to be written into the calibration set. Based on the validation set and the calibration set selected through the method, an obtained model can predict unknown samples more accurately.