100 results about "Multivariate calibration" patented technology

The present invention further relates to the selection of the specific filter combinations, which can provide sufficient information for multivariate calibration to extract accurate analyte concentrations in complex biological systems. The present invention also describes wavelength interval selection methods that give rise to the miniaturized designs. Finally, this invention presents a plurality of wavelength selection methods and miniaturized spectroscopic apparatus designs and the necessary tools to map from one domain (wavelength selection) to the other (design parameters). Such selection of informative spectral bands has a broad scope in miniaturizing any clinical diagnostic instruments which employ Raman spectroscopy in particular and other spectroscopic techniques in general.

A method of analysis, analysis system, program product, apparatus, and method of supplying analysis of value incorporating the use of at least one data acquisition device, a central processor, and a communication link that is connectable between the data acquisition device and the central processor. The central processor is loaded with multivariate calibration models developed for predicting values for various properties of interest, wherein the calibration models are capable of compensating for variations in an effectively comprehensive set of measurement conditions and secondary material characteristics. As so configured, the calibration models can compensate for instrument variance without instrument-specific calibration transfer. Measurement results generated by the central processor can be transmitted to an output device of a user interface.

A device and method for identifying solid and powdered materials use near-infrared reflection spectroscopy combined with multivariate calibration methods for analysis of the spectral data. Near-infrared reflection spectroscopy is employed within either the 700-1100 nm or the 900-1700 nm wavelength range to identify solid or powdered materials and determine whether they match specific known materials. Uses include identifying solid and powdered materials with a fast measurement cycle time of about 2 to 15 seconds and with a method that requires no sample preparation, as well as quantitative analysis to determine the concentration of one or more chemical components in a solid or powdered sample that consists of a mixture of components. A primary application involving identification analysis verification of the identify and purity of powdered materials used for fabricating drug tablets and capsules for quality control purposes.

The present invention is a method for optimizing multivariate calibrations such as those used on FT-IR analyzers. The method involves the selection of an optimum subset of samples to use in the calibration from a larger set of available samples. The subset selection is done so as to minimize the bias of the resulting calibration while simultaneously maintaining acceptable standard errors and ensuring maximal range for the model.

A novel multivariate model for analysis of absorbance spectra allows for each wavelength or spectral region to be modeled with just enough factors to fully model the analytical signal without the incorporation of noise by using excess factors. Each wavelength or spectral region is modeled utilizing its own number of factors independently of other wavelengths or spectral regions. An iterative combinative PCR algorithm allows a different number of factors to be applied to different wavelengths. In an exemplary embodiment, a three-factor model is applied over a given spectral region. The residual of the three-factor model is calculated and used as the input for an additional five-factor model. Prior to the additional five factors being applied, some of the wavelengths are removed. This leads to a three-factor model over the first region and an eight-factor model over the second region. This analysis of residuals can be repeated such that a one to n factor model could be applied to any given wavelength, or rather any number of factors may be employed to model any given frequency or spectral region. A method of predicting concentration of a target analyte from sample spectra applies a calibration developed using the inventive PCR algorithm to a matrix of sample spectral to generate a vector of predicted concentrations for the target analyte.

A method for generating a net analyte signal calibration model for use in detecting and/or quantifying the amount of an analyte in a test subject. The net analyte signal can be generated by providing a set of in vivo infrared spectra for a test subject during a period in which an analyte concentration is essentially constant; calculating an optimal subspace of spectra that at least substantially describes all non-analyte dependent spectral variance in the in vivo spectra; providing a pure component infrared spectrum for the analyte; and calculating a net analyte signal spectrum from a data set comprising the optimal subspace spectra and the pure analyte spectrum. The net analyte signal calibration model can be used, for example, in measuring the concentration of analyte in a test subject, and/or for evaluating the analytical significance of an in vivo multivariate calibration model.

The invention relates to a fruit maturity degree evaluation method based on spectrum and color measurement. The method comprises the following steps that (1) the degrade division is carried out on a certain fruit, and the color measurement parameter, the visible near infrared spectroscopy and the internal quality evaluation index of each grade of fruit are determined; (2) firstly, the visible near infrared spectroscopy and the internal quality evaluation index are combined, a fruit internal quality quantitative analysis model is built by adopting a multivariate calibration method, then, the color measurement parameter is fused, the fruit maturity degree degrade is used as the standard reference, and a fruit maturity degree evaluation model is built by using a machine learning method; and (3) the visible near infrared spectroscopy and the color measurement parameter of fruit samples to be evaluated are determined and are respectively input into the fruit internal quality quantitative analysis model and the fruit maturity degree evaluation model, and a computer carries out intelligent degrade evaluation on the fruit maturity degree. The fruit maturity degree evaluation method is simple, the fruit damage is avoided, and accuracy and high efficiency are realized.

The invention provides a novel fast and non-destructive analysis method for the nutritional quality of cottonseed. The method comprises the following steps: 1) selecting whole cottonseeds of representative cotton varieties as experimental samples; 2) using a near-infrared spectrometer to collect near-infrared spectral data of whole cottonseeds, and the spectral scanning range is 800-2500nm; 3) using the national standard method Accurately analyze the nutritional quality of cottonseed; 4) Preprocess the near-infrared spectrum data; 5) Select spectral variables to eliminate non-informative variables; 6) Use the multivariate correction method to establish the near-infrared spectrum of whole cottonseed and the nutritional value of whole cottonseed 7) Performance evaluation of the calibration model; 8) Collect near-infrared spectral data of the whole cottonseed to be tested, and use the calibration model to predict its nutritional quality after preprocessing the spectral data. The invention has fast analysis speed, does not destroy samples, does not require sample pretreatment, has high detection accuracy, and can analyze various nutritional quality components in cotton seeds at the same time, and provides a convenient, fast and efficient analysis method for cotton breeding and cotton seed inspection.

The invention relates to a screening method for near infrared spectrum wavelength and Raman spectrum wavelength. The method comprises that acquired near infrared spectrum or Raman spectrum and data of concentration of corresponding composition to be detected are divided into a training set, a check set and a prediction set; a PLS model is established by using original spectrum and the concentration of the composition to be detected to obtain a real PLS model coefficient; the concentration of the composition to be detected is ordered randomly and a great number of PLS models are established by using the vectors of the concentration of the composition to be detected and original spectrum matrices; according to the models, the times that a single model coefficient is larger than the real PLS model coefficient are respectively calculated to obtain a corresponding probability value; the wavelength that the probability value is less than a threshold value is reserved; an optimum model is established by using the reserved wavelength to predict the concentration of the composition to be detected of a sample in the prediction set. By adopting the method, the invention has the advantages that the wavelength containing spectral information can be accurately extracted, the quantitative analysis model is simplified, the prediction accuracy of the quantitative analysis model is improved and the new wavelength screening technique is provided for the multivariate calibration analysis of the near infrared spectrum and the Raman spectrum.

Systems and methods for establishing and/or maintaining the accuracy of a multivariate calibration model designed for quantitative optical spectroscopic measurement of attributes or analytes in bodily tissues, bodily fluids or other biological samples, which are particularly useful when the spectral absorbance of the attribute or analyte is small relative to the background. The present invention provides an optically similar reference sample to reduce the effect of instrument or environment variation on the measurement capability of the model. The optically similar reference can be a gel composition having scattering particles suspended therein. The reference gel can be directly applied to a spectroscopic instrument sampler, or can be in a container specifically designed for optimal coupling to a spectroscopic instrument.

The invention relates to an in-use lubricating oil quality rapid testing method which comprises the following steps that: (1) a representative lubricating oil sample is collected as a training set; (2) the infrared spectrum of the training set lubricating oil sample is determined, corresponding pre-treatment is carried out, and the spectrum data after pre-treatment serves as a variable; (3) through a multivariate calibration method, the relationship model between the in-use lubricating oil quality index and the spectrum is built; and (4) for the quality testing of the lubricating oil sample to be tested, first the infrared spectrum is determined, and then pre-treatment which is the same with that of Step (2) is carried out, and the lubricating oil quality index is determined through the lubricating oil quality analysis model built in Step (3). The method of the invention can rapidly determine 7 quality indexes, i.e. the in-use lubricating oil water content, TAN, TBN, 40DEG C viscosity, 100DEG C viscosity, the flash point, the pour point and the like, and the lubricating oil quality is monitored. The in-use lubricating oil quality rapid testing method has high analysis speed, is convenient to operate, and greatly improves the lubricating oil quality monitoring capability.

A method of non-destructively determining the amount of ultraviolet degradation of a surface and/or paint adhesion characteristics of the surface corresponding with UV damage including determining a physical property of a composite material/surfacing film by providing a series of composite materials/surfacing films which are subjected to increasing UV light experience to create a set of UV damage standards, collecting mid-IR spectra on those standards, performing data pre-processing and then multivariate calibration on the spectra of the composite materials/surfacing films, and using that calibration to predict the UV damage for samples in question.

The invention discloses a method for measuring the content of substance in a process of anaerobic fermentation, comprising the steps of: 1) scanning fermentation liquor in the process of anaerobic fermentation by a near-infrared spectrometer, and obtaining a near-infrared spectrum signal; 2) measuring the content of the substance to be measured in the fermentation liquor of the step 1) by the existing technology; 3) building a near-infrared spectrum multivariate calibration model about the near-infrared spectrum signal and the content of the substance to be measured; 4) scanning the fermentation liquor in the process of the anaerobic fermentation by the near-infrared spectrometer again, and obtaining another near-infrared spectrum signal; and 5) obtaining the content of the substance in the process of the anaerobic fermentation according to the near-infrared spectrum multivariate calibration model and the near-infrared spectrum signal obtained in the step 4). After the method disclosed by the invention is adopted, the simple pretreatment is only needed to be carried out for a sample; and compared with the traditional method, a great deal of analysis time and expense can be saved, so that a good tool is provided for monitoring the fermentation process of anaerobic reaction.

The invention discloses a module transfer method based on multiscale modeling. The method comprises the following steps: collecting original spectra of a host instrument and a slave instrument; by combining the characteristic of wavelet basis and the characteristic of the original spectrum of a sample, selecting the optimal wavelet basis to carry out wavelet multiscale decomposition on the spectrum to obtain wavelet coefficient; reconstructing the wavelet coefficient; respectively performing multivariate calibration on each layer of reconstructed spectrum; setting up a prediction model based on partial least squares method and leaving one cross-validation method for the spectrum after performing multivariate calibration to obtain a cross-validation root-mean-square error of the prediction model; performing model fusion on the prediction model by using weight number, and calculating the prediction root-mean-square error and related coefficient to evaluate the model transfer effect. Compared with a conventional model transfer method, the method greatly improves the model transfer efficiency and performance, and can be widely applied to fields of near-infrared and Raman spectrum.

A target composition determination apparatus includes, an x-ray emission element for concurrently transmitting x-ray photons with a plurality of different energy levels at a target, and an x-ray detection element for receiving x-rays interacting with the target and for determining the energy levels of the received x-rays, the x-ray detection element generating at least one output signal indicative of the number of x-rays interacting with the target and the energy levels determined for each of the x-rays, and a processing element receiving the output signal and indicating the presence of at least one component material within the target using the received detection element signal and a multivariate calibration prepared for the target, the apparatus including that the output signal of the x-ray detection element is batched over a pre-selected time period to define the physical resolution of the target composition apparatus when a target is moved relative to the apparatus.

A method of determining a film coating thickness on a substrate including making near-IR spectra of a series of coating thickness or coating weight standards on an appropriate substrate material to match sample material in question, pre-processing the data to prepare it for multivariate calibration methods, performing the multivariate calibration, saving the calibration model in the hand-held near-IR device in an appropriate format, and using the calibration model to predict sample material thickness in question from their near IR spectra.

The invention provides an online detection method for a double-effect concentration process of danhong injection. The method comprises the following steps: acquiring near infrared transmittance spectroscopy of danhong injection concentrate online by arranging a near infrared online detection device and collecting a concentrate sample; respectively measuring various pieces of quality control index information of the concentrate sample by a drying weighing method and high-performance liquid chromatography; removing abnormal spectrums; selecting a near infrared spectroscopy modeling waveband and a preprocessing method; establishing various quality control index quantitative models by a multivariate calibration algorithm; investigating the performance of the models by adopting various model evaluation indexes; online analyzing the variation trend of each quality control index in the concentration process by using the established models. According to the method, a near infrared online analysis technology is introduced into the concentration process of the danhong injection, so that the real-time monitoring of each quality control index (tanshinol, protocatechualdehyde, hydroxysafflor yellow A, rosmarinic acid, salvia acid B and moisture content) can be realized, the improvement on the quality control level in the danhong injection concentration process is facilitated, and the stable and reliable product quality is fully guaranteed.