Method for screening characteristic wavelength of near infrared spectrum features based on heredity kernel partial least square method
A partial least squares and near-infrared spectroscopy technology, which is applied in the measurement of color/spectral properties, material analysis by optical means, measurement devices, etc. Achieve the effects of high prediction performance and accuracy, reduction of modeling operation time, and strong generalization ability
Inactive Publication Date: 2012-01-04
JIANGSU UNIV
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However, in addition to the information of the sample itself, the data collected by the spectroscopic instrument also contains a lot of irrelevant and noise information, which is difficult to completely eliminate in the spectral preprocessing
If these data are all involved in the establishment of the model, not only the amount of calculation is large, the model is complex, but also the prediction acc
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[0013] See figure 1 , The present invention is implemented as follows:
[0014] 1) Use the Fourier transform near-infrared spectrometer to collect the near-infrared spectrum data of all the samples to be tested, obtain the original near-infrared spectrum data of the samples to be tested, and preprocess the spectrum data to eliminate factors such as spectral shift or baseline change The impact on the performance of the built model ensures a good correlation between the spectral data and the quality indicators of the sample to be tested. At present, the commonly used spectral preprocessing methods include standard normal variable transformation, mean centering, first-order derivative, and second-order derivative. Then, physical and chemical analysis methods are used to determine the concentration values of the components to be tested for all samples to be tested, and based on the concentration values of the components to be tested, the pre-processed raw near-infrared spectro...
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The invention discloses a method for screening the characteristic wavelength of a near infrared spectrum based on a heredity kernel partial least square method, which is used for detecting quality of food and farm products. The method comprises the following steps of: by utilizing a physicochemical analytical method, determining the concentration values of components to be detected of samples to be detected, and then dividing a calibration set and a predication set of the samples; by utilizing a genetic algorithm, carrying out global search on the preprocessed calibration set spectral data points; finally determining characteristic variable number participated in modeling according to a minimum cross-verification root-mean-square error value in the kernel partial least square method cross-verification process; by utilizing characteristic variables screened out from the genetic algorithm, forming a new data matrix again to be used as input of a model; taking a component concentration array of the sample to be detected of the calibration set as standard output of the model, so as to establish the best calibration analysis model; by virtue of the model, predicting the concentration values of components to be detected of the predicated set sample; and reducing the modeling operation time by screening the characteristic wavelength, and removing a large amount of noise and redundant variables, thus prediction performance and accuracy of the finally established model are higher.
Description
technical field [0001] The invention relates to a method for screening characteristic wavelengths of near-infrared spectroscopy, in particular to a method for screening characteristic wavelengths based on genetic nuclear partial least squares method for quality detection of food and agricultural products. Background technique [0002] Near Infrared Spectroscopy (NIR, Near Infrared Spectroscopy) analysis technology, as a fast, non-destructive, accurate, multi-component simultaneous detection green analysis technology, has been widely used in the quality inspection of food and agricultural products. With the help of advanced near-infrared spectroscopy instruments, it is convenient to obtain a large amount of spectral data in a short time. However, in addition to the information of the sample itself, the data collected by the spectroscopic instrument also contains a lot of irrelevant and noise information, which is difficult to completely eliminate in the spectral preprocessin...
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Login to view more IPC IPC(8): G01N21/35G01N21/3563G01N21/359
Inventor 朱伟兴江辉李新城
Owner JIANGSU UNIV
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