Method for screening characteristic wavelength of near infrared spectrum features based on heredity kernel partial least square method

Abstract

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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Problems solved by technology

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 accuracy of the model will be affected

In addition, since most of the components to be tested are very complex natural components, and the bands in the near-infrared spectral region are complex and overlapping, there will be a discrepancy between the collected spectral data and the measured concentration of the components to be tested. presents a relatively complex nonlinear mapping relationship between

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