Near infrared spectrum useful information distinguishing method based on wavelet transform

Abstract

The invention provides a near infrared spectrum useful information distinguishing method based on wavelet transform. The near infrared spectrum useful information distinguishing method comprises the following steps: firstly, obtaining a near infrared spectrum detection signal of a sample; performing the wavelet transform to obtain a maximum wavelet transform result of a wavelength signal of each point, wherein a scale factor corresponding to the maximum wavelet transform result is used as an optimal scale factor of the wavelength signal of the point; replacing a primary detection signal by using an optimal wavelet transform result of the wavelength signals of all the points, wherein the replaced signal is used as a new sample detection signal; selecting the new signal by using a UVE-PLS method, and establishing a prediction model; and detecting the predictive ability of the model by using a minimum root mean square error (RMSE). According to the distinguishing method, useful information in an original wavelength detection signal is maximally extracted by virtue of the continuous wavelet transform, and the prediction model is established by virtue of the useful information, so that the quality of the prediction model is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of chemometrics, and relates to a method for distinguishing useful information of near-infrared spectrum based on wavelet transform. Background technique [0002] Due to its fast, non-destructive and pretreatment-free characteristics, near infrared spectroscopy (NIR) analysis technology has been widely used in the analysis of complex samples in many fields, such as the analysis of samples in many fields such as agriculture, food and medicine. Multivariate correction methods for chemometrics, such as partial least squares (PLS), are essential analytical tools in quantitative near-infrared spectroscopy. The PLS analysis method can be expressed as follows: [0003] [0004] in, X is the near-infrared spectrum detection information matrix of the sample, Y is the measured value corresponding to each sample, B is the coefficient vector of PLS. [0005] In the quantitative analysis of NIR spectroscopy, the ...

AI Technical Summary

Problems solved by technology

But often researchers just randomly specify a smaller value of a when selecting the value of a (because if the value of a is larger, it cannot reflect the details of the detection signal), such as a=5 or a=10

This means that for the entire detection signal, when the wavelet transform is used for analysis, the value of a is fixed, and it is easy to lose the useful information of the point wavelength signal

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