Drug detector standardization method based on dual-tree complex wavelet algorithm

Abstract

The invention discloses a drug detector standardization method based on the dual-tree complex wavelet algorithm. Firstly, multi-scale spectrum decomposition and reconstruction are conducted with the dual-tree complex wavelet, then each layer of reconstructed spectrum is corrected through piecewise direct standardization, predication models based on partial least squares and leave one cross validation are established, and finally all the predication models are fused and evaluated according to a calculated weight. According to the dual-tree complex wavelet and direct standardization combined method, due to the translation invariance and multi-scale property of the dual-tree complex wavelet, the defects of existing model transfer methods are overcome, the capacity of correcting drifting in the X-axis direction and Y-axis direction is excellent, and the method also has the advantages of being exquisite, precise and efficient and can be widely applied to the fields including near-infrared and Raman spectrum.

Description

technical field [0001] The invention relates to the technical field of chemometrics, in particular to a method for standardizing drug detectors based on a dual-tree complex wavelet algorithm. Background technique [0002] In recent years, with the continuous attention to the drug problem in our country, Raman spectroscopy has begun to be introduced into the field of drug testing, becoming a new weapon and one of the most preferred technologies for fast and accurate drug testing. Raman spectral analysis technology is a molecular structure characterization technology based on the Raman effect. The peak position, quantity and intensity of the spectrum directly reflect the composition and conformation information of the molecule. It is fast, simple, non-destructive in situ and free of reagents. And other characteristics, can directly test the samples of different forms. By analyzing the Raman fingerprints of complex systems, qualitative and quantitative information of multiple ...

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Problems solved by technology

The segmented direct normalization algorithm corrects the entire spectrum and does not divide the spectrum in the frequency domain. Such a model transfer is very general and not fine enough.

However, the wavelet multi-scale segmental direct normalization algorithm based on the segmental direct normalization algorithm has the characteristics of multi-scale correction to overcome the general and not precise shortcomings of the segmental direct normalization method, but the translational variability of wavelet has caused This method is poor at correcting for drift in the x-axis of the spectra of the two instruments

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