Spectrum pretreatment method

Abstract

The invention relates to a spectrum pretreatment method comprising the steps of: de-noising the spectrum, which is inputting the spectrum into a convolution de-noising layer C1; and performing baseline correction on the spectrum, which is inputting the spectrum after the de-noising into a baseline correction layer C2. According to the spectrum pretreatment method, the de-noising and the baseline correction are combined in a convolution manner, so that the pre-processing process and the authentication problem are transformed into a unified model framework to be solved, thereby realizing the adaptive processing of data and overcoming the defects of traditional methods; each convolution layer in the de-noising and baseline correction processes have only one convolution kernel, so that the interpretation is higher than traditional networks, and the output of the convolution layer is more easily to be monitored to see if the expected effect is achieved.

Description

technical field [0001] The invention relates to the field of spectral analysis methods, in particular to a spectral preprocessing method. Background technique [0002] The original spectrum measured by the instrument is usually accompanied by serious noise and baseline drift, which limits the extraction of useful information in the spectral data during identification. Therefore, the process of spectral analysis usually involves cumbersome denoising and baseline correction processes. [0003] Most of the current denoising and baseline correction methods are independent of the model, which increases the risk of the model's predictive ability being reduced due to improper preprocessing. At the same time, affected by the physical properties of the substance to be measured (such as particle size, packing density and uniformity, etc.), the ambient temperature and the nonlinear response of the detector, there is a certain nonlinear relationship between the spectrum and the properti...

AI Technical Summary

Problems solved by technology

[0002] The original spectrum measured by the instrument is usually accompanied by serious noise and baseline drift, which limits the extraction of useful information in the spectral data during identification. Therefore, there are usually cumbersome denoising and baseline correction processes in the spectral analysis process.

[0003] Most of the current denoising and baseline correction methods are independent of the model, which increases the risk of the model's predictive ability being reduced due to improper preprocessing

At the same time, affected by the physical properties of the substance to be measured (such as particle size, packing density and uniformity, etc.), the ambient temperature and the nonlinear response of the detector, there is a certain nonlinear relationship between the spectrum and the properties and composition of the substance to be measured. However, most of the spectral qualitative or quantitative correction methods at this stage are linear models, which cannot be well represented for this nonlinear relationship.

[0004] In the prior art, when establishing a classification model, most of the model parameters are preset based on experience. On the one hand, this method limits the accuracy of spectral analysis. On the other hand, according to the characteristics of the substances to be classified, the parameters of the classification model also need to be adjusted. , so the prediction model parameters set according to empirical values ​​can only be used to classify some specific substances, and the parameters of the classification model need to be reset every time different substances are measured, which not only leads to the generalization of the classification model The performance is poor, and more importantly, the classification accuracy of the classification model is also difficult to guarantee

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