A Raman spectroscopic analysis method based on a convolution neural network

Abstract

The invention relates to a Raman spectrum analysis method based on a convolution neural network. Firstly, the classification model is established. The Raman spectrum of the material is pretreated, andthen the pretreated Raman spectrum is input to the neural network for training to determine the weights of each layer of the network, and the classification model is named RS-CNN. Secondly, the Ramanspectra of the substances to be predicted are pretreated, and then the pretreated Raman spectra with predicted substances are inputted into the classification model, and the output of the classification model is the classification result. Convolution neural network denoising and baseline correction are integrated into the convolution neural network in a convolution manner, so that the preprocessing and identification problems are solved in a unified model framework, and the adaptive data processing is realized, which makes up for the shortcomings of the traditional methods.

Description

technical field [0001] The invention relates to the field of spectral analysis methods, in particular to a convolutional neural network-based Raman spectral analysis method. Background technique [0002] The original Raman spectrum obtained by instrument measurement 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 Raman spectrum analysis usually requires cumbersome denoising and baseline correction. the process of. [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 certa...

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Problems solved by technology

[0002] The original Raman spectrum obtained by instrument measurement 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 Raman spectrum analysis usually requires cumbersome denoising and baseline correction. the process of

[0003] Most of the current denoising and baseline correction methods are independent of the model, which increases the risk of model prediction ability decline 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 nonlinearity between the Raman spectrum and the properties and composition of the substance to be measured. relationship, but most of the qualitative or quantitative correction methods for Raman spectroscopy 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 Raman spectral analysis. On the other hand, according to the characteristics of the substances to be classified, the parameters of the classification model also need Therefore, 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 classification model The versatility of the classification model is poor, and more importantly, the classification accuracy of the classification model is difficult to guarantee

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