Near infrared spectrum analysis method based on CC-PLS-RBFNN optimization model

Abstract

The invention belongs to the field of spectral analysis and especially relates to a near infrared spectrum analysis method based on a CC-PLS-RBFNN optimization model. According to the method, a correlation coefficient method (CC), partial least squares (PLS) and a radial basis function neural network (RBFNN) are combined for optimizing important parameters. The method comprises the following steps: utilizing three-order Savitzky-Golay convolution smooth filtering and first-order derivative correction to preprocess the original spectrum; establishing a PLS model on a whole wavelength section, and optimizing and selecting window width and PLC extracted main constituent quantity; calculating a correlation coefficient of each wavelength variable, selecting the wavelength variable with the correlation coefficient more than a preset threshold value, participating in modeling, and optimizing and selecting the threshold size; utilizing the optimized and selected window width, the main constituent quantity and the wavelength variable to acquire an optimized PLS model; and using a main constituent score and a nature matrix extracted by the optimized PLS model to train the RBF neural network, thereby acquiring the final CC-PLS-RBFNN optimization model. According to the method, the robustness and precision of near infrared spectroscopy analysis can be obviously promoted.

Description

technical field [0001] The invention relates to the field of near-infrared spectrum analysis, in particular to a near-infrared spectrum analysis method based on a CC-PLS-RBFNN optimization model. Background technique [0002] As a fast and non-destructive quantitative analysis method, near-infrared spectroscopy has been successfully applied in many fields such as agriculture, food, chemical industry and biological science, creating considerable economic and social benefits. The near-infrared spectrum contains a wealth of sample group information. However, the spectral absorption bands in the near-infrared region are wide and there are serious spectral overlaps. Statistical methods are usually introduced to establish a suitable multivariate calibration model, so as to realize the spectral data. Predict associations between target data. [0003] At present, for the research on the calibration model in near-infrared spectroscopy, typical linear modeling methods include multipl...

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Problems solved by technology

The limitation of multiple linear regression is that the modeling process includes the step of inverting the spectral matrix or sample property matrix, which cannot be completed when the matrix is ​​singular, and the multiple linear regression model itself does not have the ability to remove data noise; principal components Regression ensures that the matrix is ​​non-singular by decomposing the spectral matrix or the sample property matrix, but the correlation between the spectral data and the sample property data is not considered during the decomposition, so it is not suitable for the situation where the analysis target information is weak in the spectral information ; Partial least squares method considers the correlation between spectral data and sample property data on the basis of principal component regression, but as a linear correction method, the model established by it always has certain nonlinear errors; regression based on support vector machine The model has a good nonlinear fitting ability, but in the modeling process, quadratic programming is used to solve the support vector, which involves the matrix calculation of the order of the number of samples, and consumes a lot of machine memory and computing time when the number of samples is too large; The neural network model can map any complex nonlinear relationship, and the learning rules are simple and easy to implement by computer, but it has high requirements for the adequacy of data. When the amount of data is insufficient, it is easy to overfit and reduce the robustness of the model. There are method limitations that affect the robustness and accuracy of the model

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