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The Application of Chemometrics in the Simultaneous Determination of Multicomponent

Author: TaoQiuBo
Tutor: ShenZuo
School: Zhengzhou University
Course: Analytical Chemistry
Keywords: Multicomponent analysis Particle Swarm Optimization Wavelength selection Support Vector Machine Partial least squares algorithm
CLC: O657
Type: Master's thesis
Year: 2010
Downloads: 104
Quote: 1
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Abstract


The emergence of modern photometric analysis instrument to provide for our fast wavelength of thousands or even millions . Because of these characteristics , these chemical samples contain high -dimensional and linear data variables , compared to the large number of data variables , data related to the number of variables as the dependent variable . This will affect the predictive ability of the model or caused overfitting . In the the luminosity analysis , it is necessary to eliminate the the unnecessary redundancy background or noise interference . Made wavelength selection is not only beneficial to the robustness of the model , can also be a better explanation of their linear . Multicomponent analysis , especially structurally similar substances , due to serious overlap as well as the mutual interference between the components of each component spectrum , need to build robust predictive models . Partial least squares is used to solve the multiple correlation problems and is an effective method of using the valid information . However , a large number of experimental results show that , even if the application of partial least squares method to eliminate spectral model is not conducive to the establishment of unrelated variables of information is still very important , wavelength selection with the predictive ability of the PLS , the lack of The variable Selection will undermine its soundness . This paper presents an improved particle swarm optimization algorithm for wavelength selection , and for the ortho, meta nitrophenol and cephalosporins substances content determination . The results show that the particle swarm optimization wavelength selection results superior to the modeling of the full wavelength model robustness and reliability . There is a non-linear characteristics of interaction between components in multicomponent analysis , linear methods can not be satisfied with the results . Is a small sample of the machine learning problem , the use of artificial neural networks need to select the relevant independent variables and subject to certain restrictions , are prone to over-fitting problems , the above two cases will produce errors . In this paper , we trial new development , both to limit over-fitting can fit nonlinear data support vector machine . And partial least squares algorithm , the results show that the prediction error of support vector machine algorithm useful method of support vector regression is a multivariate correction .

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CLC: > Mathematical sciences and chemical > Chemistry > Analytical Chemistry > Instrument analysis ( physics and physical chemistry )
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