M3GPSpectra: A novel approach integrating variable selection/construction and MLR modeling for quantitative spectral analysis
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- @Article{YANG:2021:ACA,
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author = "Yu Yang and Xin Wang2 and Xin Zhao and Min Huang and
Qibing Zhu",
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title = "{M3GPSpectra:} A novel approach integrating variable
selection/construction and {MLR} modeling for
quantitative spectral analysis",
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journal = "Analytica Chimica Acta",
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volume = "1160",
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pages = "338453",
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year = "2021",
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ISSN = "0003-2670",
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DOI = "doi:10.1016/j.aca.2021.338453",
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URL = "https://www.sciencedirect.com/science/article/pii/S0003267021002798",
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keywords = "genetic algorithms, genetic programming, Spectral
analytical method, Variable selection, Variable
construction, Optimal combination",
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abstract = "Quantitative analysis of the physical or chemical
properties of various materials by using spectral
analysis technology combined with chemometrics has
become an important method in the field of analytical
chemistry. This method aims to build a model
relationship (called prediction model) between feature
variables acquired by spectral sensors and components
to be measured. Feature selection or transformation
should be conducted to reduce the interference of
irrelevant information on the prediction model because
original spectral feature variables contain redundant
information and massive noise. Most existing feature
selection and transformation methods are single linear
or nonlinear operations, which easily lead to the loss
of feature information and affect the accuracy of
subsequent prediction models. This research proposes a
novel spectroscopic technology-oriented, quantitative
analysis model construction strategy named M3GPSpectra.
This tool uses genetic programming algorithm to select
and reconstruct the original feature variables,
evaluates the performance of selected and reconstructed
variables by using multivariate regression model (MLR),
and obtains the best feature combination and the final
parameters of MLR through iterative learning.
M3GPSpectra integrates feature selection,
linear/nonlinear feature transformation, and subsequent
model construction into a unified framework and thus
easily realizes end-to-end parameter learning to
significantly improve the accuracy of the prediction
model. When applied to six types of datasets,
M3GPSpectra obtains 19 prediction models, which are
compared with those obtained by seven linear or
non-linear popular methods. Experimental results show
that M3GPSpectra obtains the best performance among the
eight methods tested. Further investigation verifies
that the proposed method is not sensitive to the size
of the training samples. Hence, M3GPSpectra is a
promising spectral quantitative analytical tool",
- }
Genetic Programming entries for
Yu Yang
Xin Wang2
Xin Zhao
Min Huang
Qibing Zhu
Citations