M3GPSpectra: A novel approach integrating variable selection/construction and MLR modeling for quantitative spectral analysis

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@Article{YANG:2021:ACA,

• author = "Yu Yang and Xin Wang2 and Xin Zhao and Min Huang and Qibing Zhu",
• title = "{M3GPSpectra:} A novel approach integrating variable selection/construction and {MLR} modeling for quantitative spectral analysis",
• journal = "Analytica Chimica Acta",
• volume = "1160",
• pages = "338453",
• year = "2021",
• ISSN = "0003-2670",
• DOI = "doi:10.1016/j.aca.2021.338453",
• URL = "https://www.sciencedirect.com/science/article/pii/S0003267021002798",
• keywords = "genetic algorithms, genetic programming, Spectral analytical method, Variable selection, Variable construction, Optimal combination",
• 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