Citation: ZHU Hong-Qiu, ZHOU Tao, LI Yong-Gang, CHEN Jun-Ming. An Ultraviolet-Visible Absorption Spectrometric Method for Detection of Zinc and Cobalt Ions Concentration Based on Boosting Modeling[J]. Chinese Journal of Analytical Chemistry, ;2019, 47(4): 576-582. doi: 10.19756/j.issn.0253-3820.181650 shu

An Ultraviolet-Visible Absorption Spectrometric Method for Detection of Zinc and Cobalt Ions Concentration Based on Boosting Modeling

School of Information Science & Engineering, Central South University, Changsha 410083, China

Received Date: 15 October 2018
Revised Date: 27 December 2018

Fund Project: This work was supported by the National Natural Science Foundation of China (No. 61533021) and the Fundamental Funds for the Central Universities of Central South University (No.2018zzts551).

Ultraviolet-visible (UV-vis) absorption spectrophotometry is used to detect the concentration of mixed solution of Zn and Co ions, where the problems of spectrum overlaps and interferes with each other due to their similar chemical properties are well solved. A boosting modeling method based on least absolute shrinkage and selection operator (LASSO) regression is proposed. The method first obtains sub-data sets by weighting the calibration samples, and then uses the sub-data sets to establish LASSO sub-model sets with different penalty factors, and uses Akaike Information Criterion (AIC) to judge the sub-model sets. According to the error of sub-model to modeling sample, the sample weight is updated. The iterations are repeated until sub-model convergence. Finally, the sub-models are given different weights according to the prediction performance of the sub-model, and the final prediction model is obtained by weighting fusion of the sub-model. A total of 80 groups of UV-Vis spectral data sets of mixed solution of Zn and Co are obtained by experiments. The method is compared with PLS of the whole spectrum, Monte Carlo uniformative variable elimination- partial least square(MCUVE-PLS) and competitive adaptive reweighted sampling (CARS)-PLS, and it is found that that the proposed method can greatly retain the wavelength variables with high contribution to the model and improve both the explanatory and predictive performance of the model. The method can be used to establish a stable and efficient prediction model for spectral data analysis.
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