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| A Wavelength Selection Method of UV-Vis Based on Variable Stability and Credibility |
| SUN Tao, YANG Chun-hua, ZHU Hong-qiu*, LI Yong-gang, CHEN Jun-ming |
| School of Automation, Central South University, Changsha 410083，China |
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Abstract This paper proposes a wavelength selection method based on stability and credibility partial least squares (SCPLS), to solve the problem that the ultraviolet visible （UV-Vis） spectra of multi-metal ion mixture solution were seriously overlapped and difficult to separate. In SCPLS, an exponentially decreasing function (EDF) is applied to select the variables in an iterative manner. In each iteration, a series of models are built with the sub-datasets sampled using the Monte Carlo strategy. Then, the stability and credibility of each variable are calculated, and the variables with high stability and credibility are selected by the EDF. Subsequently, the selected variables are used to construct a new variable subset for the next iteration. After the selection iterations are terminated, the root mean square error of cross validation (RMSECV) of each subset is calculated. The variable subset with the minimum RMSECV value is considered to be the optimal variable subset. The performance of SCPLS is evaluated with UV-Vis Spectral data set of Zn(Ⅱ), Cu(Ⅱ) and Co(Ⅱ) mixture solution and UV-Vis Spectral data set of Zn(Ⅱ) and Co(Ⅱ) mixture solution, and compared with that of full spectrum partial least squares (PLS) modeling and the moving window PLS (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) methods. The results show that SCPLS can not only reduce the complexity of the wavelength selection, but also ensure the stability of the wavelength selection process. And it can select the subset with the minimum RMSECV value. Thus, the RMSECV of Zn(Ⅱ), Cu(Ⅱ) and Co(Ⅱ) models obtained by SCPLS are 60.5%，40.2% and 31.8% respectively lower than that of full spectrum PLS, and 29.8%，26.1% and 0.8% respectively lower than that of SCARS. The average relative error of Zn(Ⅱ), Cu(Ⅱ) and Co(Ⅱ) is 2.14%, 1.25% and 0.74% respectively, of which the maximum relative error of Zn(Ⅱ) is 4.67%, the maximum relative error of Cu(Ⅱ) is 3.99%, and the maximum relative error of Co(Ⅱ) is 3.12%. And the RMSECV of Zn(Ⅱ) and Co(Ⅱ) models obtained by SCPLS are 39.4% and 24.9% respectively lower than that of full spectrum PLS, and 35.3% and 13.3% respectively lower than that of SCARS. The average relative error of Zn(Ⅱ) and Co(Ⅱ) are 1.23% and 1.10% respectively, of which the maximum relative error of Zn(Ⅱ) is 4.45% and the maximum relative error of Co(Ⅱ) is 4.57%. The proposed method can efficiently improve modeling accuracy.
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Received: 2018-09-19
Accepted: 2019-01-25
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Corresponding Authors:
ZHU Hong-qiu
E-mail: hqcsu@csu.edu.cn
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