光谱学与光谱分析, 2018, 38 (8): 2638, 网络出版: 2018-08-26
Development a Spectrophotometric of Fe(Ⅲ), Al(Ⅲ) and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters
Development a Spectrophotometric of Fe(Ⅲ), Al(Ⅲ) and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters
摘要
Abstract
Simultaneous determination of heavy metal cations and accurate quantitative prediction of them are of great interest in analytical chemistry. This work has focused on a comprehensive comparison of partial least squares (PLS-1) and artificial neural networks (ANN) as two types of chemometric methods. For this purpose, aluminum, iron and copper were studied as three analytes whose UV-Vis absorption spectra highly overlap each other. Accordance with determined parameters (ligand concentration, pH, waiting times, the relationship between absorbance and concentration of metal ion effect and foreign ions) are provided and the optimum conditions. After establishing the optimum conditions for Fe3+, Al3+ and Cu2+ containing mixtures spectrophotometric determinations and the data calibration method of least squares (PLS-1) regression, and artificial neural network (ANN) methods were used. Chemometric methods are applied in a fast, simple, and the results are applicable.
A. Hakan AKTA瘙塁. Development a Spectrophotometric of Fe(Ⅲ), Al(Ⅲ) and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters[J]. 光谱学与光谱分析, 2018, 38(8): 2638. A. Hakan AKTA瘙塁. Development a Spectrophotometric of Fe(Ⅲ), Al(Ⅲ) and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters[J]. Spectroscopy and Spectral Analysis, 2018, 38(8): 2638.