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

doi:10.3964/j.issn.1000-0593(2018)08-2638-07

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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.

Key words：UV-Vis spectrophotometry；Partial least squares；Artificial neural network；Aluminum；Iron；Copper

Received: 2016-01-25 Accepted: 2016-08-22

ZTFLH:

O657.3

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)08-2638-07 OR https://www.gpxygpfx.com/EN/Y2018/V38/I08/2638

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