Evaluation of Two Statistical Tools (Least Squares Regression andArtificial Neural Network) in the Multivariate Optimization of Solid-Phase Extractionfor Cadmium Determination in Leachate Samples

Journal of the Brazilian Chemical Society

Hélio R. Sousa Filho¹  Daniel M. Oliveira¹  Valfredo A. Lemos¹  Marcos A. Bezerra¹ 

关键词: cadmium;    landfill leachate;    solid-phase extraction;    Doehlert design;    least squares regression;    artificial neural network;   

DOI  :  10.5935/0103-5053.20140211

来源: SciELO

【 摘 要 】

This work proposes the use of multivariate optimization as a procedure for cadmiumdetermination in leachate samples via flame atomic absorption spectrometry aftersolid phase extraction using a minicolumn packed with Amberlite XAD-4 modified with3,4-dihydroxybenzoic acid. The variables related with the preconcentration (pH,sampling flow rate and buffer concentration) were optimized using Doehlert design.Two statistical modeling tools (least squares regression and artificial neuralnetworks) have been applied to the data and their performances were compared.Digestion procedures of the leachate by heating in acid medium and ultravioletradiation were evaluated being the latter more appropriate to prevent loss of Cd byvolatilization. The developed procedure has promoted an enrichment factor of 9, withdetection and quantification limits (3sb) of 0.72 and 2.4 µg L-1, respectively, and precision - expressed as relative standarddeviation percentage - of 4.0 and 6.4% (RSD%, n = 4 for 5.0 and 20.0 µg L-1, respectively). Addition/recovery tests for Cd were carried out andvalues between 97 and 112% were obtained. The procedure was applied for cadmiumdetermination in leachate samples collected at the sanitary landfill ofJaguaquara-BA, Brazil.

【 授权许可】

CC BY   
 All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License