Document Detail

Simultaneous spectrophotometric determination of Fe and Ni with xylenol orange using principal component analysis and artificial neural networks in some industrial samples.
MedLine Citation:
PMID:  18967466     Owner:  NLM     Status:  In-Data-Review    
Artificial neural networks (ANNs) are among the most popular techniques for nonlinear multivariate calibration in complicated mixtures using spectrophotometric data. In this study, Fe and Ni were simultaneously determined in aqueous medium with xylenol orange (XO) at pH 4.0. In this way, after reducing the number of spectral data using principal component analysis (PCA), an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. Adjustable experimental and network parameters were optimized, 30 calibration and 20 prediction samples were prepared over the concentration ranges of 0-400 mug l(-1) Fe and 0-300 mug l(-1) Ni. The resulting R.S.E. of prediction (S.E.P.) of 3.8 and 4.7% for Fe and Ni were obtained, respectively. The method has been applied to the spectrophotometric determination of Fe and Ni in synthetic samples, some Ni alloys, and some industrial waste waters.
M Kompany-Zareh; A Massoumi; S Pezeshk-Zadeh
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Talanta     Volume:  48     ISSN:  0039-9140     ISO Abbreviation:  Talanta     Publication Date:  1999 Feb 
Date Detail:
Created Date:  2008-10-30     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  2984816R     Medline TA:  Talanta     Country:  England    
Other Details:
Languages:  eng     Pagination:  283-92     Citation Subset:  -    
Department of Chemistry, Shahid Beheshti University, Evin, Tehran 19839, Iran.
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