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Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP‐AES
Author(s) -
KHAYATZADEH MAHANI Mohamad,
CHALOOSI Marzieh,
GHANADI MARAGHEH Mohamad,
KHANCHI Ali Reza,
AFZALI Dariush
Publication year - 2007
Publication title -
chinese journal of chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.28
H-Index - 41
eISSN - 1614-7065
pISSN - 1001-604X
DOI - 10.1002/cjoc.200790306
Subject(s) - partial least squares regression , chemometrics , inductively coupled plasma atomic emission spectroscopy , chemistry , principal component analysis , principal component regression , analytical chemistry (journal) , inductively coupled plasma , artificial neural network , linear regression , calibration , least squares function approximation , biological system , pattern recognition (psychology) , statistics , artificial intelligence , chromatography , mathematics , plasma , computer science , physics , quantum mechanics , estimator , biology
Simultaneous determination of several elements (U, Ta, Mn, Zr and W) with inductively coupled plasma atomic emission spectrometry (ICP‐AES) in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network (ANN) and partial least squares regression (PLS) for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line (263.553 nm) with a 0.06 nm spectral window by ICP‐AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5‐5‐5 (input, hidden and output neurons) network was used with linear transfer function after both hidden and output layers. The PLS model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions (REP) in test set were 3.75% and 3.56% for ANN and PLS respectively.