Comparison of artificial neural networks with PLS regression for simultaneous determinations by ICP-AES

Comparison of artificial neural networks with PLS regression for simultaneous determinations by ICP-AES

Chinese Journal of Chemistry 2007, 25, 1658-1662
Mohamad Khayatzadeh Mahani, Marzieh Chaloosi, Mohamad Ghanadi Maragheh, Ali Reza Khanchi, Dariush Afzali

Abstract: 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.

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