Abstract

A sensitive and selective method using combination of principal component analysis (PCA), artificial neural network (ANN) and UV-Visible spectroscopy has been developed for the simultaneous determination of acetaminophen (AMP) and codeine (COD) in plasma samples. The ANN trained by the back-propagation learning was employed to model the complex non-linear relationship between the PCs extracted from UV-visible spectra of medications and the absorbance values.A Optimal ANN model were as follows: Number of input PCs: 6, number of neurons in hidden layer: 5. The linear calibration range was 10-70 Aµg ml-1 and 40-700 Aµg ml-1, and the detection limit were 0.3 Aµg ml-1A and 1.3 Aµg ml-1, for AMP and COD, respectively. The results have been compared with those obtained by the HPLC method.

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