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. 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 μg ml‐1 and 40‐700 μg ml‐1, and the detection limit were 0.3 μg ml‐1 and 1.3 μg ml‐1, for AMP and COD, respectively. The results have been compared with those obtained by the HPLC method.
Keywords
Acetaminophen Codeine Artificial neural network Principal component Analysis UV‐VisSpectroscopy
Copyright
© 2014, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.