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Volume 26 (2011), Issue 2, Pages 105-114

Comparison of partial least squares and artificial neural network chemometric techniques in determination of sulfamethoxazole and trimethoprim in pharmaceutical suspension by ATR–FTIR spectrometry

M. Khanmohammadi,1 N. Dallali,2 A. Bagheri Garmarudi,1,3 M. Zarnegar,2 and K. Ghasemi1

1Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
2Department of Chemistry, Faculty of Science, Zanjan University, Zanjan, Iran
3Department of Chemistry and Polymer Laboratory, Engineering Research Institute, Tehran, Iran

Copyright © 2011 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Partial Least Square (PLS) and Artificial Neural Network (ANN) techniques were compared during development of an analytical method for quantitative determination of sulfamethoxazole (SMX) and trimethoprim (TMP) in Co-Trimoxazole® suspension. The procedure was based on Attenuated Total Reflectance Fourier Transform Infrared (ATR–FTIR) spectrometry. The 800–2500 cm−1 spectral region was selected for quantitative analysis. R2 and relative error of prediction (REP) in PLS technique were (0.989, 2.128) and (0.986, 1.381) for SMX and TMP, respectively. These statistical parameters were improved using the ANN models considering the complexity of the sample and the speediness and simplicity of the method. R2 and RMSEC in modified method were (0.997, 1.064) and (0.997, 0.634) for SMX and TMP, respectively.