Wydział Chemii

Sylwester Mazurek

Roman Szostak

2008

Journal of Pharmaceutical and Biomedical Analysis

48

814-821

10.1016/j.jpba.2008.08.013

Naukowa

Angielski

Artykuł

The FT-Raman quantification of diclofenac sodium in tablets and capsules was performed with the help of the partial least squares (PLS), principal component regression (PCR) and counter-propagation artificial neural networks (CP-ANN) methods. For the analysed tablets, calibration models were built using unnormalised spectra and spectra normalised by the intensity of a selected band of an internal standard. Different pre-processing methods were applied for the capsules. To compare the predictive ability of the models, the relative standard errors of prediction (RSEP) were calculated. The 5 × 5 CP-ANN and PLS methods gave models of comparable quality, which were usually more efficient than the PCR ones. The RSEP error values for the tablets were in the range of 2.4–3.8% for the calibration and 2.6–3.5% for the validation data sets and for the three procedures applied. For capsules, the RSEP errors were in the range of 0.8–1.9% and 1.4–1.7% for the calibration and validation samples, respectively. Five commercial products containing 25, 50 or 75 mg of diclofenac sodium per tablet/capsule were quantified. Concentrations found from the Raman data analysis agree with the results of the reference analysis and correlate strongly with the declared values with the recovery of 99.5–101.3%, 99.7–102.0% and 99.9–101.2% for the PLS, PCR and CP-ANN methods, respectively. The proposed procedure can be a fast and convenient alternative to the standard pharmacopoeial methods of diclofenac sodium quantification in solid dosage forms.

Active ingredient quantification, FT-Raman spectroscopy, Diclofenac sodium, Neural networks, Chemometrics

https://doi.org/10.1016/j.jpba.2008.08.013

http://www.elsevier.com