Simultaneous spectrophotometric determination of carbidopa and levodopa by partial least squares regression, principal component regression and least squares support vector machine methods

Kuldeep Kaur, Ashok Kumar Malik, Baldev Singh, M. Godarzi

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Three chemometric methods; partial least squares regression (PLS), principal component regression (PCR) and least-squares support vector machines (LS-SVM) were applied for simultaneous determination of carbidopa and levodopa in synthetic mixtures and real samples. The simultaneous determination of these drugs is a difficult problem due to spectral interferences. The proposed methods were used for multivariate calibration of the spectrophotometric data. The calibration graphs were linear in the ranges of 1-30 μg mL-1 for both carbidopa and levodopa. The relative standard error of prediction (RSEP %) for applying the three methods to 8 synthetic samples in the linear calibration ranges for carbidopa and levodopa was 11.747 and 7.783 for PLS, 14.764 and 9.869 for PCR, 0.229 and 0.170 for LS-SVM respectively. The root mean square errors of prediction (RMSEP) for carbidopa and levodopa with PLS, PCR and LS-SVM were 1.900, 2.388, 0.037 and 1.314, 1.667, 0.029 respectively. The LS-SVM methods were successfully applied for determination of these drugs in commercial pharmaceutical preparations and human urine samples.

Original languageEnglish
Pages (from-to)123-136
Number of pages14
JournalThai Journal of Pharmaceutical Sciences
Volume33
Issue number4
StatePublished - 2009 Oct 1

Keywords

  • Carbidopa
  • Levodopa
  • Simultaneous determination
  • Spectrophotometry

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