Least-Squares Support Vector Machine and its Application in the Simultaneous Quantitative Spectrophotometric Determination of Pharmaceutical Ternary Mixture

This paper proposes the least-squares support vector machine (ls-svm) as an intelligent method applied on absorption spectra for the simultaneous determination of paracetamol (pct), caffeine (caf), and ibuprofen (ib) in novafen. the signal to noise ratio (s/n) increased. also, in the ls - svm model, kernel parameter (𝜎2) and capacity factor (c) were optimized. excellent prediction was shown using ls-svm, with lower root mean square error (rmse) and relative standard deviation (rsd). in addition, regression coefficient (r2), correlation coefficient (r), and mean recovery (%) of this method obtained for pct, caf, and ib. ls- svm / spectrophotometry method is reliable for simultaneous quantitative analysis of components in commercial samples. the results obtained from analyzing the real sample by the proposed method compared to the high- performance liquid chromatography (hplc) as a reference method. one-way analysis of variance (anova) test at 95% confidence level used and results showed that there was no significant difference between suggested and reference methods.