Simultaneous Determination of Estradiol Cypionate and Medroxyprogesterone Acetate Hormones in Injectable Suspension by UV Spectrophotometry Based on Least-Squares Support Vector Machine and Fuzzy Inference System: Comparison with HPLC

Abstract

Background The combination of estradiol cypionate (ECA) and medroxyprogesterone acetate (MPA) is used to prevent pregnancy in women. The analysis of the ECA and MPA combination reveals a challenge due to the strong overlap of the spectra of these compounds. Objective Spectrophotometry technique along with chemometrics methods are simple, fast, precise, and low-cost for the simultaneous determination of ECA and MPA in a combined pharmaceutical dosage form. Methods Two developed approaches, including the least squares support vector machine (LSSVM) and fuzzy inference system (FIS) along with the spectrophotometric method were proposed to solve such a challenging overlap. Results Based on the cross-validation method, the regularization parameter (γ) and width of the function (σ) in the LSSVM model were optimized and their root mean square error (RMSE) values were found to be 0.3957 and 0.2839 for ECA and MDA, respectively. The mean recovery values were 99.87% and 99.63% for ECA and MDA, respectively. FIS coupled with principal component analysis (PCA) showed the mean recovery percentage equal to 99.05% and 99.50% for ECA and MDA, respectively. Also, the RMSE of both components was lower than 0.3. Conclusion The analysis results of a real sample (injection suspension) using the proposed methods were compared with high-performance liquid chromatography (HPLC) by one-way analysis of variance (ANOVA) test, and no significant differences were found in the results. Highlights Intelligent methods were proposed for the simultaneous determination of ECA and MPA. Least squares support vector machine and fuzzy inference system along with spectrophotometry were used. HPLC as a reference method was performed and compared with chemometrics methods. Rapid, simple, low-cost, and accurate are the benefits of the proposed approaches.