An intelligent approach based on hybrid of principal component analysis-fuzzy inference system-adaptive neuro-fuzzy inference system for the simultaneous spectrophotometric determination of sofosbuvir and velpatasvir as antiviral drugs in pharmaceutical formulation and urine sample

Abstract

In this study, a simple, fast, accurate, and inexpensive chemometrics-assisted spectrophotometric method was developed for the simultaneous determination of sofosbuvir (SOF) and velpatasvir (VEL) as antiviral drugs. Fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) along with principal component analysis (PCA) were used for the analysis of SOF and VEL in binary mixtures, pharmaceutical formulation, and urine sample. Root mean square error (RMSE) of FIS model was 0.1518 and 0.075 for SOF and VEL, respectively. Also, the average testing error related to the ANFIS method was achieved 2.23 ​× ​10−5 and 5.58 ​× ​10−6 for SOF and VEL, respectively. The range of mean recovery percentage was between 99.76 and 100.41 for both methods. Relative standard deviation (RSD) of the real sample was lower than 0.06% for FIS and ANFIS for both components. Analysis of variance (ANOVA) was applied to the results of the real sample that there was no significant difference between the suggested approaches. PCA-FIS and PCA-ANFIS as efficient and accurate methods can be useful for the simultaneous estimation of binary and ternary mixtures in quality control of pharmaceutical formulations.