Artificial Neural Network and Fuzzy Systems-Based Spectrophotometric Determination of Anti-Parkinson Drugs

A simple and accurate spectrophotometric method was developed for the simultaneous determination of levodopa (LD), carbidopa (CD), and entacapone (ENT) in pharmaceutical formulations. Because of the extensive overlap among their absorption spectra, three chemometric techniques – Artificial Neural Network (ANN), Fuzzy Inference System (FIS), and Adaptive Neuro-Fuzzy Inference System (ANFIS) – were applied to extract quantitative information and resolve spectral interferences. The ANN model, optimized using the Levenberg–Marquardt (LM) algorithm, demonstrated excellent predictive performance, with a coefficient of determination (R2) of 0.9999 for all analytes. The corresponding mean recoveries were 100.25, 99.42, and 99.65%, and the root mean square error (RMSE) values were 0.02, 0.006, and 0.05 for LD, CD, and ENT, respectively. The FIS model produced R² values of 0.9996 (LD and ENT) and 0.9998 (CD), with mean recoveries of 99.06, 99.42, and 99.25% and RMSE values of 0.14, 0.018, and 0.13, respectively. The ANFIS model yielded R2 values of 0.9981, 0.9970, and 0.9983, mean recoveries of 99.09, 98.56, and 99.34%, and RMSE values of 0.19, 0.05, and 0.22 for LD, CD, and ENT, respectively. Statistical comparison with a reference high-performance liquid chromatography (HPLC) method using one-way analysis of variance (ANOVA) indicated no significant difference between the results (p > 0.05). These findings demonstrate that the proposed spectrophotometric–chemometric approaches provide reliable and cost-effective alternatives to chromatographic techniques for the routine quality control of multicomponent pharmaceutical formulations.

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