Artificial intelligence-based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

Authors : Abdullahi Garba USMAN, Selin ISİK, Sani Isah ABBA, Filiz MERİCLİ

Pages : 1339-1351

View : 25 | Download : 8

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica insert ignore into journalissuearticles values(mango);, Rheum nobile, Annona squamosal, Camellia sinensis insert ignore into journalissuearticles values(tea);, and coriander insert ignore into journalissuearticles values(Coriandrum sativum L.);. It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography insert ignore into journalissuearticles values(HPLC); technique. In this paper, three different nonlinear models including artificial neural network insert ignore into journalissuearticles values(ANN);, adaptive neuro-fuzzy inference system insert ignore into journalissuearticles values(ANFIS);, and support vector machine insert ignore into journalissuearticles values(SVM);,in addition to a classical linear model [multilinear regression analysis insert ignore into journalissuearticles values(MLR);], were used for the prediction of the retention time insert ignore into journalissuearticles values(tR); and peak area insert ignore into journalissuearticles values(PA); for isoquercitrin using PLC. The simulation uses concentration of the standard, composition of the mobile phases insert ignore into journalissuearticles values(MP-A and MP-B);, and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error insert ignore into journalissuearticles values(RMSE);, mean square error insert ignore into journalissuearticles values(MSE);, determination coefficient insert ignore into journalissuearticles values(DC);, and correlation coefficient insert ignore into journalissuearticles values(CC);. The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS-M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN-M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.
Keywords : High performance liquid chromatography, retention time, isoquercitrin, artificial intelligence, multilinear regression