Abstract
Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile , Annona squamosal , Camellia sinensis (tea), and coriander ( 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 (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (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 (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (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.
Highlights
Coriander (Coriandrum sativum L., family Apiceae) is an annual plant generally found in Western Asia and Mediterranean Europe [1]
The results showed that artificial neural network (ANN) offered significant possibilities for method development in High performance liquid chromatography (HPLC)
This study proposes the application of four data-driven algorithms including the classical and most commonly used linear model, multilinear regression analysis (MLR), and three nonlinear models: ANN, adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM)
Summary
Coriander (Coriandrum sativum L., family Apiceae) is an annual plant generally found in Western Asia and Mediterranean Europe [1]. This plant originated in North America and was distributed and cultivated in various temperate regions of the world. The presence of various bioactive compounds gives this planta wide range of pharmacological properties including antihypertensive, neuro-protective, antidepressant, antimutagenic, antiinflammatory, and antioxidant properties as well as anticancer activities[2]. Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile, Annona squamosal, Camellia sinensis (tea), and coriander (Coriandrum sativum) [3]. It is critically important to determine the bioactive compound by qualitatively identifying its retention time and quantitatively determining its absorbance. High performance liquid chromatography (HPLC) is among the useful techniques deployed todetermine the qualitative as well as quantitative properties of isoquercitrin[5]
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