A Novel Approach to Evaluate the Quality and Identify the Active Compounds of the Essential Oil from Curcuma longa L.

Abstract

Composition-Activity Relationship (CAR) modeling is a novel approach to evaluate the quality and identify active components of herbal medicine. In this study, Grid Search Method (GSM) and Heuristics algorithms, particularly Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), were adopted to determine the optimal parameters automatically. Then, support vector regression (SVR) combined with a linear kernel function or a radial basis kernel function (RBF) and back propagation artificial neural networks (BPANN) were employed to construct the model that correlated the main chemical components with the cytotoxicity of the essential oil from Curcuma longa L., respectively. Considering the robustness and predictive ability, the ν-SVR-RBF-PSO model had the best performance in various tests performed in this paper. Nine components were then identified to have significant cytotoxicity based on the superior model and Mean Impact Value (MIV) analysis. An optimal model can therefore be a useful tool to predict the bioactivity for quality evaluation and active components identification of herbal medicine.