OBJECTIVE: The objective of this study was to develop an ultrasound-assisted extraction (UAE) method for achieving a high extraction yield of anthraquinones using the response surface methodology (RSM), Box-Behnken design (BBD), and a recycling preparative high-performance liquid chromatography (HPLC) protocol for isolation of anthraquinones from C. singueana.
METHODOLOGY: Optimisation of UAE was performed using the Box-Behnken experimental design. Recycling preparative HPLC was employed to isolate anthraquinones from the root extract of C. singueana.
RESULTS: The BBD was well-described by a quadratic polynomial model (R2 = 0.9751). The predicted optimal UAE conditions for a high extraction yield were obtained at: extraction time 25.00 min, temperature 50°C and solvent-sample ratio of 10 mL/g. Under the predicted conditions, the experimental value (1.65 ± 0.07%) closely agreed to the predicted yield (1.64%). The obtained crude extract of C. singueana root was subsequently purified to afford eight anthraquinones.
CONCLUSION: The extraction protocol described here is suitable for large-scale extraction of anthraquinones from plant extracts.
OBJECTIVES: Here, we explored the phytochemical diversity of the seven varieties from Peninsular Malaysia using Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS) analyses and correlated it with the α-glucosidase inhibitory activity.
METHODOLOGY: The Nuclear Overhauser Effect Spectroscopy (NOESY) One-Dimensional (1D)-NMR and LC-MS data were processed, annotated, and correlated with in vitro α-glucosidase inhibitory using multivariate data analysis.
RESULTS: The α-glucosidase results demonstrated that different varieties have varying inhibitory effects, with the highest inhibition rate being F. deltoidea var. trengganuensis and var. kunstleri. Furthermore, diverse habitats and plant ages could also influence the inhibitory rate. The heat map from NMR and LC-MS profiles showed unique patterns according to varying levels of α-glucosidase inhibition rate. The Partial Least Squares (PLS) model constructed from both NMR and LC-MS further confirmed the correlation between the α-glucosidase inhibition rate of F. deltoidea varieties and its metabolite profiles. The Variable Influence on Projection (VIP) and correlation coefficient (p(corr)) values values were used to determine the highly relevant metabolites for explaining the anticipated inhibitory action.
CONCLUSION: NMR and LC-MS annotations allow the identification of flavan-3-ols and proanthocyanidins as the key bioactive factors. Our current results demonstrated the value of multivariate data analysis to predict the quality of herbal materials from both biological and chemical aspects.