Metabolite profiling of Sida rhombifolia with different extracting solvents using LC-MS/MS and their antioxidant activity Alfi Hudatul Karomah (a,b), Auliya Ilmiawati (a,c), Utami Dyah Syafitri (c,d), Mohamad Rafi (a,b,c)
(a) Department of Chemistry, Faculty of Mathematics and Natural Sciences, Jalan Tanjung-Dramaga Campus, IPB University, Bogor 16680, Indonesia
mra[at]apps.ipb.ac.id
(b) Advance Research Laboratory, Institute of Research and Community Services, Jalan Palem-Dramaga Campus, IPB University, Bogor 16680, Indonesia
(c) Tropical Biopharmaca Research Center, Institute of Research and Community Services, Jalan Taman Kencana No 3, Taman Kencana Campus, IPB University, Bogor 16128, Indonesia
(d) Department of Statistics, Faculty of Mathematics and Natural Sciences, Jalan Meranti-Dramaga Campus, IPB University, Bogor 16680, Indonesia
Abstract
Sida rhombifolia is one of the herbal plants that is often used as traditional medicine and also has antioxidant activity. The effectiveness of S. rhombifolia extract as a traditional medicine can be caused by the large number of active compounds extracted. Therefore, it is important to choose a solvent that can extract the most optimum bioactive metabolites. This study aimed to evaluate differences in antioxidant activity and metabolite profiles in S. rhombifolia extracts due to differences in extraction solvents. Samples were extracted with EtOH pa, EtOH 70 percent, EtOH 50 percent, EtOH 30 percent, and water. Free radical scavenging activity of S. rhombifolia extract was determined using the DPPH method. UHPLC-Q-Orbitrap HRMS combined with multivariate analysis was used to evaluate the different metabolites in each extract. S. rhombifolia extract has free radical inhibitory activity ranging from 45.18 to 68.62 percent. A total of 34 metabolites were putatively identified, most of which were ecdysteroids, flavonoid, and fatty acids. The 70 percent EtOH extract contained the highest number of identified metabolites. Using the peak intensity of the whole chromatogram, PCA succeeded in classifying each extract based on the extraction solvent. The combination of metabolite profiles and multivariate analysis showed that there were differences in the distribution of metabolites in each extract.
