FTIR-based metabolomics for characterization of antioxidant activity of different parts of Sesbania grandiflora plant

Abstract

Sesbania grandiflora, one of the flowering plants with great potential as a source of natural antioxidants because it contains chemicals such as tannin, phenolics, and flavonoids. However, there has been no extensive investigation on the antioxidant activity of isolated from different parts of this plant. This study aims to investigate the correlation between antioxidant activity and secondary metabolites extracted from three different parts (leaves, stem barks, and roots) of S. grandiflora plant using Fourier-transform infrared spectroscopy (FTIR) based metabolomics approach. The FTIR is a very useful technique for identifying the functional groups present in the mixtures, while antioxidant assay provides the base to select the part of the plant as the most potential source of antioxidant. The antioxidant properties were determined using 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2’-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid (ABTS), and potassium ferricyanide reduction method. The multivariate data - analyses using Principal Component Analysis (PCA) and Partial Least Square (PLS) were conducted to compare the distribution of metabolites extracted from different parts of the S. grandiflora plant investigated. The PLS was performed to evaluate the relationship between the components of the extracts obtained from different parts of the plant and their antioxidant activities. The results exhibited that antioxidant activities of the extract of the stem barks, and roots are higher than that of the extract of the leaves. Also, the PLS model indicated that the functional group absorption data were significantly correlated with the IC50 values of antioxidant activity. Subsequently, based on the results of PLS analysis displayed that C=C, C=O, and along with C-O functional groups are proposed as the main contributors to the antioxidant activity of the extracts tested. The extracts of different parts were grouped using PCA analysis with a total of principal components (PC) of 94%