Profiling and identification of phytochemicals from erythrina fusca extracts and their activity against skin infectious bacterial strains through proton NMR-based chemometric approach / Adiez Sapura Azmi

• Main Author: Azmi, Adiez Sapura
• Format: Thesis
• Language: English
• Published: 2023
• Subjects: QK Botany
• Online Access: https://ir.uitm.edu.my/id/eprint/91034/1/91034.pdf; https://ir.uitm.edu.my/id/eprint/91034/
• Institution: Universiti Teknologi Mara

Erythrina fusca (Fabaceae) is a red flowering plant belonging to the Erythrina genus. Traditionally, this plant has been claimed to have therapeutic effects on certain symptoms associated with bacterial infections. Although the reported therapeutic activities of the plant are attributed to its alkaloidal content, there is currently no scientific data available to support these claims, particularly regarding the antibacterial activity of the plant against bacterial strains commonly responsible for human infections. Therefore, the objectives of this study were; 1) to extracts alkaloids from E. fusca's, leaves using a tartaric and hydrochloric acids through acid-base extraction, 2) to evaluate the antibacterial activity of extracts obtained from different parts of E.fusca (leaves, twigs, and flowers) against four human infectious bacterial strains, namely Staphylococcus aureus, Staphylococcus haemolyticus, Enterobacter cloacae, and Enterobacter aerogenes; 3) to profile and identify the phytochemicals present in the extracts, 4) to determine the variation of phytochemicals between the extracts and the correlation with their antibacterial activity. The antibacterial activity was evaluated using well diffusion method with ciprofloxacin as the positive control. The phytochemicals were profiled and identified using proton nuclear magnetic resonance (JH NMR)-based data analysis, while the phytochemicals variation between different extracts and their correlation with antibacterial activity were determined using principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and partial least square (PLS) regression.