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Optimizing Ultrasound-Assisted Extraction Methods of Etlingera elatior Using Response Surface Methodology for High Performance Liquid Chromatography Fingerprinting

Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Dr. Soeparno Street, 53122, Purwokerto, Indonesia

Received: 21 Mar 2025; Revised: 28 Jul 2025; Accepted: 1 Aug 2025; Published: 10 Sep 2025.
Open Access Copyright 2025 Jurnal Kimia Sains dan Aplikasi under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract

Kecombrang flower (Etlingera elatior) is widely used in traditional medicine and contains various metabolites. High-performance liquid chromatography (HPLC) fingerprinting can be employed as an analytical technique to comprehensively reveal the metabolite profile, while ultrasound-assisted extraction (UAE) was developed to optimize metabolite extraction. This study aims to determine the optimal extraction conditions for E. elatior and apply these conditions in HPLC fingerprinting. This study utilized central composite design (CCD) and response surface methodology (RSM) to optimize the extraction of E. elatior flowers, focusing on extraction time and the simplicia-to-solvent ratio. The optimal extraction results were applied to HPLC fingerprints of the flowers, leaves, and stems of E. elatior. The chromatograms were further analyzed using chemometric methods, namely principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), and hierarchical cluster analysis (HCA) to classify and interpret the variability of metabolite profiles in different parts of E. elatior. The optimal UAE conditions were determined to be a time of 46 minutes and a simplicia-to-solvent ratio of 1:25 (g/mL). Chemometric analysis revealed that the samples were well clustered, which reflects the similarity of metabolites among them. The HCA further showed that the metabolite profile of E. elatior flowers is closely related to that of the stems.

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Keywords: Chemometrics; Chromatography; Etlingera elatior; Extraction; Fingerprinting
Funding: Jenderal Soedirman University under contract 1133/UN23/HK.PT.01.02/2022

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