Improving phenolic compound extraction from Arnica montana flowers through multivariate optimization of heat and ultrasound-assisted methods

Abstract

This study aimed to optimize phenolic compound extraction from Arnica montana (AM) L. flowers,comparing heat- and ultrasound-assisted extraction (HAE and UAE) through a multivariateapproach. Critical parameters, including time, temperature or ultrasonic power, and ethanol concentration, were evaluated through a circumscribed central composite design. Unsupervised multivariate analysis of LC-MS/MS data identified key extraction conditions influencing the phenolicprofile. Response surface methodology (RSM) determined optimal levels of enhancing yield andtotal phenolic content. Among the 24 identified phenolic compounds, dicaffeoylquinic acid wasthe most abundant. Ethanol concentration proved crucial in extracting specific phenolic compounds, supported by multivariate and RSM analyses. Optimal HAE conditions outperformedUAE, resulting in a 26% increase in phenolic compounds. Utilizing extraction cycles under theseconditions, especially two cycles for HAE and three for UAE, surpassed traditional Soxhlet extraction, indicating potential industrial applications for AM flower extracts with improved efficiency and resource utilization compared to conventional methods.