Validation of a dispersive liquid–liquid microextraction (DLLME) method for the extraction of oleuropein from olive leaves
Year: 2025
Type: article
Abstract: The olive tree (Olea europaea L.) is among the oldest cultivated plants, with primary production concentrated in the Mediterranean region. In recent decades, olive cultivation has expanded in Brazil, primarily for olive oil production. However, large quantities of olive leaves are discarded during pruning, despite being a rich source of bioactive phenolic compounds, particularly oleuropein, a secoiridoid recognized for its health-promoting properties. This study aimed to validate a dispersive liquid-liquid microextraction (DLLME) method for the quantification of oleuropein in olive leaves. The method was evaluated based on parameters recommended by current analytical validation guidelines, including selectivity, linearity, limits of detection (LOD) and quantification (LOQ), recovery, and precision. The LOD and LOQ were established at 100 µg kg⁻¹ and 300 µg kg⁻¹, respectively. Due to the wide concentration range of oleuropein in the samples, two calibration curves were constructed: 300 µg kg⁻¹ to 11 mg kg⁻¹, and another from 11 mg kg⁻¹ to 800 mg kg⁻¹. Recovery and precision were assessed at five concentration levels (0.3, 5, 11, 390, and 800 mg kg⁻¹), yielding recovery rates between 99.44% and 106.43%, with coefficients of variation ranging from 2.38% to 5.38%. The method demonstrated excellent linearity in both analytical ranges, high recovery, and low variability, confirming its robustness, accuracy, and reproducibility. Overall, the validated DLLME method proved suitable for the quantification of oleuropein in olive leaf samples and can be reliably applied in studies focusing on the valorization and utilization of oleuropein from this agricultural residue.
Institution: Universidade Estadual de Campinas (UNICAMP)
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: diamond
Award: G3071026015
OpenAlex ID: W4413966017