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Automated green sample preparation for quantitative extraction of lipids in different sample matrices
•Quantitative extraction of lipids was evaluated using different protocols.•Recoveries were determined in different matrices.•Ethyl acetate and ethanol showed comparable recoveries to other established methods.•AGREE tool was used to evaluate the greenness of the method. An essential step in all ana...
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Published in: | Green Analytical Chemistry 2024-09, Vol.10, p.100128, Article 100128 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | •Quantitative extraction of lipids was evaluated using different protocols.•Recoveries were determined in different matrices.•Ethyl acetate and ethanol showed comparable recoveries to other established methods.•AGREE tool was used to evaluate the greenness of the method.
An essential step in all analytical workflows is the quantitative and robust extraction of the molecules of interest. While typical lipid extractions use hazardous solvents from non-renewable sources, in this work, an extraction protocol utilizing green solvents such as ethanol and ethyl acetate is presented. The workflow was also automated to reduce variability and increase robustness as well as sample throughput. Moreover, this minimizes the contamination risks and lowers the operator's exposure to organic solvents. The developed workflow was critically evaluated by the recovery of various lipid standards from different matrices (human plasma, human serum, HepG2 cells) yielding quantitative values around 80–90 % for most lipid classes. The investigation into concentration dependency reveals the robustness of the method, demonstrating consistent recoveries across various lipid concentrations and matrix contents. In non-targeted lipidomics analyses, the green solvents proved to be comparable to established protocols that use conventional solvents such as methyl‑tert-butylether (MTBE) for lipid extraction.
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ISSN: | 2772-5774 2772-5774 |
DOI: | 10.1016/j.greeac.2024.100128 |