PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 10, 2026European Journal of Lipid Science and Technology0 citationsOpen Access

Advancing Fatty Acid Profiling in Microalgae: A Comparison of Greener Approaches and Enhanced Chromatographic Separation Versus Conventional Methods

View Full Paper
NCNatalia CastejónDFDonatella FerraraICIgor Ciriviri

Key Points

  • The study aims to evaluate advanced, environmentally friendly methods for fatty acid extraction and analysis from microalgae, as alternatives to conventional techniques.
  • Compared ultrasound-assisted enzymatic extraction and microwave-assisted extraction and derivatization methods.
  • Assessed various microalgal species for yield and fatty acid profiling.
  • Evaluated methods against traditional Folch extraction and gas chromatography techniques.
  • Ultrasound-assisted enzymatic strategies yielded high fatty acid recoveries in species like M. gaditana and P. tricornutum.
  • The one-step MAED method achieved comparable fatty acid profiles to traditional methods without prior lipid extraction.
  • Bidimensional gas chromatography provided better identification of fatty acids, including minor compounds.

Abstract

ABSTRACT Extracting and characterizing fatty acids from microalgae presents several challenges, particularly due to the reliance on toxic and hazardous solvents in conventional methods, along with the complexity of microalgal cell walls, which complicates the implementation of greener techniques. To address these issues, we compared a range of advanced strategies as alternatives to conventional methods, categorized into two main approaches: (i) ultrasound and enzymatic strategies for applications that need lipid extraction, and (ii) a one‐step microwave‐assisted extraction and derivatization (MAED) method for cases requiring rapid analysis. The applicability of the proposed methods was assessed in different microalgal species of interest, including Microchloropsis gaditana , Tisochrysis lutea , Porphyridium cruentum , and Phaeodactylum tricornutum . The outcomes of ultrasound‐assisted enzymatic approaches varied by microalgal species. For instance, high yields were obtained for M. gaditana using the enzymatic solution Saczyme Yield, while the combination of ethanol and ultrasound proved efficient for lipid recovery in P. tricornutum . On the other hand, the single‐step MAED method was successfully applied across the four species, yielding a fatty acid profile comparable to that obtained using the Folch method but without requiring an additional step for lipid extraction. Moreover, thanks to bidimensional gas chromatography (GC×GC), we reported a more comprehensive characterization of the fatty acid profiles for these microalgal species compared to the widely used GC‐MS technique and previously reported data in the literature. Practical applications : Our results show that environmentally friendly methods, such as ultrasound‐assisted enzymatic approaches and the one‐step MAED method, can be effectively applied for the recovery and analysis of fatty acids in different microalgae species of interest. These methods achieved high yields and reproducible fatty acid profiles across various species while avoiding the use of toxic and hazardous solvents. The MAED method, in particular, enables rapid routine analysis without requiring prior lipid extraction, making it suitable for high‐throughput screening of microalgal biomass. In addition, GC×GC enabled improved resolution and detection of minor fatty acids, which can help unlock the complete fatty acid profile of lesser‐known microalgal species and identify compounds with potential nutritional or health‐promoting effects. These findings represent meaningful progress in the analytical characterization of microalgae, supporting their potential use in functional food and nutraceutical formulations.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Castejón et al. (2026) studied this question.

synapsesocial.com/papers/69af94e870916d39fea4bf45https://doi.org/10.1002/ejlt.70103
Ask AI
Helpful
Bookmark
Share
View Full Paper