Determination of olive oil purity based on triacylglycerols profiling by UHPLC-CAD and principal component analysis
Applications | 2019 | Thermo Fisher ScientificInstrumentation
Olive oil adulteration threatens consumer safety and product authenticity. Conventional purity assays using fatty acid and sterol profiling are resource-intensive, often outsourced, and generate chemical waste. A streamlined in-house method can enhance quality control and reduce costs.
The study aimed to establish a rapid approach for detecting olive oil adulteration by profiling triacylglycerols (TAGs) via ultra-high-performance liquid chromatography with charged aerosol detection (UHPLC-CAD), followed by principal component analysis (PCA). It evaluated pure extra virgin olive oils, five common adulterant oils, and their binary blends.
Sample Preparation:
Instrumentation:
UHPLC-CAD combined with PCA of TAG profiles provides a robust, efficient, and eco-friendly method for olive oil purity verification. The approach enables detection of low-level adulteration, streamlines laboratory workflows, and supports quality assurance in the olive oil industry.
HPLC
IndustriesFood & Agriculture
ManufacturerThermo Fisher Scientific
Summary
Significance of the Topic
Olive oil adulteration threatens consumer safety and product authenticity. Conventional purity assays using fatty acid and sterol profiling are resource-intensive, often outsourced, and generate chemical waste. A streamlined in-house method can enhance quality control and reduce costs.
Objectives and Overview of the Study
The study aimed to establish a rapid approach for detecting olive oil adulteration by profiling triacylglycerols (TAGs) via ultra-high-performance liquid chromatography with charged aerosol detection (UHPLC-CAD), followed by principal component analysis (PCA). It evaluated pure extra virgin olive oils, five common adulterant oils, and their binary blends.
Methodology and Instrumentation
Sample Preparation:
- Oil diluted to 1 % (v/v) in methanol/chloroform (50:50, v/v).
- Vortexed and transferred to amber autosampler vials.
Instrumentation:
- UHPLC System: Thermo Scientific Vanquish Flex with quaternary pump, split sampler, column compartment.
- Detector: Vanquish Charged Aerosol Detector (CAD) at 50 °C, 10 Hz collection.
- Column: Accucore C18 (2.6 μm, 100 × 2.1 mm) at 50 °C.
- Mobile Phase: Acetonitrile (A) and isopropanol (B) gradient from 10 % to 90 % B over 35 min at 0.5 mL/min.
- Data Analysis: Peak area percentages computed in Chromeleon 7.2.6; PCA executed in OriginPro 2016.
Main Results and Discussion
- PCA of eleven TAGs separated six oil types into distinct clusters with over 81 % variance explained by the first two components.
- Adulteration detection limits ranged from approximately 5–10 % depending on the adulterant.
- Optimized three‐TAG PCA models improved quantification of specific blends (e.g., LLL, OLL, OOO for grapeseed oil).
- A blind test accurately predicted a 45 % olive oil content in an unknown blend with grapeseed oil.
Benefits and Practical Applications
- Rapid adulteration screening without extensive sample prep.
- Single TAG profiling replaces separate fatty acid and sterol tests.
- Reduced solvent use enhances environmental sustainability.
- Cost-effective in-house implementation avoids external laboratory fees.
Future Trends and Applications
- Expansion to other edible oils and complex blends.
- Integration with advanced chemometric and machine learning algorithms.
- Potential coupling with high-resolution MS for deeper compositional insights.
- Miniaturized or on-line UHPLC-CAD systems for real-time quality control.
Conclusion
UHPLC-CAD combined with PCA of TAG profiles provides a robust, efficient, and eco-friendly method for olive oil purity verification. The approach enables detection of low-level adulteration, streamlines laboratory workflows, and supports quality assurance in the olive oil industry.
Reference
- Arlorio M. et al. Food Addit Contam A. 2010;27(1):11–18.
- International Olive Council. Determination of TAG ECN 42. 2001.
- Green HS et al. Food Control. 2020;107:106773.
- Indelicato S. et al. J Chromatogr A. 2017;1515:1–16.
- Lucci P. et al. J Food Comp Anal. 2018;66:230–236.
- Lísa M. et al. J Chromatogr A. 2007;1176(1-2):135–142.
- De la Mata-Espinosa P. et al. Food Anal Methods. 2011;4(4):574–581.
- De la Mata-Espinosa P. et al. Talanta. 2011;85(1):177–182.
- Abdi H., Williams LJ. WIREs Comput Stat. 2010;2(4):433–459.
- Yang Y. et al. J Agric Food Chem. 2013;61:3693–3702.
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