Determination of olive oil purity based on triacylglycerols profiling by UHPLC-CAD and principal component analysis

Importance of the Topic

With high demand and premium value of extra virgin olive oil, unscrupulous blending with cheaper vegetable oils poses economic fraud and consumer health risks. Traditional protocols for verifying purity rely on time-consuming fatty acid and sterol profiling, often necessitating external laboratories, delaying results and inflating costs. Rapid, accurate methods to confirm olive oil authenticity are vital for regulatory compliance, brand protection, and environmental sustainability.

Goals and Study Overview

This study aimed to establish a streamlined approach to identify olive oil adulteration by profiling triacylglycerol (TAG) compositions via ultra-high-performance liquid chromatography coupled with charged aerosol detection (UHPLC-CAD), followed by principal component analysis (PCA). The objectives included:

• Developing a single analytical workflow to replace traditional fatty acid and sterol assays
• Quantifying detection sensitivity for common adulterants (grapeseed, soybean, canola, high oleic sunflower and safflower oils)
• Optimizing chemometric models to determine adulterant type and concentration down to 5–10%

Methodology and Instrumentation

Sample Preparation:
Oil samples were diluted to 1% (v/v) in methanol/chloroform (1:1), vortexed and directly injected without derivatization.

Chromatographic System:
The analysis employed a Thermo Scientific Vanquish Flex UHPLC with an Accucore C18 column (100 × 2.1 mm, 2.6 μm) at 50 °C. A quaternary pump delivered a gradient of acetonitrile (mobile phase A) and isopropanol (mobile phase B) at 0.5 mL/min. A charged aerosol detector (CAD) operated at 50 °C with a 10 Hz collection rate and power function of 1.00.

Data Analysis:
Chromeleon 7.2.6 quantified relative TAG peak areas. PCA was performed in OriginPro 2016, using the first two principal components to discriminate oil types and assess adulteration levels.

Main Results and Discussion

• UHPLC-CAD effectively separated 11 major TAGs across pure extra virgin olive oil and five potential adulterants.
• PCA biplots generated distinct clusters for each oil, with olive oil clearly segregated within a 95% confidence ellipse.
• Optimized PCA models using three key TAG markers (e.g. LLL, OLL, OOO for grapeseed blends) enhanced sensitivity, detecting adulteration as low as 5–10% depending on the oil type.
• Validation with blind samples accurately predicted both the adulterant and its proportion (e.g. a 45% olive/55% grapeseed blend).

Benefits and Practical Applications

• Rapid detection: analysis completes without extensive sample preparation, enabling same-day results.
• Cost efficiency: replaces separate fatty acid and sterol assays, reducing reagent consumption and outsourcing needs.
• Environmental impact: lower solvent usage and waste generation compared to conventional methods.
• In-house implementation: laboratories can adopt the UHPLC-CAD workflow using existing equipment with minimal modifications.

Future Trends and Potential Uses

• Integration of advanced aerosol detectors (e.g. Corona Veo) to improve sensitivity and dynamic range.
• Expanded chemometric frameworks incorporating machine learning for automated adulteration screening.
• Real-time monitoring platforms for on-line quality control in production facilities.
• Adaptation of the TAG profiling approach to other high-value oils and lipid-based food products.

Conclusion

Combining UHPLC-CAD with PCA offers an efficient, cost-effective, and environmentally conscious platform for olive oil purity verification. This method accurately distinguishes genuine extra virgin olive oil from common adulterants and quantifies blending ratios at low levels, supporting quality assurance and regulatory compliance.

References

• Green HS, Li X, De Pra M, Lovejoy K, Steiner F, Acworth IN, Wang SC. Rapid detection of extra virgin olive oil adulteration via UHPLC-CAD TAG profiling and PCA. Food Control. 2020;107:106773.
• Abdi H, Williams LJ. Principal component analysis. WIREs Comput Stat. 2010;2(4):433–459.
• Lísa M, Lynen F, Holčapek M, Sandra P. Quantitation of triacylglycerols using charged aerosol detection. J Chromatogr A. 2007;1176(1–2):135–142.
• De la Mata-Espinosa P, Bosque-Sendra JM, Cuadros-Rodríguez L. Triacylglycerol quantification in olive oils by HPLC-CAD. Food Anal Methods. 2011;4(4):574–581.
• Yang Y, Ferro MD, Cavaco I, Liang Y. Detection of olive oil adulteration by GC–MS and chemometrics. J Agric Food Chem. 2013;61:3693–3702.