Olive Oil Characterization using Agilent GC/Q-TOF MS and Mass Profiler Professional Software

Significance of the Topic

The rapid growth in olive oil consumption and the premium value placed on extra virgin olive oil make reliable screening methods essential. Traditional sensory tests are time consuming, costly and subjective. Developing a chemical model to predict sensory performance can streamline quality control, reduce certification costs and ensure consistent product quality.

Aims and Study Overview

This study aimed to build a classification model that predicts whether an olive oil will pass the International Olive Council sensory test. Ten samples with known sensory outcomes were analyzed in non targeted fashion to identify chemical markers associated with test failure.

Methodology and Data Processing

Samples were diluted in cyclohexane and analyzed by gas chromatography coupled to accurate mass quadrupole time of flight mass spectrometry in electron ionization and positive chemical ionization modes. Chromatographic deconvolution extracted around 150 peaks per sample. Data were aligned and filtered using Mass Profiler Professional, reducing 442 initial entities to 91 by frequency criteria and then to 5 by fold change and statistical significance. Multivariate analysis including principal component analysis and partial least squares discrimination analysis was used to build and validate a classification model.

Instruments Used

• Agilent 7890A gas chromatograph
• Agilent 7200 series GC Q TOF mass spectrometer
• Agilent MassHunter Qualitative Analysis software
• Agilent Mass Profiler Professional software

Main Results and Discussion

PCA revealed clear clustering of pass and fail samples. Volcano plot analysis highlighted five compounds with significant abundance differences. A PLSDA model based on these markers achieved 100 percent classification accuracy on both training and independent validation samples. Library searches and accurate mass data identified four compounds as hexadecanoic acid, ethyl octadecanoate, squalene and alpha cubebene. A fifth diol was assigned formula C14H26O2. Odor attributes of the identified markers support their influence on sensory failure.

Benefits and Practical Applications

This approach offers a rapid chemical prescreen for extra virgin olive oil quality. It can reduce reliance on expert tasting panels, lower testing costs, and accelerate market release of high quality oils.

Future Trends and Opportunities

Expanding sample size will enhance model robustness. Combining high resolution MS data with advanced machine learning could improve predictive power. The strategy could be extended to detect adulteration and classify oils by geographic origin.

Conclusion

The study demonstrates the feasibility of using GC Q TOF MS and statistical modeling to predict olive oil sensory performance. With larger data sets and refined models this method can provide reliable, cost effective quality control for the olive oil industry.

References

• Packaged Facts Olive Oil in the U S 3rd Edition Apr 2009
• Frankel E N Mailer R J Shoemaker C F Wang S C Flynn J D UC Davis Olive Center report July 2010
• Vaclavik L Lacina O Hajslova J Zweigenbaum J Anal Chim Acta 685 45-51 2011
• Boccard J Veuthey J L Rudaz S J Sep Sci 33 290-304 2010