Comparison of two analytical methods validated for the determination of volatile compounds in virgin olive oil: GC-FID vs GC-MS.

The utility of volatile compounds to explain virgin olive oil aroma descriptors is fully accepted and demanded by the olive oil sector. However, the methodology, and particularly the kind of detector to be used, is a matter of discussion because the high number of volatiles and their different nature. The SPME-GC-MS method has recently been validated for the most relevant volatiles but SPME-GC-FID method still needs to be validated to evaluate its performance in this application. A comparison between these two GC methods in determining 26 volatiles has been carried out in terms of analytical quality parameters (repeatability, intermediate precision, calibration curves, limits of detection and quantification, linear working ranges, selectivity and sensitivity). Good selectivity, linearity and higher upper values of the working range are the main advantages of SPME-GC-FID versus low bottom values of working ranges, better sensitivity and lower limits of detection and quantification of SPME-GC-MS. The limit of blank associated to each individual volatile was also determined and it allowed perfecting the empirical limit of detection. This procedure was carried out for SPME-GC-FID, which resulted in 21 volatiles with empirical limits of detections lower than their odor thresholds, and hence they can be used as markers of virgin olive oil sensory descriptors. Finally, with all the analytical quality parameters checked, a practical example of the ability of the volatiles quantified by SPME-GC-FID to discriminate the different categories (extra-virgin, virgin and lampante) and their main aroma descriptors is also provided.

Validation of SPME-GCMS method for the analysis of virgin olive oil volatiles responsible for sensory defects.

Volatile compounds are responsible for the aroma of virgin olive oil and also for its quality. The high number and different nature of volatile compounds drive to the need of a reliable analytical method that allows their proper quantification to explain the standard method of panel test. Although there are some analytical solutions available, they have not been validated and the regulatory bodies are reluctant to adopt them since they can be subjected to unknown errors. In this regards, the European Union has encouraged the validation of these analytical tools through the research program Horizon2020, which involves gaining knowledge from the analytical properties of the chemical methods for sensory assessment. This work is focused on the analytical validation of the methodology used to determine the actual concentration of volatiles in virgin olive oils when applying SPME-GCMS. The validation process includes the calibration curves for 29 volatile compounds responsible for the most common sensory perceptions in virgin olive oils, the determination of their working ranges with linear response, the detection and quantification limits, the sensitivity, the accuracy estimated as trueness and precision and the selectivity. Sixty-seven percent of the compounds presented a relative standard deviation in repeatability lower than 10%, and this percentage rises to 95% in lampante virgin olive oils. The accuracy was established in 97% of the studied volatile compounds. Finally, an empirical example of the ability of the method to discriminate virgin olive oils of different categories (extra virgin, virgin, ordinary and lampante) by the quantification of their volatiles is provided.

Detection of the presence of refined hazelnut oil in refined olive oil by fluorescence spectroscopy.

The fluorescence spectroscopy technique has been tested as regards its ability to differentiate between refined hazelnut and olive oils. Classification of these oils based on their excitation-emission fluorescence spectra data (spectral range 300-500 nm of the excitation spectra at lambdaem=655 and spectral range 650-900 of the emission spectra at lambdaex=50 nm) was performed using principal component analysis and artificial neural networks. Both methods provided good discrimination between the refined hazelnut and olive oils. The results have also pointed out the possibilities of a spectrofluorimetric method joined to multivariate analysis, to differentiate refined oils, and even to detect the presence of refined hazelnut oils in refined olive oils at percentages higher than 9%.