Quantification of the intensity of virgin olive oil sensory attributes by direct coupling headspace-mass spectrometry and multivariate calibration techniques

The main sensory defects of virgin olive oils (rancid, vinegary, winey, muddy sediment, musty and vegetable water) and one positive attribute (fruity) characteristic of three monovarietal extra virgin olive oils (Arbequina, Picual and Frantoio) have been quantified using the direct coupling headspac...

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Bibliographic Details
Published in:Journal of Chromatography A 2007-04, Vol.1147 (2), p.144-152
Main Authors: LOPEZ-FERIA, S, CARDENAS, S, GARCIA-MESA, J. A, FERNANDEZ-HERNANDEZ, A, VALCARCEL, M
Format: Article
Language:English
Subjects:
Analytical chemistry
Biological and medical sciences
Calibration
Chemistry
Chromatographic methods and physical methods associated with chromatography
chromatography
Exact sciences and technology
Fat industries
flavor compounds
flavor intensity
Food industries
Fundamental and applied biological sciences. Psychology
Gas chromatographic methods
headspace analysis
Headspace-mass spectrometry
Intensity quantification
mass spectrometry
Mass Spectrometry - methods
Multivariate Analysis
odor compounds
Olive Oil
Plant Oils - chemistry
prediction
quantitative analysis
Sensory attributes
sensory properties
Virgin olive oils
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Summary:The main sensory defects of virgin olive oils (rancid, vinegary, winey, muddy sediment, musty and vegetable water) and one positive attribute (fruity) characteristic of three monovarietal extra virgin olive oils (Arbequina, Picual and Frantoio) have been quantified using the direct coupling headspace-mass spectrometry. The results obtained were compared with those provided by the panel test for the same samples. Taking into account that no chromatographic separation exists, multivariate calibration techniques (partial least squares, PLS, and principal components regression, PCR) were used to create the appropriate models. The best results, in terms of standard error of prediction and prediction residual error sum of squares were obtained by PLS and therefore it was used for the prediction of a new set of samples with the above-mentioned positive and negative attributes at different concentration levels. The samples were also assessed by the panel test and good correlations were obtained in all cases. In order to extend the applicability of the model with the time, a multiplicative calibration transfer was used. The benefit of this approach was found to be more marked for the negative than the positive attributes.
ISSN:0021-9673
DOI:10.1016/j.chroma.2007.02.107