ArticleFoods (Basel, Switzerland)2020
The Application of Chemometrics to Volatile Compound Analysis for the Recognition of Specific Markers for Cultivar Differentiation of Greek Virgin Olive Oil Samples.
Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 21 citations in OpenAlex.
- Optimisation of the HS-SPME/GC-MS Approach by Design of Experiments Combined with Chemometrics for the Classification of Cretan Virgin Olive Oils.Metabolites · 2022Article
- New Insights into the Specificity, Authenticity, and Traceability Analysis of Olive Oils.Foods (Basel, Switzerland) · 2021Article
- Alkylamide Profiling of Pericarps Coupled with Chemometric Analysis to Distinguish Prickly Ash Pericarps.Foods (Basel, Switzerland) · 2021Article
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3 authors at 1 institution in 1 country.
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Abstract
In the present study, volatile compound analysis of olive oil samples belonging to ten Greek cultivars was carried out. A total of 167 olive oil samples collected from two consecutive harvest years were analyzed by Head Space-Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS). Volatile compound data were combined with chemometric methods (Multivariate Analysis of Variance (MANOVA) and Linear Discriminant Analysis (LDA)) with the aim not only to differentiate olive oils but also to identify characteristic volatile compounds that would enable differentiation of botanical origin (marker compounds). The application of Stepwise LDA (SLDA) effectively reduced the large number of statistically significant volatile compounds involved in the differentiation process, and thus, led to a set of parameters, the majority of which belong to compounds that are highly dependent on variety. In addition, the use of these marker compounds resulted in an increased correct classification rate (85.6%) using the cross-validation method indicating the validity of the model developed despite the use of a large number of dependent variables (cultivars).
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