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Updated: Apr 24, 2026

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry
Published on: June 9, 2021
Multivariate analysis of volatile compounds detected by headspace solid-phase microextraction/gas chromatography: A
Chantal Prat1, Emili Besalú2, Lluís Bañeras3
1Department of Chemistry, University of Girona, Campus Montilivi s/n, E-17071 Girona, Spain; Molecular Microbial Ecology Group (Institute of Aquatic Ecology), University of Girona, Campus Montilivi s/n, E-17071 Girona, Spain.
Abstract:
The volatile fraction of aqueous cork macerates of tainted and non-tainted agglomerate cork stoppers was analysed by headspace solid-phase microextraction (HS-SPME)/gas chromatography. Twenty compounds containing terpenoids, aliphatic alcohols, lignin-related compounds and others were selected and analysed in individual corks. Cork stoppers were previously classified in six different classes according to sensory descriptions including, 2,4,6-trichloroanisole taint and other frequent, non-characteristic odours found in cork. A multivariate analysis of the chromatographic data of 20 selected chemical compounds using linear discriminant analysis models helped in the differentiation of the a priori made groups. The discriminant model selected five compounds as the best combination. Selected compounds appear in the model in the following order; 2,4,6 TCA, fenchyl alcohol, 1-octen-3-ol, benzyl alcohol and benzothiazole. Unfortunately, not all six a priori differentiated sensory classes were clearly discriminated in the model, probably indicating that no measurable differences exist in the chromatographic data for some categories. The predictive analyses of a refined model in which two sensory classes were fused together resulted in a good classification. Prediction rates of control (non-tainted), TCA, musty-earthy-vegetative, vegetative and chemical descriptions were 100%, 100%, 85%, 67.3% and 100%, respectively, when the modified model was used. The multivariate analysis of chromatographic data will help in the classification of stoppers and provide a perfect complement to sensory analyses.
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