Headspace solid-phase microextraction (SPME) and GC-MS were used to analyze 17 commercial French Cognac brandies (9 young and 8 well-aged, ranging in age from 3 to 55 years). Sixty-four volatiles were chosen on the basis of chromatographic separation and/or known odor importance. Chromatographic peaks were manually integrated and the peak area data analyzed using partial least-squares (PLS) regression to study relationships between volatile composition (X variables) and age (Y variable). When only those compounds with the highest significance were included and from these selected the variables (a total of 33) with the highest correlation loadings on the first two principal components, principal component 1 explained 82% of the variance of the measured compounds and 85% of the variance in age. These were considered the most important volatiles to distinguish products of different ages because young and old samples were separated along principal component 1. Norisoprenoids, terpenes, and acetate esters had weaker positive and negative loadings and were therefore left out. The PLS model could predict sample age accurately with the optimum 33 volatiles as well as with a smaller subset consisting of ethyl esters and methyl ketones.

The occurrence of stuck and sluggish wine fermentations is a persisting problem in the wine industry worldwide. This study illustrates the suitability of headspace solid-phase dynamic extraction coupled with gas chromatography-mass spectrometry (HS-SPDE GC-MS) for wine analysis and the subsequent application to discriminate between control and problem fermentations using partial least-squares discriminant analysis (PLS-DA) models. The specific analytical technique is relatively new and has not yet to the authors' knowledge been evaluated for the analysis of wine within this context of problem fermentations. HS-SPDE GC-MS was used to determine 68 volatile compounds (higher alcohols, fatty acids, esters, and carbonyl compounds) in 94 monovarietal fermenting must samples consisting of 56 red and 38 white cultivars. PLS-DA models showed the potential to discriminate between control and problem fermentations using corrected peak area headspace data for the 68 analytes. This possibility to discriminate between problem and control fermentations with only the headspace data could possibly be applied for the prediction of problem fermentations in future studies and to better understand the chemical causes of problem fermentations.

Headspace solid phase microextraction and gas chromatography/mass spectrometry were used to identify and quantify four odd-numbered methylketones in commercial Cognac brandies. These ketones are in part responsible for the desirable and complex characteristic called 'rancio charentais' or 'Cognac rancio' which is found in grape brandies aged in oak barrels for several decades. The ketones 2-heptanone, 2-nonanone, 2-undecanone and 2-tridecanone form through β-oxidation and decarboxylation of longchain fatty acids originating from yeast metabolism. The concentrations of these ketones increased with Cognac age classification in the 42 brandies analysed, and 2-heptanone was present at the highest concentration in most samples. The average concentrations and rates of formation decreased with increasing chain length. Total concentrations ranged from 21 to 328 µg l −1 . The esters propyl octanoate and ethyl octanoate followed the same trend as the methylketones and appear to play an additional role in the formation of the rancio character.

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