Sensorial Impact and Distribution of 3-Methyl-2,4-nonanedione in Cognacs and Spirits.

The aim of this study was to improve our knowledge on the chemical markers of Cognac aromas. We report results concerning the distribution and sensorial impact of 3-methyl-2,4-nonanedione (MND), a well-known compound in aged red wine, reminiscent of anise or "dried fruit", according to its concentration. We assayed first this diketone (solid-phase microextraction (SPME)-gas chromatography (GC)/mass spectrometry (MS), chemical ionization (CI)) in many Cognac samples followed by grappa, brandy, rum, whisky, vodka, and fruit spirits, and concentrations ranged from traces to 11.2 µg/L. Highest concentrations were obtained in grappa and freshly distilled eaux-de-vie of Cognac samples. Exceeding its detection threshold (100 ng/L, 70 vol %), MND contributes to the anise descriptor of these spirits. Its concentration decreased over aging while being highly correlated with the total amount of fatty acid ethyl ester. In addition, we showed that MND was produced during distillation according to the oxidation state of the white wine as well as the amount of lees used.

Assessment of strawberry aroma through solid-phase microextraction-gas chromatography and artificial neuron network methods. Variety classification versus growing years.

In a previous work, the SPME-GC-MS method (chemical analysis) coupled with KSOM-ANN treatment of the results (statistical algorithm) has proved to be efficient to classify 70 strawberry samples harvested in the same year, through the 17 varieties to which they belonged, in a two-dimensional map. As an extension, the present study confirms that these results were not dependent on the year of strawberry production and discusses what effects were observed between results obtained in different years. Samples of different strawberry varieties were harvested during the three campaigns of 2000, 2001, and 2002 and analyzed independently. The chemical data matrix obtained in each case allowed the verification of the proposal that the same discriminative effect could be obtained independently of the year of production by using maps of different sizes. Therefore, 30 measures obtained from samples of 9 varieties in 2000, 54 measures from 13 varieties in 2001, and 80 measures from 20 varieties in 2002 were correctly classified by using 20, 35, and 56 hexagon maps, respectively. In a second analysis based on the 2002 production, the chemical differences between variety aromatic features were noted through the increasing size of the map used. Finally, results relative to 7 varieties cultivated in 2001 and 2002 and stored under exactly the same conditions were computed together for elaborating a single map. An interesting effect of double classification according to the year and the varieties was observed.

Assessment of strawberry aroma through SPME/GC and ANN methods. Classification and discrimination of varieties.

To provide an efficient and running analytical tool to strawberry plant breeders who have to characterize and compare the aromatic properties of new cultivars to those already known, a HS-SPME/GC-MS analysis method has been coupled with a statistical treatment method issued from the current development of artificial neuron networks (ANN), and more specifically, the unsupervised learning systems called Kohonen self-organizing maps (SOMs). So, 70 strawberry samples harvested at CIREF from 17 known varieties have been extracted by using a DVB/Carboxen/PDMS SPME fiber according to the headspace procedure, and then chromatographed. A panel of 23 characteristic aromatic constituents has been selected according to published results relative to strawberry aroma. The complex resulting matrix, collecting the relative abundance of the 23 selected constituents for each sample, has been input into the SOM software adapted and optimized from the Kohonen approach described by one of the authors. After a period of training, the self-organized system affords a map of virtual strawberries to which real samples are compared and plotted in the best matching unit (BMU) of the map. The efficiency for discriminating the real samples according to their variety is dependent on the number of units selected to define the map. In this case, a 24-unit map allowed the complete discrimination of the 17 selected varieties. Moreover, to test the validity of this approach, two additional samples were blind-analyzed and the results were computed according to the same procedure. At the end of this treatment, both samples were plotted into the same unit as those of the same variety used for training the map.