Characterization of the volatile profile of Brazilian Merlot wines through comprehensive two dimensional gas chromatography time-of-flight mass spectrometric detection.

Wine aroma is an important characteristic and may be related to certain specific parameters, such as raw material and production process. The complexity of Merlot wine aroma was considered suitable for comprehensive two-dimensional gas chromatography (GCGC), as this technique offers superior performance when compared to one-dimensional gas chromatography (1D-GC). The profile of volatile compounds of Merlot wine was, for the first time, qualitatively analyzed by HS-SPME-GCxGC with a time-of-flight mass spectrometric detector (TOFMS), resulting in 179 compounds tentatively identified by comparison of experimental GCxGC retention indices and mass spectra with literature 1D-GC data and 155 compounds tentatively identified only by mass spectra comparison. A set of GCGC experimental retention indices was also, for the first time, presented for a specific inverse set of columns. Esters were present in higher number (94), followed by alcohols (80), ketones (29), acids (29), aldehydes (23), terpenes (23), lactones (16), furans (14), sulfur compounds (9), phenols (7), pyrroles (5), C13-norisoprenoids (3), and pyrans (2). GCxGC/TOFMS parameters were improved and optimal conditions were: a polar (polyethylene glycol)/medium polar (50% phenyl 50% dimethyl arylene siloxane) column set, oven temperature offset of 10ºC, 7 s as modulation period and 1.4 s of hot pulse duration. Co-elutions came up to 138 compounds in 1D and some of them were resolved in 2D. Among the coeluted compounds, thirty-three volatiles co-eluted in both 1D and 2D and their tentative identification was possible only due to spectral deconvolution. Some compounds that might have important contribution to aroma notes were included in these superimposed peaks. Structurally organized distribution of compounds in the 2D space was observed for esters, aldehydes and ketones, alcohols, thiols, lactones, acids and also inside subgroups, as occurred with esters and alcohols. The Fischer Ratio was useful for establishing the analytes responsible for the main differences between Merlot and non-Merlot wines. Differentiation among Merlot wines and wines of other grape varieties were mainly perceived through the following components: ethyl dodecanoate, 1-hexanol, ethyl nonanoate, ethyl hexanoate, ethyl decanoate, dehydro-2-methyl-3(2H)thiophenone, 3-methyl butanoic acid, ethyl tetradecanoate, methyl octanoate, 1,4 butanediol, and 6-methyloctan-1-ol.

Influence of the vintage, clone and rootstock on the chemical characteristics of Syrah tropical wines from Brazil.

In the Northeast of Brazil, vines can produce twice a year, because annual average temperature is 26ºC, with high solar radiation and water availability for irrigation. Many cultivars have been tested according to their adaptation to the climate and soil, and the main variety used for red wines is Syrah. This work aimed to evaluate five clones of Syrah, grafted on two rootstocks, in two harvests of the second semester of 2009 and 2010, according to the chemical analyses of the wines.The clones evaluated were 100, 174, 300, 470 and 525, the rootstocks were Paulsen 1103 and IAC 313 (Golia x Vitis caribeae). Grapes were harvested in November 2009 and 2010 and the yield was evaluated. Climate characteristics of each harvest was determined and correlated to the results. Wines were elaborated in glass tanks of 20 L, with alcoholic fermentation at 25ºC for seven days, then wines were pressed and malolactic fermentation was carried out at 18ºC for 20 days. The following parameters were analyzed: alcohol content, dry extract, total anthocyanins, total phenolic index. High performance liquid chromatography was used to determine tartaric, malic, lactic and citric organic acids. Results showed that wines presented different concentrations of classical analyses, phenolics and organic acids according to the harvest date, rootstocks and clones. Principal component analysis was applied on data and clusters with wine samples were formed, explaining the variability, and results are discussed.