Impact of fermentation rate changes on potential hydrogen sulfide concentrations in wine.

The correlation between alcoholic fermentation rate, measured as carbon dioxide (CO2) evolution, and the rate of hydrogen sulfide (H2S) formation during wine production was investigated. Both rates and the resulting concentration peaks in fermentor headspace H2S were directly impacted by yeast assimilable nitrogenous compounds in the grape juice. A series of model fermentations was conducted in temperature-controlled and stirred fermentors using a complex model juice with defined concentrations of ammonium ions and/or amino acids. The fermentation rate was measured indirectly by noting the weight loss of the fermentor; H2S was quantitatively trapped in realtime using a pre-calibrated H2S detection tube which was inserted into a fermentor gas relief port. Evolution rates for CO2 and H2S as well as the relative ratios between them were calculated. These fermentations confirmed that total sulfide formation was strongly yeast strain-dependent, and high concentrations of yeast assimilable nitrogen did not necessarily protect against elevated H2S formation. High initial concentrations of ammonium ions via addition of diammonium phosphate (DAP) caused a higher evolution of H2S when compared with a non-supplemented but nondeficient juice. It was observed that the excess availability of a certain yeast assimilable amino acid, arginine, could result in a more sustained CO2 production rate throughout the wine fermentation. The contribution of yeast assimilable amino acids from conventional commercial yeast foods to lowering of the H2S formation was marginal.

Study of aged cognac using solid-phase microextraction and partial least-squares regression.

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.