Impact of malolactic fermentation on red wine color

AVF Proj. ID: 405 / /Cat.: ,
Primary Investigator(s): James Osborneby

The color of a red wine is an important sensory attribute that originates primarily from anthocyanins. However, development of stable red wine color is impacted by compounds such as p-coumaric acid, caffeic acid, catechin, and quercetin that are involved in copigmentation reactions as well as acetaldehyde and pyruvic acid. While it is known that yeast can alter the concentrations of some of these compounds, little is known regarding the impact malolactic bacteria may have on red wine color development. This project is investigating the effect of the malolactic fermentation (MLF) on red wine color and the ability of malolactic bacteria to degrade compounds important to the development of stable red wine color.

Pinot noir and Merlot wines were produced using grapes from the Oregon State University vineyard and were fermented with Saccharomyces cerevisiae VQ15. Concurrently, a third of Pinot noir musts were inoculated with Oenococcus oeni. At dryness, wines were pressed and filtered (0.45 µm nominal) with a second third of the wines being inoculated with O. oeni VFO (remaining third was was not inoculated). Some of the wine that had not undergone MLF was pH adjusted to the same final pH of wines that had completed MLF. Samples were taken before and after MLF for analysis and wines were sterile filtered, bottled, and stored at 55° F.

Prior to MLF, all Pinot noir wines had very similar concentrations of acetaldehyde and pyruvic acid. This was also the case with the Merlot wines. However, all wines that had undergone MLF, including the simultaneously fermented Pinot noir wine, had lower acetaldehyde and pyruvic acid concentrations. Pinot noir wines that had undergone MLF also had lower wine color and polymeric pigment values compared to wines that had not gone through MLF. For the Merlot wines, wines that had undergone MLF also had lower wine color, copigmentation, anthocyanins, and polymeric pigment than wines that had not undergone MLF with the differences being more pronounced then what was observed for the Pinot noir wines. The phenolic composition of wines that underwent MLF was different from wines that had not. Both Pinot noir and Merlot wines that had undergone MLF had lower levels of caftaric acid and higher levels of caffeic acid than wines that had not undergone MLF. Wines that did not undergo MLF also had lower malvidin glucoside and monomeric anthocyanin concentrations than wines that had undergone MLF. Finally, the concentration of tannin in Pinot noir wines that had undergone MLF was lower than in wines that had not. These results demonstrate that MLF as well as time of bacterial inoculation can effect the concentration of phenolic and non-phenolic compounds involved in red wine color development.

Interactions Between Nitrogen and Vitamins on Fermentation Rate and H2S Production by Saccharomyces

AVF Proj. ID: 302 / /Cat.: ,
Primary Investigator(s): Charles Edwardsby

Sluggish fermentation and hydrogen sulfide production are currently serious problems facing the wine industry. Besides nitrogen deficiency, a lack of certain vitamins such as thiamine and pyridoxine, can also impact H2S formation, coenzymes involved in yeast metabolism, play an important role in sulfur production. A comprehensive and systematic research approach was conducted to determine how nitrogen and vitamins (thiamine and pyridoxine) influences yeast growth rate, fermentation rate, and hydrogen sulfide production. Synthetic grape juice based on the amino acid composition of Cabernet Sauvignon grape must was used for fermentation was inoculated with Saccharomyces cerevisiae strain Montrachet (UCD 522). Fermentation rate (decrease in soluble solids), yeast viability, and H2S production were all affected by the availability of nitrogen and these vitamins.

Kinetics of Flavor Formation During Grape Juice Fermentations

AVF Proj. ID: 167 / /Cat.: ,
Primary Investigator(s): Susan Ebelerby

Using solid phase microextraction coupled with gas chromatotgraphic analysis, we are able to “continuously” monitor ester production throughout grape juice fermentations. In previous studies we used this technique to monitor differences in production of acetate and fatty acid ethyl esters that could be related to the progression of the fermentation. In addition a multi-peak pattern of ester production was observed which had not previously been reported. During the past year (2001-2002) our studies showed that:

  • Ethanol concentrations did not have a significant effect on measured concentrations of most esters studied using the SPME technique. However, at ethanol levels greater than 5%, measured concentrations of ethyl decanoate were significantly decreased. This may indicate that SPME analysis underestimates concentrations of this ester as fermentations proceed and ethanol concentrations increase.
  • Carbon dioxide flow rates at levels approximating those occurring at the height of fermentation had only a minimal effect on measured ester concentrations. These results suggest that SPME sampling provides an accurate picture of total ester concentrations throughout fermentation, even when volatilization rates are expected to be high (i.e,. Logarithmic yeast growth).
  • Yeast inoculation level did not significantly impact the concentrations or production profiles of the ethyl esters and acetate esters studied, except for ethyl acetate. The reason why ethyl acetate production responds differentially to yeast inoculum levels is unknown.

PDF: Kinetics of Flavor Formation During Grape Juice Fermentations

Analysis of Sacchoromyces During Normal and Problem Fermentations

AVF Proj. ID: 173 / /Cat.: ,
Primary Investigator(s): Linda Bisson, Christian Butzke, David Block, David Mills, Hennie van Vuurenby

The aims of the first three years of this proposal were to acquire, develop and optimize technologies for the analysis of problem fermentations. The goal of this work is to develop better fermentation management strategies to reduce and hopefully eliminate the incidence of slow and incomplete fermentations. In this first phase of the research we have successfully adapted functional genomic analysis to Saccharomyces grown under enological conditions. We have identified several key differences in the physiology of yeast grown under nutrient sufficient versus nitrogen-limited conditions. We have begun identifying molecular markers associated with healthy or robust fermentations and those associated with nutritional or environmental stress. The project is well poised to complete this analysis in the next year and to identify key yeast strain and physiological input factors needed for full optimization of the predictive potential of neural networks. We have developed bacterial-specific primers for direct analysis of bacterial strains in wine. In addition, we have developed and tested several yeast specific primers and employed them on samples obtained from commercial wine fermentations. This approach has resulted in direct identification of viable but non-culturable yeast populations, a potential factor in stuck fermentations. The project is well poised to complete this analysis in the next year and to identify key yeast strain and physiological input factors needed for prediction of fermentation kinetics. In addition to the molecular and physiological work, we are currently completing detailed analysis of samples from over 200 commercial Chardonnay fermentations from the 2001 harvest. Analysis of the juice and wine from these fermentations, which ranged from normal to sluggish and stuck, will allow us to identify juice characteristics and processing choices that are critical in determining fermentation kinetics. We have also developed bacterial-specific primers for direct analysis of bacterial strains in wine. In addition, we have developed and tested several yeast specific primers and employed them on samples obtained from commercial wine fermentations. This approach has resulted in direct identification of viable but non-culturable yeast populations, a potential factor in stuck fermentations. With all yeast physiological and microbial ecology factors, juice characteristics, and processing parameters identified that are critical in determining wine fermentation kinetics, we will be able to predict problem fermentations and their resolution early in the fermentation process.

PDF: Analysis of Sacchoromyces During Normal and Problem Fermentations

Analysis of Sacchoromyces During Normal and Problem Fermentations

AVF Proj. ID: 126 / /Cat.: ,
Primary Investigator(s): Linda Bisson, Christian Butzke, David Block, David Mills, Hennie van Vuurenby

The aims of the first three years of this proposal were to acquire, develop and optimize technologies for the analysis of problem fermentations. The goal of this work is to develop better fermentation management strategies to reduce and hopefully eliminate the incidence of slow and incomplete fermentations. In this first phase of the research we have successfully adapted functional genomic analysis to Saccharomyces grown under enological conditions. We have identified several key differences in the physiology of yeast grown under nutrient sufficient versus nitrogen-limited conditions. We have begun identifying molecular markers associated with healthy or robust fermentations and those associated with nutritional or environmental stress. The project is well poised to complete this analysis in the next two years and to identify key yeast strain and physiological input factors needed for full optimization of the predictive potential of neural networks. In addition, we have demonstrated that artificial neural networks can be used to predict wine fermentation kinetics when all critical juice characteristics and processing are known. A means of using simple optical density measurements one to two days into a fermentation in order to predict problems has been identified. We have also adapted TGGE and DGGE technologies for the analysis of the microbial complement of wine samples. This capability now allows us to detect the presence of all common wine microbes in a juice, must or wine sample without the need for cultivation. This permits a more statistically robust sampling of a fermentation and will provide data of sufficient quality to be useful in the development of neural networks for the prediction of fermentation behavior.

PDF: Analysis of Sacchoromyces During Normal and Problem Fermentations

Identification of Yeast Strain Genetic Factors in the Formation of Volatile Sulfur Compounds

AVF Proj. ID: 130 / /Cat.: ,
Primary Investigator(s): Linda Bissonby

In this current grant year, the analysis of the impact of over-expression of two genes involved in consumption of reduced sulfur, CYS4 and MET17, on H2S formation in commercial and natural wine strains of Saccharomyces was completed. Interestingly, increasing the level of expression of the CYS4 gene completely eliminated hydrogen sulfide production in four strains, had no effect in others, and in a few resulted in an increase in H2S. Similar results were obtained for MET17. So far, strains that showed reduced volatile sulfur formation with CYS4 did not show any effect with MET17 and those showing an effect with MET17 showed no or increased H2S formation with over-expression of CYS4. Strains that were high produces of H2S tended to decrease sulfide release when CYS4 was present, while the moderate producers showed a stronger response with MET17. Thus, there are multiple underlying genetic causes for the production of hydrogen sulfide. This analysis does indicate that once the cause of H2S release is known for a given strain, it can be corrected genetically. It will also be possible to screen for strains naturally possessing alleles leading to reduced sulfide production to be used in conventional breeding programs.

This research has further clarified the basis for the two phases of hydrogen sulfide release observed during fermentation. The early phase of hydrogen sulfide production occurs shortly after maximal cell biomass is attained, within the first few days of active fermentation, and is related to the relative activities of the enzymes generating and consuming reduced sulfur. The later stage, which occurs at the end of fermentation, is related to the nitrogen recycling behavior of the culture. Genomic data indicates that at this point in time numerous pathways have been induced that shunt nitrogen between amino acid components. When this occurs, there is a net shift of nitrogen from the sulfur containing amino acids to the non-sulfur containing amino acids. If nitrogen levels are in ample supply, this is prevented from occurring. Interestingly, analysis of the pattern of production of hydrogen sulfide of the 12 strains used in this study revealed that many of the strains produce hydrogen sulfide continuously during fermentation. Over-expression of MET17 and CYS4 has the highest impact on the continual producers versus the transient producers.

PDF: Identification of Yeast Strain Genetic Factors in the Formation of Volatile Sulfur Compounds

Analysis of Sacchoromyces During Normal and Problem Fermentations

AVF Proj. ID: 1 / /Cat.: ,
Primary Investigator(s): Linda F. Bissonby

The aims of the first three years of this proposal were to acquire, develop and optimize technologies for the analysis of problem fermentations. The goal of this work is to develop better fermentation management strategies to reduce and hopefully eliminate the incidence of slow and incomplete fermentations. In this first phase of the research we have successfully adapted functional genomic analysis to Sacchoromyces grown under enological conditions. We have identified several key differences in the physiology of yeast grown under nutrient sufficient versus nitrogen-limited conditions. We have begun identifying molecular markers associated with healthy or robust fermentations and those associated with nutritional or environmental stress. The project is well poised to complete this analysis in the next two years and to identify key yeast strain and physiological input factors needed for full optimization of the predictive potential of neural networks. In addition, we have demonstrated that artificial neural networks can be used to predict wine fermentation kinetics when all critical juice characteristics and processing are known. A means of using simple optical density measurements one to two days into a fermentation in order to predict problems has been identified. We have also adapted TGGE and DGGE technologies for the analysis of the microbial complement of wine samples. This capability now allows us to detect the presence of all common wine microbes in a juice, must or wine sample without the need for cultivation. This permits a more statistically robust sampling of a fermentation and will provide data of sufficient quality to be useful in the development of neural networks for the prediction of fermentation behavior.

Analysis of Saccharomyces During Normal and Problem Fermentations

AVF Proj. ID: 9904 / /Cat.: ,
Primary Investigator(s): Linda F. Bisson, Christian E. Butzke, David E. Block, David M. Millsby

This is the second year of a three year proposal. In this past year, the comparative analysis of the microarray and proteome technologies was completed. For several reasons, the proteome analysis was found to be ideal for profiling the physiological status of yeast during enological fermentations. The microarray analysis will not allow comparison of expression patterns across yeast strains and growth conditions as most of the genes and proteins of interest can not be quantified by this technique. However, the microarray analysis of the French White strain under nitrogen sufficient and limiting conditions yielded valuable insights into the metabolic state of the strains. Contrary to our initial hypothesis, the low nitrogen culture does not readily enter a non-proliferative phase and rather shows a failure to adapt to grape juice conditions. In contrast, metabolic pathways associated with ethanol tolerance, are expressed in the nitrogen sufficient culture before the onset of maximal fermentation rate. During this period, the proteome gel protocols have been optimized. Comparison of the proteomes of French White and Montrachet under nutrient sufficient conditions has revealed many differences in protein patterns. For the second objective, we have experimentally verified the mechanistic model that we developed in the previous year. We have also examined the effects of juice, yeast, and initial oxygen content on maximum cell concentration attained and on fermentation rate. In all cases, cell concentration was critical. In fact, we have built a correlation between maximum viable cell concentrations (mvcc) (reached at 24 – 48 hours) and fermentation rate. Under a certain threshold of mvcc, all of our fermentations stuck, indicating that this parameter may be useful in the early prediction of sluggish or stuck fermentations. The technology for using artificial neural networks to predict kinetics has also been largely developed. We have established that neural networks show promise for prediction of wine fermentation kinetics, except in cases of extrapolation to conditions outside of the training data set when the accuracy of prediction fails. Much progress was made in the last year on methods development for direct analysis of microorganisms in wine and musts. An evaluation of the 26S ribosomal RNA gene demonstrated this locus to be more discriminatory than the 18S gene for differentiation of various wine yeasts. In addition, denaturing gradient gel electrophoresis using chemical denaturants instead of temperature (“TGGE”) was shown to provide better separation of PCR products amplified from wine yeasts. A significant achievement this year was the optimization of methods for isolation of microbial DNA directly from fermenting wine. This accomplishment made it possible to use PCR-DGGE to profile the yeast successions that occurred during a fermentation at a local winery. Moreover, sequence analysis of the DGGE bands obtained in this profile allowed identification of the yeasts involved to the genus and/or species level. Additional progress was made in laboratory fermentations where DGGE profiling was demonstrated to accurately depict the presence or absence of specific yeast strains above a threshold level (~104 cells per mL) from within a mixed culture. In addition, methods were developed for isolation of microbes from the grape surface. Initial PCR-DGGE analysis revealed different populations of fungal species present on grapes which had undergone different viticultural treatments. Methods were also optimized for discrimination of bacterial strains by PCR-DGGE and work is underway to optimize bacterial DNA purification from musts and wine. Finally methods are being developed to selectively identify and differentiate Saccharomyces species from within wine fermentations.

Impact of Fermentation Rate Changes on Hydrogen Sulfide Concentration in

AVF Proj. ID: 9932 / /Cat.: ,
Primary Investigator(s): Christian Butzke, Roger Boultonby

The correlation between alcoholic fermentation rate, measured as carbon dioxide (CO2) evolution, and the rate of hydrogen sulfide (H2S) formation during wine production has a significant impact on the H2S content of a finished wine. Both rates and the resulting concentration peaks in fermentor headspace H2S are strongly impacted by yeast assimilable nitrogenous compounds in the grape juice. We have conducted a series of model fermentations in temperature-controlled and stirred fermentors using a complex model juice with a defined combination of ammonium ions and/or amino acids. Fermentation rate was measured indirectly by weighing the fermentors on a laboratory scale. This assumes that once CO2 saturation of the juice is reached, weight loss corresponds to CO2 evolution which in return is proportional to ethanol formation. H2S production was measured using a calibrated transparent tube packed with color-indicating metal acetate. The tube was inserted into a fermentor port instead of a regular gas lock, and provided quantitative trapping of H2S formed over time. Fermentation rates for CO2 and H2S as well as the relative ratios between them were calculated. The fermentations confirmed observations that high concentrations of yeast assimilable nitrogen do not necessarily protect against elevated H2S formation. High initial concentrations of ammonium ions via addition of diammonium phosphate can cause a higher evolution of H2S as in a non-supplemented but non-deficient juice. We observed that the availability of yeast assimilable amino acids, particularly arginine, can results in a more evenly distributed CO2 production throughout the alcoholic fermentation. In addition, relative maximum H2S evolution rates can be observed earlier in the fermentation, and CO2 produced during the remainder of the fermentation may sufficiently strip out initial sulfides.

Analysis of Saccharomyces During Normal and Problem Fermentations

AVF Proj. ID: 9808 / /Cat.: ,
Primary Investigator(s): Linda Bisson, Christian E. Butzke, David E. Block, David M. Millsby

The goal of this first year of the Long-Term Research Project was to compare and develop methodologies in three key areas: analysis of global gene expression in Saccharomyces in its native habitat of grape juice; refinement of the neural network technology for prediction of problem fermentations; development of a rapid method allowing profiling of the microbial composition of industrial samples. The RNA-based microarray methodologies worked well for samples of cells grown in juice-like synthetic media, but did not work well for samples prepared from cells grown in actual juices. Further, there does not appear to be a strong correlation between actual mRNA levels and protein content in the cells. Thus the proteome analysis seems to be most useful for profiling gene expression in industrial samples. However, the microarray data provided a wealth of information on which proteins to examine in the proteome gels and has given new insights into the physiological activities of the cells under stressful conditions leading to arrest of fermentation. Neural network training methods have been established for using historical fermentation kinetics data to predict sugar utilization rates based on juice characteristics and intended processing. Small-scale fermentations have been completed to find which critical inputs to use for the neural network prediction, as well as to validate a physical and mathematical model for cell growth and sugar utilization that is likely to direct future experimentation. Temperature gradient gel electrophoresis (TGGE) works well to differentiate yeast genera using the primers that we developed and can be used to assess the microbial purity of industrial samples to be used in the proteome analyses.