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.
/wp-content/uploads/2017/09/AFV-Header-Logo.png 0 0 AVF /wp-content/uploads/2017/09/AFV-Header-Logo.png AVF2001-10-18 07:50:182017-10-18 07:51:25Analysis of Sacchoromyces During Normal and Problem Fermentations