Abstract:The main purpose of this study is to predict wine quality based on physicochemical data. In this study, two large separate data sets which were taken from UC Irvine Machine Learning Repository were used. These data sets contain 1599 instances for red wine and 4898 instances for white wine with 11 features of physicochemical data such as alcohol, chlorides, density, total sulfur dioxide, free sulfur dioxide, residual sugar, and pH. First, the instances were successfully classified as red wine and white wine with the accuracy of 99.5229% by using Random Forests Algorithm. Then, the following three different data mining algorithms were used to classify the quality of both red wine and white wine: k-nearest-neighbourhood, random forests and support vector machines. There are 6 quality classes of red wine and 7 quality classes of white wine. The most successful classification was obtained by using Random Forests Algorithm. In this study, it is also observed that the use of principal component analysis in the feature selection increases the success rate of classification in Random Forests Algorithm.

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The antagonistic effect of the isolates of Gliocladium roseum, Saccharomyces cerevisiae, Sordaria fimicola, and their mixtures, at different concentrations against the cereal damping-off pathogen, Fusarium graminearum, was examined in vitro and in vivo (foliar, seed, soil, seed + soil) treatments on the susceptible wheat cultivars "Gun 91 and Sultan 95." The 3 isolates inhibited growth of F. graminearum at a concentration of 1 × 10 9 spores/ml with inhibition rates of 84, 88, and 91%, respectively under in vitro conditions. For in vivo assays, the mixture of S. cerevisiae + S. fimicola exhibited a considerable antagonistic activity even at a concentration of 1 × 10 5 spores/ml. Particularly, at the seed + soil treatment of the mixture, the pathogen was almost completely suppressed with an inhibition rate above 96% at concentrations of 1 × 10 8 and 1 × 10 9 spores/ml for both wheat cultivars, and the percentage of emerged seedlings reached nearly 100%. The results verified that the mixture of S. cerevisiae + S. fimicola had a high potential, as a promising biocontrol agents and an eco-friendly alternative, to be used against the cereal damping-off caused by F. graminearum, to reduce the use of systemic fungicides.

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