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Methods that can provide adequate accuracy in the estimation of variables from incomplete information are desirable for the prediction of fermentation processes. A feed-forward back-propagation artificial neural network was used for modelling of thick juice fermentation. Fermentation time and starting sugar content were usedas input variables, i.e. nodes. Neural network had one output node (ethanol content, yeast cell number or sugar content). The hidden layer had nine neurons. Garson's algorithm and connection weights were used for interpreting neural network. The inadequacy of Garson's algorithm can be seen by comparing with the results of regression analysis, which indicates that the influence of the fermentation time is higher. A better agreement of the results was obtained using network connection weights, a method that can be used to determine the relative importance of input variables.

The present study examines xanthan production by Xanthomonas campestris under aerobic conditions on spent grains liquor based cultivation media containing four different amounts of maltose. All four experiments were performed simultaneously, so that all stages of the biotechnological process would be carried out under identical conditions. In addition to the cultivation parameters (pH value, contents of dissolved oxygen, reducing sugars and assimilable nitrogen, turbidity and trickling time), xanthan yield and sugar conversion percentage were also determined. Based on the results of the experiments maltose addition up to 5% to spent grains liquor does not hinder the cultivation course. On the other hand, the conversion of sugars into xanthan decreases with the increase of maltose content to 5% and a large amount of sugars lags in the cultivation broth.

Processing parameters of Kantata wheat variety indicate that it is not suitable for bread making. Therefore, an investigation was carried out to evaluate the suitability of the variety for ethanol production. Wheat was cultivated at the following sites: Kovin, Zrenjanin, Pančevo and Vrbas. Ethanol yields depend on the location on which the samples were grown and on the temperature during thermal and enzymatic preparation of flour samples. Wheat sample cultivated on the Vrbas site gave the highest ethanol yield.