Standardization of Process Norms in Baker's Yeast Fermentation through Statistical Models in Comparison with Neural Networks

Achieving consistency of growth pattern for commercial yeast fermentation over batches through addition of water, molasses and other chemicals is often very complex in nature due to its bio-chemical reactions in operation. Regression models in statistical methods play a very important role in modeling the underlying mechanism, provided it is known. On the contrary, artificial neural networks provide a wide class of general-purpose, flexible non-linear architectures to explain any complex industrial processes. In this paper, an attempt has been made to find a robust control system for a time varying yeast fermentation process through statistical means, and in comparison to non-parametric neural network techniques. The data used in this context are obtained from an industry producing baker's yeast through a fed-batch fermentation process. The model accuracy for predicting the growth pattern of commercial yeast, when compared among the various techniques used, reveals the best performance capability with the backpropagation neural network. The statistical model used through projection pursuit regression also shows higher prediction accuracy. The models, thus developed, would also help to find an optimum combination of parameters for minimizing the variability of yeast production.