Predicting acetic acid content in the final beer using neural networks and support vector machine

Abstract

Most acetic acid found in beer is produced by yeast during fermentation. It contributes significantly to beer taste, especially when its content is higher than the taste threshold in beer. Therefore, the control of its content is very important to maintain consistent beer quality. In this study, artificial neural networks and support vector machine (SVM) were applied to predict acetic acid content at the end of a commercial-scale beer fermentation. Relationships between beer fermentation process parameters and the acetic acid level in the fermented wort (beer) were modelled by partial least squares (PLS) regression, back-propagation neural network (BP-NN), radial basis function neural network (RBF-NN) and least squares-support vector machine (LS-SVM). The data used in this study were collected from 146 production batches of the same beer brand. For predicting acetic acid content, LS-SVM and RBF-NN were found to be better than BP-NN and PLS. For the comparison of RBF-NN and LS-SVM, RBF-NN had a better reliability of model, but lower reliability of prediction. SVM had better generalization, but lower reliability of model. In summary, LS-SVM was better than RBF-NN modelling for the prediction of acetic acid content during the commercial beer fermentation in this study. Copyright © 2013 The Institute of Brewing & Distilling