Abstract: The method integrating principal component analysis(PCA) with back propagation(BP) neural networks was applied to predict the beer sensory evaluation.A total of 11 physicochemical indexes was extracted by PCA to eliminate linear relevance and then used as input.Sensory evaluation scores were used as output.BP neural network was established to model the beer sensory evaluation.Finally,sensory evaluation scores of 50 beers were predicted by this model.The experimental results showed that the maximum predictive relative error was 2.68%,suggesting that the methods integrating PCA with BP networks could precisely predict the beer sensory evaluation.
2013, Vol. 39 
