The Comparison of Microbial Electronic Tongue Data Based on PCA, PLS and ANN

In this paper, we use the electronic tongue as a detection tool and Normal Pulse voltammetry (NPV) as the measuring principle to identify the sulfate-reducing bacteria and iron bacteria. The main task is to classify the two bacteria on the reference of broth culture medium. We adopted non-supervised (principal component analysis PCA) and supervised (partial least squares PLS and artificial neural network ANN) pattern recognition to process data, and compared the performance of model for supervised identification, the experimental results showed that ANN was better. The recognition rate of ANN in the training set came to 100%, and in the test set came to 91.67%. The results prove that the electronic tongue with ANN pattern recognition system is a good choice to identify the two types of bacteria.