New dimensionality reduction model (manifold learning) coupled with electronic tongue for green tea grade identification

Multivariate data analysis methods play a key role in extracting effective features to denote original tea samples. The most commonly used multivariate data analysis methods are principle component analysis and linear discriminant analysis. These methods are based on statistical learning theory and complete in mathematics. However, there is correlation and redundancy among multiple sensors of electronic tongue, and it cannot guarantee that the tea samples are linearly separable in the original data space. The aim of this study is to conduct new dimensionality reduction methods: manifold learning algorithms, to extract effective features from the responses of electronic tongue sensors, and the algorithm which gives the highest recognition accuracy is considered to be the best for tea quality gradation. Experimental results show that supervised nonlinear manifold learning algorithms outperform other methods and achieve the highest recognition accuracy for green tea with four quality grades.