Classification and Evaluation of Quality Grades of Organic Green Teas Using an Electronic Nose Based on Machine Learning Algorithms

Abstract

The quality grades of organic green teas are tightly correlated with their prices. In this work, samples of organic green teas of different quality grades are collected, and their aromas are analyzed with an electronic nose (E-nose). A multi-task model based on the back propagation neural network (MBPNN) is proposed for the simultaneous performance of the classification task (grade classification of tea) and regression task (quality prediction of tea with market price). The validity of the proposed MBPNN model is also verified; its performances of the tasks are compared with those of two classification models (random forest and support vector machine) and three regression models (partial least squares regression, kernel ridge regression, and support vector regression). Experimental results demonstrate that the MBPNN model achieves good performance both in the tasks of tea grade classification and tea quality evaluation (price regression). The study shows that the E-nose is effective for the classification and evaluation of organic green teas when an optimal pattern recognition algorithm is selected. Encouragingly, a novel application of the multi-task learning model in the tea industry is obtained to assess the tea quality in a simple, quick, and comprehensive way.