A global extended extreme learning machine combined with electronic nose for identifying tea gas information

Abstract

To enhance the detection performance of electronic nose (e-nose), a recognition method of gas feature based on a global extended extreme learning machine (GEELM) is proposed, which combines the expansion factor and global balance coefficient to expand and balance the difference between categories, and improve the classification performance. Then this method is applied to identify the quality of tea. Firstly, the dragging factor and following matrix are introduced to increase the distance between classes. Secondly, the global identification coefficient is introduced further to increase the feature differences among different types of tea, and improve the classification stability. Finally, under different feature sets, the classification performance of multi-pattern recognition methods is compared to prove the effectiveness of GEELM in e-nose gas feature recognition. The results show that GEELM has the best classification accuracy of 98.20%, F1-score of 0.9871, and Kappa coefficient of 0.9775. In conclusion, GEELM can be an effective technique to identify gas features, and it also provides a new method for tea quality measurement.