Classification of the fragrant styles and evaluation of the aromatic quality of flue-cured tobacco leaves by machine-learning methods

Abstract

During commercial transactions, the quality of flue-cured tobacco leaves must be characterized efficiently, and the evaluation system should be easily transferable across different traders. However, there are over 3000 chemical compounds in flue-cured tobacco leaves; thus, it is impossible to evaluate the quality of flue-cured tobacco leaves using all the chemical compounds. In this paper, we used Support Vector Machine (SVM) algorithm together with 22 chemical compounds selected by ReliefF-Particle Swarm Optimization (R-PSO) to classify the fragrant style of flue-cured tobacco leaves, where the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) were 90.95% and 0.80, respectively. SVM algorithm combined with 19 chemical compounds selected by R-PSO achieved the best assessment performance of the aromatic quality of tobacco leaves, where the PCC and MSE were 0.594 and 0.263, respectively. Finally, we constructed two online tools to classify the fragrant style and evaluate the aromatic quality of flue-cured tobacco leaf samples. These tools can be accessed at http://bioinformatics.fafu.edu.cn/tobacco .