title: 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 .
authors.affiliation: * Institute of Tobacco Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.|† Research Institute of Plateau Ecological, Agriculture and Animal Husbandry College, Tibet University, Linzhi 860000, China.
authors.forename: Li
authors.initials: L
authors.lastname: Gu
authors.affiliation: ‡ College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
authors.forename: Lichun
authors.initials: L
authors.lastname: Xue
authors.affiliation: ‡ College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Song
authors.affiliation: * Institute of Tobacco Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
authors.forename: Fengji
authors.initials: F
authors.lastname: Wang
authors.affiliation: ‡ College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
authors.forename: Huaqin
authors.initials: H
authors.lastname: He
authors.affiliation: * Institute of Tobacco Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
authors.forename: Zhongyi
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods; D000069550:Machine Learning; D009812:Odorants / Q000032:analysis*; D018515:Plant Leaves / Q000737:chemistry*; D060388:Support Vector Machine; D014026:Nicotiana / Q000737:chemistry*; D055549:Volatile Organic Compounds / Q000032:analysis*
chemical_list: D055549:Volatile Organic Compounds
keywords: Tobacco; aromatic quality; classification and evaluation; fragrant style