摘要
采用高效液相色谱-气相色谱-质谱联用法(HPLC-GC-MS)测定中部和下部烟叶的巨豆三烯酮、β-紫罗兰酮、氧化紫罗兰酮、茄酮等11种致香成分,应用遗传算法(GA)对筛选出的8种致香成分建立中部和下部烟叶支持向量机(SVM)分类判别模型.结果表明,中部和下部烟叶的SVM分类判别模型的建模、留一法及预报准确率分别为95.45%,89.39%和 81.25%.利用Fisher判别矢量方法考察了中部和下部烟叶的空间分布规律,分析出中部和下部烟叶致香成分中,巨豆三烯酮、β-紫罗兰酮、氧化紫罗兰酮差异显著.
Eleven different aromatic components including megastigmatrienone,beta-Ionone,Ionone oxide and solanone from middle and lower tobacco leaves were determined successfully via high performance liquid chromatography-gas chromatography-mass spectrometry (HPLC-GC-MS) system.By using genetic algorithm(GA),8 aromatic components were selected to build a support vector machine(SVM) classification model for discriminating middle and lower tobacco leaves.The results showed that the accuracies of modeling,leave-one-out,and prediction were 95.45%,89.39% and 81.25%,respectively.The spatial distribution of middle and lower tobacco leaves was investigated by Fisher discriminant vector method,which showed that megastigmatrienone,beta-Ionone,and Ionone oxide were evidently different in the middle and lower tobaccos leaves.
作者
申玉姝
曹晓卫
于洁
沙云菲
岳宝华
SHEN Yushu;CAO Xiaowei;YU Jie;SHA Yunfei;YUE Baohua(School of Materials Science and Engineering,Shanghai University,Shanghai 200444,China;College of Chemistry and Materials Science,Shanghai Normal University,Shanghai 200234,China;Technology Center,Shanghai Tobacco Group Co., Ltd.,Shanghai 200082,China)
出处
《上海师范大学学报（自然科学版）》
2019年第4期420-426,共7页
Journal of Shanghai Normal University(Natural Sciences)
基金
国家自然科学基金青年基金(21706156)
关键词
烟叶部位
致香成分
遗传算法(GA)
支持向量机(SVM)
tobacco leaves stalk positions
aromatic components
genetic algorithm(GA)
support vector machine(SVM)
