电子鼻结合GC-MS鉴别不同部位的三七粉  

Identification of Panax notoginseng Powders from Different Root Parts Using Electronic Nose and Gas Chromatography-Mass Spectrometry

作　　者：李丽霞[1] 张浩 林宇浩 史磊 李珊珊 张付杰[1] 王俊[2] LI Lixia;ZHANG Hao;LIN Yuhao;SHI Lei;LI Shanshan;ZHANG Fujie;WANG Jun(Faculty of Modern Agricultural Engineering,Kunming University of Science and Technology,Kunming 650500,China;College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]昆明理工大学现代农业工程学院,云南昆明650500 [2]浙江大学生物系统工程与食品科学学院,浙江杭州310058

出　　处：《食品科学》2023年第20期321-329,共9页Food Science

基　　金：云药之乡产业技术创新体系构建及应用项目(202102AA310045)

摘　　要：为鉴别不同部位的三七粉,采用电子鼻结合气相色谱-质谱(gas chromatography-mass spectrometry,GCMS)联用技术对三七的整根粉、剪口粉、主根粉、侧根粉和须根粉进行挥发性成分分析。通过GC-MS测定三七粉挥发物的成分和含量,并进行多重比较。利用统计学习方法提取电子鼻响应曲线的8个时域特征,并进行相关性分析,采用3种特征选择算法对特征数据进行降维。分别建立基于原始特征数据、3种特征选择数据的支持向量机(support vector machine,SVM)、最小二乘支持向量机(least square support vector machine,LSSVM)和极限学习机分类模型;引入灰狼优化(grey wolf optimization,GWO)算法对分类模型中的参数gam和sig2进行优化。结果表明:5种三七粉样品中共检测出31种挥发物成分,最优的GWO-IRIV-LSSVM模型能够对电子鼻数据进行有效区分,测试集准确率为97.5%,且能客观反映出样品种类挥发性物质的差异主要是挥发物总量、烷烃和芳香族化合物,这与GC-MS检测结果一致。本研究可用于道地产区优质三七粉混入劣质三七粉的检测。In order to identify Panax notoginseng powders from different root parts,an electronic nose and gas chromatography-mass spectrometry(GC-MS)were used to analyze the volatile components of the whole root powder,rhizome powder,taproot powder,lateral root powder and fibrous root powder of P.notoginseng.The data obtained were analyzed by multiple comparison.The statistical learning method was used to extract eight time-domain features from the response curves of the electronic nose,and correlation analysis was carried out.Three feature selection algorithms were used to reduce the dimension of the feature data.Classification models were built using support vector machine(SVM),least square support vector machine(LSSVM)or extreme learning machine(ELM)based on the original feature data or the three kinds of feature selection data.The grey wolf optimization(GWO)algorithm was introduced to optimize the parameters gam and sig2 in the classification model.The results showed that a total of 31 volatile compounds were detected in the five P.notoginseng powders.The best GWO-IRIV-LSSVM model could effectively distinguish the electronic nose data,with 97.5%accuracy for the test set.Moreover,the volatile composition of the five samples differed mainly in terms of the contents of total volatiles,alkanes,and aromatic compounds,which was consistent with the results of GC-MS.The method used in this study can be used for the detection of high-quality P.notoginseng powder from geo-authentic production areas mixed with low-quality P.notoginseng powder.

关 键 词：电子鼻 气相色谱-质谱法 三七粉 特征提取 最小二乘支持向量机 灰狼优化算法 

分 类 号：R282.5[医药卫生—中药学] TP212[医药卫生—中医学]