Cui Shaoqing, Wang Jun, Yang Liangcheng, Wu Jianfeng, Wang Xinlei
Department of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China.
Department of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China.
J Pharm Biomed Anal. 2015 Jan;102:64-77. doi: 10.1016/j.jpba.2014.08.030. Epub 2014 Sep 6.
Aroma profiles of ginseng samples at different ages were investigated using electronic nose (E-nose) and GC-MS techniques combined with chemometrics analysis. The bioactive ginsenoside and volatile oil content increased with age. E-nose performed well in the qualitative analyses. Both Principal Component Analysis (PCA) and Discriminant Functions Analysis (DFA) performed well when used to analyze ginseng samples, with the first two principal components (PCs) explaining 85.51% and the first two factors explaining 95.51% of the variations. Hierarchical Cluster Analysis (HCA) successfully clustered the different types of ginsengs into four groups. A total of 91 volatile constituents were identified. 50 of them were calculated and compared using GC-MS. The main fragrance ingredients were terpenes and alcohols, followed by aromatics and ester. The changes in terpenes, alcohols, aromatics, esters, and acids during the growth year once again confirmed the dominant role of terpenes. The Partial Least Squares (PLS) loading plot of gas sensors and aroma ingredients indicated that particular sensors were closely related to terpenes. The scores plot indicated that terpenes and its corresponding sensors contributed the most in grouping. As regards to quantitative analyze, 7 constituent of terpenes could be accurately explained and predicted by using gas sensors in PLS models. In predicting ginseng age using Back Propagation-Artificial Neural Networks (BP-ANN), E-nose data was found to predict more accurately than GC-MS data. E-nose measurement may be a potential method for determining ginseng age. The combination of GC-MS can help explain the hidden correlation between sensors and fragrance ingredients from two different viewpoints.
采用电子鼻（E-nose）和气相色谱-质谱联用（GC-MS）技术并结合化学计量学分析，研究了不同年龄人参样品的香气特征。生物活性人参皂苷和挥发油含量随年龄增长而增加。电子鼻在定性分析中表现良好。主成分分析（PCA）和判别函数分析（DFA）用于分析人参样品时均表现良好，前两个主成分（PCs）解释了85.51%的变异，前两个因子解释了95.51%的变异。层次聚类分析（HCA）成功地将不同类型的人参聚类为四组。共鉴定出91种挥发性成分。其中50种通过GC-MS进行了计算和比较。主要的香气成分是萜类和醇类，其次是芳烃和酯类。生长年份中萜类、醇类、芳烃、酯类和酸类的变化再次证实了萜类的主导作用。气体传感器与香气成分的偏最小二乘法（PLS）载荷图表明特定传感器与萜类密切相关。得分图表明萜类及其相应传感器在分组中贡献最大。在定量分析方面，通过PLS模型中的气体传感器可以准确解释和预测7种萜类成分。在使用反向传播人工神经网络（BP-ANN）预测人参年龄时，发现电子鼻数据比GC-MS数据预测得更准确。电子鼻测量可能是确定人参年龄的一种潜在方法。GC-MS的结合有助于从两个不同角度解释传感器与香气成分之间隐藏的相关性。
