A Rapid Discrimination of Authentic and Unauthentic Radix Angelicae Sinensis Growth Regions by Electronic Nose Coupled with Multivariate Statistical Analyses

作者：Liu Jie; Wang Weixin; Yang Yaojun^*; Yan Yuning; Wang Wenyi; Wu Haozhong; Ren Zihe

来源：Sensors (Switzerland), 2014, 14(11): 20134-20148.

DOI：10.3390/s141120134

• Radix Angelicae Sinensis
• electronic nose
• authentic region
• multivariate statistical analyses

Radix Angelicae Sinensis, known as Danggui in China, is an effective and wide applied material in Traditional Chinese Medicine (TCM) and it is used in more than 80 composite formulae. Danggui from Minxian County, Gansu Province is the best in quality. To rapidly and nondestructively discriminate Danggui from the authentic region of origin from that from an unauthentic region, an electronic nose coupled with multivariate statistical analyses was developed. Two different feature extraction methods were used to ensure the authentic region and unauthentic region of Danggui origin could be discriminated. One feature extraction method is to capture the average value of the maximum response of the electronic nose sensors (feature extraction method 1). The other one is to combine the maximum response of the sensors with their inter-ratios (feature extraction method 2). Multivariate statistical analyses, including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and hierarchical clustering analysis (HCA) were employed. Nineteen samples were analyzed by PCA, SIMCA and HCA. Then the remaining samples (GZM1, SH) were projected onto the SIMCA model to validate the models. The results indicated that, in the use of feature extraction method 2, Danggui from Yunnan Province and Danggui from Gansu Province could be successfully discriminated using the electronic nose coupled with PCA, SIMCA and HCA, which suggested that the electronic-nose system could be used as a simple and rapid technique for the discrimination of Danggui between authentic and unauthentic region of origin.

• 出版日期2014-11
• 单位北京中医药大学; 司法部司法鉴定科学技术研究所