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

Sensors ◽  
2014 ◽  
Vol 14 (11) ◽  
pp. 20134-20148 ◽  
Author(s):  
Jie Liu ◽  
Weixin Wang ◽  
Yaojun Yang ◽  
Yuning Yan ◽  
Wenyi Wang ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Statistical Analyses ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Radix Angelicae Sinensis ◽  
Rapid Discrimination ◽  
Angelicae Sinensis

Rapid Discrimination of Indonesian Herbal Medicines by Using Electronic Nose Based on Array of Commercial Gas Sensors

2015 ◽  
Vol 771 ◽  
pp. 209-212 ◽  
Author(s):  
Fajar Hardoyono ◽  
Kuwat Triyana ◽  
Bambang Heru Iswanto
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Herbal Medicines ◽  
Principal Component ◽  
Statistical Analyses ◽  
Local Market ◽  
Multivariate Statistical ◽  
Good Repeatability ◽  
And Cluster Analysis ◽  
Rapid Discrimination

The aim of this study is to discriminate herbal medicines (here after referred to as herbals) by an electronic nose (e-nose) based on an array of eight commercially gas sensors and multivariate statistical analyses. Seven kinds of herbal essential oils purchased from local market in Yogyakarta Indonesia, including zingiberofficinale (ZO), kaempferiagalanga (KG), curcuma longa (CL), curcuma zedoaria (CZ), languasgalanga (LG), pogostemoncablin (PO), and curcuma xanthorrizharoxb (CX) were measured by using this e-nose consecutively. Due to the use of dynamic headspace in this e-nose, data for one cycle (sampling and purging) were recorded every five second for 10 cycles. Each kind of herbals was analyzed for five replications and relative amplitude of the responses was extracted as a feature. The statistical analyses of principal component analysis (PCA) and cluster analysis (CA) were used for discriminating samples. The PCA score plot shows that these 35 essential oil samples were separated into 7 groups based on similarity of patterns. The first two components, PC1 and PC2, capture 96.2% of data variance. Meanwhile, by using 80% similarity, the CA clusters 7 herbals into 3 classes. In this case, the first class consists of ZO and CZ and the second class consists of KG, CL, LG and CX, while the PO sample is clustered in the third class. These classes need to be validated using a standard analytical instrument such as GC/MS. The technique shows some advantages including easy in operation because of without any sample preparation, rapid detection, and good repeatability.

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