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Year : 2019  |  Volume : 15  |  Issue : 63  |  Page : 392-401

Aroma characteristic analysis of Amomi fructus from different habitats using machine olfactory and gas chromatography-mass spectrometry

1 Department of Communication, School of Information Engineering, Guangdong University of Technology; Department of Computer Science, College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, China
2 Department of Communication, School of Information Engineering, Guangdong University of Technology, Guangzhou, China
3 Department of Electrical and Electronic Engineering, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
4 Department of Traditional Chinese Medicine Resources, College of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, China

Correspondence Address:
Dehan Luo
Department of Communication, School of Information Engineering,Guangdong University of Technology, Guangzhou 510006
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/pm.pm_665_18

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Background: Amomi fructus (AF Lour.) has been used to treat digestive diseases in the context of Traditional Chinese Medicine. Its aroma characteristics have been attracted attention and are considered to be effective markers for determining AF from different habitats. Materials and Methods: In this article, the odor characteristics of AF from three different habitats were investigated and analyzed using gas chromatography-mass spectrometry (GC-MS) and an electronic nose (E-nose). Results: It was found that the E-nose in conjunction with principal component analysis as an analytic tool, showed good performance and achieved a total variance of 93.90% with the first two principal components. A total of 65 aroma constituents among three groups of AF were separated, identified, and calculated using GC-MS. It was observed that the components and the contents were clearly different among the three groups. To confirm the interrelation between aroma constituents and sensors, the contents of 12 aroma ingredients and the response values of six sensors were selected to be trained and tested using the partial least squares. A satisfied quantitative prediction was presented that the contents of selected constituents were accurately predicted by corresponding E-nose sensors with the most determination coefficient of calibration and determination coefficient of prediction of >90%. Conclusion: It was revealed that the E-nose is capable of discriminating AF from different habitats, presenting an accurate, easy-operating, and nondestructive reference approach.

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