Rapid qualitative and quantitative analysis of volatile components and quality identification of amomi fructus based on bionic olfactory system

Huaying Zhou; Zhong Li; Dehan Luo; Gholamhosseini, Hami; Bin Han; Hexian Wang

Articles

Abstract

Pharmacognosy Magazine,2021,17,74,223-230.

DOI:10.4103/pm.pm_187_20

Published:July 2021

Type:Original Article

Authors:

Author(s) affiliations:

Huaying Zhou¹, Zhong Li², Dehan Luo³, Hamid Gholamhosseini⁴, Bin Han², Hexian Wang¹
¹ Department of Computer Science, College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, China
² Department of Traditional Chinese Medicine Resources, College of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, China
³ Department of Communication, School of Information Engineering, Guangdong University of Technology, Guangzhou, China
⁴ Department of Electrical and Electronic Engineering, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

Abstract:

Background: Amomi fructus (AF) (a dried fruit of Amomum villosum Lour.) has been used in the treatment of digestive diseases such as abdominal pain and dysentery and in the prevention of abortion. The active ingredient of AF is its volatile oil. The volatile oil contains bornyl acetate and (1R,4R)-(+)-camphor, which are the primary active ingredients of AF that are analyzed for the quality assessment. Therefore, it is important to find an accurate and easy method to analyze the aforementioned volatile components of AF. Materials and Methods: In this study, 8 samples (A1–A4, B5 and B6, and C7 and C8) were collected and divided into Grades A, B, and C, respectively. The characteristics of volatile oils (the aroma) in these samples were analyzed using an electronic nose (E-nose) and a gas chromatography–mass spectrometry. In this study, we proposed a bionic olfactory system based on E-nose technology combined with a convolutional neural network algorithm for component identification. This system can qualitatively evaluate AF from different quality grades and quantitatively predict the contents of the two aforementioned primary chemical components. Results: The accuracy of qualitative identification was over 95% for Grade A samples and over 90% for Grade B and Grade C samples. Discussion: Based on our identification of the quality, Grade A samples were detected with an accuracy of 86.7%. However, Grade B and C samples were identified with lower accuracies (80% and 73.3%, respectively). Conclusion: The identification of quality of AF was successfully evaluated by two primary volatile components: bornyl acetate and (1R,4R)-(+)-camphor. The bionic olfactory system combined with an appropriate prediction model might be used as a potential quality control tool for Chinese herbal medicines.