• Min Yee Lim, Jian Huang, Fu-Rong He, Bai-Xiao Zhao, Hui-Qin Zou, Yong-Hong Yan, Hui Hu, Dong-Sheng Qiu, and Jun-Jie Xie.
• aInternational School bAcupuncture Department, Dongfang Hospital, Second Affiliated Hospital of Beijing University of Chinese Medicine cSchool of Acupuncture-Moxisbution and Tuina, Fujian University of Traditional Chinese Medicine, Fujian dChinese Medicine eLibrary fSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing gAcupuncture Department, Xiamen Hospital, Affiliated Hospital of Beijing University of Chinese Medicine, Xiamen, China.
• Medicine (Baltimore). 2020 Aug 14; 99 (33): e21556.

AbstractMoxa floss is the primary material used in moxibustion, an important traditional Chinese medicine therapy that uses ignited moxa floss to apply heat to the body for disease treatment. Till date, there is no available data regarding quality control of different grades of moxa floss. The objectives of this study were to explore the probative value of the electronic nose (e-nose) in differentiating different quality grades of commercial moxa floss sold in China, and to investigate if data mining techniques could be used to optimize the sensor array while retaining classification accuracy of the samples. The e-nose with 12 metal oxide semiconductor type sensors was used to analyze the odor profiles of 15 commercial moxa floss samples of different quality grades. Feature selection algorithms using principal component analysis (PCA) and BestFirst (BC) coupled with correlation-based feature subset selection (CfsSubsetEval) method were used to obtain the most efficient feature subsets. Results for the BC feature selection method identified 3 optimized sensors (S2, S6, and S11), suggesting that aromatic compounds relate more to the identification of the samples. Radial basis function (RBF), multilayer perceptron (MLP), and random forests (RF) performed well in discriminating the samples, retaining prediction accuracies above 85%, which achieved cost-effectiveness and operational simplicity, while retaining prediction accuracy. The e-nose could be a rapid and nondestructive method for objective preliminary classification of quality grades of moxa floss and may be used for future studies related to moxa products safety and quality.

21 keywords...

hide…