Lim Min Yee, Huang Jian, He Fu-Rong, Zhao Bai-Xiao, Zou Hui-Qin, Yan Yong-Hong, Hu Hui, Qiu Dong-Sheng, Xie Jun-Jie
International School.
Acupuncture Department, Dongfang Hospital, Second Affiliated Hospital of Beijing University of Chinese Medicine.
Medicine (Baltimore). 2020 Aug 14;99(33):e21556. doi: 10.1097/MD.0000000000021556.
Moxa 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.
艾绒是艾灸中使用的主要材料，艾灸是一种重要的中医疗法，利用点燃的艾绒对身体施热以治疗疾病。迄今为止，尚无关于不同等级艾绒质量控制的可用数据。本研究的目的是探索电子鼻在区分中国市场上销售的不同质量等级商业艾绒方面的证明价值，并研究数据挖掘技术是否可用于优化传感器阵列，同时保持样品的分类准确性。使用具有12个金属氧化物半导体型传感器的电子鼻分析15个不同质量等级的商业艾绒样品的气味特征。采用主成分分析（PCA）和BestFirst（BC）结合基于相关性的特征子集选择（CfsSubsetEval）方法的特征选择算法来获得最有效的特征子集。BC特征选择方法的结果确定了3个优化传感器（S2、S6和S11），表明芳香化合物与样品的识别关系更大。径向基函数（RBF）、多层感知器（MLP）和随机森林（RF）在区分样品方面表现良好，预测准确率保持在85%以上，实现了成本效益和操作简便性，同时保持了预测准确性。电子鼻可以作为一种快速、无损的方法，用于艾绒质量等级的客观初步分类，并可用于未来与艾绒产品安全性和质量相关的研究。
