精油在各个行业都很有价值,但它们容易掺假会对健康造成不良影响。电子鼻传感器为掺假检测提供了解决方案。本文提出了一种基于低成本传感器网络和机器学习技术的表征精油的新系统。所使用的传感器属于MQ家族(MQ-2、MQ-3、MQ-4、MQ-5、MQ-6、MQ-7和MQ-8)。使用了六种精油,包括CistusLadanifer,Pinuspinaster,和掺有松果的天麻油,互花千层,茶树,红色水果总共包括7100次测量,超过118小时的33个不同的参数的测量。这些数据用于训练和比较五种机器学习算法:判别分析,支持向量机,k-最近的邻居,神经网络,当单独使用数据或包括小时平均值时,以及天真的贝叶斯。为了评估包含的机器学习算法的性能,准确度,精度,召回,并考虑了F1评分。研究发现,使用k最近的邻居,准确度,召回,F1分数,和精度值分别为1、0.99、0.99和1。对于平均数据仅使用2个参数或单个数据使用15个参数的k个最近邻居,准确度达到100%。
Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.