关于基于低成本气体传感器和机器学习技术的新系统对精油分类的建议
Sandra Viciano-Tudela1, Lorena Parra1, Paula Navarro-Garcia1
1Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paranimf, 1, 46730 Gandia, Spain.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了一种使用MQ传感器和机器学习来检测精油改的新型电子鼻子系统. k-最近邻近算法实现了100%的准确性,确保了石油的真实性.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 传感器技术 传感器技术
背景情况:
- 精油容易被改,造成健康风险.
- 电子鼻子提供了一种有希望的解决方案,用于检测杂质.
- 目前用于精油认证的方法可能是复杂和昂贵的.
研究的目的:
- 开发和验证一个低成本的电子鼻子系统,用于精油的表征和伪造检测.
- 为此应用评估各种机器学习算法的性能.
- 确定最有效的参数和算法,以准确检测伪造.
主要方法:
- 使用了MQ家族电子鼻子传感器 (MQ-2到MQ-8) 的网络.
- 测试了六种精油,包括伪造的样品.
- 在118小时内收集了7100多项测量,分析了33个参数.
- 训练并比较了五种机器学习算法:差别分析,支持向量机,k-最近邻居,神经网络和天真贝叶斯式.
主要成果:
- k-最近邻近算法表现出卓越的性能.
- 获得的准确性,精度,回忆和F1分数值分别为1,1,0.99和1.
- 使用仅2个平均参数或15个个别参数,获得了100%的准确性.
结论:
- 拟议的电子鼻子系统有效地表征精油,并检测伪造.
- 机器学习,特别是k-最近的邻居,显著提高了检测准确度.
- 这种方法提供了一种可靠和有效的方法,以确保精油的真实性.
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