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基于MOS气体传感器的电子鼻子的设计及其在果汁识别中的应用

Yafei Zhang¹, Yongli Zhao¹, Feiyang Jiang¹

¹School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Sensors (Basel, Switzerland)

|February 26, 2025

概括

这项研究引入了一种改进的电子鼻子 (E-nose) 系统,可调节工作温度和一种新的Boruta-RFE算法. 这种组合通过优化特征提取和减少数据维度来提高气体检测的准确性.

科学领域:

传感器技术传感器技术
化学测量化学测量化学测量
人工智能的人工智能

背景情况:

金属氧化物半导体 (MOS) 气体传感器对于电子鼻子 (E-nose) 系统至关重要,因为它们的成本效益和低功耗.
MOS传感器的交叉灵敏度和非最佳特征提取方法限制了E-鼻子的性能.
有效的气体传感需要解决传感器的局限性和改进数据分析技术.

研究的目的:

开发一种增强的E-鼻子系统,能够在检测过程中调节气体传感器工作温度.
引入一种新的气体特征提取算法Boruta-Recursive Feature Elimination (Boruta-RFE),以改进数据分析.
为了验证系统和算法的有效性,准确识别不同的果汁类别.

主要方法:

为气体传感器设计了一个具有可控制工作温度的E-鼻子系统.
开发并实施了Boruta-RFE算法用于气体特征选择.
将系统和算法应用于一个涉及识别果汁类别的案例研究.

主要成果:

调节传感器工作温度产生了一组更丰富的气体特征.
博鲁塔-RFE算法有效地减少了气体特征数据集的维度.
拟议的方法显著提高了果汁分类的E-nose系统的识别准确性.

结论:

调节工作温度通过提供更多信息功能来增强气体传感能力.
博鲁塔-RFE是一种高效的算法,用于在E-nose应用中减少维度和选择关键特征.
综合系统和算法为改善E-nose在复杂识别任务中的性能提供了有希望的方法.

关键词:

分类认可认可的分类.电子鼻子电子鼻子功能提取特性提取果汁标识果汁标识果汁标识

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