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材料导报  2024, Vol. 38 Issue (14): 23040072-14    https://doi.org/10.11896/cldb.23040072
  无机非金属及其复合材料 |
电子鼻技术及其应用研究进展
穆申玲1,2, 沈文锋2,3,*, 吕大伍2,3, 宋伟杰2,3,*, 谭瑞琴1,*
1 宁波大学信息科学与工程学院,浙江 宁波 315211
2 中国科学院宁波材料技术与工程研究所,浙江 宁波 315201
3 中国科学院大学,北京 100049
Advancements in Electronic Nose Technology and Its Applications
MU Shenling1,2, SHEN Wenfeng2,3,*, LYU Dawu2,3, SONG Weijie2,3,*, TAN Ruiqin1,*
1 Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, Zhejiang, China
2 Ningbo Institute of Material Technology & Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, China
3 University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 电子鼻(Electronic nose,E-nose)技术作为一种有效的嗅觉模拟与气体识别的方法被广泛应用。电子鼻系统由气体传感器阵列组成,利用其交叉敏感性对气体进行检测。电子鼻系统利用机器学习算法,对气体进行定性定量分析。传统的机器学习算法在电子鼻系统中的应用已经成熟,如今深度学习算法也慢慢在电子鼻系统中应用。电子鼻系统具有选择性高、精密度好、反应快速、稳定性和延展性好的特点,被应用于包括有毒气体检测、空气质量管理、食品新鲜度和质量预测等方面。本文从气体传感器阵列的组成、信号采集与处理单元、模式识别算法的分类以及电子鼻系统在实际中的应用等方面综述了电子鼻系统气体识别的最新研究进展,最后对电子鼻系统气体识别目前所存在的问题以及发展前景进行了总结和展望。
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穆申玲
沈文锋
吕大伍
宋伟杰
谭瑞琴
关键词:  传感器阵列  信号采集与处理  模式识别算法  电子鼻技术  气体识别    
Abstract: Electronic nose (E-nose) technology has been widely used as an effective method of olfactory simulation and odor recognition. The E-nose system is composed of an array of gas sensors that utilize their cross-reactivity for qualitative and quantitative gas analysis. Machine learning algorithms are commonly used in the E-nose system, with artificial neural networks being particularly prevalent. In addition to traditional machine learning algorithms, deep learning algorithms are also gradually being applied in E-nose systems. The E-nose system features high selectivity, precision, rapid response, stability, and scalability, and has been applied in various fields, such as toxic gas detection, air quality management, and food freshness and quality prediction. This paper reviews the latest research progress in the gas identification of E-nose systems, including the composition of gas sensor arrays based on MEMS technology, signal acquisition and processing units, and the classification of pattern recognition algorithms. Finally, the current problems and development prospects of E-nose system gas identification are summarized and discussed.
Key words:  gas array    signal sampling and processing    pattern recognition    electronic nose technology    gas recognition
出版日期:  2024-07-25      发布日期:  2024-08-12
ZTFLH:  TP212  
基金资助: 浙江省自然科学基金项目(LGF22F010008); 浙江省基础公益研究计划项目(LGG21F040001); 宁波市重点研发计划项目(2023Z021)
通讯作者:  * 沈文锋,中国科学院宁波材料技术与工程研究所副研究员、硕士研究生导师。2000年于东北大学理学院物理专业获得学士学位;2003于东北大学材料物理与化学专业获硕士学位;2010年2月于中国科学院金属研究所材料科学与工程专业获工学博士学位;2010年3月—2012年4月于中国科学院宁波材料技术与工程研究所从事博士后工作;现为中国科学院宁波材料技术与工程研究所副研究员。从事智能传感器件及应用研发。已发表研究论文50余篇,包括Sensors and Actuators B-Chemical、Materials Chemistry and Physics、Journal of Materials Chemistry C、Nanoscale、RSC Advances、Materials Letters、Journal of Materials Science & Technology、ACS Applied Materials & Interfaces等,授权中国发明10余项。wfshen@nimte.ac.cn
宋伟杰,中国科学院宁波材料技术与工程研究所研究员、博士研究生导师。2002 年清华大学物理化学专业博士毕业; 2002—2006 年,在日本物质材料研究机构工作,2006 年起加入宁波材料所任研究员,2007 年入选中国科学院百人计划。主要从事新能源技术、功能材料与纳米器件的研究工作。已完成和在研多项国家级和省部级项目及企业合作项目。发表 SCI 论文90余篇,授权中国发明专利20余项。weijiesong@nimte.ac.cn
谭瑞琴,宁波大学信息科学与工程学院研究员、博士研究生导师。目前主要从事半导体型光电功能纳米材料及相关器件、光电功能薄膜和柔性电子器件的制备及集成等相关方向的研究。主持国家自然科学基金、浙江省基础公益研究计划项目、浙江省钱江人才项目和宁波市自然科学基金项目等。已发表SCI收录论文100多篇,获得国家授权发明专利20余项。tanruiqin@nbu.edu.cn   
作者简介:  穆申玲,2021年6月于湖北工业大学获得工学学士学位。现为宁波大学和中国科学院宁波材料技术与工程研究所合培硕士研究生,在沈文锋副研究员的指导下进行研究。目前主要研究领域为室内环境监测应用的智能气体传感器阵列。
引用本文:    
穆申玲, 沈文锋, 吕大伍, 宋伟杰, 谭瑞琴. 电子鼻技术及其应用研究进展[J]. 材料导报, 2024, 38(14): 23040072-14.
MU Shenling, SHEN Wenfeng, LYU Dawu, SONG Weijie, TAN Ruiqin. Advancements in Electronic Nose Technology and Its Applications. Materials Reports, 2024, 38(14): 23040072-14.
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http://www.mater-rep.com/CN/10.11896/cldb.23040072  或          http://www.mater-rep.com/CN/Y2024/V38/I14/23040072
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