传感技术学报2018，Vol.31Issue(1)：13-18,6.DOI:10.3969/j.issn.1004-1699.2018.01.003
用于检测糖尿病标志物的电子鼻优化设计
Optimal Design of Electronic Nose for Detecting Diabetes Markers
摘要
Abstract
The content of acetone in human exhalation can be used as a marker of diabetes. In order to achieve the noninvasive diagnosis of diabetes,we design a metal oxide semiconductor gas sensors array as the core of the artifi-cial olfactory system,,which is of great significance in rapid detection of trace acetone. Through multiple Mass Flow Controller(MFC),we prepared simulated diabetic patients breath samples(30×10-6 acetone)and two interference gas samples(30×10-6 ethanol/composition of 15×10-6 acetone and 15×10-6 ethanol),Three kinds of gas qualitative identifications were carried out based on BP neural network algorithm and optimization of the original high dimen-sional feature subset was achieved through PCA algorithm. The experiment shows that the combination of PCA and BP algorithm can reduce the complexity of BP neural network and reduce the error of prediction. At the same time, the cross sensitivity of individual gas sensors can be solved,thus improve the selectivity of gas d. The identification results of trace acetone and the two interference gas samples show that the accuracy of the recognition of the three gases reaches 91％. This study provides theoretical guidance for accurate identification of diabetes markers and non-invasive diagnosis.关键词
丙酮气体/传感器阵列/BP神经网络/PCA分析Key words
acetone gas/sensor array/BP neural network/principal component analysis分类
信息技术与安全科学引用本文复制引用
奉轲,花中秋,伍萍辉,李彦,曾艳,王天赐,邱志磊..用于检测糖尿病标志物的电子鼻优化设计[J].传感技术学报,2018,31(1):13-18,6.基金项目
项目来源:天津市自然科学基金面上项目( 15JCYBJC52100) （ 15JCYBJC52100）
国家自然科学基金青年项目( 61501167) （ 61501167）
河北省自然科学基金青年项目( F2016202214) （ F2016202214）
