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用于检测糖尿病标志物的电子鼻优化设计
引用本文: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(1):13-18.
作者姓名:奉轲  花中秋  伍萍辉  李彦  曾艳  王天赐  邱志磊
作者单位:河北工业大学电子信息工程学院,天津,300401 河北工业大学电子信息工程学院,天津300401;天津市电子材料与器件重点实验室,天津300401
基金项目:项目来源:天津市自然科学基金面上项目,国家自然科学基金青年项目,河北省自然科学基金青年项目
摘    要:人体呼气中的丙酮含量可作为糖尿病的标志物.为实现无创糖尿病诊断,设计以金属氧化物半导体气敏传感器阵列为核心的人工嗅觉系统,对完成痕量丙酮的快速检测具有重要意义.通过多个气体流量控制器MFC(Mass Flow Controller)分别配制出模拟糖尿病患者呼气样本(30×10-6丙酮)与另两种干扰气体样本(30×10-6乙醇样本、15×10-6丙酮与15×10-6乙醇混合样本)进行实验,基于BP神经网络算法对3种气体定性识别,并通过主成分分析PCA(Principal Component Analysis)算法对原始的高维特征子集进行降维优化.实验表明:PCA与BP算法相结合,可降低BP神经网络的复杂性、减少预测的误差,同时能够解决单个气体传感器交叉敏感问题,进而提高对气体的选择性.对痕量丙酮样本与另两种干扰气体样本进行分析识别,识别的结果显示:对3种样本的识别准确率为91%.该研究为准确识别糖尿病标志物实现无创诊断技术提供了理论指导.

关 键 词:丙酮气体  传感器阵列  BP神经网络  PCA分析  acetone  gas  sensor  array  BP  neural  network  principal  component  analysis

Optimal Design of Electronic Nose for Detecting Diabetes Markers
FENG Ke,HUA Zhongqiu,WU Pinghui,LI Yan,ZENG Yan,WANG Tianci,QIU Zhilei.Optimal Design of Electronic Nose for Detecting Diabetes Markers[J].Journal of Transduction Technology,2018(1):13-18.
Authors:FENG Ke  HUA Zhongqiu  WU Pinghui  LI Yan  ZENG Yan  WANG Tianci  QIU Zhilei
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.
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