摘要
针对肺癌呼出挥发性有机气体(VOCs)中的特定标志物,提出了一种新型的基于荧光卟啉传感器阵列检测系统,并对4种肺癌呼出标志物进行检测研究。通过小波分析等数学工具对测得的荧光光谱数据进行特征提取,然后采用层次聚类、主成分分析等统计学方法对特征向量进行分析。不同体积分数的各类标志物在聚类分析中能够完全正确的聚到一起。通过主成分分析得到的前3个主成分包含了标志物的88%的信息,便能对不同类别的标志物进行识别。研究表明:该荧光卟啉传感器阵列系统能够快速有效地对不同肺癌标志物进行识别,有望在临床中得到应用。
In this study,a simple and rapid fluorescent detection device for lung cancer related volatile organic compounds（ VOCs） is proposed,and four specific exhaled lung cancer markers is detected by it.Through mathematical tool wavelet analysis and other technique,characteristics of the fluorescence spectra data is extracted,and then the statistical methods,including hierarchical cluster analysis（ HCA） and principal component analysis（ PCA） are used for feature vector analysis.All kinds of markers in different volume fraction can be correctly classified by HCA.Though PCA,the first three principal components representing 88 % of the amount of markers information are obtained,which have the ability to distinguish different markers from each other.The preliminary study demonstrated that the proposed fluorescent porphyrin sensor array system could identify different lung cancer marker quickly and effectively,and it had infinite potential for clinical application of early diagnosis of lung cancer in the future.
出处
《传感器与微系统》
CSCD
北大核心
2014年第7期28-31,共4页
Transducer and Microsystem Technologies
基金
国家科技支撑计划资助项目(2012BAI19B03)
国家自然科学基金资助项目(81271930
81171414)
重庆大学大型仪器设备开放基金资助项目
关键词
荧光卟啉传感器阵列
肺癌呼出标志物
小波分析
层次聚类分析
主成分分析
fluorescent porphyrin sensor array
exhaled lung cancer markers
wavelet analysis
hierarchical cluster analysis（HCA）
principal component analysis（PCA）
