肺癌的早期快速诊断对于肺癌患者的治疗至关重要.针对肺癌患者所呼出的特定标志物,建立可视化传感器阵列系统,对4种肺癌标志物进行了实验研究.采用分层聚类分析、主成分分析的统计学方法对检测结果进行分析.对不同肺癌标志物、不同体积分数的样本在聚类分析中可以正确分类,且结构相似体积分数相近的样本能优先聚到一簇.利用主成分分析获得的前2个主成分所代表的肺癌标志物72.0％的信息量即可以实现不同类标志物样本区分.研究表明:这种可视化传感器阵列系统是一种快速有效的检测识别肺癌标志物的方法.%The early rapid diagnosis of lung cancer is very important for the treatment of patients with lung cancer. Colorimetric array sensor is used for detecting four specific breath gas markers of patients with lung cancer. The digital data library generated is analyzed with statistical and chemometric methods, including hierarchical cluster analysis ( HCA ) and principal component analysis ( PCA ) . Different substances, volume fraction samples can be correctly classified in cluster analysis, and the different substances with similar structure and the same substances with approximate volume fraction will be preferentially gathered in a cluster. Though the first two principal components obtained by PCA only represent 72. 0 % of the amount of markers information, all the samples can be distinguished from each other. It is a simple and efficient detection and identification method for lung cancer markers.
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