【作者】 段玉娟；

【导师】 侯长军；

【作者基本信息】 重庆大学 ， 药物化学， 2008， 硕士

【摘要】 呼吸气体诊断是一种无创诊断技术,通过检测呼吸气体中的特征成份及浓度实现对病人的诊断和筛选,是一种很有前景的疾病诊断与监护方法。本课题根据国内外关于呼吸气体中的某些挥发性有机化合物成份与肺癌相关的研究工作报告,重点参考了Phillips博士建立的22种挥发性有机化合物标志物筛选诊断肺癌病人的模型,并以此为出发点选出了5种目标检测物质,有:苯、庚醛、苯乙烯、对二甲苯、α-甲基苯乙烯。采用卟啉及金属卟啉作为敏感物质对5种目标检测物质进行检测,重点研究其相互作用特征响应的传感方法。针对这5种目标检测物质,本论文主要完成了以下两方面的工作:首先,制备得到了9种卟啉及金属卟啉。利用卟啉及金属卟啉溶液与5种目标检测物质溶液做了紫外可见光谱液体敏感实验的筛选工作,比较单种卟啉针对单种检测物质的紫外可见光谱变化,明确了检测限和相互作用后的特征响应,筛选出了检测效果较好的卟啉有P1,P2和P3三种;采用SEMICA-P软件对单种卟啉分别与5种检测物质相互作用的光谱数据做了主成分分析,发现单种卟啉检测的检测限虽然较低,但是特异性并不理想。其次,利用毛细管在疏水的PVDF膜上进行卟啉和指示剂溶液的点样,制备了4×3卟啉染料阵列,搭建了气体发生和反应系统实验装置,使卟啉染料阵列与单种检测物质的气体分别作用。通过采集卟啉染料阵列与不同浓度的检测物质气体相互作用前后的图像,用matlab软件处理得到其响应特征。发现卟啉染料阵列对单种检测物质气体的响应时间为几分钟,检测限可达ppm以下,且对不同物质的响应特征存在很大的差异,实现了定性分析。而湿度变化对卟啉染料阵列上的卟啉几乎没有造成影响,但是指示剂受到了一定程度的影响。对卟啉染料阵列的RGB特征响应数据进行了主成分分析,发现检测具有较好的重现性,5种检测物质能完全相互区分开,并形成了聚类。鉴于卟啉光学传感器性能稳定,响应时间短,检测限低,灵敏度高,几乎不受湿度影响的优势,利用卟啉阵列的光学性质来检测人体呼吸气体中的特征挥发性有机气体成分,为实现对肺癌病人的筛选及对肺癌病人的病情的监控打下了良好的基础。更多还原

【Abstract】 Breath Diagnosis is a noninvasive diagnostic technique. Through the identification of featured components and the determination of their concentrations, diagnosis and screening of patients can be realized, so it is a prospective diagnostic and tutelary method.According to some domestic and abroad research reports which focused on the relationship between certain volatile organic compound(sVOCs) in breath and the lung cancer, this thesis mainly took the lung cancer diagnostic model established by Dr. Phillips as a reference. In his research, 22 diagnostic VOCs were screened out for lung cancer diagnosis, and based on such a start we selected 5 diagnostic VOCs as the experimental target analytes: benzene, n-heptanal, styrene, p-xylene andα-methylstyrene. Porphyrins and metalloporphyrins were used as sensitive materials for the detection of the 5 target analytes, and sensing technique of their interactional responds were studied as a main part.Aimed at the five target analytes, the following work was completed:Firstly, nine kinds of porphyrins and metalloporphyrins were prepared. Liquid sensitivity screening of these pophyrins to five target analytes was carried out by UV-Vis spectra sensitive experiments. Through comparing the UV-Vis spectra change of single porphyrin exposed to single analytes, detection limit and featured interactional respond were determined and three well sensitive porphyrins were screened: P1, P2 and P3; SEMICA-P software was used for Principle Component Analysis of spectra data resulted from single porphyrin reacted with five analytes, and the result showed that, although the detection limit of single porphyrin was rather low, the specificity was not ideal.Secondly, porphyrins and indicators spotting were conducted on hydrophobic PVDF films by capillary. In order to realize the separate reaction between porphyrin-dye array and single analyte, 4×3 porphyrin-dye arrays were prepared, and gas generator and reacting system equipment were constructed. Porphyrin-dye array images before and after exposure to different concentrations of analytes were collected for MATLAB analyzing, and responding characteristic were obtained. The results showed that, respond time of porphyrin array to single analyte was several minutes and the detection limit can be lower than ppm. For different analyte, the responding characteristic was also different, so qualitative analysis was realized. Humidity change had little impact on the porphyrin-dye array, but indicators were influenced by humidity to some extent. Principle Component Analysis for featured responding RGB data of porphyrin-dye array was conducted. The results showed that the detection had good repeatability and the five analytes can be distinguished completely with good clustering.As the optical sensor has such advantages of stable performance, rapid respond time, low detection limit, high sensitivity and little humidity influence of the optical sensor, we can use the optical property of porphyrin-dye array to detect the volatile organic compounds in breath so as to realize the lung cancer patient screening and ground for the monitoring of patient with lung cancer.更多还原