| Title: | A novel device based on a fluorescent cross-responsive sensor array for detecting lung cancer related volatile organic compounds |
| Author(s): | Lei JC; Hou CJ; Huo DQ; Luo XG; Bao MZ; Li X; Yang M; Fa HB; |
| Address: | "Postdoctoral Station of Science and Technology of Instrumentation, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China. Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, China. College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China" |
| ISSN/ISBN: | 1089-7623 (Electronic) 0034-6748 (Linking) |
| Abstract: | "In this paper, a novel, simple, rapid, and low-cost detection device for lung cancer related Volatile Organic Compounds (VOCs) was constructed. For this task, a sensor array based on cross-responsive mechanism was designed. A special gas chamber was made to insure sensor array exposed to VOCs sufficiently and evenly, and FLUENT software was used to simulate the performance of the gas chamber. The data collection and processing system was used to detect fluorescent changes of the sensor arrays before and after reaction, and to extract unique patterns of the tested VOCs. Four selected VOCs, p-xylene, styrene, isoprene, and hexanal, were detected by the proposed device. Unsupervised pattern recognition methods, hierarchical cluster analysis and principal component analysis, were used to analyze data. The results showed that the methods could 100% discriminate the four VOCs. What is more, combined with artificial neural network, the correct rate of quantitative detection was up to 100%, and the device obtained responses at concentrations below 50 ppb. In conclusion, the proposed detection device showed excellent selectivity and discrimination ability for the VOCs related to lung cancer. Furthermore, our preliminary study demonstrated that the proposed detection device has brilliant potential application for early clinical diagnosis of lung cancer" |
| Keywords: | "Algorithms Chemistry Techniques, Analytical/economics/*instrumentation Cluster Analysis Early Detection of Cancer Lung Neoplasms/*chemistry/diagnosis Rotation Spectrometry, Fluorescence Time Factors Volatile Organic Compounds/*analysis/chemistry;" |
| Notes: | "MedlineLei, Jin-can Hou, Chang-jun Huo, Dan-qun Luo, Xiao-gang Bao, Ming-ze Li, Xian Yang, Mei Fa, Huan-bao eng Research Support, Non-U.S. Gov't 2015/03/03 Rev Sci Instrum. 2015 Feb; 86(2):025106. doi: 10.1063/1.4907628" |
