ABSTRACT
Volatile organic compound (VOC) recognition systems can be helpful tools in monitoring today's living environments surrounded by harmful chemicals including dangerous VOCs. By designing a mobile system where users can easily detect VOC materials in their surroundings, people can avoid VOC-contained environments or take actions to improve their living conditions. Unfortunately, current VOC detection systems require bulky devices, and the current technology does not allow this detection and classification process to take place in real-time near the user. In this work, we introduce a novel VOC recognition process using a smartphone camera and paper-based fluorometric sensors. Fluorometric sensors will change their color patterns as they are exposed to different VOC materials and the smartphone camera combined with simple machine learning algorithms can be used to classify different VOC materials. Specifically, we introduce how a fluorometric sensor dataset of different VOC materials is gathered, and present a set of preliminary machine learning algorithms for VOC classification using smartphones. Our results show up to ~88% accuracy in classifying eight different types of VOC materials using an LDA model.
- Xing Chen, Fengjuan Xu, Yue Wang, Yuefeng Pan, Deji Lu, Ping Wang, Kejing Ying, Enguo Chen, and Weimin Zhang. 2007. A study of the volatile organic compounds exhaled by lung cancer cells in vitro for breath diagnosis. Cancer 110, 4 (2007), 835--844.Google ScholarCross Ref
- Liang Feng, Christopher J Musto, Jonathan W Kemling, Sung H Lim, Wenxuan Zhong, and Kenneth S Suslick. 2010. Colorimetric sensor array for determination and identification of toxic industrial chemicals. Analytical chemistry 82, 22 (2010), 9433--9440.Google Scholar
- H Guo, SC Lee, LY Chan, and WM Li. 2004. Risk assessment of exposure to volatile organic compounds in different indoor environments. Environmental Research 94, 1 (2004), 57--66.Google ScholarCross Ref
- Michael C Janzen, Jennifer B Ponder, Daniel P Bailey, Crystal K Ingison, and Kenneth S Suslick. 2006. Colorimetric sensor arrays for volatile organic compounds. Analytical chemistry 78, 11 (2006), 3591--3600.Google Scholar
- Michael Phillips, Kevin Gleeson, J Michael B Hughes, Joel Greenberg, Renee N Cataneo, Leigh Baker, and W Patrick McVay. 1999. Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study. The Lancet 353, 9168 (1999), 1930--1933.Google Scholar
- Marco Righettoni, Alessandro Ragnoni, Andreas T Güntner, Claudio Loccioni, Sotiris E Pratsinis, and Terence H Risby. 2015. Monitoring breath markers under controlled conditions. Journal of breath research 9, 4 (2015), 047101.Google ScholarCross Ref
- Gaurav Sharma, Wencheng Wu, and Edul N Dalal. 2005. The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research & Application 30, 1 (2005), 21--30.Google Scholar
- Xianhua Zhong, Dan Li, Wei Du, Mengqiu Yan, You Wang, Danqun Huo, and Changjun Hou. 2018. Rapid recognition of volatile organic compounds with colorimetric sensor arrays for lung cancer screening. Analytical and bioanalytical chemistry 410, 16 (2018), 3671--3681.Google Scholar
Index Terms
- Volatile Organic Compounds Recognition Using a Smartphone Camera and Fluorometric Sensors
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