A Fast-Response Odor Detector for Food Analysis

Volatile organic compounds (VOCs) constitute a large group of organic chemicals that evaporate easily under normal conditions of temperature and pressure. VOCs are emitted in the atmosphere from a wide variety of natural and anthropogenic sources. The characterization and monitoring of VOCs emitted from different sources is immensely important ranging from biomedical diagnostics, homeland security, food safety and quality to environmental protection. The proposal seeks to develop a new on-site real-time odor sensor based on the principle of electronic noses. The outcome of this proposed research will have a significant impact on reducing the cost, increasing the portability, and increasing the analysis speed and resolution of current VOC detection methodologies. The proposed device will be developed by combining microtechnology, chemistry, and statistics and will be validated for high resolution detection of food adulteration which is a serious and widespread global issue. The research while advancing science and technology for a societal need, will provide invaluable experience for students as they will be trained to work in an interdisciplinary environment. Furthermore, the outreach activities will educate pre-college students how advanced science and engineering can be merged to solve real, current problems.<br/><br/>The objective of the proposed research is to develop rapid odor detector implemented by merging the significant concepts in electronic noses and fast gas chromatography (GC) into a small footprint. The focus of this system is the uniquely designed chromatographic chip with multiple capillaries working in parallel. The chip will be fabricated in silicon using microelectromechanical systems (MEMS) technology and will be functionalized using ionic liquids. The major processing steps include deep reactive ion etching followed by silicon-glass anodic bonding. The proposed chromatographic chip will be interfaced with a detector or multiple detectors to generate a rapid odor-print of the sample. The output will be an array of chromatographic responses which will be analyzed using advanced multivariate statistical and pattern recognition algorithms to determine if the food sample has been adulterated. The statistical method can simultaneously perform a variable selection in nonadditive multivariate nonparametric model for analyzing the large data generated by the chip. Olive oil and honey which have been the target of adulteration will be used to validate the technology. It is expected that the new odor detector will achieve a total analysis time of less than a minute which is orders of magnitude faster and more economical when compared to established techniques such as gas chromatography-mass spectrometry (GC/MS).