Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals

Abstract

Over the years, Ion Mobility Spectrometry (IMS) which refers to the techniques and cutting-edge instruments for characterizing of analytes by their gas phase mobility has been gaining popularity and validity among scientific researchers for detecting chemicals. This novel gas sensor with its high analytical speed, low detection limits, ease of use and ruggedness during transport has also become the dominant commercial technology in different industries. In spite of these paramount advantages, this detector has difficulty identifying matrix compounds. To overcome this problem a Gas Chromatograph (GC) can be used to introduce individual components of mixture into an IMS. The output signal of the hyphenated GC–IMS method is an extraordinary small, time-dependent current produced by mobile ions in atmospheric pressure. To exploit the qualitative and quantitative information hidden in this sensitive noisy signal, various signal processing methods have been nominated for spectrum filtration and improving signal-to-noise ratio (SNR). In the present paper an attempt is made to design and construct a GC–IMS instrument coupled with a Thermal Solid Sample Introduction (TSSI) module for injection of solid samples to capillary GC column. Moreover, various spectrum filtration methods for removing noise from GC–IMS signal would be investigated. The GC–IMS instrument used in this research was designed and constructed in Institute of Materials and Energy, Isfahan, Detector group and can be used as a chemical sensor for rapid detection of a broad range of chemical mixtures in many operational environments including on-board Volatile Organic Analyzer Sensor (VOAS) in space missions. The capability of constructed sensor for detection of complex mixtures has been proved by analyzing a mixture of three pesticides as test materials. Our proposed method for removing noise from the real-time TSSI–GC–IMS signal will be presented and its efficacy will be proved by offering real experiments.