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Low-cost and portable gas chromatography-ion mobility spectrometry (GC-IMS) has been used to identify chemicals. To accomplish this, two parameters are used. The first parameter relates to the GC retention time (RT), which is the residence time of an analyte as it passes through the column. Different chemicals have different RTs. The second parameter is the drift time of ionized species derived for a specific chemical in the IMS. Due to molecular cross section, mass, and chemical properties, different chemicals produce ionized species with different drift times. Combining these two parameters, GC-IMS has been shown to distinguish between different chemicals. Chemical detection and identification are not that easy in practice. First, the concentration of chemicals may be very low, and it may be difficult to determine the chromatographic RT and IMS drift time for chemicals under these conditions. Second, the specific ionized species produced in the IMS are concentration dependent and the IMS spectra obtained at different analyte concentrations are not easily predictable. For example, at low concentrations, chemicals seldom form dimers following atmospheric pressure ionization. The possible presence of either monomers or dimers in the IMS drift tube may confuse the chemical classification process. Third, it is important to estimate the concentration of chemicals, as this information will provide toxicity, and the linear dynamic range of typical IMS systems is relatively low In this study, an image processing approach to enhancing the GC-IMS signal quality is introduced. The key idea in this approach is to treat GC-IMS data as an image and then apply an anomaly detector to detect and enhance abnormal regions in the image. The results of a study that compares a conventional approach to chemical detection and the introduced image enhancement approach are presented. Receiver operating characteristics curves were used to compare the detection performances of the two approac- hes.