## 1 Introduction  
Near perfect performance and excellent reliability are required of any instrumental system that detects chemical and biological threats in homeland security applications such as at transportation checkpoints. Ion mobility spectrometry (IMS) is the instrumental technique of choice for trace explosives detection in high throughput environments due to the high speed of analysis (\(\sim\)1-10 s), excellent sensitivity (sub nanogram detection), low costs of acquisition and operation, robustness and portability. IMS detectors, however, have been reported to suffer from high false positive detection rates in high clutter environments, when particle swabbing is used as the sampling mode [1]. In response to this deficiency, different headspace sampling approaches that target the volatiles associated with the presence of explosives in containers have been developed. A novel sorbent coated disk coined planar solid phase microextraction (PSPME) system has been previously reported by our group for non-contact sampling of volatile organic compounds (VOCs) associated with explosives [2; 3; 4]. Unlike particle swabbing, PSPME targets the volatiles available in the headspace of large containers with fast preconcentration of the targets during the sampling. PSPME is reported to offer greater surface area and phase volume for fast sampling and larger capacity in comparison to the widely accepted solid phase microextraction (SPME) single fiber configuration. PSPME also offers the possibility of dynamic sampling of the air flow through the device with the assistance of a vacuum pump, allowing for large volume sampling, making it ideal for high throughput environments. Although the coupling of PSPME sampling to an IMS detector has been shown to perform well with respect to sensitivity and other performance metrics in the laboratory setting, an evaluation of the use of PSPME-IMS for explosives detection in real-world, high clutter settings had yet to be investigated. The construction of receiver operating characteristic (ROC) curves are an efficient method to visualize the trade-offs for the performance of a particular technique or sensor system for a given set of sensor conditions. ROC curves were developed by the U.S. military to differentiate radar signals and noise [5; 6] and this sensor performance evaluation tool has grown in popularity for use in medical diagnostic testing [7; 8; 9; 10]. The increase in data analysis using ROC curves in non-clinical fields including psychiatry [11; 12; 13], explosives detection [14; 15; 16; 17] and computer sciences [18; 19; 20] results from the ability to visualize the performance of dichotomous decisions. The Department of Defense (DoD) conducted the Chemical and Biological Sensor Standards Study [21] in which ROC curve studies for sensor devices were proposed based on a wide range of sensitivities and false positive rates. ROC curves can be constructed to display the instrument performance trade-offs of sensitivity and specificity from the true positive and false positive rates. From the DoD study, [PERSON] and his colleagues constructed ROC curves for a portable mass spectrometer system to evaluate the real-time detection of toxic compounds [5]. [PERSON] _et al._[22] also developed ROC curves for a portable IMS for vapor sampling of diesel fuels. In this study, the detection limit and performance of the instrument was determined under different defined scenarios. This current study reports, for the first time, the development of ROC curves of the non-contact sampling of PSPME coupled with IMS detection including real-world sampling scenarios. ROC curves were constructed to evaluate the performance of two field-portable sampling systems and explosive detection systems with defined real-world scenarios for the detection of smokeless powders as a model for explosives. Smokeless powders are typically encountered in gunshot residues and have been usedin improvised explosives [23, 24].