Rapid dynamic headspace concentration and characterization of smokeless powder using direct analysis in real time - mass spectrometry and offline chemometric analysis

Abstract

Improvised explosive devices (IEDs) are charged devices often used by terrorists and criminals to create public panic. When the general public is targeted by an act of terrorism, people who are not injured or killed in the explosion remain in fear until the perpetrator(s) has been apprehended. Methods that can provide investigators and first responders with prompt investigative information are required in such cases. However, information is generally not provided quickly, in part because of time-consuming techniques employed in many forensic laboratories. As a result, case report turnaround time is longer. Direct analysis in real time - mass spectrometry (DART-MS) is a promising analytical technique that can address this challenge in the Forensic Science community by permitting rapid trace analysis of energetic materials. The builder of an IED will often charge the device with materials that are readily available. The most common materials employed in the construction of IEDs are black and smokeless powder. However, other materials may include ammonia- or peroxide-based materials such as common household detergents. Smokeless powder is a propellant that is readily available to civilians. They are typically used for reloading ammunition and are sold in large quantities each year in the United States. Some states have stricter regulations than others but typically a firearms license is all that’s required to possess smokeless powder. Smokeless powder is considered a low explosive which is capable of causing an explosion if a sufficient quantity is deflagrated inside a confined container. The most commonly employed confirmatory techniques for the analysis of smokeless powder are gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS). These methods often require extensive and time-consuming sample preparation procedures to prepare the powders for analysis. In addition to lengthy sample preparation procedures, GC-MS and LC-MS often require chromatographic separations that can range anywhere from 5 to 30 minutes or longer per sample. Ion mobility spectrometry (IMS) is widely used for the field analysis of smokeless powder and can provide faster results in comparison to GC-MS or LC-MS. However, identification is limited to drift time and no structural information is provided unless coupled to a mass spectrometer. In an effort to accelerate the speed of collection and characterization of smokeless powder, an analytical approach that utilizes novel wire mesh coated with CarbopackTM X, dynamic headspace concentration and DART-MS was evaluated to determine if the approach could generate information rich chemical attribute signatures (CAS) for smokeless powder. CarbopackTM X is a graphitized carbon material that has been employed for the collection of various volatile and semi-volatile organic compounds. The goal of using CarbopackTM X coated wire mesh was to increase the collection efficiency of smokeless powder in comparison to traditional swabbing and swiping methods. DART is an ambient ionization technique that permits analysis of a variety of samples in seconds with minimal to no sample preparation and offers several advantages over conventional methods. Heating time, heating temperature and flow rate for dynamic headspace concentration were optimized using Hodgdon Lil’ Gun smokeless powder. DART-MS was compared to GC-MS and validated using the National Institute of Standards and Technology reference material 8107 (NIST RM 8107) smokeless powder standard. Additives and energetic materials from unburnt and burnt smokeless powders were rapidly and efficiently captured by the CarbopackTM X coated wire mesh and successfully detected and identified using DART-MS. The DART source temperature was evaluated with the goal of providing the most efficient desorption of the analytes adsorbed onto the wire mesh. For this to be a robust approach in forensic analysis, chemometric analysis employing predictive models was used to simplify the data and increase the confidence of assigning a mass spectrum to a particular powder. Predictive models were constructed using the machine learning techniques available in Analyze IQ Lab and evaluated for their performance in classifying three smokeless powders: Alliant Reloder 19, Hodgdon LEVERevolution and Winchester Ball 296. The models were able to accurately predict the presence or absence of these three powders from burnt residues with error rates that were less than 4%. This approach has demonstrated the capability of generating comparable data and sensitivity in a significantly shorter amount of time in comparison to GC-MS. In addition, DART-MS also permits the detection of targeted analytes that are not amenable to GC-MS. The speed and efficiency associated with both the sample preparation technique and DART-MS, and the ability to employ chemometric analysis to the generated data demonstrate an attractive and viable alternative to conventional techniques for smokeless powder analysis.