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Illegal explosives are a threat to aviation, transport sector, critical infrastructure and generally to public safety. Their detection requires extremely sensitive instruments with efficient workflows that allow large throughput of items. In this study, we built a trace explosives detection instrument that requires minimal sample treatment and reaches ultra-low picogram level detection limits for many common explosives. The instrument is based on thermal desorption of filters, which allows analysis of liquid and solid phase samples, and subsequent selective atmospheric pressure chemical ionization and detection with a mass spectrometer. We performed experiments to scope the optimal ionization chemistry for the system and selected Br− as the reagent ion, and measured the limit of detection for 14 different explosives that were generally in the picogram range. Finally, we demonstrate the usability of the system by sampling air to a filter from a storage room known to contain explosives, from which we detect four different explosives.

Natural products (NPs) research is changing and rapidly adopting cutting-edge tools, which radically transform the way to characterize extracts and small molecules. With the innovations in metabolomics, early integration of deep metabolome annotation information allows to efficiently guide the isolation of valuable NPs only and, in parallel, to generate massive metadata sets for the study of given extracts under various perspectives. This is the case for chemotaxonomy studies where common biosynthetic traits among species can be evidenced, but also for drug discovery purpose where such traits, in combination with bioactivity studies on extracts, may evidence bioactive molecules even before their isolation. One of the major bottlenecks of such studies remains the level of accuracy at which NPs can be identified. We discuss here the advancements in LC-MS and associated mining methods by addressing what would be ideal and what is achieved today. We propose future developments for reinforcing generic NPs databases both in the spectral and structural dimensions by heading towards a virtuous metabolite identification cycle allowing annotation of both known and unreported metabolites in an iterative manner. Such approaches could significantly accelerate and improve our knowledge of the huge chemodiversity found in nature.

The model plant Arabidopsis thaliana was studied for the search of new metabolites involved in wound signalling. Diverse LC approaches were considered in terms of efficiency and analysis time and a 7-min gradient on a UPLC-TOF-MS system with a short column was chosen for metabolite fingerprinting. This screening step was designed to allow the comparison of a high number of samples over a wide range of time points after stress induction in positive and negative ionisation modes. Thanks to data treatment, clear discrimination was obtained, providing lists of potential stress-induced ions. In a second step, the fingerprinting conditions were transferred to longer column, providing a higher peak capacity able to demonstrate the presence of isomers among the highlighted compounds.