Non-target analysis and characterisation of nanoparticles in spirits via single particle ICP-TOF-MS

Abstract

Nanoparticles (NPs) can be found throughout our direct environment as well as in a large range of consumer products. However, there is a lack of data on abundances and properties, which requires dedicated methods to identify and characterise NP entities. Single particle inductively coupled plasma-mass spectrometry (SP ICP-MS) is becoming one of the most relevant methods for counting NPs and studying NP composition and properties. Especially time-of-flight (TOF) technology for ICP-MS holds the key to study single particle composition and to perform non-target particle screening. This is achieved through the unique detection paradigm in TOF acquiring all isotopes across the periodic table at 35 kHz and faster. Nevertheless, non-targeted screening methods face limitations due to two factors. First, large data sets challenge data processing units and require substantial time, and second, for each isotope/element, a decision limit to distinguish between random noise and a SP signal has to be established. Here, we propose a novel lognormal approximation method to rapidly model background levels and decision limits. This method was integrated in a pre-analysis tool which detected SP data signatures of all m/z recorded via SP ICP-TOF-MS. Within seconds, particulate elements could be pinpointed in large data files and selected for more dedicated characterisation steps. In a proof of concept, we used this non-target screening method to identify inorganic NPs in selected samples of whisky, vodka, gin, and liqueur. Following qualitative analyses, number concentrations, compositions, masses, and size distributions were investigated. Besides Mn, Fe and Cu as expected NP entities, Ti, Ag, Au and Sn-based particles were found in investigated samples.