Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review.

INTRODUCTION: Untargeted direct mass spectrometric analysis of volatile organic compounds has many potential applications across fields such as healthcare and food safety. However, robust data processing protocols must be employed to ensure that research is replicable and practical applications can be realised. User-friendly data processing and statistical tools are becoming increasingly available; however, the use of these tools have neither been analysed, nor are they necessarily suited for every data type. OBJECTIVES: This review aims to analyse data processing and analytic workflows currently in use and examine whether methodological reporting is sufficient to enable replication. METHODS: Studies identified from Web of Science and Scopus databases were systematically examined against the inclusion criteria. The experimental, data processing, and data analysis workflows were reviewed for the relevant studies. RESULTS: From 459 studies identified from the databases, a total of 110 met the inclusion criteria. Very few papers provided enough detail to allow all aspects of the methodology to be replicated accurately, with only three meeting previous guidelines for reporting experimental methods. A wide range of data processing methods were used, with only eight papers (7.3%) employing a largely similar workflow where direct comparability was achievable. CONCLUSIONS: Standardised workflows and reporting systems need to be developed to ensure research in this area is replicable, comparable, and held to a high standard. Thus, allowing the wide-ranging potential applications to be realised.

A methodology for characterising nanoparticle size and shape using nanopores.

The discovery and characterisation of nanomaterials represents a multidisciplinary problem. Their properties and applications within biological, physical and medicinal sciences depend on their size, shape, concentration and surface charge. No single technology can currently measure all characteristics. Here we combine resistive pulse sensing with predictive logistic regression models, termed RPS-LRM, to rapidly characterise a nanomaterial's size, aspect ratio, shape and concentration when mixtures of nanorods and nanospheres are present in the same solution. We demonstrate that RPS-LRM can be applied to the characterisation of nanoparticles over a wide size range, and varying aspect ratios, and can distinguish between nanorods over nanospheres when they possess an aspect ratio grater then two. The RPS-LRM can rapidly measure the ratios of nanospheres to nanorods in solution within mixtures, regardless of their relative sizes and ratios i.e. many large nanospherical particles do not interfere with the characterisation of smaller nanorods. This was done with a 91% correct classification of nanospherical particles and 72% correct classification of nanorods even when the fraction of nanorods in solution is as low as 20%. The methodology here will enable the classification of nanomedicines, new nanomaterials and biological analytes in solution.