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Combining the individual analytical strengths of mass spectrometry and infrared spectroscopy, infrared ion spectroscopy is increasingly recognized as a powerful tool for small-molecule identification in a wide range of analytical applications. Mass spectrometry is itself a leading analytical technique for small-molecule identification on the merit of its outstanding sensitivity, selectivity and versatility. The foremost shortcoming of the technique, however, is its limited ability to directly probe molecular structure, especially when contrasted against spectroscopic techniques. In infrared ion spectroscopy, infrared vibrational spectra are recorded for mass-isolated ions and provide a signature that can be matched to reference spectra, either measured from standards or predicted using quantum-chemical calculations. Here we present an overview of the potential for this technique to develop into a versatile analytical method for identifying molecular structures in mass spectrometry-based analytical workflows. In this tutorial perspective, we introduce the reader to the technique of infrared ion spectroscopy and highlight a selection of recent experimental advances and applications in current analytical challenges, in particular in the field of untargeted metabolomics. We report on the coupling of infrared ion spectroscopy with liquid chromatography and present experiments that serve as proof-of-principle examples of strategies to address outstanding challenges., (Copyright © 2019. Published by Elsevier B.V.)

Computer-aided method-development programs require accurate models to describe retention and to make predictions based on a limited number of scouting gradients. The performance of five different retention models for hydrophilic-interaction chromatography (HILIC) is assessed for a wide range of analytes. Gradient-elution equations are presented for each model, using Simpson's Rule to approximate the integral in case no exact solution exists. For most compound classes the adsorption model, i.e. a linear relation between the logarithm of the retention factor and the logarithm of the composition, is found to provide the most robust performance. Prediction accuracies depended on analyte class, with peptide retention being predicted least accurately, and on the stationary phase, with better results for a diol column than for an amide column. The two-parameter adsorption model is also attractive, because it can be used with good results using only two scanning gradients. This model is recommended as the first-choice model for describing and predicting HILIC retention data, because of its accuracy and linearity. Other models (linear solvent-strength model, mixed-mode model) should only be considered after validating their applicability in specific cases., (Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.)