Algorithm for comprehensive analysis of datasets from hyphenated high resolution mass spectrometric techniques using single ion profiles and cluster analysis

Recent advances in the application of capillary electromigration methods for food analysis and Foodomics

This review work presents and discusses the main applications of capillary electromigration methods in food analysis and Foodomics. Papers that were published during the period February 2015-February 2017 are included following the previous review by Acunha et al. (Electrophoresis 2016, 37, 111-141). The paper shows the large variety of food related molecules that have been analyzed by CE including amino acids, biogenic amines, carbohydrates, chiral compounds, contaminants, DNAs, food additives, heterocyclic amines, lipids, peptides, pesticides, phenols, pigments, polyphenols, proteins, residues, toxins, vitamins, small organic and inorganic compounds, as well as other minor compounds. This work describes the last results on food quality and safety, nutritional value, storage, bioactivity, as well as uses of CE for monitoring food interactions and food processing including recent microchips developments and new applications of CE in Foodomics.

KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms

Distilling accurate quantitation information on metabolites from liquid chromatography coupled with mass spectrometry (LC-MS) data sets is crucial for further statistical analysis and biomarker identification. However, it is still challenging due to the complexity of biological systems. The concept of pure ion chromatograms (PICs) is an effective way of extracting meaningful ions, but few toolboxes provide a full processing workflow for LC-MS data sets based on PICs. In this study, an integrated framework, KPIC2, has been developed for metabolomics studies, which can detect pure ions accurately, align PICs across samples, group PICs to identify isotope and potential adducts, fill missing peaks and do multivariate pattern recognition. To evaluate its performance, MM48, metabolomics quantitation, and Soybean seeds data sets have been analyzed using KPIC2, XCMS, and MZmine2. KPIC2 can extract more true ions with fewer detecting features, have good quantification ability on a metabolomics quantitation data set, and achieve satisfactory classification on a soybean seeds data set through kernel-based OPLS-DA and random forest. It is implemented in R programming language, and the software, user guide, as well as example scripts and data sets are available as an open source package at https://github.com/hcji/KPIC2 .