The multicomponent analysis of human breast milk (BM) by metabolic profiling is a new area of study applied to determining milk composition, and is capable of associating BM composition with maternal characteristics, and subsequent infant health outcomes. A multiplatform approach combining HPLC‐MS and ultra‐performance LC‐MS, GC‐MS, CE‐MS, and ¹H NMR spectroscopy was used to comprehensively characterize metabolic profiles from seventy BM samples. A total of 710 metabolites spanning multiple molecular classes were defined. The utility of the individual and combined analytical platforms was explored in relation to numbers of metabolites identified, as well as the reproducibility of the methods. The greatest number of metabolites was identified by the single phase HPLC‐MS method, while CE‐MS uniquely profiled amino acids in detail and NMR was the most reproducible, whereas GC‐MS targeted volatile compounds and short chain fatty acids. Dynamic changes in BM composition were characterized over the first 3 months of lactation. Metabolites identified as altering in abundance over lactation included fucose, di‐ and triacylglycerols, and short chain fatty acids, known to be important for infant immunological, neurological, and gastrointestinal development, as well as being an important source of energy. This extensive metabolic coverage of the dynamic BM metabolome provides a baseline for investigating the impact of maternal characteristics, as well as establishing the impact of environmental and dietary factors on the composition of BM, with a focus on the downstream health consequences this may have for infants.

Highly polar and ionic metabolites, such as sugars, most amino acids, organic acids or nucleotides are not retained by conventional reversed-phase LC columns and polar stationary phases and hydrophilic-interaction LC lacks of robustness, which is still limiting their applications for untargeted metabolomics where reproducibility is a must. Biological samples such as blood, urine or even tissues include many hydrophilic compounds secreted from cells, their analysis is essential for biomarker discovery, disease progression or treatment effects. This review focuses on CE coupled to MS as a mature technique for untargeted metabolomics including sample pretreatment, types of matrices, analytical methods, applications and data treatment strategies for polar compound analysis in biological matrices. The main applications and results of CE-MS in untargeted metabolomics are discussed and presented in a tabulated format.