However, the biological insights (the dynamic ranges of metabolite abundance in a biological system, conservation relationships in networks of interrelated compounds, etc.) and the currently inherent analytical limitations (suboptimal quality of extraction, detection and ionization capabilities, etc.) , call for caution in handling noise elimination and redundant signals in metabolomic datasets. The effort to produce a noise-free and non-redundant data matrix (post-processing) could result in a loss of information, some of which could be informative to comprehensively assess metabolic pathways for a better understanding and description of the regulatory mechanisms underlying the global biological responses . Furthermore, as recently demonstrated, some of the ion peaks that could be regarded as a source of redundancy (e.g., adduct formation) might be very crucial and actually needed in metabolite annotation and differentiation .