The tabulated results (Table 1), from the same LC-MS raw dataset, demonstrate that changing the mass tolerance and intensity threshold parameters affects the number of defined features (X-variables). One observation to point out is that increasing the intensity threshold (counts) led to a significant decrease in the number of X-variables and noise levels. For a novice in metabolomic data analysis or any metabolomic scientist with less expertise in statistics/chemometrics, this could raise questions with regard to the “correct” method (or set of parameters) to trust and use: e.g., the one producing a matrix with less noise. A point to consider is to what extent any decrease in the number of “defined” ion peaks (variables) would bring about information loss? As a first approach, a chemometrician or statistician would advise for a cleaner data matrix: the less noise in the created matrix the better. Methods (set of parameters or algorithms) that could reduce the noise in the data and decrease the redundancy are often advised and preferred .