Furthermore, Figure 3 and Figure 5 then raise questions with regard to what could be the best methodological approach for data pre-processing and pre-treatment, given an untargeted metabolomics dataset (as it is in this case) that exhaustively explores the data. Could these results (including the tabulated models’ description in Table 2 and Table 3) actually be pointing to the problem of “multiplicity of good models”/“multitude of descriptions” (Rashomon Effect) ? Or is it, indeed, an indication that, currently, no single chemometric method can actually extract all of the information from an untargeted metabolomic dataset. Different chemometric methodologies and algorithms are constantly being developed to cope with systems biology-perspective demands , but maybe the chemometric principle of “largest variance is most important” might not hold true in all cases, as the total variation in the metabolomic data is multifactorial. On the other hand, to extract relevant information may require searching the “needle in the haystack” methods .