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The Multiple Criteria Decision Aiding (MCDA) method allows the derivation of a model that can be used for classification of existing or new production processes for silver nanoparticles in preference-ordered classes on the basis of the principles of green chemistry.

DRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data.

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