Extraction of meaningful biological information from longitudinal metabolomic studies is a major challenge and typically involves multivariate analysis and dimensional reduction methods for data visualization such as Principal Component Analysis or Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Besides, a variety of computational tools have been developed to identify changes in metabolic pathways including functional analysis and pathway analysis. In this work, the joint analysis of results from MCR-ALS and metabolic pathway analysis is proposed to facilitate the interpretation of dynamic changes in longitudinal metabolomic data. The strategy is based on the use of MCR-ALS to remove unstructured random variation in the raw data, thus facilitating the interpretation of dynamic changes observed by metabolic pathway analysis over time. A simulated data set representing dynamic longitudinal changes in the intensities of a subset of metabolites from three metabolic pathways was initially used to test the applicability of MCR-ALS to support pathway analysis for detecting pathway perturbations. Then, the strategy is applied to real data acquired for the analysis of changes during CD8+ T cell activation. Results obtained show that MCR-ALS facilitates the interpretation of longitudinal metabolomic profiles in multivariate data sets by identifying metabolic pathways associated with each detected dynamic component.