Significance testing is a crucial step in metabolic biomarker recovery from the metabolome-wide latent variables computed by multivariate statistical analysis. In this study we propose an algorithm based on the landscape of the covariance/correlation ratio of consecutive variables along the chemical shift axis to restore, prior to significance testing, the spectral dependency and recouple variables in clusters which correspond to physical, chemical, and biological entities: statistical recoupling of variables (SRV). Variables are associated into a series of clusters, which are then considered as individual objects for the control of the false discovery rate. Compared to classical procedures, it is found that SRV allows efficient recovery of statistically significant metabolic variables. The proposed SRV method when associated with the Benjamini-Yekutieli correction retains a low level of significant variables in the noise areas of the nuclear magnetic resonance (NMR) spectrum, close to that observed using the conservative Bonferroni correction (false positive rate), while also allowing successful identification of statistically significant metabolic NMR signals in cases where the classical procedures of Benjamini-Yekutieli and Benjamini-Hochberg (false discovery rate) fail. This procedure improves the interpretability of latent variables for metabolic biomarker recovery.