Sparse partial-least-squares discriminant analysis for different geographical origins of Salvia miltiorrhiza by (1) H-NMR-based metabolomics

Introduction: (1) H nuclear magnetic resonance (NMR) spectroscopy has clear advantages in respect of detecting various primary and secondary metabolites in plants simultaneously, non-targeted and non-destructively.

Objective: To establish a method for detecting both primary and secondary metabolites in Salvia miltiorrhiza and screening potential geographical biomarkers effectively.

Methods: Primary and secondary metabolites of S. militiorrhiza were detected and identified by (1) H-NMR fingerprint. Sparse partial-least-squares discriminant analysis (sPLS-DA) was undertaken for classification and variable selection in a one-step procedure and the classification error rates were implemented to estimate the cluster validation of sPLS-DA. Potential candidate metabolites by characterised different geographical origins of S. miltiorrhiza were identified according to the sparse loading vectors. The levels of these metabolites were quantified and evaluated by Kruskal-Wallis tests and also showed significant difference.

Results: Twenty-six primary and secondary metabolites were identified in samples from different regions. The results suggest that malonate and succinate can be possibly recognised as the key markers for discriminating the geographical origin of S. miltiorrhiza based on the regulation and influence on the root respiratory rates of plants.

Conclusion: (1) H-NMR metabolic profiling combination with PLS-DA provided a very efficient and visualised representation of similarities and dissimilarities between S. miltiorrhiza samples.