Comparative metabolic profiling of pigmented rice (Oryza sativa L.) cultivars reveals primary metabolites are correlated with secondary metabolites

Abstract

Gas-chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS) was used to analyze the relationships between primary metabolites and phenolic acids in rice (Oryza sativa L.), including six black cultivars and one white cultivar. A total of 52 metabolites were identified, including 45 primary metabolites and seven phenolic acids from rice seeds. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Pearson's correlation analysis, and hierarchical clustering analysis (HCA). PCA could fully distinguish between these cultivars. HCA of these metabolites resulted in clusters derived from common or closely related biochemical pathways. There was a positive relationship between all phenolic and shikimic acids. Projection to latent structure using partial least squares (PLS) was applied to predict the total phenolic content based on primary metabolite profiles from rice grain. The predictive model showed good fit and predictability. The GC-TOFMS-based metabolic profiling approach could be used as an alternative method to predict food quality and identify metabolic links in complex biological systems.