Discrimination of conventional and organic rice using untargeted LC-MS-based metabolomics

Abstract

A metabolite profiling method was developed for rice samples under conventional and organic agricultural practices. In this study, an ultra-high performance liquid chromatography combined with quadrupole time-of-flight MS based (UHPLC-QTOF based) metabolite approach in combination with multivariate statistical analyses, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA), was applied to determine metabolite patterns among rice samples. In addition, an orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to identify key constituents to efficiently distinguish between cultivation methods. In total, 30 discriminant components were chosen from these two kinds of rice samples, in which 8 secondary metabolites could be considered to be potential biomarkers for the discrimination of organic and conventional rice. These results suggest that a metabolomics approach could be a reliable, precise, and effective method for the identification of rice under different cultivation practices.