Discrimination of conventional and organic rice using untargeted LC-MS-based metabolomics. (July 2018)

• Record Type: Journal Article
• Authors: Xiao, Ran; Ma, Ying; Zhang, Dongjie; Qian, Lili
• Is Part Of: Journal of cereal science. Volume 82(2018)
• Page Start: 73
• Page End: 81
• Publication Date: 2018-07
• Subjects: Organic rice -- Metabolite profiling -- UHPLC-QTOF MS -- Multivariate statistical analysis; Grain -- Periodicals; Cereal products -- Periodicals; Céréales -- Périodiques; Produits céréaliers -- Périodiques; Cereal products; Grain; Periodicals; 664.705
• DOI: 10.1016/j.jcs.2018.05.012
• Languages: English
• ISSNs: 0733-5210

Abstract

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. Graphical abstract: Image 1 Highlights: Untargeted metabolite profiling based on UHPLC-MS analysis was established. Three chemometric methods were applied to modeling. The regression model can distinguish organic and conventional rice samples. Eight potential biomarkers were chosen. Online database was applied for initial identification.