Liquid chromatography combined with patter recognition to detect the metabolic profiling of corn kernels
2019
Qiaoliang Li | Zhuoying He | Ruitian Luo | Tao Nie | Quejian Zhang | Ying Xu | Huimin Guan | Suwen Qi
The aim of this study was to compare the components of waxy and non-waxy corn kernels from a metabolomic perspective. All samples were analysed by liquid chromatography-mass spectrometry (LC-MS) with mode+ and mode− to obtain spectral data. Unsupervised principal component analysis (PCA), Partial least squares discrimination analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to identify significant components of waxy and non-waxy corn kernels. A total of 1589 features in (ESI+) ion mode and 2310 features in (ESI-) ion mode were obtained in this project. OPLS-DA identified 117 differential metabolites, including citric acid, alpha-linolenic metabolites, distinguished non-waxy corn from waxy corn. Compared with waxy corn, non-waxy corn expressed the enhanced metabolites such as guanine, guanosine and the reduced ones represented by citric acid, and oleic acid. This study offer new clues for the study of the taste and nutritional value of waxy and non-waxy corn.
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