1H NMR-based metabolomics for discrimination of rice from different geographical origins of China. (July 2017)

• Record Type: Journal Article
• Authors: Huo, Yinqiang; Kamal, Ghulam Mustafa; Wang, Jie; Liu, Huili; Zhang, Gaonan; Hu, Zhengyi; Anwar, Farooq; Du, Hongying
• Is Part Of: Journal of cereal science. Volume 76(2017)
• Page Start: 243
• Page End: 252
• Publication Date: 2017-07
• Subjects: Rice -- 1H NMR -- Linear discriminant analysis -- PCA -- Geographical discrimination; Grain -- Periodicals; Cereal products -- Periodicals; Céréales -- Périodiques; Produits céréaliers -- Périodiques; Cereal products; Grain; Periodicals; 664.705
• DOI: 10.1016/j.jcs.2017.07.002
• Languages: English
• ISSNs: 0733-5210

Abstract

Abstract: Food frauds related to the mislabeling and mixing of products of inferior quality with those of superior quality are a serious concern nowadays. NMR-based metabolomics has great potential in the authentication of foods for quality assurance and the tracing of fraudulent labeling. The present study was conducted to discriminate rice from geographically different provinces of China. The study reports the potential use of 1 H NMR spectroscopy coupled with PCA and a discriminant analysis method, LDA for metabolomic fingerprinting of Chinese rice. A total of 106 rice samples from nine different provinces of China were analyzed for 1 H NMR-based metabolomics. Both the whole variable analysis (heat map) and the Principal Component Analysis (PCA) showed a clear separation among the samples. Linear Discriminant Analysis (LDA) was conducted to extract the variables majorly responsible for this separation, such as sucrose, fructose, glucose, succinate, polyphenols, trigonelline and asparagine. The discrimination was explained on the basis of variations in latitude, temperature and rainfall in these provinces. The study highlights the application of 1 H NMR for geographical discrimination of rice and its usefulness for consumers while choosing their desired variety of rice. Highlights: Rice from different origins separated by 1 H-NMR combined with multivariate analysis. Sugar and non-sugar data subsets provided efficient discriminant analysis. Different sugar and nutritive compounds were found responsible for the discrimination. The discrimination of rice was explained based on various environmental factors.