Fast nutritional characterization of different pigmented rice grains using a combination of NMR and decision tree analysis

ABSTRACT Different rice cultivars contain various metabolites which are closely related with their nutritional and functional effects. In the current study, NMR and chemometrics methods – decision tree analysis (DTA) were applied to explore the chemical characteristics in different rice cultivars. The pigmented rice were completely discriminated from each other, and the key metabolites that mostly contributed to the rice discrimination were explored, including sugars compounds (sucrose, fructose and glucose) and non-sugar compounds (γ-amino butyric acid (GABA), asparagine, citric acid and malic acid) with DTA approach and 1HNMR metabolite fingerprints. The purple rice has the highest concentrations of sucrose, citric acid and asparagine (p < 0.05), but lowest fructose (p < 0.05); red rice has the lowest GABA concentration comparing with the other three groups (p < 0.05). This study implies that DTA method is a powerful tool to discriminate different pigmented rice varieties. Furthermore, it also reveals the crucial nutrients based on 1H-NMR metabolic profiling.

[1]  T. Fan Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures , 1996 .

[2]  T. Yamaya,et al.  Organ and cellular localization of asparagine synthetase in rice plants. , 2000, Plant & cell physiology.

[3]  Kozo Nakamura,et al.  Analysis of phenolic compounds in white rice, brown rice, and germinated brown rice. , 2004, Journal of agricultural and food chemistry.

[4]  S. Helliwell,et al.  The distribution of phenolic acids in rice , 2004 .

[5]  Olivier Lavialle,et al.  1H NMR and chemometrics to characterize mature grape berries in four wine-growing areas in Bordeaux, France. , 2005, Journal of agricultural and food chemistry.

[6]  Shim KangBo,et al.  Effect of Sowing Dates on Flowering and Maturity of Sesame , 2006 .

[7]  Christophe Malabat,et al.  Application of support vector machines to 1H NMR data of fish oils: methodology for the confirmation of wild and farmed salmon and their origins , 2007, Analytical and bioanalytical chemistry.

[8]  Huiru Tang,et al.  Revealing the metabonomic variation of rosemary extracts using 1H NMR spectroscopy and multivariate data analysis. , 2008, Journal of agricultural and food chemistry.

[9]  D. Wishart Metabolomics: applications to food science and nutrition research , 2008 .

[10]  M. Darvishi,et al.  Metabolome Comparison of Transgenic and Non-transgenic Rice by Statistical Analysis of FTIR and NMR Spectra , 2009 .

[11]  Won-Mok Park,et al.  Metabolomic studies on geographical grapes and their wines using 1H NMR analysis coupled with multivariate statistics. , 2009, Journal of agricultural and food chemistry.

[12]  E. Etxeberria,et al.  Metabolomic analysis in food science: a review , 2009 .

[13]  Choong Hwan Lee,et al.  Metabolomics analysis reveals the compositional differences of shade grown tea (Camellia sinensis L.). , 2010, Journal of agricultural and food chemistry.

[14]  J. Shipp,et al.  Food Applications and Physiological Effects of Anthocyanins as Functional Food Ingredients , 2010 .

[15]  H. Yoshida,et al.  Lipid components, fatty acids and triacylglycerol molecular species of black and red rices. , 2010 .

[16]  J. Vichapong,et al.  High performance liquid chromatographic analysis of phenolic compounds and their antioxidant activities in rice varieties. , 2010 .

[17]  E. Berghofer,et al.  Physicochemical and antioxidative properties of red and black rice varieties from Thailand, China and Sri Lanka , 2011 .

[18]  K. Engel,et al.  Metabolite profiling of colored rice (Oryza sativa L.) grains , 2012 .

[19]  X. Wan,et al.  Discrimination of the production season of Chinese green tea by chemical analysis in combination with supervised pattern recognition. , 2012, Journal of agricultural and food chemistry.

[20]  D. Karladee,et al.  γ-Aminobutyric acid (GABA) content in different varieties of brown rice during germination , 2012 .

[21]  RICHA SHARMA,et al.  Decision tree approach for classification of remotely sensed satellite data using open source support , 2013, Journal of Earth System Science.

[22]  Yuan Zhang,et al.  Phenolic Compounds and Bioactivities of Pigmented Rice , 2013, Critical reviews in food science and nutrition.

[23]  R. Julkunen‐Tiitto,et al.  Quantitative metabolite profiling of edible onion species by NMR and HPLC-MS. , 2014, Food chemistry.

[24]  Guangren Shi Chapter 5 – Decision Trees , 2014 .

[25]  Thomas Kuballa,et al.  Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools , 2014 .

[26]  S. Muthayya,et al.  An overview of global rice production, supply, trade, and consumption , 2014, Annals of the New York Academy of Sciences.

[27]  supJin Xiaming,et al.  Discrimination of Rice Varieties using LS-SVM Classification Algorithms and Hyperspectral Data , 2015 .

[28]  U. Roessner,et al.  Current and Emerging Applications of Metabolomics in the Field of Agricultural Biotechnology , 2015 .

[29]  Hyun-Jung Chung,et al.  A (1)H HR-MAS NMR-Based Metabolomic Study for Metabolic Characterization of Rice Grain from Various Oryza sativa L. Cultivars. , 2016, Journal of agricultural and food chemistry.

[30]  J. Im,et al.  Detection of tropical cyclone genesis via quantitative satellite ocean surface wind pattern and intensity analyses using decision trees , 2016 .

[31]  B. Jiang,et al.  13C-NMR-Based Metabolomic Profiling of Typical Asian Soy Sauces , 2016, Molecules.

[32]  S. D. De Pascali,et al.  (1)H NMR metabolomic profiling of the blue crab (Callinectes sapidus) from the Adriatic Sea (SE Italy): A comparison with warty crab (Eriphia verrucosa), and edible crab (Cancer pagurus). , 2016, Food chemistry.

[33]  H. Feng,et al.  Enhancing Contents of γ-Aminobutyric Acid (GABA) and Other Micronutrients in Dehulled Rice during Germination under Normoxic and Hypoxic Conditions. , 2016, Journal of agricultural and food chemistry.

[34]  Hu Zhengyi,et al.  1H NMR-based metabolomics for discrimination of rice from different geographical origins of China , 2017 .

[35]  Huili Liu,et al.  Specific patterns of spinal metabolites underlying α-Me-5-HT-evoked pruritus compared with histamine and capsaicin assessed by proton nuclear magnetic resonance spectroscopy. , 2017, Biochimica et Biophysica Acta - Molecular Basis of Disease.

[36]  Maryam Tayefi,et al.  The application of a decision tree to establish the parameters associated with hypertension , 2017, Comput. Methods Programs Biomed..

[37]  Yue Liu,et al.  NMRSpec: An integrated software package for processing and analyzing one dimensional nuclear magnetic resonance spectra , 2017 .

[38]  F. Priego-Capote,et al.  Establishing compositional differences between fresh and black garlic by a metabolomics approach based on LC–QTOF MS/MS analysis , 2017 .

[39]  Luca Sebastiani,et al.  1H NMR and PCA-based analysis revealed variety dependent changes in phenolic contents of apple fruit after drying. , 2017, Food chemistry.

[40]  H. Du,et al.  Evaluation of metabolites extraction strategies for identifying different brain regions and their relationship with alcohol preference and gender difference using NMR metabolomics. , 2018, Talanta.