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      Untargeted NMR Spectroscopic Analysis of the Metabolic Variety of New Apple Cultivars

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          Abstract

          Metabolome analyses by NMR spectroscopy can be used in quality control by generating unique fingerprints of different species. Hundreds of components and their variation between different samples can be analyzed in a few minutes/hours with high accuracy and low cost of sample preparation. Here, apple peel and pulp extracts of a variety of apple cultivars were studied to assess their suitability to discriminate between the different varieties. The cultivars comprised mainly newly bred varieties or ones that were brought onto the market in recent years. Multivariate analyses of peel and pulp extracts were able to unambiguously identify all cultivars, with peel extracts showing a higher discriminative power. The latter was increased if the highly concentrated sugar metabolites were omitted from the analysis. Whereas sugar concentrations lay within a narrow range, polyphenols, discussed as potential health promoting substances, and acids varied remarkably between the cultivars.

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          Most cited references14

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          Metabolite profiling on apple volatile content based on solid phase microextraction and gas-chromatography time of flight mass spectrometry.

          A headspace SPME GC-TOF-MS method was developed for the acquisition of metabolite profiles of apple volatiles. As a first step, an experimental design was applied to find out the most appropriate conditions for the extraction of apple volatile compounds by SPME. The selected SPME method was applied in profiling of four different apple varieties by GC-EI-TOF-MS. Full scan GC-MS data were processed by MarkerLynx software for peak picking, normalisation, alignment and feature extraction. Advanced chemometric/statistical techniques (PCA and PLS-DA) were used to explore data and extract useful information. Characteristic markers of each variety were successively identified using the NIST library thus providing useful information for variety classification. The developed HS-SPME sampling method is fully automated and proved useful in obtaining the fingerprint of the volatile content of the fruit. The described analytical protocol can aid in further studies of the apple metabolome. Copyright © 2011 Elsevier B.V. All rights reserved.
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            Liquid chromatography–mass spectrometry-based metabolomics for authenticity assessment of fruit juices

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              Utility of metabolomics toward assessing the metabolic basis of quality traits in apple fruit with an emphasis on antioxidants.

              A gas chromatography-mass spectrometry approach was employed to evaluate the use of metabolite patterns to differentiate fruit from six commercially grown apple cultivars harvested in 2008. Principal component analysis (PCA) of apple fruit peel and flesh data indicated that individual cultivar replicates clustered together and were separated from all other cultivar samples. An independent metabolomics investigation with fruit harvested in 2003 confirmed the separate clustering of fruit from different cultivars. Further evidence for cultivar separation was obtained using a hierarchical clustering analysis. An evaluation of PCA component loadings revealed specific metabolite classes that contributed the most to each principal component, whereas a correlation analysis demonstrated that specific metabolites correlate directly with quality traits such as antioxidant activity, total phenolics, and total anthocyanins, which are important parameters in the selection of breeding germplasm. These data sets lay the foundation for elucidating the metabolic basis of commercially important fruit quality traits.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                19 September 2016
                September 2016
                : 6
                : 3
                : 29
                Affiliations
                [1 ]Institute of Organic Chemistry, Karlsruhe Institute of Technology, Fritz-Haber-Weg 6, 76131 Karlsruhe, Germany; philipp-michael.eisenmann@ 123456kit.edu (P.E.); pavleta.tzvetkova@ 123456kit.edu (P.T.); mara.silber@ 123456student.kit.edu (M.S.); burkhard.luy@ 123456kit.edu (B.L.)
                [2 ]Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Haid-und-Neu-Straße 9, 76131 Karlsruhe, Germany; ehlers.mona@ 123456web.de (M.E.); christoph.weinert@ 123456mri.bund.de (C.H.W.)
                [3 ]Institute for Biological Interfaces 4, Karlsruhe Institute of Technology, P.O. Box 3640, 76021 Karlsruhe, Germany
                [4 ]Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Haid-und-Neu-Straße 9, 76131 Karlsruhe, Germany; manuela.rist@ 123456mri.bund.de
                Author notes
                [* ]Correspondence: claudia.muhle-goll@ 123456kit.edu ; Tel.: +49-721-608-45344
                Article
                metabolites-06-00029
                10.3390/metabo6030029
                5041128
                27657148
                0dfae90d-1cb7-4c62-917c-8b090ef99b13
                © 2016 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 31 May 2016
                : 13 September 2016
                Categories
                Article

                metabolomics,multivariate analysis,quality control,malus domestica

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