LC-MS based global metabolite profiling of grapes: solvent extraction protocol 
optimisation

Theodoridis, Georgios; Gika, Helen; Franceschi, Pietro; Caputi, Lorenzo; Arapitsas, Panagiotis; Scholz, Mattias; Masuero, Domenico; Wehrens, Ron; Vrhovsek, Urska; Mattivi, Fulvio

doi:10.1007/s11306-011-0298-z

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

学術論文

LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

There are no locators available

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

フルテキスト (公開)

公開されているフルテキストはありません

付随資料 (公開)

There is no public supplementary material available

引用

Theodoridis, G., Gika, H., Franceschi, P., Caputi, L., Arapitsas, P., Scholz, M., Masuero, D., Wehrens, R., Vrhovsek, U., & Mattivi, F. (2012). LC-MS based global metabolite profiling of grapes: solvent extraction protocol optimisation. Metabolomics, 8(2), 175-185. doi:10.1007/s11306-011-0298-z.

引用: https://hdl.handle.net/21.11116/0000-0009-C761-9

要旨

Optimal solvent conditions for grape sample preparation were investigated for the purpose of metabolite profiling studies, with the aim of obtaining as many features as possible with the best analytical repeatability. Mixtures of water, methanol and chloroform in different combinations were studied as solvents for the extraction of ground grapes. The experimental design used a two stage study to find the optimum extraction medium. The extracts obtained were further purified using solid phase extraction and analysed using a UPLC full scan TOF MS with both reversed phase and hydrophilic interaction chromatography. The data obtained were processed using data extraction algorithms and advanced statistical software for data mining. The results obtained indicated that a fairly broad optimal area for solvent composition could be identified, containing approximately equal amounts of methanol and chloroform and up to 20% water. Since the water content of the samples was variable, the robustness of the optimal conditions suggests these are appropriate for large scale profiling studies for characterisation of the grape metabolome.