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Structural Identification of Ginsenoside Based on UPLC-QTOF-MS of Black Ginseng (Panax Ginseng C.A. Mayer).
Oh, Hyo-Bin; Jeong, Da-Eun; Lee, Da-Eun; Yoo, Jong-Hee; Kim, Young-Soo; Kim, Tae-Young.
  • Oh HB; Institute of Jinan Red Ginseng, Jinan-gun 55442, Republic of Korea.
  • Jeong DE; Department of Food Science and Technology, Jeonbuk National University, Jeonju 54896, Republic of Korea.
  • Lee DE; Institute of Jinan Red Ginseng, Jinan-gun 55442, Republic of Korea.
  • Yoo JH; Institute of Jinan Red Ginseng, Jinan-gun 55442, Republic of Korea.
  • Kim YS; Institute of Jinan Red Ginseng, Jinan-gun 55442, Republic of Korea.
  • Kim TY; Department of Food Science and Technology, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Metabolites ; 14(1)2024 Jan 18.
Article en En | MEDLINE | ID: mdl-38248865
ABSTRACT
Black ginseng (BG) is processed ginseng traditionally made in Korea via the steaming and drying of ginseng root through three or more cycles, leading to changes in its appearance due to the Maillard reaction on its surface, resulting in a dark coloration. In this study, we explored markers for differentiating processed ginseng by analyzing the chemical characteristics of BG. We elucidated a new method for the structural identification of ginsenoside metabolites and described the features of processed ginseng using UPLC-QTOF-MS in the positive ion mode. We confirmed that maltose, glucose, and fructose, along with L-arginine, L-histidine, and L-lysine, were the key compounds responsible for the changes in the external quality of BG. These compounds can serve as important metabolic markers for distinguishing BG from conventionally processed ginseng. The major characteristics of white ginseng, red ginseng, and BG can be distinguished based on their high-polarity and low-polarity ginsenosides, and a precise method for the structural elucidation of ginsenosides in the positive ion mode is presented.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Año: 2024 Tipo del documento: Article