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µ-PESI-based MS profiling combined with untargeted metabolomics analysis for rapid identification of red wine geographical origin.
Pu, Keyuan; Wang, Yue; Wei, Huiwen; Hu, Jun; Qiu, Jiamin; Chen, Siyu; Liu, Qian; Lin, Yan; Ng, Kwan-Ming.
Afiliação
  • Pu K; Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, China.
  • Wang Y; Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, China.
  • Wei H; Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, China.
  • Hu J; Guangdong RangerBio Technologies Co. Ltd, Dongguan, China.
  • Qiu J; Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, China.
  • Chen S; Guangdong RangerBio Technologies Co. Ltd, Dongguan, China.
  • Liu Q; Department of Biology, Shantou University, Shantou, China.
  • Lin Y; Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Shantou, China.
  • Ng KM; Guangdong RangerBio Technologies Co. Ltd, Dongguan, China.
J Sci Food Agric ; 104(1): 546-552, 2024 Jan 15.
Article em En | MEDLINE | ID: mdl-37647550
ABSTRACT

BACKGROUND:

The commercial value of red wine is strongly linked to its geographical origin. Given the large global market, there is great demand for high-throughput screening methods to authenticate the geographical source of red wine. However, only limited techniques have been established up to now.

RESULTS:

Herein, a sensitive and robust method, namely probe electrospray ionization mass spectrometry (µ-PESI-MS), was established to achieve rapid analysis at approximately 1.2 min per sample without any pretreatment. A scotch near the needle tip provides a fixed micro-volume for each analysis to achieve satisfactory ion signal reproducibility (RSD < 26.7%). In combination with a machine learning algorithm, 16 characteristic ions were discovered from thousands of detected ions and were utilized for differentiating red wine origin. Among them, the relative abundances of two characteristic metabolites (trigonelline and proline) correlated with geographical conditions (sun exposure and water stress) were identified, providing the rationale for differentiation of the geographical origin.

CONCLUSION:

The proposed µ-PESI-MS-based method demonstrates a promising high-throughput determination capability in red wine traceability.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vinho Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: J Sci Food Agric Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vinho Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: J Sci Food Agric Ano de publicação: 2024 Tipo de documento: Article