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- Title
μ‐PESI‐based MS profiling combined with untargeted metabolomics analysis for rapid identification of red wine geographical origin.
- Authors
Pu, Keyuan; Wang, Yue; Wei, Huiwen; Hu, Jun; Qiu, Jiamin; Chen, Siyu; Liu, Qian; Lin, Yan; Ng, Kwan‐Ming
- 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.
- Subjects
RED wines; ELECTROSPRAY ionization mass spectrometry; MACHINE learning; SUNSHINE; IDENTIFICATION; METABOLOMICS
- Publication
Journal of the Science of Food & Agriculture, 2024, Vol 104, Issue 1, p546
- ISSN
0022-5142
- Publication type
Article
- DOI
10.1002/jsfa.12951