Determination of Flavonoid Glycoside Isomers Using Vision Transformer and Tandem Mass Spectrometry

  • Park, Ji In; 
  • Kim, Myeong Ji; 
  • Lee, Kyu Hyeong; 
  • Oh, Seung Hyun; 
  • Kang, Young Hoon; 
  • ... Kim, Hyunwoo
Citations

WEB OF SCIENCE

7
Citations

SCOPUS

7

초록

A vision transformer (ViT)-based deep neural network was applied to classify the flavonoid glycoside isomers by analyzing electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra. Our model successfully classified the flavonoid isomers with various substitution patterns (3-O, 6-C, 7-O, 8-C, 4 '-O) and multiple glycosides, achieving over 80% accuracy during training. In addition, the experimental spectra from flavonoid glycoside standards were acquired with different adducts, and our model showed robust performance regardless of the experimental conditions. As a result, the vision transformer-based computer vision model is promising for analyzing mass spectrometry data.

키워드

flavonoid; artificial intelligence; vision transformer; DIFFERENTIATION
제목
Determination of Flavonoid Glycoside Isomers Using Vision Transformer and Tandem Mass Spectrometry
저자
Park, Ji In; Kim, Myeong Ji; Lee, Kyu Hyeong; Oh, Seung Hyun; Kang, Young Hoon; Kim, Hyunwoo
DOI
10.3390/plants13233401
발행일
2024-12
유형
Article
저널명
Plants
권
13
호
23
페이지
1 ~ 13