Your browser doesn't support javascript.
loading
Statistical discrimination using different machine learning models reveals dissimilar key compounds of soybean leaves in targeted polyphenol-metric metabolomics in terms of traits and cultivation.
Rha, Chan-Su; Jang, Eun Kyu; Lee, Jong Suk; Kim, Ji-Sung; Ko, Min-Ji; Lim, Sol; Park, Gun Hwan; Kim, Dae-Ok.
Affiliation
  • Rha CS; AMOREPACIFIC Research and Innovation Center, Yongin 17074, Republic of Korea. Electronic address: teaman@amorepacific.com.
  • Jang EK; Gyeonggi-do Agricultural Research and Extension Services, Hwaseong 18388, Republic of Korea.
  • Lee JS; Biocenter, Gyeonggido Business and Science Accelerator, Suwon 16629, Republic of Korea.
  • Kim JS; AMOREPACIFIC Research and Innovation Center, Yongin 17074, Republic of Korea.
  • Ko MJ; Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
  • Lim S; Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
  • Park GH; Gyeonggi-do Agricultural Research and Extension Services, Hwaseong 18388, Republic of Korea.
  • Kim DO; Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea. Electronic address: dokim05@khu.ac.kr.
Food Chem ; 404(Pt A): 134454, 2023 Mar 15.
Article in En | MEDLINE | ID: mdl-36240552
Subject(s)
Key words
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Isoflavones / Fabaceae Language: En Journal: Food Chem Year: 2023 Document type: Article
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Isoflavones / Fabaceae Language: En Journal: Food Chem Year: 2023 Document type: Article