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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.
Afiliação
  • 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 em En | MEDLINE | ID: mdl-36240552
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Isoflavonas / Fabaceae Idioma: En Revista: Food Chem Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Isoflavonas / Fabaceae Idioma: En Revista: Food Chem Ano de publicação: 2023 Tipo de documento: Article