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Cooperative combination of LIBS-based elemental analysis and near-infrared molecular fingerprinting for enhanced discrimination of geographical origin of soybean paste.
Jeong, Seongsoo; Seol, Daun; Kim, Hyang; Lee, Yonghoon; Nam, Sang-Ho; An, Jae-Min; Chung, Hoeil.
Afiliação
  • Jeong S; Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea.
  • Seol D; Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea.
  • Kim H; Plasma Spectroscopy Analysis Center, Mokpo National University, Jeonnam 58554, Republic of Korea.
  • Lee Y; Department of Chemistry, Mokpo National University, Jeonnam 58554, Republic of Korea; Plasma Spectroscopy Analysis Center, Mokpo National University, Jeonnam 58554, Republic of Korea.
  • Nam SH; Department of Chemistry, Mokpo National University, Jeonnam 58554, Republic of Korea; Plasma Spectroscopy Analysis Center, Mokpo National University, Jeonnam 58554, Republic of Korea. Electronic address: shnam@mokpo.ac.kr.
  • An JM; Division of Origin Identification, Experiment & Research Institute, National Agricultural Products Quality Management Service, Gimcheon 39660, Republic of Korea.
  • Chung H; Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea. Electronic address: hoeil@hanyang.ac.kr.
Food Chem ; 399: 133956, 2023 Jan 15.
Article em En | MEDLINE | ID: mdl-36027807
ABSTRACT
Laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy were combined to enhance discrimination of soybean paste samples according to geographical origin. Since element and organic component compositions of soybean pastes depend on soybean cultivation areas and fermentation conditions, utilization of two complementary spectroscopic signatures would be synergetic for the discrimination. When the areas of C (AC) and Ca (ACa) peaks in the LIBS spectra were used as the inputs for linear discriminant analysis, the accuracy was 95.4%. The accuracy became 92.1%, when the principal component (PC) scores obtained by principal component analysis of the NIR spectra were employed. To enhance NIR discrimination, two-trace two-dimensional (2T2D) correlation analysis was adopted to recognize minute spectral differences. With using the 1st/2nd PC scores of 2T2D slice spectra, accuracy increased to 95.0%. When the ratios of ACa/AC and the 2nd PC scores of the samples were combined together, the accuracy improved to 99.6%.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Glycine max / Fabaceae Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Glycine max / Fabaceae Idioma: En Ano de publicação: 2023 Tipo de documento: Article