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들깨 잎의 FT-IR 스펙트럼 데이터로부터 다변량 통계분석을 이용한 생산연도 판별
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- 영문명
- Determination of Production Year Using Multivariate Statistical Analysis from FTIR Spectrum Data of Perilla Leaves
- 발행기관
- 한국육종학회
- 저자명
- 서혜영(Hye-Young Seo) 서은지(Eun Ji Suh) 최은빈(Eun Bin Choi) 이미자(Mi Ja Lee) 이한결(Han Gyeol Lee) 서우덕(Woo Duck Seo) 김정인(Jung In Kim) 송승엽(Seung-Yeob Song)
- 간행물 정보
- 『한국육종학회지』Vol.56 No.1, 11~18쪽, 전체 8쪽
- 주제분류
- 농수해양 > 기타농수해양
- 파일형태
- 발행일자
- 2024.03.01
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국문 초록
영문 초록
This study used perilla seeds produced in 2019, 2020, and 2021 to determine the year of production using multivariate statistical analysis of Fourier-transform infrared (FTIR) spectral data of perilla leaves. Spectral analysis based on multivariate statistical analysis of whole-cell extracts was used to distinguish the perilla leaves at the metabolic level. FT-IR spectral data of the leaves were analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The FTIR spectrum identified spectral differences between the frequency regions of 1,700 to 1,500, 1,500 to 1,300, and 1,100 to 950 cm-1. This spectral region reflects quantitative and qualitative changes in amides I, II in amino acids and proteins (1,700–1,500 cm-1), phosphodiester groups from nucleic acids and phospholipids (1,500–1,300 cm-1), and carbohydrate compounds (1,100–950 cm-1). PCA revealed separate clusters corresponding to production traceability relationships. Therefore, PCA can be used to distinguish between production in 2019, 2020, and 2021 based on different metabolite contents. PLS-DA showed a similar production traceability classification for the perilla seeds. In addition, this metabolic identification system can be used to rapidly select and classify useful perilla seed varieties.
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