들깨 잎의 FT-IR 스펙트럼 데이터로부터 다변량 통계분석을 이용한 생산연도 판별

• 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: PDF
• 발행일자: 2024.03.01

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.

목차

서언

재료 및 방법

결과 및 고찰

적요

사사

References

키워드

perilla fourier transform infrared (FT-IR) spectral analysis principal component analysis (PCA) partial least square discriminant analysis (PLS-DA) production traceability