Oil content analysis of corn seeds using a hand-held Raman spectrometer and spectral peak decomposition algorithm.

Gaussian curve fitting Raman spectroscopy corn seed oil content spectral peak decomposition

Journal

Frontiers in plant science
ISSN: 1664-462X
Titre abrégé: Front Plant Sci
Pays: Switzerland
ID NLM: 101568200

Informations de publication

Date de publication:
2023
Historique:
received: 27 02 2023
accepted: 22 03 2023
medline: 20 4 2023
pubmed: 20 4 2023
entrez: 20 04 2023
Statut: epublish

Résumé

Rapid, non-destructive and reliable detection of the oil content of corn seeds is important for development of high-oil corn. However, determination of the oil content is difficult using traditional methods for seed composition analysis. In this study, a hand-held Raman spectrometer was used with a spectral peak decomposition algorithm to determine the oil contents of corn seeds. Mature and waxy Zhengdan 958 corn seeds and mature Jingke 968 corn seeds were analyzed. Raman spectra were obtained in four regions of interest in the embryo of the seed. After analysis of the spectra, a characteristic spectral peak for the oil content was identified. A Gaussian curve fitting spectral peak decomposition algorithm was used to decompose the characteristic spectral peak of oil at 1657 cm

Identifiants

pubmed: 37077627
doi: 10.3389/fpls.2023.1174747
pmc: PMC10106593
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1174747

Informations de copyright

Copyright © 2023 Jin, Tian, Gao, Yang and Dong.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Yuan Jin (Y)

School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China.
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

Hongwu Tian (H)

Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China.
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

Zhen Gao (Z)

Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China.
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
College of Information and Electrical Engineering, China Agricultural University, Beijing, China.

Guiyan Yang (G)

Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China.
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China.

Daming Dong (D)

Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Beijing, China.
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

Classifications MeSH