Fatty Acid Profile-Based Chemometrics to Differentiate Metabolic Variations in Sorghum

Endalkachew Mengistie; Abdulbaset M. Alayat; Farid Sotoudehnia; Norbert Bokros; Seth DeBolt; Armando G. McDonald

doi:10.1021/acsfoodscitech.1c00320

Fatty Acid Profile-Based Chemometrics to Differentiate Metabolic Variations in Sorghum

Endalkachew Mengistie, Abdulbaset M. Alayat, Farid Sotoudehnia, Norbert Bokros, Seth DeBolt, Armando G. McDonald

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

The extractive content and fatty acid profiles of Della and REDforGREEN (RG) sweet Sorghum varieties grown in two different seasons have been evaluated. The stalk internodes and nodes were quantitatively extracted with CH₂Cl₂. The extracts were converted to their fatty acid methyl ester (FAME) derivatives and analyzed by gas chromatography-mass spectrometry (GCMS). The main fatty acids detected were azelaic (C9:0), lauric (C12:0), myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), and eicosanoic acids (C20:1). Fatty acids were considered as chemical descriptors of varieties to evaluate metabolic variations, where principal component analysis (PCA) and linear discriminant analysis (LDA) multivariate analysis methods were applied. LDA allowed discrimination between Della and RG varieties with higher prediction accuracy, suggesting metabolic variations between them. The high predictive power suggests the use of a fatty acid composition as a fingerprint to reveal metabolic variations.

Original language	English
Pages (from-to)	2127-2134
Number of pages	8
Journal	ACS Food Science and Technology
Volume	1
Issue number	11
DOIs	https://doi.org/10.1021/acsfoodscitech.1c00320
State	Published - Dec 17 2021

Bibliographical note

Publisher Copyright:
© 2021 American Chemical Society.

Funding

The authors would like to acknowledge the University of Idaho Equipment and Infrastructure Support (EIS) Award Program from the Office of Research and Economic Development (ORED) RISE Funding Program and the College of Natural Resources to support the purchase of the GCMS. The authors are grateful for the financial support of this research from the National Science Foundation Grant # 1826715.

Funders	Funder number
Division of Community and Natural Resources Community College
GCMS
National Science Foundation (NSF)	1826715
Office of Research and Economic Development, Mississippi State University
Office of Research and Economic Development, Louisiana State University

Keywords

FAME
fatty acids
linear discriminant analysis
principal component analysis
sorghum stalks

ASJC Scopus subject areas

Food Science
Analytical Chemistry
Organic Chemistry
Chemistry (miscellaneous)

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10.1021/acsfoodscitech.1c00320

Cite this

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title = "Fatty Acid Profile-Based Chemometrics to Differentiate Metabolic Variations in Sorghum",

abstract = "The extractive content and fatty acid profiles of Della and REDforGREEN (RG) sweet Sorghum varieties grown in two different seasons have been evaluated. The stalk internodes and nodes were quantitatively extracted with CH2Cl2. The extracts were converted to their fatty acid methyl ester (FAME) derivatives and analyzed by gas chromatography-mass spectrometry (GCMS). The main fatty acids detected were azelaic (C9:0), lauric (C12:0), myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), and eicosanoic acids (C20:1). Fatty acids were considered as chemical descriptors of varieties to evaluate metabolic variations, where principal component analysis (PCA) and linear discriminant analysis (LDA) multivariate analysis methods were applied. LDA allowed discrimination between Della and RG varieties with higher prediction accuracy, suggesting metabolic variations between them. The high predictive power suggests the use of a fatty acid composition as a fingerprint to reveal metabolic variations.",

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author = "Endalkachew Mengistie and Alayat, {Abdulbaset M.} and Farid Sotoudehnia and Norbert Bokros and Seth DeBolt and McDonald, {Armando G.}",

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AU - Sotoudehnia, Farid

AU - Bokros, Norbert

AU - DeBolt, Seth

AU - McDonald, Armando G.

PY - 2021/12/17

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N2 - The extractive content and fatty acid profiles of Della and REDforGREEN (RG) sweet Sorghum varieties grown in two different seasons have been evaluated. The stalk internodes and nodes were quantitatively extracted with CH2Cl2. The extracts were converted to their fatty acid methyl ester (FAME) derivatives and analyzed by gas chromatography-mass spectrometry (GCMS). The main fatty acids detected were azelaic (C9:0), lauric (C12:0), myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), and eicosanoic acids (C20:1). Fatty acids were considered as chemical descriptors of varieties to evaluate metabolic variations, where principal component analysis (PCA) and linear discriminant analysis (LDA) multivariate analysis methods were applied. LDA allowed discrimination between Della and RG varieties with higher prediction accuracy, suggesting metabolic variations between them. The high predictive power suggests the use of a fatty acid composition as a fingerprint to reveal metabolic variations.

AB - The extractive content and fatty acid profiles of Della and REDforGREEN (RG) sweet Sorghum varieties grown in two different seasons have been evaluated. The stalk internodes and nodes were quantitatively extracted with CH2Cl2. The extracts were converted to their fatty acid methyl ester (FAME) derivatives and analyzed by gas chromatography-mass spectrometry (GCMS). The main fatty acids detected were azelaic (C9:0), lauric (C12:0), myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1), stearic (C18:0), oleic (C18:1), linoleic (C18:2), and eicosanoic acids (C20:1). Fatty acids were considered as chemical descriptors of varieties to evaluate metabolic variations, where principal component analysis (PCA) and linear discriminant analysis (LDA) multivariate analysis methods were applied. LDA allowed discrimination between Della and RG varieties with higher prediction accuracy, suggesting metabolic variations between them. The high predictive power suggests the use of a fatty acid composition as a fingerprint to reveal metabolic variations.

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Fatty Acid Profile-Based Chemometrics to Differentiate Metabolic Variations in Sorghum

Abstract

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Keywords

ASJC Scopus subject areas

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