TY  - JOUR
AU  - Ilić, Marko
AU  - Pastor, Kristian
AU  - Ilić, Aleksandra
AU  - Vasić, Mirjana
AU  - Nastić, Nataša
AU  - Vujić, Đura
AU  - Ačanski, Marijana
PY  - 2023
UR  - http://fiver.ifvcns.rs/handle/123456789/4219
AB  - This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.
PB  - Basel : MDPI
T2  - Foods - Basel
T1  - Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics
IS  - 24
SP  - 4420
VL  - 12
DO  - 10.3390/foods12244420
ER  - 

@article{
author = "Ilić, Marko and Pastor, Kristian and Ilić, Aleksandra and Vasić, Mirjana and Nastić, Nataša and Vujić, Đura and Ačanski, Marijana",
year = "2023",
abstract = "This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.",
publisher = "Basel : MDPI",
journal = "Foods - Basel",
title = "Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics",
number = "24",
pages = "4420",
volume = "12",
doi = "10.3390/foods12244420"
}

Ilić, M., Pastor, K., Ilić, A., Vasić, M., Nastić, N., Vujić, Đ.,& Ačanski, M.. (2023). Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics. in Foods - Basel
Basel : MDPI., 12(24), 4420.
https://doi.org/10.3390/foods12244420

Ilić M, Pastor K, Ilić A, Vasić M, Nastić N, Vujić Đ, Ačanski M. Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics. in Foods - Basel. 2023;12(24):4420.
doi:10.3390/foods12244420 .

Ilić, Marko, Pastor, Kristian, Ilić, Aleksandra, Vasić, Mirjana, Nastić, Nataša, Vujić, Đura, Ačanski, Marijana, "Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics" in Foods - Basel, 12, no. 24 (2023):4420,
https://doi.org/10.3390/foods12244420 . .