Classification of Cereal Flour by Gas Chromatography – Mass Spectrometry (GC-MS) Liposoluble Fingerprints and Automated Machine Learning

Pastor, Kristian; Ilić, Marko; Kojić, Jovana; Ačanski, Marijana; Vujić, Djura

doi:10.1080/00032719.2022.2050921

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Reconstitution was performed by adding 1 mL of dichloromethane (DCM), followed by trans-derivatization using a 0.2 M trimethylsulfonium hydroxide (TMSH) solution in methanol. Trans-derivatization was performed in the injection port of the gas chromatography device [20].…”

Section: Lipid Extractionmentioning

confidence: 99%

“…There are many examples of the application of a GC/MS technique (gas chromatography with mass spectrometric detection) coupled with various multivariate data analysis approaches for the purpose of food authentication and fraud detection. Thus, lipid profiles showed to be a useful input in: the authentication of the botanical origin of various types of edible oils [16][17][18][19]; classification of cereal flours [20], cereal and pseudocereal flours [21], and common and sourdough bread [22]; Parmigiano Reggiano authentication [23]; differentiation between organically raised lamb and broiler compared to conventionally raised meats [24,25]; characterization of different mushroom species [26]; and many other applications.…”

Section: Introductionmentioning

confidence: 99%

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Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics

Ilić,

Pastor,

Ilić

et al. 2023

Foods

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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.

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Section: Lipid Extractionmentioning

confidence: 99%