Hypenathed Techniques and Chemometrics for Food Authentication

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: closed (26 February 2024) | Viewed by 1159

Special Issue Editors


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Guest Editor
Department of Analytical Chemistry, University of Jaén, Jaén, Spain
Interests: food quality; food safety; vegetable oils; chemometrics; chromatography; spectroscopy; spectrometry

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Guest Editor
Chemometrics and Qualimetry Research Group, Universidad de Burgos, Burgos, Spain
Interests: chromatographic techniques; mass spectrometry detectors; chemometrics; PARAFAC decomposition to solve the unequivocal identification; foodstuff area (food security); analytical chemistry area; quality management

Special Issue Information

Dear Colleagues,

It is our great pleasure to invite you to contribute to a Special Issue in Foods titled: “Hypenathed Techniques and Chemometrics for Food Authentication”.

Food authentication is the analytical process used to verify that a food product complies with its label description. Fraudulent food activities are characterised by their intentional nature and end goal of economic gain; for that reason, public awareness concerning food quality and food safety has increased in recent years, as well as scientific interest in providing solutions.

Advanced analytical techniques, such as coupling two (or more) methods to solve complex analytical challenges, provide the rapid and reliable separation of compounds in complex food matrices. The addition of chemometrics and multivariate analysis to this equation will allow the development of solutions to any kind of food authentication issue.

This Special Issue will publish recent research based on a combination of hypenathed techniques and chemometrics for food authenticity assessments. We encourage you to submit both reviews and original research articles. Share your great science!

Dr. Cristina Ruiz Samblás
Dr. Lucía Valverde Som
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • food authenticity
  • chromatography
  • spectrometry
  • chemometrics
  • multivariate analysis
  • food fraud
  • food adulteration

Published Papers (1 paper)

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Research

11 pages, 1663 KiB  
Article
Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics
by Marko Ilić, Kristian Pastor, Aleksandra Ilić, Mirjana Vasić, Nataša Nastić, Đura Vujić and Marijana Ačanski
Foods 2023, 12(24), 4420; https://doi.org/10.3390/foods12244420 - 09 Dec 2023
Viewed by 759
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Hypenathed Techniques and Chemometrics for Food Authentication)
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