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Foods
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Novel Techniques for Food Authentication
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Novel Techniques 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: 31 May 2024 | Viewed by 3956

Special Issue Editors

Prof. Dr. Athanasios Ladavos

E-Mail Website
Guest Editor
Department of Food Science & Technology, University of Patras, 30100 Agrinio, Greece
Interests: geographical origin of agricultural products and foods; isotopic ratio of stable isotopes/IRMS; multi-element analysis/ICP-MS; antimicrobial-antioxidant packaging; polymer-clay nanocomposite films; chemical technology
Dr. Eleni Mazarakioti

E-Mail Website
Guest Editor
Department of Food Science & Technology, University of Patras, 30100 Agrinio, Greece
Interests: geographical origin authentication; multi-elemental analysis; stable isotope analysis; intelligent food packaging materials; nanoporous materials

Special Issue Information

Dear Colleagues,

Food adulteration is a phenomenon with deep historical roots and occurs in diverse ways (i.e., substituting, mixing, blending, and mislabeling products) by producers and suppliers attempting to maximize their financial profits. In our time, the high consumers’ awareness of food quality with the concomitant food regulations, which have been established by all developed countries, has led to the requirement for appropriate scientific solutions of food fraud. Food authenticity represents the process of proving the genuineness of a food product, particularly to verify that the label information corresponds to the original product and/or its composition. Developing highly sophisticated analytical methods is considered the solution for food authenticity. This Special Issue is dedicated to original research on novel analytical techniques and methods for food authentication which can include: physical/chemical methods (i.e., purity control), enzymatic and DNA-based techniques, spectroscopic techniques (i.e., IR, Raman, UV-Vis, NMR, ICP-MS, IRMS), chromatographic techniques (i.e., GC, HPLC) and thermal techniques (i.e., DSC). We invite authors to contribute to the present Special Issue with innovative research or review articles.

Prof. Dr. Athanasios Ladavos
Dr. Eleni Mazarakioti
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 authentication
  • geographical origin
  • food safety and quality
  • food fraud/adulteration
  • physical/chemical methods
  • molecular biology techniques
  • multi-elemental analysis
  • stable isotope analysis
  • fingerprinting / profiling
  • food production methods

Published Papers (2 papers)

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Research

19 pages, 3125 KiB  
Article
Non-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy
by Diding Suhandy, Dimas Firmanda Al Riza, Meinilwita Yulia and Kusumiyati Kusumiyati
Foods 2023, 12(16), 3067; https://doi.org/10.3390/foods12163067 - 15 Aug 2023
Cited by 3 | Viewed by 1244
Abstract
Stingless bee honey (SBH) is rich in phenolic compounds and available in limited quantities. Authentication of SBH is important to protect SBH from adulteration and retain the reputation and sustainability of SBH production. In this research, we use portable LED-based fluorescence spectroscopy to [...] Read more.
Stingless bee honey (SBH) is rich in phenolic compounds and available in limited quantities. Authentication of SBH is important to protect SBH from adulteration and retain the reputation and sustainability of SBH production. In this research, we use portable LED-based fluorescence spectroscopy to generate and measure the fluorescence intensity of pure SBH and adulterated samples. The spectrometer is equipped with four UV-LED lamps (peaking at 365 nm) as an excitation source. Heterotrigona itama, a popular SBH, was used as a sample. 100 samples of pure SBH and 240 samples of adulterated SBH (levels of adulteration ranging from 10 to 60%) were prepared. Fluorescence spectral acquisition was measured for both the pure and adulterated SBH samples. Principal component analysis (PCA) demonstrated that a clear separation between the pure and adulterated SBH samples could be established from the first two principal components (PCs). A supervised classification based on soft independent modeling of class analogy (SIMCA) achieved an excellent classification result with 100% accuracy, sensitivity, specificity, and precision. Principal component regression (PCR) was superior to partial least squares regression (PLSR) and multiple linear regression (MLR) methods, with a coefficient of determination in prediction (R2p) = 0.9627, root mean squared error of prediction (RMSEP) = 4.1579%, ratio prediction to deviation (RPD) = 5.36, and range error ratio (RER) = 14.81. The LOD and LOQ obtained were higher compared to several previous studies. However, most predicted samples were very close to the regression line, which indicates that the developed PLSR, PCR, and MLR models could be used to detect HFCS adulteration of pure SBH samples. These results showed the proposed portable LED-based fluorescence spectroscopy has a high potential to detect and quantify food adulteration in SBH, with the additional advantages of being an accurate, affordable, and fast measurement with minimum sample preparation. Full article
(This article belongs to the Special Issue Novel Techniques for Food Authentication)
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18 pages, 379 KiB  
Article
Detection and Quantification of Botanical Impurities in Commercial Oregano (Origanum vulgare) Using Metabarcoding and Digital PCR
by Antoon Lievens, Valentina Paracchini, Linda Garlant, Danilo Pietretti, Alain Maquet and Franz Ulberth
Foods 2023, 12(16), 2998; https://doi.org/10.3390/foods12162998 - 09 Aug 2023
Viewed by 2019
Abstract
DNA technology for food authentication is already well established, and with the advent of Next Generation Sequencing (NGS) and, more specifically, metabarcoding, compositional analysis of food at the molecular level has rapidly gained popularity. This has led to several reports in the media [...] Read more.
DNA technology for food authentication is already well established, and with the advent of Next Generation Sequencing (NGS) and, more specifically, metabarcoding, compositional analysis of food at the molecular level has rapidly gained popularity. This has led to several reports in the media about the presence of foreign, non-declared species in several food commodities. As herbs and spices are attractive targets for fraudulent manipulation, a combination of digital PCR and metabarcoding by NGS was employed to check the purity of 285 oregano samples taken from the European market. By using novel primers and analytical approaches, it was possible to detect and quantify both adulterants and contaminants in these samples. The results highlight the high potential of NGS for compositional analysis, although its quantitative information (read count percentages) is unreliable, and other techniques are therefore needed to complement the sequencing information for assessing authenticity (‘true to the name’) of food ingredients. Full article
(This article belongs to the Special Issue Novel Techniques for Food Authentication)
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