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Foods
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Food Integrity and Authenticity
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Food Integrity and Authenticity

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: 10 June 2024 | Viewed by 2146

Special Issue Editor

Dr. Adriana Dehelean

E-Mail Website
Guest Editor
National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania
Interests: food authentication; food traceability; geographical origin; elemental analysis; ICP-MS; chemometric methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food integrity and authenticity represent important components for increasing consumers’ trust regarding the quality and safety of food products. Consumers want to know if the products they buy are of quality, contain what they claim, and that the country of origin is the one declared on the label. Foods, as complex matrices, require, for their assessment control, adequate analysis methods combined with statistical tools in order to develop differentiation models, according to distinct criteria (e.g., geographical origin, botanical origin, producer fingerprint, etc.). The analytical methods used in the authentication process and quality control of food items (isotope ratio mass spectrometry (IRMS), inductively coupled plasma-mass spectrometry (ICP-MS), gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), near-infrared spectroscopy (NIR), Raman, nuclear magnetic resonance spectroscopy (2H-NMR), ultraviolet–visible spectroscopy (UV–Vis)) alongside different chemometric methods (i.e., PCA, LDA, PLS-DA, SIMCA, etc.) represent useful tools in building predictive models for both quantitative (calibration) and qualitative (classification) responses based on the experimental profiles.

This Special Issue aims to include the manuscripts focusing on new approaches regarding to food quality and safety assessment.

Dr. Adriana Dehelean
Guest Editor

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
  • food safety
  • elemental composition
  • IRMS
  • contaminants
  • chemometrics

Published Papers (2 papers)

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Research

15 pages, 2682 KiB  
Article
Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market
by Florina-Dorina Covaciu, Camelia Berghian-Grosan, Ariana Raluca Hategan, Dana Alina Magdas, Adriana Dehelean and Gabriela Cristea
Foods 2023, 12(23), 4237; https://doi.org/10.3390/foods12234237 - 23 Nov 2023
Viewed by 892
Abstract
Food composition issues represent an increasing concern nowadays, in the context of diverse food commodity varieties. The contents and types of fatty acids are a constant preoccupation among consumers because of their reflections of nutrition and health problems. This study aims to find [...] Read more.
Food composition issues represent an increasing concern nowadays, in the context of diverse food commodity varieties. The contents and types of fatty acids are a constant preoccupation among consumers because of their reflections of nutrition and health problems. This study aims to find the best tool for the rapid and reliable identification of similarities and differences among several food items from a fatty acid profile perspective. An acknowledged GC-FID method was considered, while, for a better interpretation of the analytical results, machine learning algorithms were used. It was possible to develop a recognition model able to simultaneously differentiate, with an accuracy of 79.3%, nine product types using the bagged tree ensemble model. The low number of samples or some similarities among the classes could be responsible for the wrong assignments that occurred, especially in the biscuit, wafer and instant soup classes. Better accuracies values of 95, 86.1, and 97.8% were obtained when the products were grouped into three categories: (1) sunflower oil, mayonnaise, margarine, and cream cheese; (2) biscuits, cookies, margarine, and wafers; and (3) sunflower oil, chips, and instant soup. Full article
(This article belongs to the Special Issue Food Integrity and Authenticity)
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19 pages, 6694 KiB  
Article
Simulated and Verification of Mass and Heat Transfer Coupled Model of Jujube Slices Dried by Hot Air Combined with Radio Frequency Heat Treatment at Different Drying Stages
by Shuaitao Cao, Chenyan Yang, Yongzhen Zang, Yang Li, Jiangwei Gu, Haiyang Ding, Xuedong Yao, Rongguang Zhu, Qiang Wang, Wancheng Dong and Yong Huang
Foods 2023, 12(16), 3025; https://doi.org/10.3390/foods12163025 - 11 Aug 2023
Viewed by 850
Abstract
This study investigates the impact of radio frequency (RF) heat treatment on heat and mass transfer during the hot air drying of jujube slices. Experiments were conducted at different drying stages, comparing single-hot air drying with hot air combined with RF treatment. Numerical [...] Read more.
This study investigates the impact of radio frequency (RF) heat treatment on heat and mass transfer during the hot air drying of jujube slices. Experiments were conducted at different drying stages, comparing single-hot air drying with hot air combined with RF treatment. Numerical models using COMSOL Multiphysics® were developed to simulate the process, and the results were compared to validate the models. The maximum difference between the simulated value of the center temperature and the experimental value was 6.9 °C, while the minimum difference was 0.1 °C. The maximum difference in average surface temperature was 1.7 °C, with a minimum of 0.3 °C. The determination coefficient (R2) between the simulated experimental values of HA and the early (E-HA + RF), middle (M-HA + RF), and later (L-HA + RF) groups was 0.964, 0.987, 0.961, and 0.977, respectively. The study demonstrates that RF treatment reduces drying time, enhances internal temperature, promotes consistent heat and mass transfer, and accelerates moisture diffusion in jujube slices. Furthermore, the later the RF treatment is applied, the greater the increase in internal temperature and the faster the decrease in moisture content. This research elucidates the mechanism by which RF heat treatment influences heat transfer in hot air-dried jujube slices. Full article
(This article belongs to the Special Issue Food Integrity and Authenticity)
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