Advance of Rapid Analysis Technology for Detecting Food Contaminants

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 11915

Special Issue Editors


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Guest Editor
LAQV/REQUIMTE, Laboratory of Bromatology and Hydrology, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
Interests: analytical methods; chromatography; human digestion; food chemistry; food safety
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
LAQV/REQUIMTE, Laboratory of Bromatology and Hydrology, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
Interests: analytical methods; food lipids; food technology; food authenticity; food safety; food waste
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unsafe food containing deleterious chemicals and pathogenic microorganisms is responsible for foodborne diseases that affects millions every year. Increased awareness and concerns about food safety has skyrocketed the development of novel and rapid technologies for detecting contamination. In loco quality evaluation and assurance of agrifoods for the processing industries and fresh market or even for curious consumers has also driven technological advancements. Conventional methods are apparently less expensive and simple but can be laborious and time consuming. Therefore, rapid methods offering higher sensitivity and specificity, while reducing human errors are necessary to overcome the limitations of conventional approaches.

From single analysis to multi-residue methods, from chemical to microbiological evaluations, this Special Issue entitled “Advance of Rapid Analysis Technology for Detecting Food Contaminants” aims to assemble the most innovative research including new method of analysis and new applications of analytical methods in the field of food safety.

Dr. Rebeca Cruz
Prof. Dr. Susana Casal
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 safety
  • food processing contaminants
  • environmental contaminants
  • pathogenic microorganisms
  • anthropogenic contaminants
  • allergens
  • sample preparation
  • multi-residue methods
  • biosensors
  • rapid methods
  • high-throughput screening
  • non-invasive technology
  • sustainable methods
  • New applications of analytical methods

Published Papers (3 papers)

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Research

15 pages, 2556 KiB  
Article
Selection and Application of ssDNA Aptamers for Fluorescence Biosensing Detection of Malachite Green
by Miaojia Xie, Zanlin Chen, Fengguang Zhao, Ying Lin, Suiping Zheng and Shuangyan Han
Foods 2022, 11(6), 801; https://doi.org/10.3390/foods11060801 - 10 Mar 2022
Cited by 11 | Viewed by 2979 | Correction
Abstract
Malachite green oxalate (MG) is a kind of veterinary drug, which is freely soluble in water and hazardous to aquatic products, resulting in food toxicity and human health problems. The demand for effective and sensitive detection of MG residues is increasing in food [...] Read more.
Malachite green oxalate (MG) is a kind of veterinary drug, which is freely soluble in water and hazardous to aquatic products, resulting in food toxicity and human health problems. The demand for effective and sensitive detection of MG residues is increasing in food safety. In this work, three DNA aptamers MG-36-12/16/17 targeting MG with good affinity (Kd values were 169.78, 71.94, and 102.46 μM, respectively) were obtained by Capture-SELEX. Furthermore, MG-36-12, MG-76-16-6A, and MG-36-17 were found to perform sensitively and specifically to detect MG as a sensing probe in a FRET fluorescent aptasensor, where the FAM-labeled aptamer and GO were employed as efficient energy donor–acceptor pair. The linear range of this aptasensor using aptamer MG-36-12 was from 1.71 to 514.29 ng/mL and the LOD was as low as 0.79 ng/mL. Additionally, the fluorescent assay using aptamer MG-36-17 to detect MG exhibited a linear relationship from 1.71 to 857.14 ng/mL and a LOD of 2.13 ng/mL. Meanwhile, the aptasensor showed high specificity to MG with no cross-reactivity to other veterinary drugs and had a mean recovery of 81.54% to 100.96% in actual water samples from the aquatic product market. Full article
(This article belongs to the Special Issue Advance of Rapid Analysis Technology for Detecting Food Contaminants)
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12 pages, 965 KiB  
Article
Offline Solid-Phase Extraction and Separation of Mineral Oil Saturated Hydrocarbons and Mineral Oil Aromatic Hydrocarbons in Edible Oils, and Analysis via GC with a Flame Ionization Detector
by José Luis Hidalgo Ruiz, Javier Arrebola Liébanas, José Luis Martínez Vidal, Antonia Garrido Frenich and Roberto Romero González
Foods 2021, 10(9), 2026; https://doi.org/10.3390/foods10092026 - 28 Aug 2021
Cited by 4 | Viewed by 4098
Abstract
A method was developed for the determination of mineral oil saturated hydrocarbons (MOSH) and mineral oil aromatic hydrocarbons (MOAH) in edible oils, achieving similar limits of quantification than those obtained by online extraction methodologies, i.e., 0.5 mg/kg. The isolation of MOSH and MOAH [...] Read more.
A method was developed for the determination of mineral oil saturated hydrocarbons (MOSH) and mineral oil aromatic hydrocarbons (MOAH) in edible oils, achieving similar limits of quantification than those obtained by online extraction methodologies, i.e., 0.5 mg/kg. The isolation of MOSH and MOAH was performed in a silver nitrated silica gel stationary phase prior to their analysis by gas chromatography–flame ionization detector (GC-FID). To improve the sensitivity, the simulated on-column injection method, using a suitable liner, was optimized. The method was validated at 0.5, 10.0 and 17.9 mg/kg, and recoveries ranged from 80 to 110%. Intra and inter-day precision were evaluated at the same levels, and relative standard deviation (RSD) was lower than 20%. The method was applied to a total of 27 samples of different types of oil previously analyzed in an accredited laboratory, detecting MOSH up to 79.2 mg/kg and MOAH up to 22.4 mg/kg. Full article
(This article belongs to the Special Issue Advance of Rapid Analysis Technology for Detecting Food Contaminants)
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13 pages, 3009 KiB  
Article
Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain
by Clíssia Barboza da Silva, Alysson Alexander Naves Silva, Geovanny Barroso, Pedro Takao Yamamoto, Valter Arthur, Claudio Fabiano Motta Toledo and Thiago de Araújo Mastrangelo
Foods 2021, 10(4), 879; https://doi.org/10.3390/foods10040879 - 16 Apr 2021
Cited by 20 | Viewed by 3709
Abstract
The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of [...] Read more.
The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection. Full article
(This article belongs to the Special Issue Advance of Rapid Analysis Technology for Detecting Food Contaminants)
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