Emerging Analytical Technologies for Food Contaminants Detection—Volume II

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

Deadline for manuscript submissions: 10 August 2024 | Viewed by 777

Special Issue Editor


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Guest Editor
School of Food Science and Engineering, Hainan University, Haikou, China
Interests: food contaminants; immunoassay; biosensor; nanobody; peptidomimetic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food contaminants, such as mycotoxin, pesticides, heavy metals, veterinary drugs and illegal additives pose serious threats to public health and food safety. Developing rapid and sensitive detection methods is critical to minimize exposure to food contaminants. Instrumental techniques for food analysis, such as the combination of high-performance liquid chromatography or gas chromatography with mass spectrometry, are well established. Immunoassay technologies have become beneficial supplements to instrumental techniques because of their time-saving properties for the routine laboratory that requires high-throughput, cost-effective and non-expensive instrumentation, immediacy in decision making and field detection. Due to the advances in gene engineering and material engineering, various novel recognition elements, such as nanobodies, aptamers and peptidomimetics, as well as nanomaterials such as magnetic beads, quantum dots, metal–organic frameworks and aggregation-induced emission probes have facilitated the development of many novel immunoassay technologies. Therefore, this Special Issue aims to publish the latest research on the emerging immunoassay technologies which involve novel recognition elements and nanomaterials to detect food contaminants. Additionally, reviews in the field of immunoassay for food contaminants are welcomed.

Prof. Dr. Xing Liu
Guest Editor

Manuscript Submission Information

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Keywords

  • antibody
  • aptamer
  • peptidomimetic
  • immunoassay
  • immunosensor
  • nanomaterial
  • food contaminants

Published Papers (1 paper)

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Research

12 pages, 8861 KiB  
Article
Enzyme Cascade Amplification-Based Immunoassay Using Alkaline Phosphatase-Linked Single-Chain Variable Fragment Fusion Tracer and MnO2 Nanosheets for Detection of Deoxynivalenol in Corn Samples
by Guifang Xie, Fujing Mao, Yirui Huang, Li Wen, Zhichang Sun, Zhenyun He and Xing Liu
Foods 2024, 13(13), 2009; https://doi.org/10.3390/foods13132009 - 25 Jun 2024
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Abstract
Deoxynivalenol (DON) is a common mycotoxin that contaminates cereals. Therefore, the development of sensitive and efficient detection methods for DON is essential to guarantee food safety and human health. In this study, an enzyme cascade amplification-based immunoassay (ECAIA) using a dual-functional alkaline phosphatase-linked [...] Read more.
Deoxynivalenol (DON) is a common mycotoxin that contaminates cereals. Therefore, the development of sensitive and efficient detection methods for DON is essential to guarantee food safety and human health. In this study, an enzyme cascade amplification-based immunoassay (ECAIA) using a dual-functional alkaline phosphatase-linked single-chain fragment variable fusion tracer (scFv-ALP) and MnO2 nanosheets was established for DON detection. The scFv-ALP effectively catalyzes the hydrolysis of ascorbyl-2-phosphate (AAP) to produce ascorbic acid (AA). This AA subsequently interacts with MnO2 nanosheets to initiate a redox reaction that results in the loss of oxidizing properties of MnO2. In the absence of ALP, MnO2 nanosheets can oxidize 3,3′,5,5′-tetramethylbenzidine (TMB) to produce the blue oxidized product of TMB, which exhibits a signal at a wavelength of 650 nm for quantitative analysis. After optimization, the ECAIA had a limit of detection of 0.45 ng/mL and a linear range of 1.2–35.41 ng/mL. The ECAIA exhibited good accuracy in recovery experiments and high selectivity for DON. Moreover, the detection results of the actual corn samples correlated well with those from high-performance liquid chromatography. Overall, the proposed ECAIA based on the scFv-ALP and MnO2 nanosheets was demonstrated as a reliable tool for the detection of DON in corn samples. Full article
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