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: 25 February 2025 | Viewed by 1867

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
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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

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Keywords

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

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Published Papers (2 papers)

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Research

14 pages, 3603 KiB  
Article
A Lincomycin-Specific Antibody Was Developed Using Hapten Prediction, and an Immunoassay Was Established to Detect Lincomycin in Pork and Milk
by Yuhan Shang, Dandan Zhang, Yun Shen, Yuanhu Pan, Jing Wang and Yulian Wang
Foods 2024, 13(19), 3118; https://doi.org/10.3390/foods13193118 - 29 Sep 2024
Viewed by 582
Abstract
Prolonged consumption of animal-derived foods containing high levels of lincomycin (LIN) residues can adversely impact human health. Therefore, it is essential to develop specific antibodies and immunoassay methods for LIN. This study utilized computational chemistry to predict the efficacy of LIN haptens prior [...] Read more.
Prolonged consumption of animal-derived foods containing high levels of lincomycin (LIN) residues can adversely impact human health. Therefore, it is essential to develop specific antibodies and immunoassay methods for LIN. This study utilized computational chemistry to predict the efficacy of LIN haptens prior to chemical synthesis, with subsequent confirmation obtained through an immunization experiment. A hybridoma cell line named LIN/1B11 was established, which is specific to LIN. The optimized indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) method exhibited high specificity for detecting LIN residues, with an IC50 value of 0.57 ± 0.03 µg/kg. The method effectively detected LIN residues in pork and milk samples, achieving a limit of detection (LOD) ranging from 0.81 to 1.20 µg/kg and a limit of quantification (LOQ) ranging from 2.09 to 2.29 µg/kg, with recovery rates between 81.9% and 108.8%. This study offers a valuable tool for identifying LIN residues in animal-derived food products. Furthermore, the efficient hapten prediction method presented herein improves antibody preparation efficiency and provides a simple method for researchers in screening haptens. Full article
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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
Viewed by 911
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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