Non-Destructive Analysis for the Detection of Contaminants in Food

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

Deadline for manuscript submissions: 30 January 2026 | Viewed by 1255

Special Issue Editors


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Guest Editor
Department of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: Raman spectroscopy; fluorescence spectroscopy; near-infrared spectroscopy; hyperspectral image; data mining; chemometrics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: enzymatic catalysis; high-value-added nutritional chemicals; nutritional food preparation; food component detection; food contaminants detection; food additives detection; enzyme; biosensor
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Food Science and Technology, Henan University of Technology, Zhengzhou 450001, China
Interests: food safety and rapid testing; electrochemical sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid detection technology has become a transformative solution to address the current challenges in global food safety monitoring. This Special Issue, "Non-Destructive Analysis for the Detection of Contaminants in Food", will focus on breakthrough methods for detecting chemical and biological contaminants in complex food matrices, including heavy metals, pesticides, antibiotics, mycotoxins, viruses, bacteria, fungi, etc. We welcome the submission of original research articles that demonstrate significant progress in areas such as detection speed, sensitivity, and field applicability, especially studies that combine nanotechnology, microfluidic technology, and artificial intelligence algorithms.

The technical foundation of effective and rapid detection systems requires careful optimization through multiple parameters. Submitted manuscripts must provide comprehensive validation data, including detection limits, quantitative ranges, cross-reactivity characteristics, and recovery rates. The following research will receive priority: (1) new signal amplification strategies using quantum dots or plasma nanoparticles; (2) detection platforms based on smartphones combined with cloud data integration;  (3) microfluidic chips that can realize the sample-to-result processing flow; and (4) food analytical methods combined with new data mining algorithms.

In addition to methodological innovation, this Special Issue also emphasizes practical application scenarios in the food supply chain. We encourage researchers to produce case studies demonstrating technology deployment, covering the following aspects: (1) border inspection at import and export, (2) quality control points in processing factories, (3) monitoring systems at the retail level, and (4) detection equipment for consumers.

Dr. Kaiyi Zheng
Prof. Dr. Bin Zou
Dr. Yiwei Xu
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 250 words) can be sent to the Editorial Office for assessment.

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

  • non-destructive detection
  • spectral analysis
  • electrochemical analysis
  • photoelectrochemical analysis
  • colorimetric method
  • microfluidics
  • molecular imprinting
  • nanobody
  • aptamer
  • pesticides detection
  • fungicides detection
  • antibiotics detection
  • mycotoxin detection

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Published Papers (1 paper)

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Review

32 pages, 2334 KB  
Review
Recent Advances in SERS-Based Detection of Organophosphorus Pesticides in Food: A Critical and Comprehensive Review
by Kaiyi Zheng, Xianwen Shang, Zhou Qin, Yang Zhang, Jiyong Shi, Xiaobo Zou and Meng Zhang
Foods 2025, 14(21), 3683; https://doi.org/10.3390/foods14213683 - 29 Oct 2025
Viewed by 1001
Abstract
Surface-enhanced Raman spectroscopy (SERS) has rapidly emerged as a powerful analytical technique for the sensitive and selective detection of organophosphorus pesticides (OPPs) in complex food matrices. This review summarizes recent advances in substrate engineering, emphasizing structure–performance relationships between nanomaterial design and molecular enhancement [...] Read more.
Surface-enhanced Raman spectroscopy (SERS) has rapidly emerged as a powerful analytical technique for the sensitive and selective detection of organophosphorus pesticides (OPPs) in complex food matrices. This review summarizes recent advances in substrate engineering, emphasizing structure–performance relationships between nanomaterial design and molecular enhancement mechanisms. Functional groups such as P=O, P=S, and aromatic rings are highlighted as key determinants of Raman activity through combined chemical and electromagnetic effects. State-of-the-art substrates, including noble metals, carbon-based materials, bimetallic hybrids, MOF-derived systems, and emerging liquid metals, are critically evaluated with respect to sensitivity, stability, and applicability in typical matrices such as fruit and vegetable surfaces, juices, grains, and agricultural waters. Reported performance commonly achieves sub-μg L−1 to low μg L−1 detection limits in liquids and 10−3 to 10 μg cm−2 on surfaces, with reproducibility often in the 5–15% RSD range under optimized conditions. Persistent challenges are also emphasized, including substrate variability, quantitative accuracy under matrix interference, and limited portability for real-world applications. Structure–response correlation models and data-driven strategies are discussed as tools to improve substrate predictability. Although AI and machine learning show promise for automated spectral interpretation and high-throughput screening, current applications remain primarily proof-of-concept rather than routine workflows. Future priorities include standardized fabrication protocols, portable detection systems, and computation-guided multidimensional designs to accelerate translation from laboratory research to practical deployment in food safety and environmental surveillance. Full article
(This article belongs to the Special Issue Non-Destructive Analysis for the Detection of Contaminants in Food)
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