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Foods
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Rapid Detection Technology Applied in Food Safety
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Rapid Detection Technology Applied in Food Safety

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 2930

Special Issue Editors

Dr. Qi Zhao

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Guest Editor
National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
Interests: nanomaterials; solid phase extraction
Special Issues, Collections and Topics in MDPI journals
Dr. Huiyu Li

E-Mail Website
Guest Editor
Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: analytical chemistry; nanotechnology; sensors
Dr. Xi Yu

E-Mail Website
Guest Editor
Faculty of Medicine, Macau University of Science and Technology, Avenida Wai Long Taipa, Macau 999078, China
Interests: food science; analytical chemistry; nanotechnology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ligang Chen

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Guest Editor
College of Chemistry, Chemical Engineering and Resource Utilization, Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, China
Interests: analytical chemistry; nanotechnology; sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food safety has always been an important issue of public concern. In recent years, driven by policies and markets, the rapid testing market has grown rapidly. Rapid food safety testing technology is an important support for food safety protection, but conventional laboratory testing methods and instruments find it difficult to monitor all aspects of food safety conditions in a timely, rapid, and comprehensive manner, which requires a large number of fast, convenient, accurate, and sensitive food safety analysis as well as rapid testing technologies that can meet this requirement.

This Special Issue focuses on the progress in rapid food safety testing technology, rapid food safety testing supporting the development of pretreatment technology, the rapid food safety testing of core raw materials and equipment, rapid food safety testing technology, advanced applications, pesticide/veterinary drug residuals rapid detection technology, the rapid detection of biological toxins, the rapid detection of pathogenic micro-organisms, the rapid detection of new food hazards and other topics, and jointly exploring new food safety rapid testing technologies as well as applications. This has successfully built a bridge and quality platform for communication and exchange in the field of rapid food safety testing, which is of great significance in realizing the forward movement of the supervision of hazardous factors in food, enhancing the effectiveness of food safety supervision and boosting the high-quality development of the food industry.

Dr. Qi Zhao
Dr. Huiyu Li
Dr. Xi Yu
Prof. Dr. Ligang Chen
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

  • sample preparation
  • nondestructive testing
  • mass spectrum
  • electrochemistry
  • sensors
  • pesticide and veterinary drug residuals
  • biological toxins
  • hazardous substances produced by processing

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Published Papers (3 papers)

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Research

19 pages, 16378 KiB  
Article
Classification of Chicken Carcass Breast Blood-Related Defects Using Hyperspectral Imaging Combined with Convolutional Neural Networks
by Liukui Duan, Juanfang Bao, Hao Yang, Liuqian Gao, Xu Zhang, Shengjie Li and Huihui Wang
Foods 2024, 13(23), 3745; https://doi.org/10.3390/foods13233745 - 22 Nov 2024
Viewed by 348
Abstract
For chicken carcass breast blood-related defects (CBDs), which occur with high frequency, the visual features are approximated in terms of the similarity of the composition of these defects, making it challenging to classify them, either manually or automatically, using conventional machine vision. The [...] Read more.
For chicken carcass breast blood-related defects (CBDs), which occur with high frequency, the visual features are approximated in terms of the similarity of the composition of these defects, making it challenging to classify them, either manually or automatically, using conventional machine vision. The aim of this paper was to introduce a method of CBD classification based on hyperspectral imaging combined with Convolutional Neural Networks (CNNs). To process hyperspectral data, the Improved Firefly Band Selection Algorithm was constructed with the 1-D CNN CBD classification model as the objective function, achieving a reduction in the dimensionality of hyperspectral data. The multidimensional data CBD classification models were developed based on YOLOv4 and Faster R-CNN, incorporating the 1-D CNN CBD classification model and the feature fusion layer. The combination of hyperspectral data and CNN can effectively accomplish the classification of CBDs, although different model architectures emphasize classification speed and accuracy differently. The multidimensional data YOLOv4 CBD classification model achieves an mAP of 0.916 with an inference time of 41.8 ms, while the multidimensional data Faster R-CNN CBD classification model, despite having a longer inference time of 58.2 ms, reaches a higher mAP of 0.990. In practical production scenarios, the appropriate classification model can be selected based on specific needs. Full article
(This article belongs to the Special Issue Rapid Detection Technology Applied in Food Safety)
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16 pages, 3010 KiB  
Article
Determination of Free Fatty Acids in Krill Oil during Storage Based on NH2-MMS
by Shibing Zhang, Yiran Wang, Chunyu Yang, Xi Wang, Siyi Wang, Jiping Yin, Yinan Du, Di Wu, Jiangning Hu and Qi Zhao
Foods 2024, 13(17), 2736; https://doi.org/10.3390/foods13172736 - 28 Aug 2024
Viewed by 737
Abstract
In this study, amino-modified micro-mesoporous silica (NH2-MMS) with hierarchical pores was prepared by modifying micro-mesoporous silica ZSM-5 with 3-aminopropyltriethoxysilane and used as an adsorbent in solid-phase extraction to analyze free fatty acids (FFAs) in krill oil during storage for an initial [...] Read more.
In this study, amino-modified micro-mesoporous silica (NH2-MMS) with hierarchical pores was prepared by modifying micro-mesoporous silica ZSM-5 with 3-aminopropyltriethoxysilane and used as an adsorbent in solid-phase extraction to analyze free fatty acids (FFAs) in krill oil during storage for an initial time. The Brunner Emmet Teller adsorption experiment and Fourier transform infrared spectroscopy demonstrate that NH2-MMS, with a hierarchical pore structure, was successfully synthesized. The adsorption experiments, especially static adsorption, indicate that the absorption ability of the prepared NH2-MMS, with a hierarchical pore structure, toward FFAs was better than that of traditional amino-modified mesoporous silica (SBA-15) with a mesoporous structure at all temperature and concentrations. Fairly low limits of detection (0.06–0.15 μg g−1), acceptable recoveries (85.16–94.31%), and precision (0.08–5.26%) were attained under ideal circumstances. Moreover, NH2-MMS has the advantages of easy preparation and being environmentally friendly. As a result, this method offers an alternative to the current method for determining FFAs in different kinds of oil specimens. Full article
(This article belongs to the Special Issue Rapid Detection Technology Applied in Food Safety)
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14 pages, 3664 KiB  
Article
Development of a Time-Resolved Fluorescent Microsphere Test Strip for Rapid, On-Site, and Sensitive Detection of Picoxystrobin in Vegetables
by Junjie Chen, Lidan Chen, Yongyi Zhang, Siyi Xiang, Ruizhou Zhang, Yudong Shen, Jiaming Liao, Huahui Xie and Jinyi Yang
Foods 2024, 13(3), 423; https://doi.org/10.3390/foods13030423 - 28 Jan 2024
Viewed by 1267
Abstract
Picoxystrobin (PIC) is a fungicide extensively used for disease control in both crops and vegetables. Residues of PIC in vegetables pose a potential threat to human health due to their accumulation in the food chain. In this study, a specific PIC monoclonal antibody [...] Read more.
Picoxystrobin (PIC) is a fungicide extensively used for disease control in both crops and vegetables. Residues of PIC in vegetables pose a potential threat to human health due to their accumulation in the food chain. In this study, a specific PIC monoclonal antibody (mAb) was developed by introducing a carboxylic acid arm into PIC and subsequently preparing a hapten and an artificial antigen. A sensitive and rapid time-resolved fluorescence immunochromatographic assay (TRFICA) was established based on the mAb. Subsequently, using a time-resolved fluorescent microsphere (TRFM) as signal probe, mAbs and microspheres were covalently coupled. The activated pH, the mAb diluents, the mAb amount, and the probe amount were optimized. Under optimized conditions, the quantitative limits of detection (qLOD) of PIC in cucumber, green pepper, and tomato using TRFICA were established at 0.61, 0.26, and 3.44 ng/mL, respectively; the 50% inhibiting concentrations (IC50) were 11.76, 5.29, and 37.68 ng/mL, respectively. The linear ranges were 1.81–76.71, 0.80–35.04, and 8.32–170.55 ng/mL, respectively. The average recovery in cucumber, green pepper, and tomato samples ranged from 79.8% to 105.0%, and the corresponding coefficients of variation (CV) were below 14.2%. In addition, 15 vegetable samples were selected and compared with the results obtained using ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS). The results revealed a high degree of concordance between the proposed method and UPLC-MS/MS. In conclusion, the devised TRFICA method is a valuable tool for rapid, on-site, and highly sensitive detection of PIC residues in vegetables. Full article
(This article belongs to the Special Issue Rapid Detection Technology Applied in Food Safety)
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