Advanced Technology Application in Food Quality and Safety and Intelligent Inspection Equipment

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Engineering and Technology".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 721

Special Issue Editors


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Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: non-destructive intelligent detection; origin traceability of agricultural and livestock products; agricultural electrification and automation; machine vision
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: flexible sensor; surface-enhanced raman spectroscopy; non-destructive testing

Special Issue Information

Dear Colleagues,

This Special Issue addresses the growing demands of the modern food industry by focusing on the innovative integration of advanced technologies for enhancing food quality and safety. It explores how the convergence of Artificial Intelligence, spectral imaging, IoT, and blockchain is transforming quality control paradigms and fostering the development of a new generation of intelligent inspection equipment. Topics of interest include intelligent sensing, data analytics, process optimization, full-chain traceability, and the development of novel inspection devices. By providing a platform for cutting-edge research, this collection aims to showcase pivotal advancements and support the intelligent and sustainable evolution of the food sector.

Prof. Dr. Qiaohua Wang
Guest Editor

Dr. Yingli Wang
Guest Editor Assistant

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

  • technology integration
  • food quality and safety
  • intelligent inspection
  • artificial intelligence
  • spectral imaging
  • internet of things
  • intelligent equipment
  • non-destructive testing

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Published Papers (1 paper)

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Research

15 pages, 2768 KB  
Article
Non-Destructive Detection Model and Device Development for Duck Egg Freshness
by Qian Yan, Qiaohua Wang, Meihu Ma, Zhihui Zhu, Weiguo Lin, Shiwei Liu and Wei Fan
Foods 2026, 15(7), 1211; https://doi.org/10.3390/foods15071211 - 2 Apr 2026
Viewed by 442
Abstract
To address the low accuracy of traditional freshness detection/grading and poor adaptability to different shell colors in the duck egg industry, this study developed a non-destructive detection model and an integrated device for duck egg freshness based on machine vision combined with eggshell [...] Read more.
To address the low accuracy of traditional freshness detection/grading and poor adaptability to different shell colors in the duck egg industry, this study developed a non-destructive detection model and an integrated device for duck egg freshness based on machine vision combined with eggshell optical property analysis. A four-sided yolk transmission imaging system was designed, and accurate yolk region segmentation was achieved via grayscale conversion, a weighted improved Otsu algorithm for whole-egg segmentation, histogram equalization enhancement, and K-means clustering in the LAB color space. A relational model between the average four-angle yolk projected area ratio and Haugh Units (HU) freshness grades was constructed, with grading thresholds determined by constrained optimization combined with the Youden index to balance food safety and grading accuracy. Experimental results showed the model achieved an overall freshness grade discrimination accuracy of 91.3%, with a sensitivity of 97.1% and specificity of 98.9% for inedible Grade B (HU < 60) duck eggs and below. An automated testing device was further developed, adopting a roller-rotating motor collaborative mechanism for automatic flipping and imaging, and equipped with a 10 W/5500 K LED cool white light source to solve the problem of poor adaptability to different shell colors. The device achieved an overall discrimination accuracy of 88.5% with a detection time of ≤5 s per egg, and its host computer can real-time output the yolk area ratio, predicted HU value, and freshness level. This study provides a high-precision and low-cost technical solution for the refined grading of the poultry egg industry. Full article
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