Seafood: Quality, Shelf Life, Sensory Analysis, and Intelligent Detection

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Foods of Marine Origin".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 942

Special Issue Editors


E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian Polytechnic University, Dalian, China
Interests: intelligent manufacturing of seafood; food intelligence detection

E-Mail Website
Guest Editor
School of Light industry & Chemical Engineering, Dalian Polytechnic University, Dalian, China
Interests: food quality testing; functional substance embedding; design and development of soft matter materials

E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian Polytechnic University, Dalian, China
Interests: food intelligent processing equipment; manufacturing of seafood

Special Issue Information

Dear Colleagues,

In recent years, significant progress has been made in the field of food quality, shelf-life, and sensory evaluation. In terms of shelf-life prediction models, the traditional model based on chemical reaction kinetics has been continuously expanded, and multifactorial integrated models incorporating microbial growth and physical change models have been widely used. For sensory evaluation technology, traditional sensory analysis methods, such as difference test and descriptive analysis, are gradually moving towards digitalization and intelligence. The rise in bionic sensing technologies such as electronic nose and electronic tongue, which can quickly and objectively detect the odor and taste characteristics of food and transform them into quantifiable data, has led to partial compensation for the shortcomings of traditional sensory evaluations in terms of strong subjectivity and poor repeatability. In addition, computer vision, hyperspectral imaging, and other technologies play an increasingly important role in the sensory evaluation of food quality and lead to accurate monitoring of food quality, shelf-life, and changes in the appearance of products through the identification and analysis of maps. Research studies on food quality, shelf-life, and sensory evaluation are of irreplaceable significance in promoting sustainable development of the food industry, protecting consumers' rights and interests, and safeguarding public health.

We look forward to your submissions.

Prof. Dr. Huihui Wang
Dr. Yao Li
Dr. Xu Zhang
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

  • food quality
  • shelf-life
  • sensory evaluation
  • multi-sensor perception
  • computer vision
  • hyperspectral imaging
  • electronic nose
  • electronic tongue
  • deep learning for seafood

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 1898 KB  
Article
Computer Vision-Based Deep Learning Modeling for Salmon Part Segmentation and Defect Identification
by Chunxu Zhang, Yuanshan Zhao, Wude Yang, Liuqian Gao, Wenyu Zhang, Yang Liu, Xu Zhang and Huihui Wang
Foods 2025, 14(20), 3529; https://doi.org/10.3390/foods14203529 - 16 Oct 2025
Viewed by 692
Abstract
Accurate cutting of salmon parts and surface defect detection are the key steps to enhance the added value of its processing. At present, mainstream manual inspection methods have low accuracy and efficiency, making it difficult to meet the demands of industrialized production. A [...] Read more.
Accurate cutting of salmon parts and surface defect detection are the key steps to enhance the added value of its processing. At present, mainstream manual inspection methods have low accuracy and efficiency, making it difficult to meet the demands of industrialized production. A machine vision inspection method based on a two-stage fusion network is proposed in this paper, aiming to achieve accurate cutting of salmon parts and efficient recognition of defects. The fish body image is collected by building a visual inspection system, and the dataset is constructed by preprocessing and data enhancement. For the part cutting, the improved U-Net model that introduces the CBAM attention mechanism is used to strengthen the extraction ability of the fish body texture features. For defect detection, the two-stage fusion architecture is designed to quickly locate the defective region by adding the YOLOv5 of the P2 small target detection layer first, and then the cropped region is fed into the improved U-Net for accurate cutting. The experimental results demonstrate that the improved U-Net achieves a mean average precision (mAP) of 96.87% and a mean intersection over union (mIoU) of 94.33% in part cutting, representing improvements of 2.44% and 1.06%, respectively, over the base model. In defect detection, the fusion model attains an mAP of 94.28% with a processing speed of 7.30 fps, outperforming the single U-Net by 28.02% in accuracy and 236.4% in efficiency. This method provides a high-precision, high-efficiency solution for intelligent salmon processing, offering significant value for advancing automation in the aquatic product processing industry. Full article
Show Figures

Figure 1

Back to TopTop