Digital, Computational, and Learning Technologies for Food Analysis
A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".
Deadline for manuscript submissions: 10 June 2026 | Viewed by 103
Special Issue Editor
Interests: artificial intelligence; computer vision; image processing; food intelligent computing (AI for food safety, quality, and sensory); medical image analysis
Special Issue Information
Dear Colleagues,
The rapid development of digital, computational, and learning technologies is reshaping the landscape of food analysis and quality assessment. Traditional analytical methods, though accurate, often rely on manual operations, specialized instruments, and time-consuming procedures, limiting their efficiency and scalability. In contrast, emerging technologies such as artificial intelligence (AI), machine learning (ML), computer vision, and Internet of Things (IoT)-based sensing are enabling more intelligent, automated, and data-driven approaches to understanding and evaluating food. These methods facilitate the faster and more precise detection of composition, freshness, adulteration, and safety issues across various stages of the food supply chain.
Deep learning techniques have demonstrated remarkable potential in visual and spectral food analysis, supporting tasks such as image-based food recognition, segmentation, and portion estimation. Computational modeling and digital twin systems further enhance predictive capabilities by simulating physical and chemical changes during processing and storage. Meanwhile, IoT-enabled sensors and real-time data analytics are improving traceability, transparency, and sustainability in food production and distribution.
The integration of these technologies represents a paradigm shift toward smart food systems, where data collection, modeling, and decision-making are performed collaboratively. Beyond improving analytical accuracy, digital and learning technologies help optimize resources, reduce waste, and promote personalized nutrition and public health.
This Special Issue, “Digital, Computational, and Learning Technologies for Food Analysis”, aims to showcase the most recent developments and innovative applications in this fast-evolving domain. We welcome contributions that advance the theoretical foundations, algorithmic methods, and practical implementations of digital intelligence in food science. Topics of interest include but are not limited to machine learning for food analysis, computer vision-based food recognition, intelligent sensing, computational modeling, data fusion, and smart systems for food quality assurance, safety monitoring, and process optimization.
Dr. Zhiyong Xiao
Guest Editor
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
- digital food analysis
- artificial intelligence
- deep learning
- machine learning
- computer vision
- food quality assessment
- smart sensing
- food safety/quality
- food sensory analytics
- computational modeling
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