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Recent Advances in the Improvement of Food Quality and Safety: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Food Science and Technology".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 414

Special Issue Editor

Special Issue Information

Dear Colleagues,

In the face of increasing consumer demand and stricter regulatory standards, the continuous improvement of methods for food production, processing, and control has become a crucial challenge for scientists and the food industry. Submissions related to novel foods are highly recommended for this Special Issue, which will focus on varied topics, including, but not limited to, the following:

  • Advanced food processing technologies: We welcome studies on innovative methods of preservation, packaging, and storage that extend the shelf life of products without compromising their quality. This includes high-pressure processing, ultrasound treatment, supercritical extraction, pulsed electric fields, and cold plasma technology.
  • Food safety: Novel approaches to detecting and eliminating microbiological, chemical, and physical contaminants are welcome. This encompasses advanced pathogen detection methods, foodborne disease prevention strategies, and the development of safer food contact materials.
  • Food quality analysis and monitoring: This includes advanced analytical techniques, diagnostic tools, and real-time monitoring systems that ensure the consistent quality of food products. Topics of interest include non-destructive testing methods, machine learning applications for quality prediction, and blockchain technology for traceability.
  • Sustainable food technologies: We invite papers on methods for minimizing the environmental impact of food production, including waste reduction technologies and efficient resource utilization. Areas such as the valorization of food by-products, water- and energy-saving techniques, and the development of eco-friendly packaging materials are highly relevant.
  • Biotechnology in food production: This includes articles on the use of microorganisms and enzymes to improve the nutritional and sensory properties of food products. This section welcomes research on probiotics, genetically modified organisms (GMOs), and fermentation technology.
  • Regulations and standards: Reviews of the latest changes in food safety and quality regulations and their impact on the industry are welcome. Papers discussing the international harmonization of standards, compliance strategies, and risk assessment methodologies are encouraged.
  • Contemporary research hot topics: This includes emerging areas, such as personalized nutrition, the role of microbiomes in food safety and quality, nanotechnology applications in food systems, and the use of artificial intelligence in food production and safety management.

Dr. Agata Urszula Fabiszewska
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 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. Applied Sciences 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 2400 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

  • bioprocesses
  • sustainability
  • novel food
  • food safety
  • artificial intelligence

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Published Papers (1 paper)

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Research

31 pages, 4140 KB  
Article
Mapping Frozen Fish Quality via Machine Learning for Predictive Spoilage Kinetics Under Subzero Conditions
by İlknur Meriç Turgut and Dilara Gerdan Koc
Appl. Sci. 2025, 15(23), 12611; https://doi.org/10.3390/app152312611 - 28 Nov 2025
Viewed by 280
Abstract
Frozen storage modulates the progression of key oxidative and nitrogenous reactions within fish muscle. We therefore identify the drivers of quality degradation in filleted whiting (Merlangius merlangus) and Atlantic bonito (Sarda sarda) during 10-month frozen storage at −12, −18, [...] Read more.
Frozen storage modulates the progression of key oxidative and nitrogenous reactions within fish muscle. We therefore identify the drivers of quality degradation in filleted whiting (Merlangius merlangus) and Atlantic bonito (Sarda sarda) during 10-month frozen storage at −12, −18, and −24 °C, and to integrate state-of-the-art machine learning architectures to predict deterioration kinetics and shelf-life trajectories. To this end, following blast freezing at −30 °C for 6 h, samples were periodically (0, 2, 4, 6, 8, and 10 months) assessed for biochemical indices—total volatile base nitrogen (TVB-N), trimethylamine nitrogen (TMA-N), thiobarbituric acid (TBA), and free fatty acids (FFA)—in which proximate composition and pH were determined solely on the same day (Day 0). Whiting displayed progressive increases in all indices, yet values at −24 °C remained within regulatory acceptability, supporting a safe storage period of up to nine months. By contrast, Atlantic bonito retained TVB-N and TMA-N values below regulatory thresholds across storage, but TBA exceeded acceptability limits from the second month onward, and FFA rose after month four. Complementing these findings, machine learning (ML) approaches, including Naïve Bayes, Support Vector Machine, Decision Tree, Multilayer Perceptron, and Extreme Gradient Boosting, were implemented to classify species and predict spoilage kinetics, with Extreme Gradient Boosting achieving the highest accuracy (98.9%, κ = 0.978) and Random Forest providing superior regression performance (R2 = 0.986, RMSE = 0.392). ML models consistently identified TVB-N as the dominant predictor for whiting and TBA for Atlantic bonito, correctly capturing the critical time points of 9 months and 2 months, respectively, and highlighting −24 °C as the most reliable condition for preserving quality. These results underscore the potential of ML as a transformative tool for accurate shelf-life prediction and smarter cold-chain management in frozen fish products. Full article
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