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Artificial Intelligence and Numerical Simulation in Food Engineering
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Artificial Intelligence and Numerical Simulation in Food Engineering

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

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 8274

Special Issue Editors

Dr. Mahdi Rashvand

E-Mail Website
Guest Editor
Centre for Business and Industry Transformation, Nottingham Trent University, Nottingham, UK
Interests: food engineering; artificial intelligence; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Hongwei Zhang

E-Mail Website
Guest Editor
Advanced Food Innovation Centre, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK
Interests: advanced process control; sustainable food processing; Industry 4.0; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Genovese

E-Mail Website
Guest Editor
Department of Agriculture, Forest, Food and Environmental Sciences, University of Basilicata, 85100 Potenza, Italy
Interests: energy saving and process analysis; cold storage room; automated systems of storage and packing; computer application in all agricultural activities and food processing; post-harvest and storage technology; packaging systems; food loss
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) and numerical simulation is revolutionizing food engineering, enabling innovative solutions in process optimization, quality control, and supply chain management. AI-based methods such as machine learning, deep learning, and computer vision enable predictive modeling and real-time decision-making with improved efficiency and product uniformity. Also, numerical simulations, from FEA to CFD, enable accurate analysis of thermal, mechanical, and biochemical processes in food processing. This Special Issue is devoted to documenting state-of-the-art research at the intersection of AI and numerical simulation in food engineering for automating, optimizing, and making more sustainable food processing. We invite submissions that report new applications, theoretical developments, and industrial case studies illustrating the potential of these technologies in transforming the food industry.

We are writing to invite you to make a contribution to the Special Issue titled "Artificial Intelligence and Numerical Simulation in Food Engineering". The issue aims to cover the latest advances in AI-driven modeling, machine learning techniques, and numerical simulation techniques such as computational fluid dynamics (CFD) and finite element analysis (FEA) in food processing and quality control. Original research articles, reviews, and case studies that report innovative solutions for automation, optimization, and sustainability in food engineering are invited. Your research experience in this field would be a valuable addition to the issue, and I would like to invite you to submit your research or collaborate with other researchers.

Dr. Mahdi Rashvand
Dr. Hongwei Zhang
Dr. Francesco Genovese
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. 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

  • artificial intelligence
  • machine learning
  • smart sensors
  • digital twins
  • sustainability

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

Published Papers (3 papers)

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Research

10 pages, 245 KB  
Article
Assessment of Egg Quality Across Seasons, Storage Durations, and Temperatures in Commercial Laying Hens
by Olusegun O. Ikusika, Hombisa Dwakasa, Sinovuyo Luphuzi, Oluwakamisi F. Akinmoladun and Conference T. Mpendulo
Appl. Sci. 2025, 15(19), 10344; https://doi.org/10.3390/app151910344 - 24 Sep 2025
Viewed by 2647
Abstract
Egg quality plays a crucial role in determining shelf life, consumer acceptability, and economic value in commercial egg production systems. This study evaluated the effects of season, storage temperature, and duration on internal and external egg quality. A total of 256 freshly laid [...] Read more.
Egg quality plays a crucial role in determining shelf life, consumer acceptability, and economic value in commercial egg production systems. This study evaluated the effects of season, storage temperature, and duration on internal and external egg quality. A total of 256 freshly laid eggs were collected during winter and spring, and stored at four temperatures (0 °C, 10 °C, 20 °C, and 30 °C) for 0, 10, 20, and 30 days. The experimental design was a 2 × 4 × 4 factorial design (season × temperature × duration), with 128 eggs collected each in both seasons. Each treatment combination included 8 eggs (2 eggs × 4 replicates). External quality (egg weight and shell thickness) and internal quality (yolk and albumen height, width, pH, Haugh units, and yolk colour) parameters were evaluated at 10-day intervals. Egg weight significantly decreased (p < 0.05) from 67.67 g on Day 0 to 59.39 g on Day 30. Similarly, shell thickness decreased (p < 0.05) from 40.00 mm to 36.00 mm over the same period. Yolk pH increased from 6.68 to 7.16 (p < 0.05), and albumen pH rose (p < 0.05) from 7.30 to 7.60, particularly at higher storage temperatures (20 °C and 30 °C). Yolk and albumen heights decreased significantly (p < 0.05), from 2.03 cm to 1.23 cm and 6.65 cm to 3.88 cm, respectively, indicating structural degradation. Yolk width increased from 2.58 cm to 3.49 cm (p > 0.05), and albumen width expanded (p < 0.05) from 5.33 cm to 9.21 cm, with a notably greater spread observed at 30 °C (14.68 cm). Haugh unit values declined markedly from 98.46 to 60.00 over 30 days (p < 0.05), indicating a significant deterioration in internal egg quality. Seasonal effects were also evident: spring eggs had greater shell thickness (40.60 mm vs. 38.45 mm in winter; p = 0.01) and brighter yolk colour, whereas winter eggs had higher yolk pH values (6.47 vs. 6.28; p = 0.009), and superior yolk and albumen heights. These findings indicate that storage beyond 10 days, particularly above 20 °C, compromises egg quality and that seasonality significantly affects multiple quality parameters. Cold storage and seasonally optimized management strategies are recommended to preserve egg integrity and marketability in commercial poultry systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Numerical Simulation in Food Engineering)
24 pages, 5968 KB  
Article
Life Cycle Assessment of a Digital Tool for Reducing Environmental Burdens in the European Milk Supply Chain
by Yuan Zhang, Junzhang Wu, Haida Wasim, Doris Yicun Wu, Filippo Zuliani and Alessandro Manzardo
Appl. Sci. 2025, 15(15), 8506; https://doi.org/10.3390/app15158506 - 31 Jul 2025
Viewed by 1119
Abstract
Food loss and waste from the European Union’s dairy supply chain, particularly in the management of fresh milk, imposes significant environmental burdens. This study demonstrates that implementing Radio Frequency Identification (RFID)-enabled digital decision-support tools can substantially reduce these impacts across the region. A [...] Read more.
Food loss and waste from the European Union’s dairy supply chain, particularly in the management of fresh milk, imposes significant environmental burdens. This study demonstrates that implementing Radio Frequency Identification (RFID)-enabled digital decision-support tools can substantially reduce these impacts across the region. A cradle-to-grave life cycle assessment (LCA) was used to quantify both the additional environmental burdens from RFID (tag production, usage, and disposal) and the avoided burdens due to reduced milk losses in the farm, processing, and distribution stages. Within the EU’s fresh milk supply chain, the implementation of digital tools could result in annual net reductions of up to 80,000 tonnes of CO2-equivalent greenhouse gas emissions, 81,083 tonnes of PM2.5-equivalent particulate matter, 84,326 tonnes of land use–related carbon deficit, and 80,000 cubic meters of freshwater-equivalent consumption. Spatial analysis indicates that regions with historically high spoilage rates, particularly in Southern and Eastern Europe, see the greatest benefits from RFID enabled digital-decision support tools. These environmental savings are most pronounced during the peak months of milk production. Overall, the study demonstrates that despite the environmental footprint of RFID systems, their integration into the EU’S dairy supply chain enhances transparency, reduces waste, and improves resource efficiency—supporting their strategic value. Full article
(This article belongs to the Special Issue Artificial Intelligence and Numerical Simulation in Food Engineering)
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23 pages, 3752 KB  
Article
Food Waste Detection in Canteen Plates Using YOLOv11
by João Ferreira, Paulino Cerqueira and Jorge Ribeiro
Appl. Sci. 2025, 15(13), 7137; https://doi.org/10.3390/app15137137 - 25 Jun 2025
Viewed by 4160
Abstract
This work presents a Computer Vision (CV) platform for Food Waste (FW) detection in canteen plates exploring a research gap in automated FW detection using CV models. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen [...] Read more.
This work presents a Computer Vision (CV) platform for Food Waste (FW) detection in canteen plates exploring a research gap in automated FW detection using CV models. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen plates images before and after lunch or dinner, and data augmentation techniques were applied to enhance the model’s robustness. Subsequently, a CV model was developed using YOLOv11 to classify the percentage of FW on a plate, distinguishing between edible food items and non-edible discarded material. To evaluate the performance of the model, we used a real dataset as well as three benchmarking datasets with food plates, in which it could be detected waste. For the real dataset, the system achieved a mean average precision (mAP) of 0.343, a precision of 0.62, and a recall of 0.322 on the test set as well as demonstrating high accuracy in classifying waste considering the traditional evaluation metrics on the benchmarking datasets. Given these promising results and the provision of open-source code on a GitHub repository, the platform can be readily utilized by the research community and educational institutions to monitor FW in student meals and proactively implement reduction strategies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Numerical Simulation in Food Engineering)
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