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Artificial Intelligence and Modeling Science in the Food Industry
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Artificial Intelligence and Modeling Science in the Food Industry

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2179

Special Issue Editors

Dr. Changsu Xu

E-Mail Website
Guest Editor
College of Engineering and Technology, Southwest University, Chongqing 400715, China
Interests: agricultural engineering; food engineering; mechanical equipment; intelligent technology; model; information perception; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Han Tang

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: intelligent equipment; food crops; agricultural engineering; discrete element simulation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Yamine Bouzembrak

E-Mail Website
Guest Editor
Information Technology, Wageningen University, Wageningen University and Research, Wageningen, The Netherlands
Interests: food quality; food security; simulation models; health foods; big data; machine learning; artificial intelligence; mathematical models; nutrition and health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) and modeling science have emerged as transformative tools in the food industry, revolutionizing processes from production to consumption. With the growing demand for sustainable food systems, enhanced quality control, and personalized nutrition, data-driven technologies such as machine learning, deep learning, predictive modeling, and computer vision are becoming increasingly utilized to address complex challenges, including optimizing supply chains, improving food safety, and innovating product development. However, gaps remain in integrating these advanced technologies into practical food science applications, particularly in translating theoretical models to real-world scenarios and standardizing data collection across diverse food matrices.

This Special Issue aims to showcase cutting-edge research that bridges AI, modeling science, and food science, fostering interdisciplinary dialogue to advance this field. Key objectives include the following:

  • Showcasing AI-driven solutions for food quality assessment, safety monitoring, and process optimization.
  • Exploring modeling approaches for predicting food properties, flavor profiles, and nutritional outcomes.
  • Promoting sustainable food systems through AI-enhanced resource efficiency and waste reduction.
  • Encouraging cross-disciplinary collaborations between food scientists, data scientists, and engineers.

Dr. Changsu Xu
Dr. Han Tang
Dr. Yamine Bouzembrak
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

  • artificial intelligence (AI)
  • machine learning (ML)
  • predictive modeling
  • food quality and safety
  • 3D food printing
  • computer vision
  • big data
  • simulation analysis
  • deep learning

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

Published Papers (2 papers)

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Research

30 pages, 8293 KB  
Article
Food Origin Authenticity Using Deep Learning and Citizen Science: Bananas Case Study
by Nikolaos Fragkos, Yamine Bouzembrak, Sara Wilhelmina Erasmus and Filipi Miranda Soares
Foods 2026, 15(10), 1628; https://doi.org/10.3390/foods15101628 - 7 May 2026
Viewed by 295
Abstract
This study introduces an Artificial Intelligence (AI)-based proof-of-concept approach to tackle food fraud by using convolutional neural networks (CNNs) and citizen science-generated imagery to predict the country of origin of Cavendish banana cultivars (Musa spp.). A total of 6000 images were collected [...] Read more.
This study introduces an Artificial Intelligence (AI)-based proof-of-concept approach to tackle food fraud by using convolutional neural networks (CNNs) and citizen science-generated imagery to predict the country of origin of Cavendish banana cultivars (Musa spp.). A total of 6000 images were collected from iNaturalist, and a CNN classifier was trained to distinguish bananas sourced from six countries. Transfer learning was leveraged, and among nine pre-trained models tested, MobileNetV1 demonstrated the best trade-off between performance and computational efficiency. Following model fine-tuning, data augmentation was implemented to mitigate class imbalance and ensure a dense feature space. The model achieved an average accuracy of 0.86 with Monte Carlo Cross Validation and 0.77 with a 5-Fold Cross Validation. The final selected model attained a validation accuracy of 0.79. Accordingly, this study should be viewed as a foundational proof-of-concept demonstrating the potential of AI for origin detection at the cultivation stage. While the current evaluation framework reflects an early-stage experimental setting, the findings introduce a promising new dimension for proactive food fraud detection. Moving forward, this pipeline provides a foundation that can be expanded and independently validated. Full article
(This article belongs to the Special Issue Artificial Intelligence and Modeling Science in the Food Industry)
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19 pages, 9951 KB  
Article
Adaptive Identification of Food Sweetness Concentration: An Electroencephalogram Feature Classification Network Under Taste Stimulation
by He Wang, Hong Men and Yan Shi
Foods 2025, 14(22), 3855; https://doi.org/10.3390/foods14223855 - 11 Nov 2025
Viewed by 1056
Abstract
Detecting and identifying consumers’ perception of food sweetness can help guide the optimization of food formulations. Electroencephalogram (EEG) detection can capture changes in brain electrical activity in response to different sweet taste stimuli. In this work, we employ EEG detection and propose an [...] Read more.
Detecting and identifying consumers’ perception of food sweetness can help guide the optimization of food formulations. Electroencephalogram (EEG) detection can capture changes in brain electrical activity in response to different sweet taste stimuli. In this work, we employ EEG detection and propose an EEG Feature Calculation and Classification Network (EFCC-Net) to recognize taste EEG signals under different sweetness concentration stimuli. First, taste-related EEG data from a subject group under varying sweetness concentration stimuli are collected. Then, an EEG Feature Calculation Module (EFCM) is proposed, which utilizes convolutional kernels of different sizes to compute local features from both temporal and spatial dimensions of EEG data. A lightweight self-attention mechanism is employed to compute global features, and a multi-branch computation approach is adopted to enhance feature extraction capability. Next, based on EEG topographic maps, qualitative analysis is conducted to examine differences in brain region activation under varying taste concentrations. Finally, leveraging the proposed EFCM, the EFCC-Net is designed to classify EEG data corresponding to different sweetness levels. Through structural optimization, ablation experiments, and comparisons with state-of-the-art EEG classification methods, EFCC-Net achieves the best classification performance, with an accuracy of 96.57%, a precision of 96.58%, and a recall of 96.53%, while also demonstrating superior stability. Full article
(This article belongs to the Special Issue Artificial Intelligence and Modeling Science in the Food Industry)
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