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Using Artificial Intelligence and Big Data Analytics to Improve Food...
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Using Artificial Intelligence and Big Data Analytics to Improve Food Safety

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 5146

Special Issue Editors

Prof. Dr. Min Zuo

E-Mail Website
Guest Editor
1. Business School, Beijing Wuzi University, Beijing 100048, China
2. National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
Interests: food quality evaluation; food flavor assessment; food quality monitoring; food sensory evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Qingchuan Zhang

E-Mail Website
Guest Editor
National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
Interests: food quality evaluation; food flavor assessment; food quality monitoring; food sensory evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ensuring food safety is a critical global challenge that impacts public health and economic stability. With increasingly complex food supply chains, traditional monitoring methods often struggle to prevent contamination and ensure compliance. The emergence of artificial intelligence (AI) and big data analytics offers transformative solutions, enabling real-time monitoring, predictive risk assessment, and enhanced traceability. These technologies are revolutionizing food safety management by improving efficiency and reducing hazards.

We invite you to contribute to the Special Issue "Using Artificial Intelligence and Big Data Analytics to Improve Food Safety". This Special Issue explores innovative applications of AI and big data for addressing food safety risks. We seek contributions that demonstrate how computational techniques can enhance pathogen detection, optimize supply chains, and improve regulatory compliance. This goal aligns with the journal’s mission of advancing food safety research and technological innovation.

In this Special Issue, we welcome original research articles and reviews. Topics of interest include, but are not limited to, the following:

  • AI-driven models for foodborne pathogen detection;
  • Big data analytics for supply chain traceability;
  • Machine learning approaches for contamination control;
  • IoT-based AI solutions for real-time food safety monitoring;
  • Risk assessment and regulatory compliance models.

Prof. Dr. Min Zuo
Dr. Qingchuan 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

  • artificial intelligence
  • big data analytics
  • food safety
  • machine learning
  • foodborne pathogens
  • supply chain traceability
  • contamination detection
  • IoT in food safety
  • risk assessment
  • food quality monitoring

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

Published Papers (4 papers)

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Research

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31 pages, 2717 KB  
Article
Quality Assessment and Prediction of Peanut Storage Life Based on Deep Learning
by Yipeng Zhou, Xingchen Sun, Wenjing Yan, Mingwen Bi, Yiwen Shao and Kexin Chen
Foods 2026, 15(3), 446; https://doi.org/10.3390/foods15030446 - 26 Jan 2026
Viewed by 820
Abstract
As a globally significant oilseed and food crop, peanuts exhibit significant quality changes influenced by storage conditions. This study monitored six key quality indicators—including fatty acid content, carbonyl content, peroxide value, acid value, phenylacetaldehyde and moisture content—in peanut samples stored for 30 weeks [...] Read more.
As a globally significant oilseed and food crop, peanuts exhibit significant quality changes influenced by storage conditions. This study monitored six key quality indicators—including fatty acid content, carbonyl content, peroxide value, acid value, phenylacetaldehyde and moisture content—in peanut samples stored for 30 weeks under varying temperature and humidity conditions. A Deep Clustering Network (DCN) was employed for quality grading, yielding superior results compared to Deep Empirical Correlation (DEC) and K-Means++ clustering methods, thereby establishing effective quality grading standards. Building upon this, a D-SCSformer time series prediction model was constructed to forecast quality indicators. Through dimensionality-segmented embedding and statistical feature fusion, it achieved strong predictive performance (MSE = 0.2012, MAE = 0.2884, RMSE = 0.4387, and R2 = 0.9998), reducing MSE by 57.9%, MAE by 35.4%, and RMSE by 34.1%, while improving R2 from 0.9996 to 0.9998 compared to the mainstream Crossformer model. This study provides technical support and a decision-making basis for temperature and humidity regulation and shelf-life management during peanut storage. Full article
(This article belongs to the Special Issue Using Artificial Intelligence and Big Data Analytics to Improve Food Safety)
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19 pages, 41470 KB  
Article
Multi-Path Attention Fusion Transformer for Spectral Learning in Corn Quality Assessment
by Jialu Li, Haoyi Wang, Hongbo Zhang and Tongqiang Jiang
Foods 2025, 14(21), 3786; https://doi.org/10.3390/foods14213786 - 4 Nov 2025
Cited by 1 | Viewed by 1111
Abstract
Accurately modeling the nonlinear relationships between near-infrared (NIR) spectral signatures and biochemical traits in corn remains a major challenge. A key difficulty lies in capturing multi-scale contextual dependencies—ranging from local absorption peaks to global spectral patterns—that jointly determine quality constituents such as protein [...] Read more.
Accurately modeling the nonlinear relationships between near-infrared (NIR) spectral signatures and biochemical traits in corn remains a major challenge. A key difficulty lies in capturing multi-scale contextual dependencies—ranging from local absorption peaks to global spectral patterns—that jointly determine quality constituents such as protein and oil. To address this, we propose SpecTran, a spectral Transformer network specifically designed for NIR regression. SpecTran integrates three key components: adaptive multi-scale patch embedding which extracts spectral features at multiple resolutions to capture both fine and coarse patterns, spectral-enhanced positional encoding which preserves wavelength order information more effectively than standard encoding, and hierarchical feature fusion for robust multi-task prediction. Evaluated on the public Eigenvector corn dataset, SpecTran had a performance across four key traits—moisture, starch, oil, and protein—with an average R2 of 0.483. It reduced the RMSE by 11.2% for protein and 10.7% for oil compared to the best-performing baseline, which is the standard Transformer model. These results demonstrate SpecTran’s superior ability to model complex spectral dynamics while providing interpretable insights, offering a reliable framework for NIR-based agricultural quality assessment. Full article
(This article belongs to the Special Issue Using Artificial Intelligence and Big Data Analytics to Improve Food Safety)
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22 pages, 1789 KB  
Article
Evaluation Model Based on the SGCNiFormer for the Influence of Different Storage Environments on Wheat Quality
by Qingchuan Zhang, Zexi Song and Mingwen Bi
Foods 2025, 14(10), 1715; https://doi.org/10.3390/foods14101715 - 12 May 2025
Cited by 3 | Viewed by 984
Abstract
Wheat is a vital staple food crop, and its post-harvest storage is paramount to maintaining its quality. However, conventional grain storage methods frequently impede the ability to promptly and accurately predict and assess quality changes. Moreover, most storage systems are ineffective in dealing [...] Read more.
Wheat is a vital staple food crop, and its post-harvest storage is paramount to maintaining its quality. However, conventional grain storage methods frequently impede the ability to promptly and accurately predict and assess quality changes. Moreover, most storage systems are ineffective in dealing with the impact of temperature and humidity fluctuations on wheat quality, which can potentially lead to quality degradation during storage. To address these challenges, this paper proposes a dual model system of “prediction-evaluation”, which integrates a dynamic quality prediction model based on SGCNiFormer with an evaluation framework based on K-Smeans clustering to establish a closed-loop mechanism from quality prediction to storage effect evaluation. The system incorporates a graph convolutional network (GCN) and a dynamic gating module, enabling precise simulation of the multidimensional evolution of wheat quality under the interaction of moisture and temperature. The experimental results demonstrate the superiority of SGCNiFormer in time-series prediction tasks, while the K-Smeans method establishes a wheat quality grading standard with physical interpretability. This integrated method provides a systematic theoretical framework for optimizing storage parameters and offers substantial support for intelligent grain storage management. Full article
(This article belongs to the Special Issue Using Artificial Intelligence and Big Data Analytics to Improve Food Safety)
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Review

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30 pages, 2009 KB  
Review
Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples
by Yerkanat Syrgabek, José Bernal and Adrián Fuente-Ballesteros
Foods 2026, 15(3), 415; https://doi.org/10.3390/foods15030415 - 23 Jan 2026
Cited by 1 | Viewed by 1252
Abstract
Reliable monitoring of pesticide residues is essential for ensuring food safety. Conventional chromatographic and spectrometric techniques remain labor-intensive, time-consuming, and costly. Recent progress in Machine Learning (ML) provides computational tools that improve the precision and efficiency of pesticide residue detection in diverse food [...] Read more.
Reliable monitoring of pesticide residues is essential for ensuring food safety. Conventional chromatographic and spectrometric techniques remain labor-intensive, time-consuming, and costly. Recent progress in Machine Learning (ML) provides computational tools that improve the precision and efficiency of pesticide residue detection in diverse food matrices. This review presents a comprehensive analysis of current ML-based approaches for pesticide analysis, with particular attention to supervised learning algorithms such as support vector machines, random forests, boosting methods, and deep neural networks. These models have been integrated with chromatographic, spectroscopic, and electrochemical platforms to achieve enhanced signal interpretation and more reliable prediction from existing analytical data, and more robust data processing in complex food systems. The review also discusses methodologies for feature extraction, model validation, and the management of heterogeneous datasets, while examining ongoing challenges that include limited training data, matrix variability, and regulatory constraints. Emerging advances in deep learning architectures, transfer learning strategies, and portable sensing technologies are expected to support the development of real-time, field-ready monitoring systems. The findings highlight the potential of ML to advance food quality assurance and strengthen public health protection through more efficient and accurate pesticide residue detection. Full article
(This article belongs to the Special Issue Using Artificial Intelligence and Big Data Analytics to Improve Food Safety)
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