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Enhancing Food Safety Through Artificial Intelligence: Innovations, Challenges, and Opportunities
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Enhancing Food Safety Through Artificial Intelligence: Innovations, Challenges, and Opportunities

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 4056

Special Issue Editors

Dr. Zhanming Li

E-Mail
Guest Editor
School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang, China
Interests: food safety; food analysis and quality control; food hazardous control; chemometrics
Special Issues, Collections and Topics in MDPI journals
Dr. Xuejin Mao

E-Mail Website
Guest Editor
College of Food Science and Technology, Nanchang University, Nanchang, China
Interests: food quality and safety; chromatography–mass spectrometry; food flavor; chemometrics

Special Issue Information

Dear Colleagues,

The field of enhancing food safety through artificial intelligence (AI) is rapidly evolving. AI can analyze historical data to predict potential outbreaks of foodborne diseases, enabling the implementation of preventive measures. Moreover, AI technologies, including deep learning algorithms, can be trained to recognize patterns and anomalies in food safety data, enhancing contaminant detection and control. AI can also be used to predict the behavior of food-related microorganisms under various distribution and storage conditions, assessing their risk. However, challenges and opportunities coexist in using AI technology in the food industry. In addition, the real-time performance, applicability, and generalization capabilities of AI algorithms need to be continuously improved to ensure food quality and safety. With the development of hardware equipment, rapid online non-destructive testing can be achieved, better addressing various food safety challenges.

Dr. Zhanming Li
Dr. Xuejin Mao
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 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

  • chemometrics and deep learning
  • spectroscopy technology and modeling
  • food adulteration
  • artificial intelligence technology
  • food fermentation and metabonomics
  • food safety and quality control
  • chromatography–mass spectrometry technology
  • nuclear magnetic resonance
  • risk assessment and management
  • microbial modeling

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Published Papers (3 papers)

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Research

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16 pages, 2767 KiB  
Article
Monitoring of the Physicochemical Properties and Aflatoxin of Aspergillus flavus-Contaminated Peanut Kernels Based on Near-Infrared Spectroscopy Combined with Machine Learning
by Yingge Wang, Mengke Li, Li Xu, Chun Gao, Cheng Wang, Lu Xu, Shaotong Jiang, Lili Cao and Min Pang
Foods 2025, 14(13), 2186; https://doi.org/10.3390/foods14132186 - 22 Jun 2025
Viewed by 421
Abstract
This study explores the application of near-infrared (NIR) spectroscopy combined with machine learning for the non-destructive detection of aflatoxin in peanuts contaminated by Aspergillus flavus (A. flavus). The key innovation lies in the development of an optimized spectral processing pipeline that [...] Read more.
This study explores the application of near-infrared (NIR) spectroscopy combined with machine learning for the non-destructive detection of aflatoxin in peanuts contaminated by Aspergillus flavus (A. flavus). The key innovation lies in the development of an optimized spectral processing pipeline that effectively overcomes moisture interference while maintaining high sensitivity to low aflatoxin concentrations. NIR spectra were collected from peanut samples at different incubation times within the spectral range of 950 to 1650 nm. Spectral data were preprocessed, and Competitive Adaptive Reweighted Sampling (CARS) selected ten characteristic bands. Correlation analysis was performed to examine the relationships between physicochemical properties, characteristic bands, and aflatoxin content. Three machine learning models—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were used to predict aflatoxin levels. The SNV-SVM model demonstrated superior performance, achieving calibration metrics (R2C = 0.9945, RMSEC = 9.92, RPDC = 14.59) and prediction metrics (R2P = 0.9528, RMSEP = 19.58, RPDP = 7.01), along with leave-one-out cross-validation (LOOCV) results (R2 = 0.9834, RMSE = 11.20). The results demonstrate that NIR spectroscopy combined with machine learning offers a rapid, non-destructive approach for aflatoxin detection in peanuts, with significant implications for food safety and agricultural quality control. Full article
(This article belongs to the Special Issue Enhancing Food Safety Through Artificial Intelligence: Innovations, Challenges, and Opportunities)
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24 pages, 5293 KiB  
Article
Smart Grain Storage Solution: Integrated Deep Learning Framework for Grain Storage Monitoring and Risk Alert
by Xinze Li, Wenfu Wu, Hongpeng Guo, Yunshandan Wu, Shuyao Li, Wenyue Wang and Yanhui Lu
Foods 2025, 14(6), 1024; https://doi.org/10.3390/foods14061024 - 18 Mar 2025
Viewed by 1171
Abstract
In order to overcome the notable limitations of current methods for monitoring grain storage states, particularly in the early warning of potential risks and the analysis of the spatial distribution of grain temperatures within the granary, this study proposes a multi-model fusion approach [...] Read more.
In order to overcome the notable limitations of current methods for monitoring grain storage states, particularly in the early warning of potential risks and the analysis of the spatial distribution of grain temperatures within the granary, this study proposes a multi-model fusion approach based on a deep learning framework for grain storage state monitoring and risk alert. This approach combines two advanced three-dimensional deep learning models, a grain storage state classification model based on 3D DenseNet and a temperature field prediction model based on 3DCNN-LSTM. First, the grain storage state classification model based on 3D DenseNet efficiently extracts features from three-dimensional grain temperature data to achieve the accurate classification of storage states. Second, the temperature prediction model based on 3DCNN-LSTM incorporates historical grain temperature and absolute water potential data to precisely predict the dynamic changes in the granary’s temperature field. Finally, the grain temperature prediction results are input into the 3D DenseNet to provide early warnings for potential condensation and mildew risks within the grain pile. Comparative experiments with multiple baseline models show that the 3D DenseNet model achieves an accuracy of 97.38% in the grain storage state classification task, significantly outperforming other models. The 3DCNN-LSTM model shows high prediction accuracy in temperature forecasting, with MAE of 0.24 °C and RMSE of 0.28 °C. Furthermore, in potential risk alert experiments, the model effectively captures the temperature trend in the grain storage environment and provides early warnings, particularly for mildew and condensation risks, demonstrating the potential of this method for grain storage safety monitoring and risk alerting. This study provides a smart grain storage solution which contributes to ensuring food safety and enhancing the efficiency of grain storage management. Full article
(This article belongs to the Special Issue Enhancing Food Safety Through Artificial Intelligence: Innovations, Challenges, and Opportunities)
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Review

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33 pages, 6590 KiB  
Review
Current Progress and Future Trends of Genomics-Based Techniques for Food Adulteration Identification
by Jing Zhao, Wei Yang, Hongli Cai, Guangtian Cao and Zhanming Li
Foods 2025, 14(7), 1116; https://doi.org/10.3390/foods14071116 - 24 Mar 2025
Cited by 2 | Viewed by 1891
Abstract
Addressing the pervasive issue of food adulteration and fraud driven by economic interests has long presented a complex challenge. Such adulteration not only compromises the safety of the food supply chain and destabilizes the market economy but also poses significant risks to public [...] Read more.
Addressing the pervasive issue of food adulteration and fraud driven by economic interests has long presented a complex challenge. Such adulteration not only compromises the safety of the food supply chain and destabilizes the market economy but also poses significant risks to public health. Food adulteration encompasses practices such as substitution, process manipulation, mislabeling, the introduction of undeclared ingredients, and the adulteration of genetically modified foods. Given the diverse range of deceptive methods employed, genomics-based identification techniques have increasingly been utilized for detecting food adulteration. Compared to traditional detection methods, technologies such as polymerase chain reaction (PCR), next-generation sequencing (NGS), high-resolution melt (HRM) analysis, DNA barcoding, and the CRISPR–Cas system have demonstrated efficacy in accurately and sensitively detecting even trace amounts of adulterants. This paper provides an overview of genomics-based approaches for identifying food adulteration, summarizes the latest applications in certification procedures, discusses current limitations, and explores potential future trends, thereby offering new insights to enhance the control of food quality and contributing to the development of more robust regulatory frameworks and food safety policies. Full article
(This article belongs to the Special Issue Enhancing Food Safety Through Artificial Intelligence: Innovations, Challenges, and Opportunities)
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