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Foods
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Application of Artificial Intelligence and Machine Learning in Food Safety
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Application of Artificial Intelligence and Machine Learning in Food Safety

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 9448

Special Issue Editor

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 Issue Information

Dear Colleagues,

In recent years, the intersection of artificial intelligence (AI) and machine learning (ML) with food safety has garnered significant attention, revolutionizing the landscape of food production, processing, and distribution. The use of AI and ML algorithms offers unprecedented opportunities to enhance the efficiency, accuracy, and effectiveness of various aspects of food safety management, ranging from early detection of contaminants to ensuring regulatory compliance throughout the supply chain. As we continue to witness advancements in technology, there arises a pressing need to consolidate and disseminate cutting-edge research and innovations in this rapidly evolving field.

This call for a Special Issue aims to provide a platform for researchers, practitioners, and stakeholders in the food chain to showcase their latest findings, methodologies, and applications pertaining to the integration of AI and ML in food safety. We invite original research articles, reviews, case studies, and perspectives that explore diverse aspects of this interdisciplinary domain, including but not limited to predictive modeling for risk assessment, sensor-based monitoring systems, intelligent food traceability, adaptive quality control, and decision support systems.

Dr. Yamine Bouzembrak
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 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

  • artificial intelligence (AI)
  • machine learning (ML)
  • food safety
  • predictive modelling in food safety
  • risk assessment
  • sensor-based monitoring
  • food traceability and smart labelling
  • quality control and inspection
  • decision support systems
  • regulatory compliance
  • smart sensors and Internet of Things (IoT)
  • foodborne pathogens
  • personalized nutrition
  • food fraud detection

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

Published Papers (6 papers)

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Research

16 pages, 4152 KiB  
Article
Computer Vision-Based Fire–Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits
by Peng Chen, Xutong Shao, Guangyu Wen, Yaowu Song, Rao Fu, Xiaoyan Xiao, Tulin Lu, Peina Zhou, Qiaosheng Guo, Hongzhuan Shi and Chenghao Fei
Foods 2025, 14(1), 5; https://doi.org/10.3390/foods14010005 - 24 Dec 2024
Viewed by 886
Abstract
The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI [...] Read more.
The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law’s texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire–ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm’s effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, p < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83–100% and 95.89–99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Machine Learning in Food Safety)
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17 pages, 13240 KiB  
Article
Assessment of Tail-Cutting in Frozen Albacore (Thunnus alalunga) Through Ultrasound Inspection and Chemical Analysis
by Masafumi Yagi, Akira Sakai, Suguru Yasutomi, Kanata Suzuki, Hiroki Kashikura and Keiichi Goto
Foods 2024, 13(23), 3860; https://doi.org/10.3390/foods13233860 - 29 Nov 2024
Viewed by 1026
Abstract
Fat content is the main criterion for evaluating albacore quality. However, no reports exist on the accuracy of the tail-cutting method, a method used to assess the fat content of albacore. Here, we evaluated this method by comparing it with chemical analysis and [...] Read more.
Fat content is the main criterion for evaluating albacore quality. However, no reports exist on the accuracy of the tail-cutting method, a method used to assess the fat content of albacore. Here, we evaluated this method by comparing it with chemical analysis and ultrasound inspection. We measured the actual fat content in albacore using chemical analysis and compared the results with those obtained using the tail-cutting method. Significant discrepancies (99% CI, t-test) were observed in fat content among the tail-cutting samples. Using chemical analysis as the ground truth, the accuracy of tail-cutting from two different companies was 70.0% for company A and 51.9% for company B. An ultrasound inspection revealed that a higher fat content reduced the amplitude of ultrasound signals with statistical significance (99% CI, t-test). Finally, machine learning algorithms were used to enforce the ultrasound inspection. The best combination of ultrasound inspection and a machine learning algorithm achieved an 84.2% accuracy for selecting fat-rich albacore, which is better than tail-cutting (73.6%). Our findings suggested that ultrasound inspection could be a valuable and non-destructive method for estimating the fat content of albacore, achieving better accuracy than the traditional tail-cutting method. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Machine Learning in Food Safety)
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16 pages, 3677 KiB  
Article
Plant Microbe Interaction—Predicting the Pathogen Internalization Through Stomata Using Computational Neural Network Modeling
by Linze Li, Shakeel Ahmed, Mukhtar Iderawumi Abdulraheem, Fida Hussain, Hao Zhang, Junfeng Wu, Vijaya Raghavan, Lulu Xu, Geng Kuan and Jiandong Hu
Foods 2024, 13(23), 3848; https://doi.org/10.3390/foods13233848 - 28 Nov 2024
Cited by 2 | Viewed by 893
Abstract
Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant–pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study [...] Read more.
Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant–pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study introduces a computational model utilizing neural networks to anticipate pathogen internalization via stomata, contrasting with previous research that emphasized biocontrol techniques. Computational modeling assesses the likelihood and duration of internalization for bacterial pathogens such as Salmonella enterica (S. enterica), considering various environmental factors including humidity and temperature. The estimated likelihood ranges from 0.6200 to 0.8820, while the internalization time varies from 4000 s to 5080 s, assessed at 50% and 100% humidity levels. The difference in internalization time, roughly 1042.73 s shorter at 100% humidity, correlates with a 26.2% increase in the likelihood of internalization, rising from 0.6200 to 0.8820. A neural network model has been developed to quantitatively predict these values, thereby enhancing the understanding of plant–microbe interactions. These methods will aid researchers in understanding plant–pathogen interactions, especially in environments characterized by varying humidity and temperature and are essential for formulating strategies to prevent pathogen ingress and tackle foodborne illnesses within a technologically advanced context. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Machine Learning in Food Safety)
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19 pages, 2835 KiB  
Article
Risk Classification of Food Incidents Using a Risk Evaluation Matrix for Use in Artificial Intelligence-Supported Risk Identification
by Sina Röhrs, Sascha Rohn and Yvonne Pfeifer
Foods 2024, 13(22), 3675; https://doi.org/10.3390/foods13223675 - 18 Nov 2024
Cited by 1 | Viewed by 1173
Abstract
Foodborne illnesses and mortalities persist as a significant global health issue. The World Health Organization estimates that one out of every ten individuals becomes ill following the consumption of contaminated food. However, in the age of digitalization and technological progress, more and more [...] Read more.
Foodborne illnesses and mortalities persist as a significant global health issue. The World Health Organization estimates that one out of every ten individuals becomes ill following the consumption of contaminated food. However, in the age of digitalization and technological progress, more and more data and data evaluation technologies are available to counteract this problem. A specific challenge in this context is the efficient and beneficial utilization of the continuously increasing volume of data. In pursuit of optimal data utilization, the objective of the present study was to develop a Multi-Criteria Decision Analysis (MCDA)-based assessment scheme to be prospectively implemented into an overall artificial intelligence (AI)-supported database for the autonomous risk categorization of food incident reports. Such additional evaluations might help to identify certain novel or emerging risks by allocating a level of risk prioritization. Ideally, such indications are obtained earlier than an official notification, and therefore, this method can be considered preventive, as the risk is already identified. Our results showed that this approach enables the efficient and time-saving preliminary risk categorization of incident reports, allowing for the rapid identification of relevant reports related to predefined subject areas or inquiries that require further examination. The manual test runs demonstrated practicality, enabling the implementation of the evaluation scheme in AI-supported databases for the autonomous assessment of incident reports. Moreover, it has become evident that increasing the amount of information and evaluation criteria provided to AI notably enhances the precision of risk assessments for individual incident notifications. This will remain an ongoing challenge for the utilization and processing of food safety data in the future. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Machine Learning in Food Safety)
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Graphical abstract

12 pages, 1643 KiB  
Article
Construction of a Food Safety Evaluation System Based on the Factor Analysis of Mixed Data Method
by Yiqiong Liu, Shengmei Cai, Xuelei He, Xiaowei He and Tianli Yue
Foods 2024, 13(17), 2680; https://doi.org/10.3390/foods13172680 - 25 Aug 2024
Viewed by 1441
Abstract
Food safety evaluation, which aims to reflect food safety status, is an important part of food safety management. Traditional food evaluation methods often consider limited data, and the evaluation process is subjective, time-consuming, and difficult to popularize. We developed a new food safety [...] Read more.
Food safety evaluation, which aims to reflect food safety status, is an important part of food safety management. Traditional food evaluation methods often consider limited data, and the evaluation process is subjective, time-consuming, and difficult to popularize. We developed a new food safety evaluation system that incorporates simple qualification degrees, food consumption, project hazard degrees, sales channels, food production regions, and other information obtained from food safety sampling and inspection to reflect the food safety situation accurately, objectively, and comprehensively. This evaluation model combined the statistical method and the machine learning method. The optimal distance method was used to calculate the basic qualification degree, and then expert elicitation via a questionnaire and the factor analysis of mixed data method (FADM) was applied to modify the basic qualification degree so as to obtain the food safety index, which indicates food safety status. Then, the effectiveness of this new method was verified by calculating and analyzing of the food safety index in region X. The results show that this model can clearly distinguish food safety levels in different cities and food categories and identify food safety trends in different years. Thus, this food safety evaluation system based on the FADM quantifies the real food safety level, screens out cities and food categories with high food safety risks, and, finally, helps to optimize the allocation of regulatory resources and provide technical and theoretical support for government decision-making. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Machine Learning in Food Safety)
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25 pages, 11774 KiB  
Article
CR-YOLOv9: Improved YOLOv9 Multi-Stage Strawberry Fruit Maturity Detection Application Integrated with CRNET
by Rong Ye, Guoqi Shao, Quan Gao, Hongrui Zhang and Tong Li
Foods 2024, 13(16), 2571; https://doi.org/10.3390/foods13162571 - 17 Aug 2024
Cited by 6 | Viewed by 1523
Abstract
Strawberries are a commonly used agricultural product in the food industry. In the traditional production model, labor costs are high, and extensive picking techniques can result in food safety issues, like poor taste and fruit rot. In response to the existing challenges of [...] Read more.
Strawberries are a commonly used agricultural product in the food industry. In the traditional production model, labor costs are high, and extensive picking techniques can result in food safety issues, like poor taste and fruit rot. In response to the existing challenges of low detection accuracy and slow detection speed in the assessment of strawberry fruit maturity in orchards, a CR-YOLOv9 multi-stage method for strawberry fruit maturity detection was introduced. The composite thinning network, CRNet, is utilized for target fusion, employing multi-branch blocks to enhance images by restoring high-frequency details. To address the issue of low computational efficiency in the multi-head self-attention (MHSA) model due to redundant attention heads, the design concept of CGA is introduced. This concept aligns input feature grouping with the number of attention heads, offering the distinct segmentation of complete features for each attention head, thereby reducing computational redundancy. A hybrid operator, ACmix, is proposed to enhance the efficiency of image classification and target detection. Additionally, the Inner-IoU concept, in conjunction with Shape-IoU, is introduced to replace the original loss function, thereby enhancing the accuracy of detecting small targets in complex scenes. The experimental results demonstrate that CR-YOLOv9 achieves a precision rate of 97.52%, a recall rate of 95.34%, and an mAP@50 of 97.95%. These values are notably higher than those of YOLOv9 by 4.2%, 5.07%, and 3.34%. Furthermore, the detection speed of CR-YOLOv9 is 84, making it suitable for the real-time detection of strawberry ripeness in orchards. The results demonstrate that the CR-YOLOv9 algorithm discussed in this study exhibits high detection accuracy and rapid detection speed. This enables more efficient and automated strawberry picking, meeting the public’s requirements for food safety. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Machine Learning in Food Safety)
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