Artificial intelligence in food safety and quality control: applications, challenges, and future perspectives

Highlights

• Artificial intelligence enables proactive and risk-based food safety management.
• AI improves contaminant detection through computer vision, biosensors, and smart sensing.
• AI enhances quality control, traceability, and decision-making across food supply chains.
• Data quality, interpretability, and regulatory compliance remain major implementation challenges.
• This review highlights current advances and future directions of AI in food safety.

1 Introduction

Food safety is one of today’s most critical global priorities regarding the protection of public health, the support of sustainable food production, and the maintenance of consumer confidence. A growing global population, shifting consumer expectations, and globalizing food supply chains are significantly increasing the demand for the production of safe, high-quality food. Consequently, ensuring food safety requires the effective control of biological, chemical, and physical hazards throughout the entire chain of production, processing, storage, transportation, and distribution (Varghese et al., 2022; Pandhi et al., 2025). The increasing complexity of modern food supply chains makes the effective management of contamination risks and product traceability significantly more challenging. Contaminants such as microbial pathogens, pesticide residues, heavy metals, mycotoxins, and foreign matter pose serious risks to food safety (Nerín et al., 2016; Abdolshahi and Yancheshmeh, 2020). Furthermore, the prevalence of food fraud, the expansion of product diversity, and the growth in international trade volumes necessitate that safety and quality control processes be conducted more rapidly, accurately, and reliably (Li et al., 2021; Femimol and Nalini Joseph, 2025). Traditional food safety practices rely heavily on laboratory analyses, manual inspections, and periodic sampling methods. Although these methods can yield high accuracy, they often fall short of meeting the demands of modern, dynamic production systems due to their time-consuming nature, high costs, and inability to provide real-time monitoring (Zhou et al., 2026; Bhat et al., 2025). These limitations have accelerated the integration of technologies such as big data analytics, biosensors, hyperspectral imaging, the Internet of Things (IoT), blockchain, and artificial intelligence into food safety processes. In recent years, artificial intelligence, leveraging advanced data analysis approaches such as machine learning, deep learning, and computer vision, has demonstrated successful applications in numerous areas, including contaminant and pathogen detection, quality classification, production process monitoring, early warning systems, and supply chain traceability. By rapidly analyzing large volumes of multidimensional data, these technologies support decision-making processes, reduce human error, and enable the earlier identification of risks (Qian et al., 2023; Dhal and Kar, 2025). Furthermore, the integration of artificial intelligence with blockchain, biosensors, and IoT-based systems contributes significantly to the development of more transparent, traceable, data-driven, and proactive food safety management across the food supply chain.

Although the literature contains numerous studies addressing various applications of artificial intelligence technologies in food safety, the majority of these studies focus on specific technologies or a single application area. However, few reviews provide an integrated evaluation of artificial intelligence applications in food hygiene, contamination control, quality management, traceability, and risk management within a single framework. Furthermore, there is a need for up-to-date review studies that comparatively examine the performance, advantages, limitations, and industrial applicability of various artificial intelligence approaches.

To provide a clearer overview of the current state of the literature, Table 1 summarizes the major research gaps identified in previous studies and highlights the main contributions of the present review.

TABLE 1. Current state of the literature, existing research gaps, and contribution of the present review.

This review article aims to comprehensively synthesize the existing literature by evaluating, from a holistic perspective, current applications of artificial intelligence technologies in food hygiene, contamination control, quality control, and traceability, with particular emphasis on the roles of machine learning, deep learning, computer vision, biosensors, hyperspectral imaging, smart packaging systems, the Internet of Things (IoT), and blockchain technologies, as well as their advantages, limitations, and future research needs.

In this context, the review focuses on the following research questions:

• What key advantages do artificial intelligence technologies provide in food safety management, and what are the primary limitations encountered during their implementation?
• How do artificial intelligence technologies contribute to the development of systems for food hygiene, contamination control, quality control, and traceability?
• What is the potential for future research and industrial application of artificial intelligence technologies in the field of food safety?

2 Literature review methodology

In this review study, applications of artificial intelligence technologies in the fields of food hygiene, safety, and quality control were evaluated through a comprehensive literature review. The literature search was conducted using the Scopus, Web of Science, PubMed, and Google Scholar databases, focusing primarily on peer-reviewed scientific publications indexed in SCI/SCIE. The keywords “Artificial Intelligence and Food Hygiene,” “Food Safety,” and “Food Contamination” were used to identify relevant studies. The majority of the reviewed works consisted of recent articles published between 2015 and 2026, although foundational studies contributing to the theoretical development of the field were also included in the evaluation.

Publications identified during the literature search were screened for duplicates, and redundant studies were excluded. Publications were evaluated based on reviews of their titles, abstracts, and full texts to ensure alignment with the scope of the study. The primary selection criteria required that the included publications link artificial intelligence applications to food hygiene, food safety, or quality control and address topics such as contamination detection, quality assessment, early warning systems, hygiene management, or traceability. While peer-reviewed journal articles formed the bulk of the literature reviewed, conference proceedings and book chapters were also included where deemed appropriate.

The selected studies were evaluated based on publication year, research topic, artificial intelligence methods employed, and application areas. The findings were then synthesized to outline current applications, advantages, limitations, and future research trends regarding artificial intelligence technologies in the field of food safety. All tables, performance metrics, comparative evaluations, and synthesized findings presented in this review were compiled exclusively from previously published peer-reviewed studies. No original experiments, unpublished datasets, original experimental data, or new quantitative analyses were included or conducted as part of this review.

To provide a concise overview of the principal application areas identified in the literature, Table 2 summarizes representative original research studies together with the corresponding artificial intelligence technologies and their practical contributions across major food safety applications. The studies presented in Table 2 were selected as representative examples of each application area rather than an exhaustive list of all available publications.

TABLE 2. Representative applications of artificial intelligence in food safety management based on original research studies.

As summarized in Table 2, artificial intelligence has evolved beyond addressing isolated food safety problems and is now being applied across multiple stages of the food supply chain, including risk prediction, contaminant detection, quality assessment, traceability, and smart packaging. Collectively, the reviewed studies indicate that research has gradually shifted from developing highly accurate models for specific analytical tasks toward more integrated and application-oriented approaches capable of supporting comprehensive food safety management. This trend also reflects the increasing emphasis on combining advances in sensing technologies with artificial intelligence to improve both analytical performance and practical applicability under real-world conditions. To better illustrate how these research priorities have evolved over time, Table 3 presents a chronological overview of the major research trends in artificial intelligence applications for food safety management reported between 2018 and 2026.

TABLE 3. Evolution of major research trends in artificial intelligence applications for food safety management (2018–2026).

3 Digital transformation and the transition to artificial intelligence

Increasing production volumes, complex supply chains, and rising consumer expectations in the food industry have necessitated the use of more advanced technologies for safety and quality management. This transformation is not limited to the automation of production processes; it also encompasses enhancing resource efficiency, reducing waste, improving product quality, and supporting sustainable production goals (Guo et al., 2024; Wang et al., 2022). In agri-food systems, big-data approaches can support sustainability management by integrating data architecture and sustainability assessment to improve supply-chain analysis and decision-making (Belaud et al., 2019). Traditional food safety practices rely heavily on manual sampling, visual inspection, and laboratory-based analysis methods. Although methods such as chromatography, mass spectrometry, plate counting, and real-time PCR offer high accuracy, they fail to fully meet real-time monitoring needs due to long analysis times, the requirement for expert personnel, and limitations in field applications (Adade et al., 2025; Kar and Dhal, 2025; Kumar et al., 2024). Furthermore, increasing product diversity and complex food matrices make it difficult to rapidly and reliably identify foreign matter, quality defects, and microscopic contamination (Chen I.-C. et al., 2025). To overcome these limitations, digital technologies—such as blockchain, the Internet of Things (IoT), biosensors, and advanced sensing systems—have begun to be integrated into food production processes. These technologies enable more effective management of contamination risks by facilitating the tracking of product movements and production processes, as well as the real-time monitoring of critical parameters such as temperature and humidity (Kumar A. et al., 2020; Verdouw et al., 2016). Furthermore, nanotechnology-based sensing systems and biosensors have enhanced the effectiveness of food safety practices by providing high sensitivity in the detection of microbial and chemical contaminants (Velusamy et al., 2010; Liu et al., 2018). With digitalization, the analysis of vast amounts of data obtained from diverse systems and their transformation into decision-making processes have become a critical necessity. The surge in data volume and the holistic evaluation of multi-source data have accelerated the development of artificial intelligence-based decision support systems (Kanicheril Ambikalekshmi et al., 2026). Accordingly, artificial intelligence makes significant contributions to analyzing large datasets, predicting risks, identifying contaminants and pathogens, supporting quality control processes, and strengthening decision-making mechanisms (Dhal and Kar, 2025; Shehzad et al., 2025). The integrated use of artificial intelligence with IoT, blockchain, and biosensor technologies enhances the effectiveness of real-time monitoring, early warning systems, predictive analytics, and traceability applications (Naseem and Rizwan, 2025; Hua et al., 2021; Frankish et al., 2022). Furthermore, blockchain-based applications and automated monitoring systems facilitate the conduct of production processes in a more transparent and traceable manner (Ellahi et al., 2023). Nevertheless, issues such as data quality, data security, explainability, and regulatory compliance remain crucial for the widespread implementation of these technologies (Kanicheril Ambikalekshmi et al., 2026; Naseem and Rizwan, 2025). Overall, artificial intelligence is currently recognized not merely as an analytical tool, but as a strategic technology that supports the development of more reliable, sustainable, and data-driven food safety systems (Abass et al., 2024). The overall conceptual framework of artificial intelligence applications in food safety management is illustrated in Figure 1.

4 Artificial intelligence technologies and their applications in food safety

Recent advances in artificial intelligence (AI) have considerably expanded its role in food safety and quality management by enabling the analysis of large and heterogeneous datasets, the identification of complex patterns, and data-driven decision-making (Qian et al., 2023). As a result, AI applications have evolved beyond conventional quality control to encompass hygiene management, contamination detection, risk analysis, and automated monitoring through the integration of expert systems and data analytics tools (Kumar et al., 2021; Puroy Vásquez et al., 2019).

Rather than functioning as standalone technologies, AI approaches—including machine learning, deep learning, computer vision, and natural language processing—are increasingly integrated with sensors, biosensors, spectroscopic systems, hyperspectral imaging, and Internet of Things (IoT)-based infrastructures. This integration enables the acquisition and analysis of large-scale data streams, thereby enhancing monitoring accuracy and supporting more effective decision-making throughout food safety management (Parihar et al., 2024; Furstenau et al., 2023).

The reviewed studies indicate that these integrated AI systems facilitate automated contaminant and quality defect detection, improve risk prediction, and support real-time monitoring applications (Wang X. et al., 2022; Shehzad et al., 2025; Deng et al., 2023; Chhetri, 2024). Furthermore, integrating AI with IoT, computer vision, and data analytics enables the analysis of heterogeneous data sources, supporting early warning systems and predictive analytics that shift food safety management from conventional reactive inspection toward more proactive, rapid, and data-driven approaches (Cao L. et al., 2025; Yu et al., 2025).

The major application domains, data inputs, analytical workflow, and expected outcomes of artificial intelligence-based food safety and quality management systems are conceptually summarized in Figure 2.

As illustrated in Figure 2, artificial intelligence supports multiple food safety functions through integrated data acquisition, predictive analytics, and decision-support mechanisms. Building upon this conceptual framework, Table 4 summarizes the principal artificial intelligence technologies currently applied in food safety management together with their representative applications, key advantages, current limitations, and representative original studies.

TABLE 4. Applications, key advantages, and major limitations of artificial intelligence technologies used in food safety management.

The reviewed studies also demonstrate that the performance and suitability of AI algorithms vary according to data characteristics and application requirements. Consequently, selecting an appropriate algorithm requires consideration not only of predictive performance but also of computational requirements, model interpretability, and data availability. Since no single algorithm is universally suitable for all food safety applications, the choice of AI method should be guided by the specific objectives and characteristics of the target application.

When the findings summarized in Tables 2–4 are considered together, it becomes evident that no single artificial intelligence algorithm is universally suitable for every food safety application. Instead, the effectiveness of an algorithm largely depends on the characteristics of the food matrix, the nature of the target problem, the available data, and computational constraints. Therefore, selecting an appropriate AI approach requires balancing predictive performance with practical considerations such as interpretability, computational efficiency, and applicability under real-world conditions. From this perspective, Table 5 comparatively summarizes representative AI algorithms reported in the literature according to their principal application areas, representative studies, and key characteristics.

TABLE 5. Representative artificial intelligence algorithms used in food safety applications and their major characteristics.

5 Artificial intelligence application areas in food safety

Artificial intelligence (AI) technologies are increasingly applied across various areas of food safety management, including contaminant detection, quality assessment, fraud identification, risk prediction, and process monitoring. By enabling the analysis of large datasets, these technologies facilitate the rapid identification of food safety risks and support data-driven decision-making (Shehzad et al., 2025). Furthermore, the integration of AI with Internet of Things (IoT) technologies enables the continuous monitoring of critical environmental parameters, such as temperature and humidity, thereby contributing to product quality preservation and risk mitigation (Suthar et al., 2024).

Among these application areas, hyperspectral imaging (HSI), Raman spectroscopy, X-ray imaging, and computer vision are widely used for detecting chemical, microbial, and physical hazards. HSI enables the detection of chemical and microbial contaminants through spectral fingerprint analysis, whereas X-ray imaging and computer vision are effective for identifying foreign materials such as glass, metal, and plastic. In addition, the integration of Raman spectroscopy with AI enables the rapid and accurate detection of contaminants, particularly in dairy products and edible oils (Shehzad et al., 2025). Computer vision systems provide automated, non-destructive, and cost-effective approaches for evaluating food quality and can be applied to a wide range of fresh and processed foods, including meat, fish, pizza, cheese, and bread (Ogidi et al., 2025).

The reviewed literature also indicates that combining AI algorithms with spectroscopic techniques enhances analytical performance. For example, Du et al. (2022) developed a hybrid model combining generative adversarial networks (GANs) and support vector machines (SVMs), which improved the classification accuracy of foodborne pathogens. Similarly, HSI has been successfully applied to quality assessment, defect detection, and contamination monitoring in fruits, vegetables, and meat products. However, studies investigating HSI for chemical contaminant detection remain limited, with current research focusing primarily on pesticide residues and melamine (Soni et al., 2022).

Beyond contaminant detection, machine learning and deep learning methods are also employed in quality assessment, defect analysis, fraud detection, and production efficiency improvement. By enabling the rapid analysis of large datasets, these approaches contribute to faster, more accurate, and data-driven decision-making processes (Zhu L. et al., 2021).

5.1 Early warning and risk prediction applications

Studies in the literature demonstrate that artificial intelligence technologies offer significant advantages in identifying, monitoring, and managing food safety risks. In this context, Sadilek et al. (2018) developed a machine learning-based system named FINDER for the early detection of foodborne illnesses using real-world data. Leveraging anonymized web search logs and location data, the system successfully identified high-risk restaurants and facilitated faster interventions to curb the spread of foodborne illnesses. These findings indicate that AI-based data analytics serves as an effective tool for the early identification of food safety risks and the support of preventive interventions. Furthermore, such applications enable the holistic evaluation of diverse data sources, thereby contributing to the development of more proactive and data-driven approaches to food safety management. Predictive microbiology models can estimate microbial growth and associated risks in food products and can be integrated into HACCP protocols to support food safety risk assessment (Taiwo et al., 2024).

5.2 Contaminant and pathogen detection

The rapid and reliable identification of contaminants and pathogens is critical for ensuring food safety. In recent years, artificial intelligence systems based on machine learning and deep learning have enabled more effective assessment of contamination risks by analyzing large datasets derived from sensors, biosensors, spectroscopic methods, and imaging technologies. However, research indicates that there is no single universal model applicable to all food products; rather, there is a need for customized artificial intelligence models tailored to product structure, microbial characteristics, and the specific sensor technology employed (Kahraman et al., 2025). Artificial intelligence algorithms yield successful results in detecting bacterial pathogens such as Escherichia coli, Salmonella, and Listeria, as well as mycotoxins, pesticide residues, heavy metals, microplastics, and physical foreign objects (Janga et al., 2023; Aggarwal et al., 2024; Darwish et al., 2022; Bernardes et al., 2024; Jin et al., 2024; Yadav et al., 2023; Sharif et al., 2021). Furthermore, AI-integrated biosensor systems can continuously monitor food samples to assess signs of contamination in real time, thereby contributing to the development of early warning mechanisms (Gutierrez et al., 2023; Huang et al., 2019). Spectroscopic methods and imaging technologies are widely used in AI-assisted contaminant detection efforts. Raman spectroscopy, hyperspectral imaging (HSI), X-ray imaging, and computer vision systems offer significant advantages in identifying contaminants and detecting quality defects (Wang et al., 2022; Chang et al., 2021). Although traditional microbial culture methods provide high accuracy, they entail various limitations due to long analysis times and complex procedures (Wang H. et al., 2023). Consequently, researchers are developing new approaches that utilize spectroscopic data in conjunction with artificial intelligence algorithms. For instance, Du et al. (2022) improved pathogen classification performance by combining Generative Adversarial Networks (GANs) and Support Vector Machines (SVMs) in the analysis of Raman data. Similarly, Yan et al. (2021) achieved an accuracy of 87.1%–95.8% using a multi-level classification model based on Raman spectra, thereby demonstrating the method’s potential for rapid pathogen detection (Baker, 2023). The combination of Raman spectroscopy and deep learning has enabled the multiplex detection and classification of foodborne bacterial pathogens, demonstrating the potential of AI-assisted spectroscopic approaches for rapid pathogen identification (Kang et al., 2023).

Deep learning-based systems have also made significant strides in identifying microbial contamination. The Detect-Cells-Rapidly-Net (DCRNet) model developed by Zheng H. et al. (2024) achieved a mean precision of 81.2% in identifying stained cells, while Chun et al. (2024) detected Botrytis cinerea infection in strawberries with 96.86% accuracy using hyperspectral fluorescence imaging and a ResNet-50-based model. Furthermore, Liu et al. (2023) reduced the analysis time to less than 30 min using an AI-powered portable biosensor they developed for the detection of Salmonella in poultry products. Meanwhile, the web-based application MeatReg assists in selecting appropriate machine learning methods to identify microorganisms that cause spoilage in meat products (Estelles-Lopez et al., 2017).

Image processing technologies also have significant applications in the detection of microbial contamination. Park et al. (2025) demonstrated that yeast species could be classified with 96.0% accuracy in just 6 hours by combining traditional culture methods and micro-colony imaging techniques with Convolutional Neural Networks (CNNs). The study also reported that incorporating synthetic images—generated using Generative Adversarial Networks (GANs)—into the dataset improved model performance. Similarly, Ravindhiran and Dhandapani (2025) developed a YOLOv5-based deep learning system for the detection of *Listeria monocytogenes*. Utilizing PALCAM agar images, the system achieved approximately 94% accuracy under optimized lighting conditions and offered a cost-effective solution. The researchers noted that this approach could contribute not only to food safety but also to reducing unnecessary antibiotic use. Deep learning methods are also employed in monitoring foodborne outbreaks and analyzing their origins. The DeepSANet model, developed by Liang et al. (2025), combined the Swin Transformer architecture with hierarchical prediction mechanisms to determine the geographical origins of Salmonella enterica serovar Enteritidis strains. The model demonstrated performance levels ranging from 80% to 92% across different geographical scales and successfully generalized to large datasets. These results indicate that deep learning-based approaches can be effectively utilized in outbreak surveillance and epidemiological analyses. Artificial intelligence systems employing advanced neural network architectures and multimodal data analysis offer faster, more precise, and scalable solutions for contaminant detection (Ding et al., 2025). AI-based portable devices such as CSI-D yield effective results in detecting surface contamination and contribute to reducing the risk of foodborne illnesses (Ismail et al., 2025).

The integration of artificial intelligence (AI) with microscopy and medical imaging techniques has supported imaging-based pathogen detection by enabling automated feature extraction and classification, while also allowing for the identification of patterns not easily discernible to the human eye (Pinto-Coelho, 2023). In the context of food safety, machine learning (ML) and deep learning models have been employed to accelerate image analysis and classify foodborne pathogens (Kang et al., 2021; Ma et al., 2023; Yi et al., 2023). The integration of artificial intelligence with advanced nanobiosensor technologies provides promising approaches for enhanced bacterial detection and monitoring (Khan et al., 2024). Overall, the diversity of preprocessing strategies, segmentation methods, model architectures, and validation approaches demonstrates the adaptability of AI-assisted bacterial imaging across various datasets and applications, while simultaneously highlighting the lack of standardized workflows for objective performance comparison and pathogen image optimization (Papa et al., 2026).

Similar to the selection of artificial intelligence models, studies have employed combinations of various preprocessing techniques and emphasized that preprocessing depends heavily on the imaging modality, data characteristics, and application context. In particular, multimodal imaging generates high-dimensional and heterogeneous datasets characterized by varying imaging conditions, feature representations, and biological variability, thereby necessitating comprehensive and application-specific preprocessing (Cozzolino et al., 2023; Fong et al., 2020; Kang et al., 2020; Park et al., 2023; Tao et al., 2023).

In the field of computer vision, standardized benchmark datasets such as ImageNet and COCO are used to evaluate model performance under controlled conditions. In contrast, AI applications in food safety must account for biological variability, strain diversity, and heterogeneous sample conditions. Therefore, it is of great importance that models developed for food safety applications maintain their reliability not only in terms of computational performance but also amidst the biological complexity found in real-world sample conditions (Papa et al., 2026).

To enhance reproducibility, future studies should comprehensively document bacterial strains, incubation conditions, sample concentrations, imaging settings, dataset characteristics, sample selection procedures, and the implementation of both AI-based and reference methods (Papa et al., 2026). Such documentation will reduce potential sources of bias and facilitate meaningful comparisons across studies. Correlating AI-assisted imaging workflows with established microbiological reference methods, and reporting incubation history and imaging conditions in sufficient detail, will further improve the interpretation of diagnostic performance across different experimental settings. Furthermore, establishing standardized criteria for the rigorous and consistent evaluation of AI-assisted pathogen detection models is essential. From a computational perspective, this can be facilitated through publicly available benchmark datasets hosted in centralized food safety and agricultural repositories, enabling the development, evaluation, and comparison of AI models (Joshi et al., 2023; Qian et al., 2025).

A systematic quality assessment conducted by Papa et al. (2026) revealed that comparative methods in AI-assisted pathogen detection studies are used infrequently and inconsistently, hindering standardized benchmarking against established reference methods. Although many of the reviewed studies reported high performance metrics, variations in evaluation methods and deficiencies in reporting limit the assessment of model generalizability and the ability to make reliable comparisons between different AI-based approaches. Specifically, significant methodological shortcomings were identified, including inadequate descriptions of data preprocessing, data splitting strategies, and dataset characteristics; incomplete reporting on the implementation of reference methods and their comparison with AI results; and a lack of sufficient standardization regarding sample preparation processes and testing conditions. These findings highlight the need for more transparent and standardized reporting practices in AI-assisted pathogen detection research, emphasizing that such an approach would support reproducibility, cross-study comparability, and real-world application (Papa et al., 2026).

Another significant challenge is the limited interpretability of many artificial intelligence models. The “black box” nature of deep learning systems reduces transparency in the decision-making process and can limit confidence in model predictions, particularly regarding food safety regulations. Consequently, explainable AI (XAI) has emerged as a promising approach to enhance model interpretability by providing insights into the factors underlying AI predictions (Hassija et al., 2024). Although XAI methods have begun to appear in a small segment of the broader AI-based imaging literature, their application in pathogen detection remains limited (Ding et al., 2025).

In food safety applications, XAI methods—such as feature importance visualizations, class activation or saliency maps, and local surrogate models (e.g., Local Interpretable Model-agnostic Explanations [lime])—can help clarify the spatial or image-based features underpinning model decisions, thereby enhancing transparency for operators and regulatory bodies (Dwivedi et al., 2023). As these technical and interpretability challenges are addressed, the integration of multimodal imaging and AI-driven detection systems into production environments could enable food safety programs to shift from periodic sampling—which often relies on third-party laboratories—to continuous in-house monitoring with rapid detection capabilities. This approach could reduce response times while facilitating the generation of richer data streams for verification and monitoring processes. Such data could enhance traceability and contribute to a more comprehensive, automated audit trail that is consistent with Hazard Analysis and Critical Control Point (HACCP) principles and aligned with FDA expectations regarding documentation, transparency, and system validation (Papa et al., 2026).

Despite these advancements, more comprehensive validation studies are required to translate AI-driven pathogen detection methods into routine food safety practices. The majority of existing studies have been conducted under controlled laboratory conditions and on limited datasets. However, real-world food production environments are far more complex due to factors such as diverse food matrices, varying production and processing workflows, and the biological variability inherent in natural microbiota. Since food processing steps—such as heat treatment, drying, fermentation, and freezing—can alter microbial characteristics and the data acquired by imaging systems, AI models developed in laboratory settings may not necessarily achieve the same level of performance under actual production conditions. Therefore, supporting future research with broader, real-world datasets that represent diverse food matrices and production conditions will enhance the generalizability of AI models, thereby facilitating their reliable application in industrial settings.

5.3 Detection of chemical contaminants

Chemical contamination is a significant food safety issue encompassing pesticides, fungicides, heavy metals, toxic additives, environmental pollutants, and packaging-derived components. For instance, the fungicide thiram, widely used in agricultural production, can leave residues in foods (Sun et al., 2021), while the illegal use of melamine in dairy products poses serious health risks (Alizadeh et al., 2023). Furthermore, the transfer of microplastics into the food chain has emerged as another area of ​​concern in recent years (Udovički et al., 2022). Artificial intelligence-based systems are increasingly being used to detect pesticide residues, heavy metals, toxins, and allergens. In particular, integrating advanced analytical techniques—such as hyperspectral imaging, Raman spectroscopy, near-infrared (NIR) spectroscopy, and mass spectrometry—with artificial intelligence algorithms enables rapid and highly sensitive analysis (Kang et al., 2021; Wang et al., 2023). Although traditional chromatography and spectroscopy methods offer high accuracy, they may face limitations regarding the detection of trace-level contaminants and the interpretation of multidimensional data. Deep learning techniques, however, enhance contaminant detection performance by enabling more effective data analysis through capabilities such as non-linear modeling and adaptive feature extraction (Tian et al., 2022). Prominent machine learning-based methods include Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). These algorithms yield successful results in classifying unknown samples and identifying potential contaminants by analyzing spectral signatures (Taneja et al., 2023; Zatsu et al., 2024). The literature reports that deep learning-based applications achieve high accuracy. Lin et al. (2023) achieved 99.6% accuracy with the MM-IR-AlexNet model they developed to detect trace amounts of contaminants in flour samples. These findings demonstrate that AI-powered systems hold significant potential for the detection of chemical contaminants.

AI-driven analytical methods are widely employed to enhance the accuracy and speed of spectroscopic and imaging-based techniques in the quantitative analysis of pesticide and veterinary drug residues. In particular, the integration of fluorescence imaging, hyperspectral imaging (HSI), near-infrared (NIR) spectroscopy, and Raman spectroscopy with machine learning and deep learning algorithms has enabled the rapid and precise determination of pesticide residues. For instance, Mousavizadegan et al. (2023) achieved high prediction accuracy in the determination of tetracycline by combining fluorescence imaging with Bagging-ANN and Bagging-DT algorithms. Regarding HSI-based deep learning applications, Jiang et al. (2019) utilized a CNN-based model to detect four different pesticides, reporting accuracies of 99.09% and 95.35% on test and validation datasets, respectively. Portable spectroscopic techniques have also demonstrated promising analytical performance in pesticide residue analysis.

Villanueva et al. (2023) achieved detection limits as low as 9.8 μg/kg in the analysis of pesticide residues in cocoa samples using NIR spectroscopy and the PLSR algorithm, whereas Zhu J. et al. (2021) ensured high accuracy, stability, and sensitivity in the in-situ analysis of pesticide residues in tea by integrating surface-enhanced Raman spectroscopy (SERS) with a one-dimensional convolutional neural network (1D-CNN). Similarly, Wei et al. (2023) optimized the QuEChERS method using the GA-ANN algorithm, achieving detection limits ranging from 0.023 to 0.0644 mg/kg and regression coefficients exceeding 0.9960 in the analysis of multiple pesticide residues in Lycium barbarum samples.

Artificial intelligence-based methods also demonstrate promising performance in the detection and quantitative analysis of mycotoxins. Machine learning algorithms are employed not only for predicting toxin levels but also for forecasting the risks of fungal growth and mycotoxin formation. Tarazona et al. (2021) successfully modeled the effects of ethylene-vinyl alcohol copolymer films containing essential oils on the growth of Fusarium culmorum and F. proliferatum and the production of zearalenone and fumonisin, using the random forest (RF) algorithm.

Regarding imaging-based approaches, Gao et al. (2021) integrated HSI with a 1D-CNN model and reported test accuracies of 96.35%, 92.11%, and 94.64% for aflatoxin detection in peanut and maize samples, respectively. Similarly, in a study utilizing NIR-HSI combined with PCA-SVM and SPA-SVM algorithms, maize kernels were classified based on fungal infection with an accuracy exceeding 92%. In a method combining multispectral imaging with the GA-BPNN algorithm, the zearalenone content in maize was estimated non-destructively, yielding values ​​of R = 0.95, RMSE = 3.66 μg/kg, and RPD = 5.39. Furthermore, Ma et al. (2023) integrated a whole-cell biosensor array based on volatile organic compounds with the RF algorithm, successfully distinguishing between healthy and moldy grains with 100% accuracy and identifying early stages of mold growth with an accuracy exceeding 95%. These studies demonstrate that artificial intelligence holds significant potential not only for determining mycotoxin levels but also for the early detection of contamination and risk assessment.

Artificial intelligence-based approaches also enhance the accuracy of spectroscopic analysis methods in the determination of heavy metal contamination. Lu et al. (2022) successfully predicted the removal performance of chitosan-based flocculants for heavy metals such as Cu, Pb, Cd, Zn, and Ni using an RF model, achieving values ​​of R ² = 0.9354 and RMSE = 5.67. In a study integrating NIR reflectance spectroscopy with the REELM algorithm, a prediction accuracy of 99.27% ​​was achieved for Pb detection in mussels, while Zhou et al. (2023) predicted lead content in rapeseed leaves using fluorescence HSI and SNV-WT-SDAE algorithms with accuracy levels of R ² = 0.9388, RMSEP = 0.0199 mg/kg, and RPD = 3.275. Furthermore, mercury stress in tobacco plants was successfully identified in a study employing Vis-NIR-HSI in conjunction with the CARS-LS-SVM algorithm. These findings demonstrate that AI-assisted spectroscopic approaches offer rapid, non-destructive, and highly accurate alternative methods for heavy metal analysis.

When the studies reviewed are evaluated collectively, it is evident that integrating artificial intelligence with spectroscopic, imaging-based, and biosensor-based techniques—as well as sample preparation methods—holds significant potential for the detection and quantitative analysis of pesticide residues, mycotoxins, and heavy metal contamination. However, the majority of existing research has been conducted on specific food matrices and under controlled experimental conditions; furthermore, variations in sensor technologies, data preprocessing approaches, and artificial intelligence algorithms make it difficult to directly compare performance across studies. In this context, conducting more comprehensive validation studies that encompass diverse food matrices, alongside standardizing analytical methods and evaluation criteria, will facilitate a more thorough assessment of the reliability and practical applicability of AI-driven analytical approaches in food safety.

5.4 Detection of physical contaminants

Contamination by physical foreign matter can lead to serious consequences, such as product recalls, consumer complaints, a decline in brand credibility, and physical injuries. Foreign materials—such as glass, metal, plastic, rubber, insects, and bone fragments—can find their way into food products during production, transport, packaging, and preparation, posing significant health risks to consumers (Shimonomura et al., 2021).

The fact that physical contaminants are often small makes them difficult to detect via traditional visual inspections. Consequently, AI-powered image processing systems offer a significant alternative for the automated identification of foreign matter. Based on the analysis of image data, these systems enable the rapid and reliable detection of physical contaminants (Liu, 2022).

Shimonomura et al. (2021) developed a system designed to identify hard foreign objects smaller than a millimeter, utilizing cylindrical photosensors and high-sensitivity reflective membrane sensor points. The study results demonstrated the successful detection of small foreign objects in samples such as raw shrimp and fish bones.

In addition, methods such as hyperspectral imaging, Near-Infrared (NIR) spectroscopy, and Raman spectroscopy are widely used to identify physical contaminants. AI-driven data analysis systems process the data obtained from these technologies to enhance detection performance and contribute to more reliable inspection processes (Pandhi et al., 2025).

AI-powered hyperspectral imaging systems demonstrate significant potential for the rapid and non-destructive detection of physical foreign objects. Research in this field indicates that hyperspectral imaging technology can be successfully integrated with machine learning and chemometric methods to identify various foreign objects across different food matrices. For instance, Saeidan et al. (2021) utilized hyperspectral imaging technology to detect four distinct types of foreign objects—wood, plastic, stone, and plant matter—in the cocoa processing industry, achieving an accuracy of 89.10% using a support vector machine (SVM) classifier. Similarly, Sun et al. (2022) combined a hyperspectral imaging system with a linear discriminant analysis (LDA) model, reporting 100% precision, 100% recall, and 97.56% accuracy in detecting tea stems and insect-related foreign objects in finished tea products.

Studies conducted on various product groups yield similar results. Shu et al. (2023) evaluated foreign objects—such as stones, plastic, paper, string, and stems—in pepper products by integrating hyperspectral imaging with spectral preprocessing, chemometric analysis, and target detection algorithms, reporting that the Random Forest (RF) algorithm achieved over 96% accuracy in identifying these foreign objects. Additionally, Tunny et al. (2023) compared visible-near-infrared (VNIR), short-wave infrared (SWIR), and fluorescence hyperspectral imaging systems for fresh-cut vegetables, demonstrating that an overall detection accuracy of 99% was achieved by using the SWIR hyperspectral imaging system in conjunction with the SPA-PLS-DA model.

When the studies reviewed are considered collectively, AI-assisted hyperspectral imaging appears to be a promising approach for detecting physical contaminants in various food products under controlled experimental conditions. However, directly comparing the reported performance is difficult, as the studies differ significantly in terms of the food matrices used, contaminant characteristics, imaging systems, AI algorithms, and experimental protocols.

Despite these promising findings, various technical and operational issues must be resolved for hyperspectral imaging to be widely implemented in industrial food production. Li et al. (2025) state that hyperspectral imaging technology holds significant potential for foreign object detection, yet various technical challenges persist in industrial applications. They emphasize that the high-dimensional nature of hyperspectral data increases computational costs, while the limited availability of samples containing foreign objects under real-world production conditions—along with class imbalance in datasets—can negatively affect the generalizability of artificial intelligence models. Furthermore, they note that the lack of comprehensive, standardized databases representing diverse food products and foreign object types complicates the comparison and validation of developed models. Consequently, the authors propose the development of standardized databases and data-sharing platforms, alongside transfer learning and few-shot learning approaches, as key research directions to support the industrial-scale deployment of artificial intelligence applications in this field.

5.5 AI-powered smart food packaging systems

Artificial intelligence has redefined the functional boundaries of food packaging by enabling data-driven, dynamic systems that go beyond static protection. These systems aim to proactively ensure safety, extend shelf life, and enhance traceability. Through integration with sensors, cloud platforms, and Internet of Things (IoT) networks, AI-powered smart packaging systems offer greater accuracy, sensitivity, and reliability throughout the food supply chain (Li et al., 2024; Rosca and Stancu, 2025).

The rapid advancement of flexible sensor technologies has further enabled smart packaging systems to continuously generate multidimensional, real-time data related to food quality indicators, including temperature, humidity, pH, and volatile compounds. However, the large volume and complexity of these datasets require advanced analytical approaches for effective interpretation. Machine learning-based artificial intelligence facilitates the extraction of hidden patterns from sensor data, predicts spoilage processes, and supports adaptive decision-making throughout the food supply chain. Consequently, the integration of flexible sensors with AI-driven analytics transforms smart packaging from a passive monitoring tool into an active, predictive, and responsive system (Jia et al., 2025). Artificial neural network models have also been successfully applied to predict changes in the overall quality of processed cheese during storage, demonstrating their potential for shelf-life and quality assessment (Stangierski et al., 2019). Waday and Aklilu (2023) applied artificial neural network (ANN) and response surface methodology (RSM) to model and optimize the tensile strength, water vapor permeability, and antimicrobial properties of packaging films based on avocado seed starch and orange peel essential oil, with both models demonstrating suitable predictive performance.

AI-powered smart packaging systems leverage various artificial intelligence approaches, such as machine learning (ML), deep learning (DL), computer vision (CV), and natural language processing (NLP). These technologies are utilized to analyze data derived from packaging systems, predict shelf life and contamination risks, assess packaging integrity, and facilitate interaction between consumers and regulatory information (Oyegbade et al., 2025). Artificial neural networks and image processing were used to predict the shelf life of cauliflower based on total color changes under modified atmosphere packaging conditions (Mohi Alden et al., 2019). Computer vision systems integrated into packaging lines facilitate the verification of packaging integrity by automatically detecting micro-cracks, sealing defects, and foreign contaminants. In particular, convolutional neural networks (CNNs)—a prominent deep learning architecture—can identify subtle visual differences often imperceptible to human inspectors; furthermore, by analyzing thermal images of packages, they contribute to the detection of hotspots indicative of bacterial activity or irregular sealing. These capabilities offer significant advantages in monitoring the effects of variability in storage and processing conditions on food quality and safety (Liakos et al., 2025; Young et al., 2020; Oyegbade et al., 2025).

Although AI-powered smart packaging systems offer significant advantages in applications such as quality control, defect detection, and label verification, several technical, regulatory, and ethical challenges continue to limit their widespread adoption in industrial settings (Kumar et al., 2025). Among these, interpretability is one of the most prominent challenges. Interpretability refers to the ability of machine learning models to explain their decision-making processes, enabling users to understand how a model reaches a particular prediction. However, many deep learning models operate as “black-box” systems, making their decision-making processes difficult to interpret and potentially limiting user trust and accountability. In particular, the ability to provide transparent explanations for product acceptance or rejection decisions is essential in real-time quality control and defect detection applications (Kumar et al., 2025).

Another important challenge is regulatory compliance. As the packaging industry is subject to various safety, quality, and environmental regulations, AI-based systems must be developed and implemented in accordance with these regulatory requirements. Therefore, the development of transparent and interpretable AI models capable of demonstrating compliance with existing regulations is critical to enhancing the reliability and industrial acceptance of these technologies (Kumar et al., 2025).

In addition to technical and regulatory considerations, ethical issues should also be addressed in smart packaging applications. The collection and use of consumer-related data require appropriate consent procedures and adequate protection of data privacy. Furthermore, biases present in training datasets may influence model predictions and decision-making. Therefore, ensuring privacy, transparency, fairness, and accountability throughout the development and deployment of AI-based smart packaging solutions is essential for the establishment of trustworthy and responsible AI applications (Naik et al., 2022).

Overall, the reviewed studies suggest that AI-powered smart packaging systems have considerable potential to enhance food safety by supporting continuous quality monitoring, shelf-life prediction, packaging integrity assessment, active packaging applications, and intelligent decision-making throughout the food supply chain. However, translating these promising findings into large-scale industrial applications remains challenging. Current evidence indicates that factors such as sensor reliability, long-term operational stability, data integration, compatibility between sensing technologies and packaging materials, implementation costs, and interoperability may influence the practical adoption of these systems. In addition, data heterogeneity, limited model interpretability, and the absence of widely accepted validation frameworks continue to present challenges for broader industrial use. Future research should therefore focus on developing more robust, explainable, and generalizable AI models while evaluating their performance under real-world manufacturing and supply chain conditions.

5.6 Food fraud and authenticity verification

Verifying the authenticity of food products and ensuring their traceability are of great importance in preventing fraud and mislabeling practices. Artificial intelligence systems can determine unique identity information regarding a product’s origin, variety, and processing characteristics by evaluating spectral images, chemical composition profiles, and other analytical data. This enhances product traceability throughout the supply chain, reduces the risk of fraud, and contributes to bolstering consumer confidence (Zhu et al., 2024). For the practical implementation of machine learning-based spectroscopic systems for food adulteration detection, model performance depends not only on algorithm selection but also on dataset size, preprocessing, feature selection and extraction, model validation, and optimization of performance criteria (Goyal et al., 2024). One of the methods employed for this purpose is the use of AI-supported Nuclear Magnetic Resonance (NMR) spectroscopy. By facilitating the interpretation of complex NMR data, AI algorithms enable more precise and efficient analyses in areas such as verifying food authenticity, tracking origin, and detecting contamination (Cao R. et al., 2025). These applications offer significant potential for authenticity verification and anti-fraud efforts, particularly regarding high-value-added products.

5.7 Supply chain and traceability

Ensuring transparency and traceability within the food supply chain is critical for mitigating contamination risks, effectively managing product recalls, and boosting consumer confidence. In this context, blockchain technology enhances the traceability of product movements by enabling transactions—from production to consumption—to be stored as secure, immutable records. Consequently, in the event of potential contamination or an outbreak, it becomes possible to rapidly identify the source of the risk and implement necessary interventions in a timely manner (Duan et al., 2024; Kamath, 2018). Meanwhile, artificial intelligence technologies contribute to the identification of product defects, operational errors, and potential safety risks by analyzing the vast volumes of data generated throughout the supply chain. Machine learning algorithms, in particular, can predict risks of spoilage and contamination by evaluating data related to temperature fluctuations, humidity levels, storage conditions, and transportation processes (Qian et al., 2023; Li et al., 2024; Zhang Z. et al., 2024). The integration of blockchain and artificial intelligence technologies offers significant advantages regarding data security and risk management. While blockchain’s decentralized record-keeping structure preserves data integrity, artificial intelligence algorithms analyze this data to enable the early detection of anomalies, safety risks, and potential sources of contamination (Joseph, 2025; Joseph and Pandeeswari, 2025). Applications such as IBM Food Trust demonstrate that this integration serves as an effective approach for enhancing reliability and traceability within the food supply chain (Kamath, 2018). Thanks to AI-powered labeling and tracking systems, all processes—from harvest to processing, packaging, and distribution—can be monitored; notably, the source of contaminated products can be rapidly identified in the event of a recall (Ling et al., 2021; Gbashi and Njobeh, 2024). Real-time monitoring systems developed in recent years offer more integrated solutions by combining the Internet of Things (IoT), artificial intelligence, and blockchain technologies. By ensuring the continuous monitoring of critical safety parameters, these systems contribute to the early identification of potential risks and the timely implementation of preventive measures (Kanicheril Ambikalekshmi et al., 2026). Overall, the integration of artificial intelligence and blockchain supports the establishment of a more transparent, reliable, and proactive traceability infrastructure within food supply chains.

Beyond traceability applications, recent advances have also focused on integrating artificial intelligence with emerging sensing and monitoring technologies to improve food safety management.

6 New technologies integrated with artificial intelligence

Integrating traditional analytical methods with digital technologies has enabled the development of faster, portable, and user-friendly systems for food safety applications. In particular, biosensors integrated with smartphone technologies facilitate low-cost, on-site analysis. Smartphone-based aptasensor systems support real-time data sharing and contribute to global food safety surveillance thanks to their semi-automated structures, user-friendly interfaces, and wireless data transmission capabilities (Abdelbasset et al., 2023).

6.1 Biosensors, nanotechnology, and smart sensing systems

The combined use of artificial intelligence, biosensors, and nanotechnology has enabled the development of next-generation sensing systems for food safety applications. Compared to traditional laboratory-based methods, these systems offer faster, more sensitive, and portable solutions for detecting contaminants and pathogens. Nanotechnology-based applications offer significant advantages in food safety analysis. Nanomaterials such as gold and silver nanoparticles, quantum dots, graphene-based structures, and nanozymes are widely used to detect contaminants. Meanwhile, biosensor technologies enable target-specific analyses through structures based on enzymes, DNA, aptamers, and functional nucleic acids. The development of electrochemical, optical, and paper-based biosensor platforms has facilitated the widespread adoption of these technologies in commercial applications (Pandhi et al., 2025; Jafrasteh et al., 2023). Integrating nanocomposites into biosensor systems contributes to enhanced analytical sensitivity and specificity. In particular, nanobiosensors demonstrate high performance in detecting pathogens and toxins, thereby supporting the prevention of foodborne illnesses (Kaur et al., 2024; Kumar H. et al., 2020; Naresh and Lee, 2021). Furthermore, machine learning algorithms enable the analysis of complex data derived from biosensors, facilitating improvements in sensor performance, the identification of low-level contamination patterns, and predictive analytics (Inês and Cosme, 2025).

Zhu et al. (2022) reported successful results using colorimetric sensor arrays combined with artificial intelligence-based models for the detection of aflatoxin B1 (AFB1) in peanuts. The optimized SSA-SVR model achieved high prediction accuracy and demonstrated reliable performance in the quantitative analysis of AFB1. Likewise, Tamale et al. (2026) developed an Inception-ResNet-V2-based deep learning model for the early detection of aflatoxin-related defects in Ugandan groundnuts, achieving an accuracy of 99.29% and AUC values ranging from 0.97 to 1.00. Deshmukh et al. (2025) further highlighted the role of AI in aflatoxin detection and risk prediction, emphasizing the effectiveness of machine learning, deep learning, smartphone-based diagnostic systems, and image analysis technologies. Alongside these developments, metal-organic framework (MOF)-based sensors have emerged as promising platforms for detecting pesticides, mycotoxins, antibiotics, food additives, and heavy metals. Zhang L. et al. (2024) highlighted the potential of multi-parameter, multi-modal approaches integrating biosensors and artificial intelligence for the rapid detection of food contaminants. In addition, Zhang Z. et al. (2024) highlighted the potential of MOF-based sensing systems for food contaminant detection using fluorescence, electrochemical, electrochemiluminescence, surface-enhanced Raman spectroscopy (SERS), and colorimetric sensing approaches.

Advances in sensing technologies extend beyond biosensors and nanomaterials. Deep eutectic solvents (DES) have emerged as environmentally friendly alternatives for contaminant extraction because of their low volatility, high thermal stability, and favorable physicochemical properties (Kalogiouri et al., 2024; Wang and Liu, 2025). For example, Fattahi et al. (2024) reported extraction efficiencies of 55%–73% for pesticides in water and fruit juice using a hydrophobic DES system. In addition, aggregation-induced emission (AIE) technologies, functional nucleic acid-based biosensors, and 3D printing-assisted sensing platforms have attracted increasing attention. AIE-based systems have shown promising performance in detecting pesticides, mycotoxins, and pathogens (Chen X. et al., 2025), whereas aptamer- and quantum dot-based biosensors enable highly sensitive detection of heavy metals and pesticide residues (Jafrasteh, Farmani and Mohamadi, 2023). The integration of 3D printing with biosensor technologies further enhances contamination monitoring during food production (Addanki et al., 2022). Moreover, AI-supported nanosensors integrated with IoT, cloud computing, 5G, and edge computing facilitate centralized data management, remote monitoring, and intelligent analysis of sensor outputs, while also supporting smart packaging applications throughout the food supply chain (Zhang L. et al., 2024). Overall, the integration of artificial intelligence, nanotechnology, and biosensors represents one of the most promising directions for next-generation food safety systems by enabling real-time monitoring, high-precision analysis, and data-driven decision support (Pandhi et al., 2025).

6.2 Real-time monitoring systems

Real-time monitoring systems enable the continuous tracking of critical parameters—such as temperature, humidity, pH, gas composition, and contamination levels—during food production and storage processes. This allows for the early detection of process deviations and the timely implementation of necessary corrective actions (Boukid, 2022). The integration of sensor technologies with artificial intelligence has significantly enhanced the effectiveness of real-time monitoring applications. By analyzing large datasets obtained from sensors using machine learning algorithms, it has become possible to detect anomalies, evaluate process performance, and predict potential risks in advance. Furthermore, AI-driven systems support predictive maintenance practices, thereby contributing to improved equipment performance and enhanced production continuity (Roy, 2026). These systems contribute not only to strengthening food safety but also to increasing production efficiency and reducing food waste. Continuous data collection and automated analysis capabilities facilitate a more reliable, sustainable, and data-driven approach to food safety management (Roy, 2026; Boukid, 2022).

7 Advantages and limitations

Although a significant portion of artificial intelligence applications in the literature remains at the proof-of-concept and pilot-scale study stages, their use in areas such as quality control, contamination detection, organoleptic assessment, and process optimization is becoming increasingly widespread (Mir et al., 2026). The performance of artificial intelligence systems depends not only on the algorithms employed but also on data quality, data diversity, and the selection of an appropriate model (Sapienza and Vedder, 2023). Artificial intelligence offers significant advantages over traditional methods—particularly in spectroscopic and image-based applications—due to its ability to analyze complex and non-linear data structures (Zhou et al., 2026). Thanks to advantages such as high processing speed, consistent decision-making capabilities, and operational efficiency, artificial intelligence systems demonstrate significant potential in food safety applications. Furthermore, by contributing to the reduction of human error, they enable the more consistent implementation of quality and safety standards (Dara et al., 2022).

The studies reviewed indicate that the performance of artificial intelligence models can vary depending on the application area and the type of input data used. Deep learning models such as CNN, YOLOv5, ResNet-50, and Inception-ResNet-V2 have yielded successful results in image-based applications, including pathogen detection, contaminant identification, and food quality assessment (Park et al., 2025; Ravindhiran and Dhandapani, 2025; Chun et al., 2024; Tamale et al., 2026). In contrast, machine learning algorithms such as SVM and ANN have primarily been applied to spectroscopic data, demonstrating effective performance in classification and prediction tasks (Du et al., 2022; Lin et al., 2023). Furthermore, the hybrid SSA-SVR model improved aflatoxin prediction accuracy by combining feature optimization with regression analysis (Zhu et al., 2022).

When these studies are evaluated collectively, it is evident that various artificial intelligence models are employed across different food safety applications and data types. This indicates that the performance of artificial intelligence models must be assessed within the context of the application area and the type of data used. Table 6 summarizes the performance metrics of the artificial intelligence models discussed in this review, alongside the computational characteristics (such as computational cost and inference time) reported in the original studies.

TABLE 6. Performance comparison and computational characteristics of representative artificial intelligence algorithms used in food safety applications.

Although the artificial intelligence models summarized in Table 6 generally demonstrate high predictive performance across various food safety applications, direct comparison of the reported performance metrics should be interpreted with caution because the reviewed studies employed different food matrices, datasets, experimental conditions, and validation protocols. Furthermore, although computational characteristics were available for some studies, the majority did not report standardized quantitative measures such as inference time, computational complexity, memory requirements, or energy consumption. Consequently, predictive accuracy alone is insufficient to evaluate the practical industrial applicability of AI models. Future studies should therefore report standardized computational performance metrics together with predictive performance to facilitate objective benchmarking and support industrial implementation.

7.1 Technical and operational limitations

One of the primary obstacles to the widespread deployment of artificial intelligence systems at an industrial scale is the requirement for digital infrastructure capable of supporting the generation and processing of high-quality data. Deficiencies in the infrastructure for collecting, storing, and analyzing large datasets can directly impact model performance and reliability (Liu et al., 2023). Furthermore, transferring many imaging and spectroscopy-based systems—which yield successful results in laboratory settings—to actual production environments presents various challenges. Factors such as portability, durability, and cost limit the widespread adoption of these systems; specifically, hyperspectral cameras, Raman spectrometers, and advanced sensor systems entail high investment, maintenance, and calibration costs (Li et al., 2025). Additionally, data transmission capacity, storage requirements, and the computational needs for running AI models on-device pose significant constraints, particularly for small and medium-sized enterprises (Deng et al., 2023; Zheng et al., 2025). Another significant challenge is the inability of developed models to achieve sufficient generalizability across different product groups and production environments. Most current applications have been developed for specific products or contaminants, and the development of models adaptable to diverse conditions is considered a key focus area for future research (Qian et al., 2023).

7.2 Regulatory and ethical challenges

With the widespread adoption of artificial intelligence technologies in the field of food safety, regulatory compliance and ethical issues have gained increasing importance. In particular, the “black box” nature of most deep learning models complicates the explanation of decision-making processes and creates various challenges regarding their acceptance by regulatory authorities (Rudin, 2019; Marvin et al., 2022). Consequently, research into Explainable Artificial Intelligence (XAI) approaches has accelerated in recent years (Li et al., 2021). From a regulatory perspective, organizations such as the US Food and Drug Administration (FDA), the European Food Safety Authority (EFSA), and the Food Safety and Standards Authority of India (FSSAI) require comprehensive risk assessments to verify the reliability of AI- and nanotechnology-based systems. This necessitates the strengthening of standardization efforts, the establishment of validation protocols, and the development of international regulatory frameworks (Malarkodi et al., 2017; Pearson et al., 2024; Pandhi et al., 2025).

7.3 Data privacy and security

Artificial intelligence applications require the processing of vast amounts of data regarding production processes, supply chain activities, and consumer behaviors. Consequently, issues such as data privacy, data ownership, and cybersecurity are critical for the sustainable and reliable use of these systems (Dara et al., 2022; Kanicheril Ambikalekshmi et al., 2026). To ensure the secure sharing and processing of data, it is necessary to comply with data protection regulations such as the GDPR (General Data Protection Regulation), establish robust cybersecurity infrastructures, and conduct data management processes transparently (Kudashkina et al., 2022). Furthermore, obtaining user consent and clearly defining data usage policies are considered essential requirements for fostering trust in AI systems and facilitating the broader adoption of these technologies (Liu et al., 2023). Overall, AI technologies have the potential to transform food safety management into a more proactive, rapid, and data-driven system. However, it is evident that technological advancements are not being reflected in industrial applications at the same pace. Therefore, it is crucial that future studies focus not only on model accuracy but also on performance under real-world production conditions, scalability, and regulatory acceptance processes. To provide an overall perspective on the current status of artificial intelligence applications in food safety, Table 7 summarizes the major strengths and current limitations reported in the literature.

TABLE 7. Strengths and current limitations of artificial intelligence-based applications in food safety.

8 Future perspectives and contribution to the literature

This review evaluates the applications of artificial intelligence technologies in food safety—specifically regarding contaminant and pathogen detection, quality control, traceability, risk estimation, and early warning systems—from a holistic perspective. Literature findings indicate that AI-driven approaches significantly contribute to the development of faster, more precise, and data-oriented decision-making processes in food safety management (Mir et al., 2026; Yu et al., 2025; Zhou et al., 2026). The integrated use of artificial intelligence, big data analytics, advanced sensor systems, biosensors, and nanotechnology constitutes a fundamental component of future food safety systems. In particular, real-time monitoring systems, smart sensing technologies, portable biosensors, and advanced data analysis methods are expected to gain increasing importance in the coming years (Pandhi et al., 2025; Cao L. et al., 2025). However, the majority of existing studies are based on laboratory or pilot-scale applications. The limited number of studies conducted in actual production environments necessitates further research into the industrial-scale feasibility of artificial intelligence systems. Furthermore, issues such as data quality, model generalizability, explainable AI, data security, and regulatory requirements are among the key research areas highlighted in the literature (Qian et al., 2023). In this context, it is important for future research to focus on the performance of AI applications under real-world production conditions and their industrial viability. Additionally, increasing research into integrated systems that combine various AI technologies, as well as into explainable and trustworthy AI approaches, will contribute to the advancement of food safety practices. It is also anticipated that collaborations established among AI experts, food engineers, industry representatives, and regulatory bodies will support the more widespread and effective adoption of these technologies.

8.1 Transfer learning and model generalizability

Most of the reviewed studies developed and validated artificial intelligence models using datasets derived from the same food products under similar experimental conditions. Although transfer learning has considerable potential to improve model performance in applications with limited labeled data by leveraging knowledge learned from previously trained models (Gandorfer et al., 2022), its successful application depends on the similarity between the source and target domains. Transfer learning can help address limited labeled data by adapting pre-trained models to related applications, thereby reducing the need for large task-specific datasets (Hossen et al., 2025). The visual characteristics of food products may vary considerably according to ripening stage, environmental conditions, season, geographical region, and imaging system. Therefore, the transferability of models developed using a specific food product or dataset to different food products, independent datasets, or real-world imaging conditions should be carefully evaluated. Source–target domain discrepancies may result in domain shift, reducing the representativeness of transferred features and increasing the risk of negative transfer, particularly when models pretrained on general-purpose datasets such as ImageNet and COCO are transferred to food-specific applications (Gandorfer et al., 2022; Al Sahili and Awad, 2022).

To improve model generalizability, the inclusion of diverse datasets collected from different geographical regions, seasons, environmental conditions, and imaging systems has been recommended (Attallah, 2023; Liu and Wang, 2021). In addition, the development of domain-specific and more generalizable pretrained models covering diverse food matrices may enhance the effectiveness of transfer learning in food safety and quality control applications (Al Sahili and Awad, 2022). Few-shot and zero-shot learning have also been identified as promising approaches for adapting models to new applications where only limited labeled data are available (Rezaei et al., 2024).

Singh et al. (2020) reported that models pretrained using laboratory-specific datasets may exhibit variable performance when applied to different field datasets, highlighting the importance of validating model generalizability using independent datasets. In the reviewed studies, models were predominantly evaluated using the same or similar datasets, whereas validation using independent datasets was reported less frequently. Accordingly, previous studies have emphasized the importance of using more diverse datasets collected under different environmental conditions and imaging settings, together with the development of domain-specific pretrained models, to improve model generalizability (Attallah, 2023; Liu and Wang, 2021; Al Sahili and Awad, 2022).

9 Conclusion

Artificial intelligence has emerged as a transformative technology for modern food safety management by enabling faster, more accurate, and data-driven approaches to contaminant detection, quality assessment, traceability, and risk prediction. The literature reviewed in this study demonstrates that the integration of machine learning, deep learning, computer vision, biosensors, smart packaging, the Internet of Things (IoT), and blockchain technologies has substantially improved the capability to monitor food safety throughout the supply chain. Beyond summarizing current applications, this review critically evaluated representative artificial intelligence technologies and algorithms with respect to their predictive performance, computational characteristics, industrial applicability, and current limitations. The findings indicate that no single AI model is universally optimal for all food safety applications. Instead, the selection of appropriate AI approaches should consider not only predictive accuracy but also computational efficiency, interpretability, data characteristics, and the specific requirements of the intended application. Despite these advances, the transition of AI technologies from laboratory research to routine industrial practice remains limited. Challenges related to heterogeneous food matrices, limited external validation, insufficient model interpretability, lack of standardized benchmark datasets, high computational requirements, infrastructure costs, and regulatory acceptance continue to restrict large-scale implementation. Addressing these challenges will require standardized validation protocols, comprehensive evaluation under real-world production conditions, wider adoption of explainable artificial intelligence (XAI), improved model generalizability through transfer learning, and stronger collaboration among researchers, industry stakeholders, and regulatory authorities.

Overall, artificial intelligence should be regarded as a complementary technology that strengthens existing food safety management systems rather than replacing them. Continued advances in explainable AI, integrated sensing technologies, standardized data infrastructures, and digital food systems are expected to further enhance the reliability, transparency, and sustainability of food safety management, supporting the transition from reactive quality control toward predictive, risk-based, and intelligent food safety systems.

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Footnotes

Edited by: Qingli Dong, University of Shanghai for Science and Technology, China

Reviewed by: Ntsoaki Malebo, Central University of Technology, South Africa

Xidong Jiao, Chinese Academy of Fishery Sciences, China