# From lab to market: real-time food safety monitoring via spectroscopy, blockchain and Artificial Intelligence

Nikos Chorianopoulos, Anastasia Lytou, Lemonia-Christina Fengou, Shintaro Kinoshita, Pengcheng Dong, Yimin Zhang, Chrysoula Tassou, Fady Mohareb,

George-John Nychas

<figure><img src="image_1.png" /></figure>

## To cite this version:

Nikos Chorianopoulos, Anastasia Lytou, Lemonia-Christina Fengou, Shintaro Kinoshita, Pengcheng Dong, et al. From lab to market: real-time food safety monitoring via spectroscopy, blockchain and Artificial Intelligence. Trends in Food Science and Technology, 2025, 166, pp.105386. 10.1016/j.tifs.2025.105386. hal-05334009

HAL Id: hal-05334009

https://utc.hal.science/hal-05334009v1

Submitted on 5 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

# From lab to market: Real-time food safety monitoring via spectroscopy, blockchain and artificial intelligence

Nikos Chorianopoulos<sup>a,*</sup>, Anastasia Lytou<sup>a</sup>, Lemonia-Christina Fengou<sup>a</sup>, Shintaro Kinoshita<sup>e</sup>, Pengcheng Dong<sup>b,c</sup>, Yimin Zhang<sup>b,c</sup>, Chrysoula Tassou<sup>d</sup>, Fady Mohareb<sup>e,f</sup>, George-John Nychas<sup>a,b,c,**</sup> ◐

## ARTICLE INFO

Handling Editor: Dr. S Charlebois

### Keywords:

* Artificial intelligence

* Spectroscopy-based sensors

* Big data

* Cloud computing

* Blockchain technologies

* Food safety

## ABSTRACT

**Background:** The food system is a complex concept that involves various stages and processes from farm-to-fork, where the assessment of quality and safety at every stage is of great importance. The current safety and quality controls rely on microbiological and chemical analyses, which are time-consuming, laborious and provide retrospective results.

**Scope and approach:** This review critically examines the current developments and practical applications of spectroscopy-based sensors integrated with Artificial Intelligence (AI) and Information Technologies (IT), emphasising their role in ensuring food integrity throughout the entire supply chain, from production to consumption. It provides a comprehensive analysis of the state-of-the-art in non-destructive spectroscopic instrumentation, AI-driven analytical tools, Blockchain technology, and other emerging digital innovations aimed at enhancing real-time food safety and quality monitoring. Particular attention is given to the progression from laboratory-scale experiments to commercially viable solutions, highlighting the technological advancements achieved, as well as the logistical, economic, and infrastructural challenges impeding their large-scale deployment. This review underscores the transformative potential of these integrated technologies in establishing transparent, traceable, and resilient food systems on a global scale.

**Key findings and conclusions:** In this context, spectroscopy-based sensors in tandem with AI tools and Information Technologies, such as cloud computing, Internet of Things, big data and Blockchain, are a very promising topic for the food industry to assure food quality and safety.

## 1. Introduction

The agricultural and food industry is confronted with significant challenges at the beginning of the 21st century, these are: first, providing the world's population with enough food to eat (Food Security) and second, ensuring that this food is safe to eat (Food Safety), while maintaining a production process within environmental constraints. These objectives must be realised in the context of tremendous

technological change, a growing lack of natural resources, and a continuous evolution of consumers' lifestyles and consumption habits, across the globe. The food industry is obliged to operate under seemingly contradictory expectations, i.e., consumers prefer foods that are (i) convenient and fresh (minimally processed and packaged); (ii) all "natural" - with no preservatives (iii) healthy without bad health effects (i.e., low in fat, salt, and sugar) and (iv) produced in an environmentally sustainable manner, that will follow the principles of European Green

<footnote><sup>a</sup> Agricultural University of Athens, Department of Food Science and Human Nutrition, Lab of Microbiology and Biotechnology of Foods, Iera Odos 75, 11855, Athens, Greece</footnote>

<footnote><sup>b</sup> Shandong Agricultural University College of Food Science and Engineering, Lab of Beef Processing and Quality Control, Tai'an, Shandong, 271018, China</footnote>

<footnote><sup>c</sup> International Joint Research Lab (China and Greece) of Digital Transformation as an Enabler for Food Safety and Sustainability, Tai'an, Shandong, 271018, China</footnote>

<footnote><sup>d</sup> Institute of Technology of Agricultural Products, Hellenic Agricultural Organisation - DIMITRA, S. Venizelou 1, Lycovrissi, 14123, Athens, Greece</footnote>

<footnote><sup>e</sup> E1 Datascience LTD, MK404RZ Bedfordshire, United Kingdom</footnote>

<footnote><sup>f</sup> Université de Technologie de Compiègne, CNRS, UPJV, GEC, 60203, Compiègne, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author. Agricultural University of Athens, Department of Food Science and Human Nutrition, Lab of Microbiology and Biotechnology of Foods, Iera Odos 75, 11855, Athens, Greece.</footnote>

<footnote>E-mail addresses: nchorian@aua.gr (N. Chorianopoulos), gjn@aua.gr (G.-J. Nychas).</footnote>
Deal priorities, the farm to fork strategy and Food2030 priorities for a fair, healthy and environmentally friendly food system, as well as with the EU's Climate ambition for 2030 and 2050 (Hassoun, 2024c, Hassoun, 2025a; Hassoun et al., 2025a,b; Parra-Lopez et al., 2025).

The above-mentioned issues are challenging for the food business and its stakeholders, particularly in light of the recent findings from the FAO, which indicate that approximately one-third of food produced for human use is lost or wasted worldwide (FAO, 2011). A report on hazard mapping in the food chain (including primary production in the EU and China) has clarified the differences and similarities between China and Europe in the limit standards and testing standards of different types of hazards in the four industrial chains, as well as the provisions of relevant laws and regulations (Chen et al., 2015; DiTECT, 2020).

The food system is a complex and broad concept that involves various stages and processes, starting from primary production and ending with consumption (from farm to fork). The final products are packed, labelled, stored, and distributed through different channels. Consumption involves the preparation and serving of food at restaurants or further storage and processing at the household level. Transportation and distribution of food play a crucial role in ensuring food safety and quality throughout the food supply chain (Nychas et al., 2016; 2021). Ensuring the integrity of food supply networks is of great importance for consumer and environmental protection. Maintaining food quality, safety, and authenticity throughout the entire food chain is a challenge for producers, industries, and governments, as some food safety issues, such as mycotoxins, may arise at the cultivation stage (Zhang et al., 2022) and persist in the final product.

The assessment of quality and safety for highly perishable products still predominantly depends on traditional regulatory inspections and conventional analytical methods, such as those outlined by the International Organisation for Standardisation for total viable count. These approaches are often costly, time-intensive, destructive, and retrospective in nature, making them unsuitable for delivering real-time insights into a product's remaining shelf life or safety status throughout its lifecycle.

Spectroscopy-based techniques, however, are emerging as powerful, non-destructive alternatives that can revolutionise food safety monitoring. By enabling rapid, in situ analysis of chemical and microbial properties, spectroscopy offers the potential for continuous, real-time surveillance of food products—from production to consumption. This shift toward optical sensing and spectral fingerprinting aligns with the growing demand for smarter, data-driven quality control in the agri-food sector and the integration of advanced spectroscopic technologies becomes increasingly critical. Thus, assessing and ensuring food safety quality and security at each stage, including production, processing, packaging, distribution, and handling, which is particularly important for perishable food items like meat, fish, fruits, and vegetables, it is evident that the implementation of these techniques is not just a regulatory necessity—it is a technological imperative for the 21st century. Recent publications are dealing either with data derived from the agri-food sector (Nychas et al., 2021; Liu et al., 2021; Hassoun et al., 2023a, 2023b; Hassoun, Jagtap, et al., 2023b) or with AI tools (Qian et al., 2023), indicating a gap in the literature of the potential for commercialisation of these approaches. Thus, this review focuses on how several spectroscopy-based sensors coupled with AI tools and Information Technologies have been used for assuring food integrity from farm to fork and presents implementations within the food sector.

## 2. Current food safety and quality management system vs the framework of the food industry 4.0

The current safety and quality controls in the food chain are largely relying on the good design for the quality and safety management of

food industries/manufacturing practices (EFSA, 2004) and on chemical and microbiological analyses, which are applied in the context of sampling regimes delineated by regulatory requirements (EFSA, 2005, 2023). In this context, the existing analyses have certain disadvantages, such as being time-consuming, providing retrospective results, costly, often requiring high-tech instruments and highly trained personnel, and usually destructive to test products, thus limiting their potential to be used on, in or at-line (Nychas et al., 2016, 2021; Villegas-Ch et al., 2025). These disadvantages, combined with the rapid evolution of technology, as well as with the potential provided in legislation 2007/2005 i.e., use of alternative (rapid) methods of analysis, have led to new needs and the pursuit of implementing new methodologies in the food industry, as it adapts to the new 4th Industrial Revolution (Hassoun, 2024b). Industry 4.0, the era of cyber-physical systems, is defined by the integration of physical, digital, and virtual domains through the Industrial Internet of Things (IIoT). Unlike consumer or medical IoT, IIoT targets manufacturing, where the transition from Industry 3.0's automation to Industry 4.0's cognition hinges on the systematic capture and utilization of data. Global initiatives reflect this paradigm shift: China's Made in China 2025; Li, 2018), the European Union's (EU) "Factories of the Future" (European Commission, 2014), and the U.S. Manufacturing USA (2014) (Baily & Bosworth, 2014). These programs mobilise governments, industry, and academia to advance manufacturing technologies, foster collaborative R&D, and mitigate risks in technology adoption.

Within the spirit of "Factories of the Future" mentioned above, the EU has recently mandated the implementation of process analytical technology (PAT) in food manufacturing, processing and production lines (Nychas et al., 2016). This requirement addresses the need for agri-food industries to ensure the safety of raw and in-process materials by controlling processing contaminants and existing or emerging hazards. Thus, it is of great research interest to study the use of non-targeted sensors for the development of rapid methods that could be implemented online. In that framework, spectroscopy-based sensors in combination with machine learning methodologies are investigated. The application of novel technologies and methodologies applied in the field or on-line through the food value chain using portable devices in tandem with the current rapid development of Information Technologies, which include cloud computing, Internet of Things (IoT), big data and Blockchain, draws new potential for assuring food integrity from farm to fork. Also, the wide use of applications of Quick Response codes (QR) that can be implemented in smartphones and/or devices could lead to a new way of food chain supervision and monitoring of quality, safety and authenticity (Nychas et al., 2016, 2021). These technologies will contribute to better and continuous monitoring of food quality and safety, while optimising operations and decreasing costs.

To elucidate the magnitude of progress within the field, a systematic search of the literature was conducted in the Scopus database using a set of the aforementioned terms, covering the period from 2001 to 2025. The purpose of this search was simply to delineate and map the principal domains in which these approaches have emerged and advanced, while the systematic analysis was not within the scope of this review.

For this reason an "advanced document search" i.e., {((food OR meat OR Beef OR Pork OR poultry OR fish OR milk OR seafood OR dairy OR vegetables OR Fruits OR corn OR oil OR plant OR Grains OR seeds OR herbs OR spices OR tea OR Juices OR sauces OR Alcohol OR honey OR eggs OR oil OR nuts OR coffee OR cocoa) AND (safety OR spoilage OR quality OR fraud OR fraudulent OR "food crime") AND (spectroscop* OR sensors OR FTIR OR multispectral OR hyperspectral OR fluorescence) AND (ML OR "machine learning" OR "big data" OR "Internet of Things" OR "data science" OR "digital twins" OR "LLM" OR "blockchain" OR "distributed ledger")) AND PUBYEAR > 2001 AND PUBYEAR < 2026), occurred in which 9586 documents were indicated. Among them, more than 60% were published in the last
three and a half years (2022: 1155; 2023: 1557; 2024: 2133; May 2025: 1044), showing the growing research interest in the implementation of the above-mentioned approaches in the food sector. The majority (approximately 65 %) of these published articles originate from China (3092), India (1695), the USA (950), Italy (337), and the UK (305).

### 3. Non-destructive sensors based on spectroscopy

The quick assessment of food quality, safety, and fraud is crucial because official methods are slow, labour-intensive, costly, and only provide retrospective results. Specifically, the current official and certified methods include those proposed by regulations, ISO, and AOAC, where the final product is tested. The potential of employing non-destructive techniques, such as spectroscopic and imaging technologies, to address the limitations of traditional food microbiology is attracting increasing interest.

The ongoing advancements in sensor technology in the food business strongly facilitate the industry's shift towards comprehensive control of production parameters (Raju et al., 2020). These sensors might vary from basic temperature, humidity, and pressure recorders to comprehensive arrays of meteorological sensors positioned in the field where the crops are cultivated. Furthermore, a multitude of modern sensors integrate supplementary technologies to enhance their functionality. Optical sensors, such as spectrophotometers and infrared cameras, are employed for the purpose of rapid examination of the chemical composition and microbial contamination of food products. FTIR, a biochemical fingerprinting technique and Multispectral imaging (MSI), an optical sensing technique, emerge as highly promising techniques since they give insight into the biochemical composition of the surface of the samples. Both have shown significant potential in the detection and quantification of spoilage bacteria in muscle foods. Such techniques aim to provide the food industries with rapid (<1 min) and non-invasive tools to classify the microbial status of the products with no sample preparation, since they deliver fingerprints of an active biological system containing information for the discrimination of food products in microbiological class qualities. This approach may serve as a substitute for laborious and time-consuming microbiological analysis (>48 h).

Non-destructive instruments monitoring safety, quality and fraud; from laboratory to practice and commercialisation.

Food safety assessment covers a wide range of biological, chemical, and physical hazards (EFSA, 2005). For the rapid evaluation of biological hazards, more than half of the articles refer to the estimation of mycotoxins (Adade et al., 2025). Efforts have been reported for the rapid detection and identification of foodborne pathogens as well as spoilage bacteria (Dimopoulou et al., 2021).

Chemical hazards encompass the evaluation of pesticides, heavy metals, biogenic amines, food additives, and chemical substances that may function as food allergens or pose health risks (Nychas et al., 2021). The least examined category of threats is the physical ones (Sun et al., 2021). On the other hand, antibiotic residues in animal-derived foods are not immediately classified as dangers; they represent risks to human health, including chemical poisoning and antimicrobial resistance; thus, continuous monitoring across the food supply chain is essential (European Union, 2024).

The fast assessment of food quality has evaluated the following parameters: moisture, fat, lipid, colour, freshness, microbial population, pH, TVB-N, nutrients, and soluble solid content (Quelal-Vásconez et al., 2020; Hassoun et al., 2023a, 2023b).

In the context of food fraud, instances of adulteration, origin, mislabeling, and identification of the sample's condition or species were evaluated (Cantarelli et al., 2020; Chen et al., 2022; Power & Cozzolino, 2020). It is important to note that food safety, fraud, and quality factors frequently intersect (Khan et al., 2021; Ndlovu et al., 2021). In this

regard, elements of food quality may influence aspects of food safety. The flavour of numerous traditional Chinese dishes, including preserved eggs, is enhanced with prolonged storage duration (Guo et al., 2022).

Several food categories have been examined for the swift evaluation of fraud, quality, and safety through the integration of sensors (e.g., NIR, HIS, MSI, MIR, RAMAN, VIS, Fluorescence, THz, X-Ray), either handled or portable, combined with AI techniques, are reported in the literature (Beć et al., 2022; Wang et al., 2020; da Silva Medeiros et al., 2022; Wold, 2016; Cavallini et al., 2022).

Indeed, spectroscopy is widely used across the food and beverage industry, as well as in agriculture, offering a fast, cost-effective alternative to traditional chemical testing. Several equipment and instruments are available in the market (New Food, 2025) and contribute to the optimization of processes, ensuring safety, quality, and automation in the food supply chain.

More specifically, there are available sensors for the rapid detection of fat, protein and moisture, seeds discrimination, fat trimming, and other (Ramachandran et al., 2025).

The accompanied software increases the capabilities of the commercial systems, contributing to the digitalisation and direct decision-making. The examples demonstrate the evolution and applicability of spectroscopic-based sensors, but recent research articles encompass a wider range of applications related to quality, safety, and fraud detection (Edwards et al., 2021; Guo & Jayan, 2023).

More specifically, non-destructive spectroscopy-based sensors applied for quality and safety aspects in food and agricultural industries have been commercialised by several companies e.g. Foss, Videometer, Galaxy and Mantispectra.

Unlike prior reviews focusing on technical efficiency, this work emphasises commercialisation pathways and real-world adoption. Having this in mind, it needs to be noted that there is a limited deployment of these sensors in food processing companies (small and medium-sized enterprises -SMEs), mainly due to their difficulty and/or hesitation to invest in equipment and manpower for automation, digitisation, and process modernisation.

### 4. Artificial intelligence

A big problem for food safety is the huge amount of data that is acquired by different analytical and high-throughput systems. Monitoring and real-time sensor data should be kept in a central library with a semi-structured design. This will provide stakeholders with a flexible way to store these very different types of data. A flexible online interface that works on smartphones and/or tablets, and cloud computing can be added to this data repository.

#### 4.1. Machine learning

The domain of AI has demonstrated a strong presence in food science research in recent years, especially in relation to assessing the quality and safety of food products using rapid sensors such as multi- and hyperspectral imaging. This is due to the fact that such sensors often output a multi-variable output (e.g. formed of multiple wavelengths) which range from tens of metrics in the case of Multi-spectral imaging, to up to thousands of wavelengths in the case of hyperspectral imaging such as FTIR and Raman.

The main challenge associated with this approach is the fact that, over the past decade, it has become evident that there no single algorithm (or a group of algorithms) that seem to be consistently providing a strong predictive powers using the forementioned sensors; and it has been demonstrated that the superior predictive power for a given algorithm is highly dependent on the nature of the food product being assessed, the packaging condition and indeed the sensor being applied.
For this reasons, there has been multiple initiatives in establishing a data exchange protocols for managing, storing and reusing validated models such as the Food Safety Knowledge Exchange Format (FSKX) for model storage and exchange, in tandem with platforms which facilitate and automate the process of developing, optimising and validating classification and regression models to predict freshness, safety and authenticity of food products such as SorfML (www.sorfml.com) Estelles-Lopez et al. (2017).

Commonly employed machine learning techniques in the domain of food science encompass regression, classification, and deep learning models. Regression models are often applied when the aim is to predict the bacterial count population (either total viable count or specific species count) within a given food sample encompass linear regression as well as many forms of non-linear regression, such as logistic regression. Each of the aforementioned methods can be expanded to encompass multiple regression, which involves incorporating more than one independent variable. Additionally, techniques like stepwise regression can be employed to enhance the precision of the predictive model. Classification models encompass several techniques such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naïve Bayes, K-Nearest Neighbours (KNN), decision trees, and Support Vector Machines (SVMs). A large number of such algorithms provide a classification option if the aim is to provide a nominal prediction, as is the case for predicting the sensory perception (i.e., quality) of a given food product. Efficiency-boosting techniques, such as bootstrap and ensemble learning, can be utilised with all of the aforementioned machine learning models to yield more precise predictions. Deep Learning methods, which encompass language models and neural networks, are capable of making precise quantitative predictions. Language models involve natural language processing and large language models, while neural networks are computational models inspired by the structure and function of the human brain. These networks consist of interconnected nodes (neurons) organised in layers that process data (Nychas et al., 2021).

The standards of analysis according to Ropodi et al. (2016), Torrione et al. (2014) and Goodacre et al. (2007) include pre-processing and pre-treatment of spectroscopic/imaging data, feature selection, application of machine learning methods, training and validation, parameters' tuning, and evaluation of model performance. Visualization of data is an important step for data investigation. It is worth mentioning that some of the suggested steps may be skipped either due to limitations of the acquired datasets or because they are considered redundant for the objective of an investigated use case. Specifically, machine learning approaches have been used for the qualitative or quantitative evaluation of various characteristics related to food quality, safety and fraud. During 2020 to 2022 by far the most used algorithm for the development and evaluation of regression/classification models applied on spectral/imaging data was partial least squares (PLS), followed by support vector machines (SVM) and linear discriminant analysis (LDA) (Dourou et al., 2021; Tsakanikas et al., 2020). Algorithms such as k-nearest neighbours (kNN), decision trees, random forest, soft independent modelling of class analogy (SIMCA), and extreme gradient boosting (XGBoost) have also been used. Among neural network-based algorithms, the most used are back-propagation neural network (BPNN), convolutional neural network (CNN), and extreme learning machine (ELM). Research has been intensified towards investigating deep learning, comparing the widely used algorithms with the most commonly used ones or investigating data fusion models (Heffer et al., 2025; Mishra et al., 2020; Robert et al., 2021; Shin et al., 2021; Xu et al., 2022).

The Digital Twin (Nychas et al., 2021) integrates real-time sensor data with machine learning algorithms. Digital twins, is becoming more popular because of developments in cyber-physical system

interconnections (cloud computing, the internet of things, AI, and big data analytics), particularly for the opportunities it offers in relation to Industry 5.0 (Barata & Kayser, 2023). The food business, like many others, is only now starting to reap the benefits of (i) intelligent decision-making, (ii) cost-effective solutions that fulfil the demands of stakeholders, and (iii) real-time simulations. The "Digital Twin" can be a tool of food safety management in the present technological era, enabling hitherto unattainable possibilities (Verboven et al., 2020). A digital twin is a virtual representation of a physical object or process that maintains constant communication with its real counterpart through sensors (ISO, 2019). A mathematical model that describes a process or product in a way that allows for analysis and decision-making under stakeholder needs is the basic idea. In the context of food safety, a DT can serve both as a monitoring tool and an early warning system by utilising physical assets linked to product safety and simulating different parameters (such as temperature and the risk of environmental contamination). Using this technology, stakeholders can actively observe and examine crucial quality, safety, and production data in real-time. This allows for prompt detection and resolution of issues before they have any impact on consumer safety. This can be achieved by combining real-time data from sensors and other IoT-connected services (Fig. 1). Therefore, all parties involved would have access to a model that might foretell any problems with product quality or safety. In Fig. 1, we assume that the simulated product in question is corn, which can be sold as a Processed Culinary Ingredient (i.e., corn starch) or ultra-processed food (UPF) in the case of infant food. In such a case, real-time weather stations, temperature, humidity, wind speed sensors are embedded within the corn field, to predict potential environmental stresses such as extreme weather events (high temperature, combined with high precipitation), which would increase the risk of fungal infection and the associated mycotoxin contamination. The same contamination risk remains in silos during storage; therefore, it is equally important to equip these with temperature and humidity, CO₂, sensors, in tandem with rapid and non-invasive platforms typically based on hyper- and multispectral imaging, capable of identifying early signs of contamination during storage. Following the same approach throughout the processing, packaging and distribution chain, a real-time monitoring of multiple quality and safety indices of the food product in question is capable of providing a continuous series of parameters across the entire product life-cycle. This ultimately provides the necessary knowledge base for developing a product digital twin, providing therefore the unprecedented capacity to simulate various scenarios such as different processing or storage parameters, and examine their impact on the product quality and safety resilience in silico. Several studies have been performed within the EU-Chinese collaboration [DiTECT (2020) project (www.ditect.eu)] in two pilots (Europe and China). Similar approaches, i.e., using digital twins, have been published (Song & Fu, 2024; Guo et al., 2024; Bhattacharya et al., 2022).

To summarise, "digital twin" has largely been utilised in communication and information, and it was commonly tied to IoT and blockchain due to its dependence on real-time data recording. This technology would help food safety managers to simplify operations, predict risks, and comply with regulations. As mentioned, creating a digital twin of a food preparation line is a more advanced risk assessment that uses real-time data and feedback. Examples of the implementation of DT in the food industry could be found in the recent publication of Abdurrahman and Ferrari (2025).

## 4.2. Artificial intelligence and ethics

The study and analysis of AI ethics have become crucial as AI technology has permeated every facet of our society. The European Union (EU) has recently introduced the European Union Act (2023), which is
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Digital twins in the food sector through the integration of product tracking and tracing using blockchain technologies, with product quality and safety real-time monitoring using non-invasive sensors and machine learning modelling.</figcaption></figure>

the first official legislative framework aimed at regulating the use of artificial intelligence (AI) and ensuring its safe and ethical development and application. Furthermore, novel governance and monitoring systems are being established to execute and enforce this legislation effectively. The European Commission and EU Member States collaborate to execute and oversee adherence. Additionally, there is a plan to establish a European AI Council with the purpose of offering guidance and facilitating collaboration among European Union member states. This legislation classifies AI applications into four primary risk categories: unacceptable risk, high risk, restricted risk, and minimum risk. AI systems categorised as high risk must adhere to several prerequisites, including proper documentation and maintenance of a comprehensive record and data repository, efficient monitoring and regulation of AI systems, guaranteeing transparency and user awareness, evaluating and mitigating the risks associated with such AI systems, and ensuring the precision, resilience, and security of the AI systems. Moreover, it is imperative to inform users of AI systems about the explicit presence of an AI system, particularly in cases when this may not be readily apparent. The transparency criteria aim to guarantee that users

comprehend the functionalities and constraints of the AI systems they engage with. The EU AI Act incorporates provisions to foster innovation, including the establishment of sandboxes for the advancement and experimentation of novel AI technology within regulated settings, exempt from complete regulatory limitations. This environment serves as the primary testing ground for the current proposal.

Safeguarding data privacy is a crucial pillar of AI ethics. It is imperative to safeguard personal data gathered and utilised by AI systems by stringent security protocols, guaranteeing that it will not be exploited or utilised without the explicit agreement of users. Implementing data anonymization, encryption, and access control measures are essential practices for safeguarding privacy. Simultaneously, it is crucial to guarantee that choices made by AI systems are capable of being verified and assessed for their accuracy and impartiality, as this is vital for establishing public confidence in these systems. Enhancing the dependability of AI systems is facilitated by transparency in the decision-making process and the potential for oversight by autonomous entities.
<table><caption>**Table 1**
Transforming food quality and safety with AI-Enhanced cloud platforms and traceability systems.</caption><thead><tr><th>N<sub>o</sub></th><th>Platform</th><th>Applications/Features/Services</th><th>Website/Brief description/Strengths &amp; limitations</th></tr></thead><tbody><tr><td>1</td><td>AgShift (Hydra USA (N/A)</td><td>Rapid quality assessment of strawberries, raspberries, almonds/Food waste management</td><td>https://www.agshift.com/<br>AI powered food quality analyser that uses computer vision and deep learning algorithms to assess the quality and integrity of food products. It combines two integrated technological components, the revolutionary Hydra analyser, and the powerful cloud-enabled deep learning platform Hydra AI.<br>Strengths: Fast, objective and consistent grading - Reduces manual inspection time and sample discard.<br>Limitations: Limited to products with strong visual/physical defect signals. Each commodity may need a dedicated model and training data. "Black box" trust and explainability issues, especially in regulated food sectors.<br>https://www.alchemysystems.co.uk/ provides tools for creating and delivering food safety training programs, managing compliance.</td></tr><tr><td>2</td><td>Alchemy Systems USA (1996, 2012)</td><td>Training/Consulting</td><td>Strengths: Can "see inside" produce, enabling predictions of internal quality beyond what vision alone does. Non-destructive, online/in-line capabilities for high throughput.<br>Limitations: High cost and hardware complexity for hyperspectral systems. Calibration, model drift, environmental robustness are substantial challenges. Need per-commodity models and ongoing retraining<br>https://datapaas.aliyun.com/product/software_food_safety_chains Food Safety Chain: A one-stop food safety traceability closed-loop management platform that solves the problem of full-chain record traceability of cold chain, pre-packaged, and edible agricultural products within the province/city Aliyun is for the mainland of China and Alibaba is for the oversea business.<br>Strengths: Provincial-scale implementation, cloud-native.<br>Limitations: Not widely adopted outside China<br>https://www.aptean.com/en-US</td></tr><tr><td>3</td><td>Aliyun (food safety china)</td><td>Food traceability/Supply chain management/Intelligent supervision/Data analysis/Consumer inquiry</td><td>They offer AI-enhanced enterprise software solutions tailored to food and beverage, manufacturing, as well as retail.<br>Their platform includes tools for Enterprise resource planning, transportation management, and product lifecycle management, helping businesses optimize efficiency, productivity, and operational insights.<br>Strengths: End-to-end business integration. Limitations: Complex setup; less focus on small producers<br>https://aromyx.com/ offers a sensory-based platform that uses AI and biosensors to digitally capture and analyse taste and scent data.</td></tr><tr><td>4</td><td>Aptean (USA) (N/A)</td><td>Enterprise Resource Planning, Transportation management solution, business solutions, AI solutions, traceability</td><td>Strengths: Novelty in digitizing smell/taste. Limitations: Limited adoption; proprietary tech.<br>https://aws.amazon.com/iot/<br>Generic cloud services, data storage, data analysis etc.<br>Strengths: Scalable, highly secure, well established infrastructure.<br>Limitations: Not purpose-built for food.<br>https://blueyonder.com/<br>Helps companies optimize inventory, demand forecasting, production planning, and distribution, ensuring smooth and efficient supply chain operations.<br>Strengths: Strong in retail &amp; logistics.<br>Limitations: Limited visibility into food-specific hazards<br>https://www.cancanan.cn/<br>Maisai Technology Co., Ltd (CanCanAn). is rooted in the field of AI technology and serves as a pioneering manufacturer in the smart food safety industry. The company is committed to the development of cutting-edge systems and the R&amp;D and manufacturing of AI-powered smart food safety hardware, providing comprehensive solutions that integrate AI, IoT, big data, and other advanced technologies. Its software and hardware products are primarily applied in institutional cafeterias, commercial kitchen backends, and school canteens. Strengths: Built for Chinese education sector &amp; cafeterias.</td></tr><tr><td>5</td><td>Aromyx USA (2014)</td><td>Sensory evaluation</td><td>Strengths: Limited info on openness or exportability<br>https://certusfooderp.com/<br>Combines software and hardware solutions for food quality assessment and pathogen detection. Strength: Bundled solution for safety + management.<br>Limitations: Small vendor; less global visibility<br>http://www.chinafoods.org.cn/</td></tr><tr><td>6</td><td>AWS IoT USA (N/A)</td><td>Infrastructure for IoT sensors/Compliance and auditing/ operation management</td><td>The "China Food Safety Information Traceability Platform" is a third-party information traceability service platform established by the Food Safety and Information Traceability Branch of the China National Food Industry Association. As an official domestic industry organization, the platform operates under the joint supervision of industry associations and enterprises, providing authoritative third-party traceability services and data exchange capabilities for food enterprises.</td></tr><tr><td>7</td><td>Blue Yonder (USA)</td><td>Supply chain operation system</td><td>Strengths: Scalable, highly secure, well established infrastructure.<br>Limitations: Not purpose-built for food.<br>https://blueyonder.com/<br>Helps companies optimize inventory, demand forecasting, production planning, and distribution, ensuring smooth and efficient supply chain operations.<br>Strengths: Strong in retail &amp; logistics.<br>Limitations: Limited visibility into food-specific hazards<br>https://www.cancanan.cn/<br>Maisai Technology Co., Ltd (CanCanAn). is rooted in the field of AI technology and serves as a pioneering manufacturer in the smart food safety industry. The company is committed to the development of cutting-edge systems and the R&amp;D and manufacturing of AI-powered smart food safety hardware, providing comprehensive solutions that integrate AI, IoT, big data, and other advanced technologies. Its software and hardware products are primarily applied in institutional cafeterias, commercial kitchen backends, and school canteens. Strengths: Built for Chinese education sector &amp; cafeterias.</td></tr><tr><td>8</td><td>CanCanAn</td><td>Information tracing/Supply chain management/Intelligent supervision/Data analysis/Consumer inquiry</td><td>Strengths: Scalable, highly secure, well established infrastructure.<br>Limitations: Not purpose-built for food.<br>https://blueyonder.com/<br>Helps companies optimize inventory, demand forecasting, production planning, and distribution, ensuring smooth and efficient supply chain operations.<br>Strengths: Strong in retail &amp; logistics.<br>Limitations: Limited visibility into food-specific hazards<br>https://www.cancanan.cn/<br>Maisai Technology Co., Ltd (CanCanAn). is rooted in the field of AI technology and serves as a pioneering manufacturer in the smart food safety industry. The company is committed to the development of cutting-edge systems and the R&amp;D and manufacturing of AI-powered smart food safety hardware, providing comprehensive solutions that integrate AI, IoT, big data, and other advanced technologies. Its software and hardware products are primarily applied in institutional cafeterias, commercial kitchen backends, and school canteens. Strengths: Built for Chinese education sector &amp; cafeterias.</td></tr><tr><td>9</td><td>Certus Food ERP USA</td><td>Food Manufacturing/Food Quality/Accelerate and Streamline Distribution</td><td>Strengths: Scalable, highly secure, well established infrastructure.<br>Limitations: Not purpose-built for food.<br>https://blueyonder.com/<br>Helps companies optimize inventory, demand forecasting, production planning, and distribution, ensuring smooth and efficient supply chain operations.<br>Strengths: Strong in retail &amp; logistics.<br>Limitations: Limited visibility into food-specific hazards<br>https://www.cancanan.cn/<br>Maisai Technology Co., Ltd (CanCanAn). is rooted in the field of AI technology and serves as a pioneering manufacturer in the smart food safety industry. The company is committed to the development of cutting-edge systems and the R&amp;D and manufacturing of AI-powered smart food safety hardware, providing comprehensive solutions that integrate AI, IoT, big data, and other advanced technologies. Its software and hardware products are primarily applied in institutional cafeterias, commercial kitchen backends, and school canteens. Strengths: Built for Chinese education sector &amp; cafeterias.</td></tr><tr><td>10</td><td>China food safety information traceability platform</td><td>Information tracing/Supply chain management/Data analysis/Risk early warning/Consumer inquiry</td><td>Strengths: Scalable, highly secure, well established infrastructure.<br>Limitations: Not purpose-built for food.<br>https://blueyonder.com/<br>Helps companies optimize inventory, demand forecasting, production planning, and distribution, ensuring smooth and efficient supply chain operations.<br>Strengths: Strong in retail &amp; logistics.<br>Limitations: Limited visibility into food-specific hazards<br>https://www.cancanan.cn/<br>Maisai Technology Co., Ltd (CanCanAn). is rooted in the field of AI technology and serves as a pioneering manufacturer in the smart food safety industry. The company is committed to the development of cutting-edge systems and the R&amp;D and manufacturing of AI-powered smart food safety hardware, providing comprehensive solutions that integrate AI, IoT, big data, and other advanced technologies. Its software and hardware products are primarily applied in institutional cafeterias, commercial kitchen backends, and school canteens. Strengths: Built for Chinese education sector &amp; cafeterias.</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>N<sub>o</sub></th><th>Platform</th><th>Applications/Features/Services</th><th>Website/Brief description/Strengths &amp; limitations</th></tr></thead><tbody><tr><td>11</td><td>CLCircular Spain/South America</td><td>Supply chain management/Route planning/Quality management</td><td><strong>Strengths:</strong> Authoritative, high trust domestically. <strong>Limitations:</strong> Language/local standards barrier<br>https://www.clcircular.com/<br>It can track where and how their products are during transport or storage—and thus prevent problems and losses. The platform enables users to correlate data from sensors with the information they already have, allowing them to extract all of the potential from their big data in an efficient manner.<br><br><strong>Strength:</strong> Good ability to correlate sensor data with transport and storage data; helps reduce losses in logistics.<br><br><strong>Limitations:</strong> Less known for auditing, compliance, or inspection.<br>https://www.clearlabs.com/<br>Combines genomic sequencing and AI algorithms to analyse food samples and detect contaminants, allergens, and other quality-related issues.<br><br><strong>Strengths:</strong> Fully automated NGS pipeline, high depth of microbial profiling, accuracy, ability to detect contaminants.<br><br><strong>Limitations:</strong> High cost, requires lab infrastructure; throughput constraints; not real-time for large volumes.<br>https://www.compliancequest.com/<br>Assists restaurant chains and hotels with food safety monitoring and HACCP compliance adherence. Its key features include digital checklists, data management, quality control and food traceability. It captures reliable temperature data, ensures compliance with HACCP standards and in-house standard operating procedure (SOP), sends real-time notifications when temperatures are out of specific ranges.<br><br><strong>Strengths:</strong> Strong focus on foodservice/restaurant chains; real-time alerts and data capture from temperature sensors.<br><br><strong>Limitations:</strong> May not scale to full supply-chain complexity; limited predictive modelling.<br>https://www.compliancequest.com/<br>Transform to a fully connected business with a next-generation AI-powered Product Lifecycle, Quality, Safety, and Supplier management platform, built on Salesforce.<br><br><strong>Strengths:</strong> Built on Salesforce platform, good for enterprises with existing Salesforce ecosystems. <strong>Limitations:</strong> May be heavy for small producers; dependence on Salesforce ecosystem.<br>https://compusense.com/<br>Uses AI and machine learning for sensory evaluation and consumer research.<br><br><strong>Strengths:</strong> Deep domain expertise in sensory panels; usability for new product development.<br><br><strong>Limitations:</strong> Limited to sensory dimension, not to physical safety, traceability, or compliance.<br>https://crisp.chat/en/<br>Business messaging platform that gathers teams, conversations, data and knowledge using machine learning algorithms to analyse data from various sources, including sensors, IoT devices, and production systems, providing real-time insights and predictive analytics.<br><br><strong>Strengths:</strong> Good for linking customer feedback/complaint data to operations; potential to integrate sensor &amp; production inputs.<br><br><strong>Limitations:</strong> Not a full food safety or inspection tool; more oriented to communications.<br>https://www.dotcompliance.com/<br>Includes features such as document control, non-conformance management, and risk assessment. <strong>Strengths:</strong> Strong structured QMS features, well-suited for highly regulated environments. <strong>Limitations:</strong> Limited to the "management system" side; less emphasis on inspection/sensing.<br>https://www.e2open.com/<br>provides end-to-end visibility, collaboration, and optimization capabilities.<br><br><strong>Strengths:</strong> Strong in global supply chain orchestration; recognized platform in large enterprises.<br><br><strong>Limitations:</strong> Less specific to food safety risk, pathogen detection, inspection.<br>https://fda.nkbiotech.com/#/index<br>The platform integrates various business formats such as production (including small workshops), circulation (school cloud food safety), and catering, with traceability as the core function and the implementation of corporate main responsibility as the foothold, forming a full-chain digital supervision of food safety, innovating supervision methods, and improving the timeliness of supervision. The food safety supervision cloud platform has been put into use in more than 100 counties and cities in Zhejiang, Anhui, Guangxi, Shandong, Liaoning, Jiangsu, Sichuan and other provinces and other regions across the country. Currently, there are tens of thousands of companies settled in the platform, and more than 100,000 bills are generated every day. <strong>Strengths:</strong> Integrates production, catering, regulatory supervision across provinces; large scale in China.</td></tr><tr><td>12</td><td>Clear Labs USA (2014</td><td>Food safety/Integration of the tool to food safety management systems</td><td></td></tr><tr><td>13</td><td>ComplianceMate USA (2001</td><td>Compliance with food safety regulations/Quality monitoring/Traceability/Customizable system/Real-time reporting/Training</td><td></td></tr><tr><td>14</td><td>ComplianceQuest</td><td>Audit, safety, product life cycle, product inspection</td><td></td></tr><tr><td>15</td><td>Compusense Canada (N/A</td><td>Sensory testing/Consumers consulting</td><td></td></tr><tr><td>16</td><td>Crisp France (2015</td><td>Customer support/Marketing/Training</td><td></td></tr><tr><td>17</td><td>Dot Compliance - QMS Software [USA) (N/A</td><td>Document management/Training/Supplier quality management/Audit</td><td></td></tr><tr><td>18</td><td>E2open USA (N/A</td><td>Supply chain management/Manufacturer collaboration/ Sales management</td><td></td></tr><tr><td>19</td><td>Eclipse cloud (food safety Supervision Cloud Platform)</td><td>Data sharing/Intelligent early warning/Traceability management/Supply chain monitoring/Consumer inquiry</td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>N<sub>o</sub></th><th>Platform</th><th>Applications/Features/Services</th><th>Website/Brief description/Strengths &amp; limitations</th></tr></thead><tbody><tr><td>20</td><td>ETQ Reliance USA (2000)</td><td>Food safety/Training/Audit/Risk Assessment</td><td><strong>Limitations:</strong> Likely regional specificity (China), language/regulation lock-in.<br>https://www.etq.com/<br>Helps companies manage quality processes, including document control, audits, corrective actions, and regulatory compliance; Ensures adherence to food safety regulations like FSMA, GMP, and HACCP, supporting documentation management and process alignment with global standards.<br><strong>Strengths:</strong> Strong track record across industries including food, good governance tools.<br><strong>Limitations:</strong> May require heavy customization in food-specific contexts.<br>https://eyequestion.nl/<br>Combines AI and sensory science for sensory evaluation and market research.<br><strong>Strengths:</strong> Strong specialisation in sensory &amp; consumer research; mature domain tools. <strong>Limitations:</strong> Not a full supply-chain or safety tool; less focus on inspection, traceability.<br>https://flavorwiki.com/en<br>includes tools for conducting sensory tests, analysing data, and generating sensory profiles, allowing companies to understand consumer preferences and develop products accordingly. <strong>Strengths:</strong> Enables linking sensory data to consumer segments; product innovation support.<br><strong>Limitations:</strong> Limited in safety, compliance, or traceability domains<br>https://www.foodconnected.com/<br>A customizable software solutions is available as a whole suite for centralised control or can be tailored to include only the specific solutions your organisation now requires, all interconnected. Integration with other prominent systems utilised for managing certain domains is likewise straightforward.<br><strong>Strengths:</strong> Modular, customizable to enterprise needs; integration-friendly.<br><strong>Limitations:</strong> May lack deep inspection hardware or sensing modules.<br>https://foodguard.io/<br>Simplifies HACCP, food traceability and quality management in mid-market food producers. <strong>Strengths:</strong> Simplifies compliance workflows for mid-sized producers.<br><strong>Limitations:</strong> lack advanced AI/inspection modules<br>https://www.fooddocs.com/<br>Provides tools for conducting self-assessments, creating HACCP plans, and monitoring compliance with regulations.<br><strong>Strengths:</strong> Low barrier for SMEs; intuitive, cloud-based compliance management.<br><strong>Limitations:</strong> Not hardware/inspection oriented; may lack deep AI.<br>https://foodready.ai/<br>FoodReady offers AI-powered software solutions for the food industry, focusing on traceability, production, inventory, and safety management. It helps businesses comply with key standards such as the FDA, USDA, FSMA, and GFSI while streamlining food safety processes, including HACCP plans and supplier management.<br><strong>Strengths:</strong> Focus on AI-enhanced compliance, production linkage.<br><strong>Limitations:</strong> Relatively new; adoption and robustness uncertain.<br>http://www.foodsmart.org/<br>Allows for real-time monitoring of quality metrics, tracking of non-conformances, and generating reports.<br><strong>Strengths:</strong> Useful for firms needing lightweight real-time metrics.<br><strong>Limitations:</strong> Less mature; limited to specific domains (e.g. dietary).<br>https://www.freshcheq.com/<br>help restaurants, food trucks, and other food preparation businesses to ensure safety with temperature logs and checklists.<br><strong>Strengths:</strong> Tailored to restaurant/food trucks, practical deployment.<br><strong>Limitations:</strong> Less suited to full supply chain/large-scale processing.<br>https://www.ibm.com/docs/en/food-trust?topic=overview<br>helps to enhance food safety, reduces waste, and improves supply chain efficiency by providing a shared record of information and facilitating collaboration among stakeholders.<br><strong>Strengths:</strong> Strong consortia backing; trusted ledger for wide scopes.<br><strong>Limitations:</strong> Blockchain scalability and the cost, incentive alignment.<br>https://icicletechnologies.com/<br>ICicle Technologies offers an advanced ERP solution for small to mid-sized food manufacturers, helping businesses streamline production, safety, and compliance management.<br>The platform provides tools for managing traceability, quality control, inventory, and procurement, ensuring operational efficiency and regulatory compliance.<br><strong>Strengths:</strong> Tailored to small/mid food manufacturers, combining ERP and safety. <strong>Limitations:</strong> Less strength in advanced sensing and AI modules.<br>https://www.ideagen.com/solutions/quality/quality-management</td></tr><tr><td>21</td><td>EyeQuestion Netherlands (2001)</td><td>Sensory and Consumer Research/Panel management/Data collection, analysis and reporting<br>https://eyequestion.nl/</td><td></td></tr><tr><td>22</td><td>FlavorWiki</td><td>Sensory evaluation/Consumer research/New product development</td><td></td></tr><tr><td>23</td><td>Food Connected (UK – Belfast, Londonderry)</td><td>Food compliance software/safety audits/quality management/food specifications, Traceability, reporting, analytics</td><td></td></tr><tr><td>24</td><td>Food Guard IR (N/A)</td><td>Reporting/Auditing/Traceability/Temperature monitoring/ Food display</td><td></td></tr><tr><td>25</td><td>FoodDocs USA (2017)</td><td>Compliance/Safety Management/Restaurant management/ Traceability/Monitoring</td><td></td></tr><tr><td>26</td><td>FoodReady (USA)</td><td>Food Safety/Audit/HACCP &amp; Quality Management Consulting</td><td></td></tr><tr><td>27</td><td>FoodSmart USA</td><td>Dietary assessments/Nutrition counselling</td><td></td></tr><tr><td>28</td><td>FreshCheq</td><td>Inspection/Restaurant management/Traceability/Food Service Management</td><td></td></tr><tr><td>29</td><td>IBM Food Trust (USA (N/A</td><td>Supply chain management/Resiliency and sustainability</td><td></td></tr><tr><td>30</td><td>ICicle Technologies (USA)</td><td>Food safety, Traceability, food quality</td><td></td></tr><tr><td>31</td><td>Ideagen [previous Qualtrax] USA (1996)</td><td>Accreditation/Training/Document Management/Process Management</td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>N<sub>o</sub></th><th>Platform</th><th>Applications/Features/Services</th><th>Website/Brief description/Strengths &amp; limitations</th></tr></thead><tbody><tr><td>32</td><td>Ideagen Quality Management UK (n/a)</td><td>Compliance/Risk management/Safety and Quality Management</td><td>Assists in managing documents, streamlining processes, and ensuring compliance with food safety standards and regulations.<br><b>Strengths:</b> Strong in regulated industries; robust QMS features.<br><b>Limitations:</b> Not focused on inspection hardware or traceability per se. helps to gain visibility of the supply chain with automated workflows and track supplier information, automate audit preparation, automate supplier certification tracking and self-inspection checklists.<br><b>Strengths:</b> Local/regional presence and customization strengths.<br><b>Limitations:</b> limitations in inspection with the main focus on compliance.<br>https://www.impactvi.com/ uses hyperspectral imaging and machine learning algorithms to assess the quality and freshness of food products.</td></tr><tr><td>33</td><td>ImpactVision, USA (2016)</td><td>Food quality assessment/Food waste management</td><td><b>Strengths:</b> Deep internal quality sensing.<br><b>Limitations:</b> High cost, calibration challenges.<br>https://www.inspectorio.com/ It uses machine learning algorithms to analyse data from inspections and audits, helping to identify quality issues, track corrective actions, and improve overall supply chain visibility.</td></tr><tr><td>34</td><td>Inspectorio, USA, Vietna, China, Belarus 2016</td><td>Supply chain management/Traceability</td><td><b>Strengths:</b> Emphasis on quality in supplier networks; predictive risk.<br><b>Limitations:</b> Reliant on inspection data quality; limited physical sensing.<br>https://www.intelman.com/ Document control, corrective action reporting and supplier management, ensuring compliance with food safety standards.</td></tr><tr><td>35</td><td>Intelex, Canada (1992)</td><td>Compliance/Audits/Raw material inspections/Streamline document control/Supplier management</td><td><b>Strengths:</b> Broad regulatory experience, cross-sector strength.<br><b>Limitations:</b> Less domain-specific in food safety sensing or traceability.<br>https://comply.sy.foodvip.net/ Leveraging the well-established food classification system, along with data from local sampling inspections in China, test results, and baseline data on hazardous substances, the platform apply analytical models to calculate risks associated with food categories, sampling data, test results, and hazardous substance indicators. This helps enterprises develop internal early warning and risk analysis capabilities. By collecting domestic and international food safety standards and regulatory information, The platform is building an intelligent, dynamic visualization database for analysing differences in harmful substances across global trade regulations. Through a systematic design approach, the platform also developing a comparative analysis and query platform centered on product safety indicator requirements, enabling cross-border regulatory compliance and reference. <b>Strengths:</b> National-level adoption, regulatory linkages.</td></tr><tr><td>36</td><td>Intelligent platform for food safety compliance management</td><td>Information tracing/Compliance management/Data analysis</td><td><b>Limitations:</b> Limited coverage outside China or multi-regional.<br>https://www.intellolabs.com/ Uses computer vision and machine learning to assess the quality of fruits and vegetables<br>it combines sensors with data-driven analysis to give a crystal clear picture of every nut or spice. <b>Strengths:</b> Application in fresh produce grading; scalable image-based assessment.</td></tr><tr><td>37</td><td>Intello Labs India, USA, Singapore, Sweden (N/A)</td><td>Food quality digitation/Food waste reduction</td><td><b>Limitations:</b> Limited to visual/optical quality; model drift risk.<br>https://www.jolt.com/ One-stop shop for checklists, audits, temperature logs, labelling, training, scheduling, &amp; attendance. <b>Strengths:</b> Multipurpose tool bridging operations, QA, compliance.</td></tr><tr><td>38</td><td>Jolt USA</td><td>Quality/control/Labelling/Forecasting/Traceability/ Knowledge management/Maintenance management</td><td><b>Strengths:</b> Not focused on traceability hardware or inspection.<br>https://www.leanpath.com/ Helps restaurants and foodservice providers minimize food waste.</td></tr><tr><td>39</td><td>Leanpath USA (2017)</td><td>Food waste management</td><td><b>Strengths:</b> Strong domain in waste reduction in foodservice sector.<br><b>Limitations:</b> Less full supply-chain traceability or inspection modules.<br>https://azure.microsoft.com/en-gb/ Universal cloud services, data storage, data analysis etc.</td></tr><tr><td>40</td><td>Microsoft Azure USA (2010)</td><td>Infrastructure for IoT sensors/Predictions using data/</td><td><b>Strengths:</b> Scalable, flexible, reliable cloud backbone.<br><b>Limitations:</b> Not domain-specific; needs layers on top to be tailored for food quality and safety monitoring.<br>https://www.imuyuan.com/ This is an innovative software platform specifically designed for modern ranches, integrating advanced technology with livestock management. The platform seamlessly combines the Internet of Things (IoT), big data analytics, and AI to optimize farming processes and enhance operational efficiency through intelligent solutions. By enabling real-time data collection and analysis, the platform empowers ranch owners to precisely monitor key aspects such as environmental conditions, early warnings for animal health, and feed ratio management. This helps to significantly reduce operational costs while improving the quality and yield of livestock products.</td></tr><tr><td>41</td><td>Muyuan intelligent platform</td><td>Information tracing/Supply chain management/Intelligent supervision/Data analysis/Risk management</td><td><b>Strengths:</b> Strong domain fit for agricultural/ranch chain; integrates upstream production to food chain.<br><b>Limitations:</b> region-locked; less visibility globally; complexity in livestock + food traceability.</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>N<sub>o</sub></th><th>Platform</th><th>Applications/Features/Services</th><th>Website/Brief description/Strengths &amp; limitations</th></tr></thead><tbody><tr><td>42</td><td>NutraSoft USA (2018)</td><td>Nutrition facts/Recipes/Allergens Tracking</td><td>https://nutrasoft.ca/<br>Calculates nutritional facts and create product labels in compliance with U. S. and Canadian laws and regulations.<br><strong>Strengths:</strong> Useful for regulatory compliance of labeling in Canada and USA; fits R&amp;D/compliance link.<br><strong>Limitations:</strong> Not designed for inspection, traceability, or supply chain management.</td></tr><tr><td>43</td><td>Oracle SCM cloud USA (1986)</td><td>Risk Management/Compliance, Supply chain management/ Fusion analytics</td><td>https://www.oracle.com/scm/<br>End-to-end supply chain management, restaurant solutions, cloud kitchens, marketing for restaurants. <strong>Strengths:</strong> Strong enterprise reach, interoperability, scalability.<br><strong>Limitations:</strong> requires heavy customization for food safety contexts.</td></tr><tr><td>44</td><td>Primority USA (1995)</td><td>Primority is a platform which offers a modular food safety and quality management system</td><td>https://primority.com/<br>Helps businesses efficiently manage supplier compliance, corrective actions, and food safety documentation while ensuring adherence to global standards like HACCP, BRCGS, and ISO 22000. <strong>Strengths:</strong> Focused food safety domain; modular design.<br><strong>Limitations:</strong> Less known for hardware/sensor integration, limited published case studies.</td></tr><tr><td>45</td><td>P&amp;P Optica USA (N/A)</td><td>Food inspection/Foreign material detection/Quality assessment product/Quality control for fat/lean</td><td>https://ppo.ca/<br>Provides an AI-based spectral imaging system for food quality assessment.<br><strong>Strengths:</strong> High-resolution spectral detection for foreign objects, fat/lean analysis.<br><strong>Limitations:</strong> High cost; limited to specific uses; scale and adoption may be low.</td></tr><tr><td>46</td><td>QIMA Hong Kong - 2005-2020 QimaOne</td><td>Product quality inspection/Audits/Suppliers management/ Trainings/Certification, supply chain visibility</td><td>https://www.qima.com/<br>helps food companies assess and monitor product quality, conduct supplier audits, and ensure compliance with quality standards and regulatory requirement.<br><strong>Strengths:</strong> Strong global footprint, trusted in quality control and compliance.<br><strong>Limitations:</strong> Less focus on deep AI-driven inspection (vs human audits).<br>https://repositrak.com/<br>Offers both compliance and commerce solutions, helps companies track and manage their supply chains, ensuring adherence to food safety standards and regulatory requirements.</td></tr><tr><td>47</td><td>ReposiTrak USA (2003)</td><td>safety and quality optimization - food traceability/ Automated quality management systems-automated supplier compliance/sales and sourcing optimization</td><td><strong>Strengths:</strong> Combines commerce and compliance, useful for food retail ecosystems.<br><strong>Limitations:</strong> May struggle with inspection hardware or sensor integration.<br>https://rizepoint.com/platform/<br>offers a software platform that uses AI and machine learning for food safety and compliance management.<br><strong>Strengths:</strong> Good usability; proven in food &amp; beverage verticals.<br><strong>Limitations:</strong> Less specialisation in hardware/in-line inspection.</td></tr><tr><td>48</td><td>RizePoint 2016 (USA)</td><td>Audit management/Supplier Management/Facility Quality Management/Food Safety Management</td><td>https://safefood360.com/<br>It helps companies automate and streamline food safety processes, including hazard analysis, corrective actions, and compliance monitoring. <strong>Strengths:</strong> Broad feature set, good reputation in food safety software.<br><strong>Limitations:</strong> needs customization for sensor/inspection linkages.</td></tr><tr><td>49</td><td>SafeFood 360° 2010 (IR, USA)</td><td>Compliance/Traceability/Audit/Food Safety Management/ Supplier Quality Management/Food Safety/Risk Assessment</td><td>https://www.safetraces.com/<br>Uses DNA-based tracing technology and machine learning algorithms to track and verify the safety and authenticity of food products throughout the supply chain.</td></tr><tr><td>50</td><td>SafeTraces Canada (N/A)</td><td>Inner Air Quality verification/Measurement of pathogen mitigation for safe and sustainable indoor spaces.</td><td><strong>Strengths:</strong> Novel approach using DNA tracers, highly precise tracking.<br><strong>Limitations:</strong> Cost, adoption, regulatory acceptance, scale may be limiting.</td></tr><tr><td>51</td><td>SafetyChain USA, 2011</td><td>Compliance with food safety standards/Process control/ Supplier management/Preventative maintenance</td><td>https://safetychain.com/<br>Offers features such as document management, supplier management, and real-time analytics ensuring compliance with food safety standards. It unifies production and quality teams with data and insights, tools, and delivers real-time operational visibility and control by eliminating paper and point solutions.<br>SafetyChain is a digital plant management platform trusted by more than 2500 food and beverage manufacturing facilities to improve plant-wide performance.<br><strong>Strengths:</strong> Good industrial adoption, trusted by many food production facilities.<br><strong>Limitations:</strong> Less inspection hardware; dependency on input data quality. uses AI and machine learning to detect pathogens in food products, provides pathogen detection solutions using engineering bacteriophages for the food industry. This capability is coupled with highly sensitive sensor technology that detects low levels of pathogens to see results in minutes right on-site, with no laboratory required.</td></tr><tr><td>52</td><td>Sample6 USA (2009)</td><td>Food Safety/Quick and easy detection of harmful and unwanted bacteria</td><td><strong>Strengths:</strong> Very rapid and on-site capability for microbial detection.<br><strong>Limitations:</strong> Lower maturity, validation in diverse food matrices, cost.</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>N<sub>o</sub></th><th>Platform</th><th>Applications/Features/Services</th><th>Website/Brief description/Strengths &amp; limitations</th></tr></thead><tbody><tr><td>53</td><td>SAP Ariba, USA</td><td>Integrated Business Planning for Food and Beverage (SAP Hana) Supply chain management/Industrial processing/Forecasting</td><td>https://www.sap.com/index.html<br>Offers a comprehensive supply chain management platform specifically designed for the food and beverage industry Predicts customer demands and rapidly adjust to change, improves QA with AI-enabled visual inspection and anomaly detection, mitigates risk with preventive maintenance guidance, increases production efficiency with intelligent auto-dispatching, explores relationships in datasets and predicts future events, automates and optimizes complex industry processes, receives data-driven, prescriptive guidance at critical decision points.<br>Strengths: Very strong in enterprise ecosystems, seamless connectivity.<br>Limitations: Cost, heavy licensing, adaptation to food safety domain<br>https://sensorycloud.net/<br>Allows for the collection of sensory data from trained panelists, providing insights into product quality and consumer preferences.<br>Strengths: Innovative method to capture human responses; good for product R&amp;D.<br>Limitations: not focused for inspection or safety.<br>http://www.shidongyun.com/<br>The platform specializes in intelligent agriculture IoT solutions and end-to-end product services. It provides strong support for our clients in building modern agricultural applications that enable precise sensing and refined management. It offers comprehensive hardware and software solutions for governments and enterprises across various sectors, including smart agriculture, smart farming, smart agricultural trade, product traceability, smart slaughtering, smart rural development, food safety supervision, emergency command, and VR simulation. By connecting the entire agro-food industry chain, The platform integrate production factors through digitalisation, enabling resource allocation and full-process traceability.<br>Strength: Strong domestically in China; integrates production, supervisory, and trade layers. Limitations: Limited to Chinese market, regulatory difference barriers.<br>https://www.getsignify.com/<br>Signify deploys purpose-built AI compliance agents for consumer product manufacturers—speeding approvals, minimising risk, and unlocking scalable growth across CPG and durables. Compliance Automation: Supplier Verification: Artwork and Exports Compliance: Always-On Compliance Monitoring: Document Analysis &amp; Risk Detection: Continuous Monitoring of Regulatory Changes: Audit Trail &amp; Traceability.<br>Strengths: Automates regulatory compliance tasks. Limitations: Does not directly inspect or trace physical goods. Early stages of development.<br>https://smartfoodsafe.com/<br>Offers a tech-enabled platform to demonstrate compliance with various global quality and food safety standards; Food Safety Program<br>Management: Centralizes food safety management with tools for monitoring, reporting, and corrective actions to meet regulatory and customer expectations.<br>Strengths: Doesn't directly inspect or trace physical goods; nascent stage.<br>Limitations: Less known hardware or sensor integration.<br>https://www.usestarfish.com/; Reduces manual workload, freeing up valuable time for higher-level tasks. Identify cost-saving opportunities to optimize expenses and improve the bottom line. Evaluate your financial performance against industry standards to optimize operations. Strengths: Useful for restaurant/fish business cost insights; simple, targeted value.<br>Limitations: Not a full safety or traceability platform.<br>https://www.strellabiotech.com/<br>provides a sensor for converting data to large packers of fruit for predicting produce shelf life. It helps to optimize against spoilage and downgrade fruit quality in the fresh produce supply chain. Strengths: Focused on perishables, useful in supply chain spoilage control.<br>Limitations: New, limited scale, calibration challenges.<br>https://tracegains.com/<br>This platform provides real-time access to a networked marketplace for ingredients, supplier management, and quality control, helping businesses ensure transparency, safety, and efficiency throughout their operations. Digitizes supplier management, focusing on building trust, evaluating performance, and assessing risks. Strengths: Marketplace model gives visibility across supply chain.<br>Limitations: Less low-level inspection hardware; dependent on data quality.<br>https://www.traceregister.com<br>Strengths: Tailored for seafood chain, credible domain specialisation.<br>Limitations: Focused niche; less generalization to other food types.<br>https://www.trustwell.com/foodlogig-home/<br>Provides real-time monitoring, analytics, and traceability features to ensure compliance with food safety regulations and standards.</td></tr><tr><td>54</td><td>SensoryCloud</td><td>Face, voice biometrics</td><td></td></tr><tr><td>54</td><td>Shi Dong Yun</td><td>Information tracing/Intelligent supervision/Data analysis/Risk early warning/Consumer inquiry</td><td></td></tr><tr><td>55</td><td>Signify (2024) USA</td><td>Help Inudstry 4.0 AI-Driven</td><td></td></tr><tr><td>56</td><td>Smart Food Safe Canada (2017)</td><td>Quality management/Food traceability/Auditing</td><td></td></tr><tr><td>57</td><td>Starfish</td><td>For fish restaurants &amp; bisness; Streamlines and summarizes detailed financial data into a concise, 1-page report</td><td></td></tr><tr><td>58</td><td>Strella s USA (2017) Start-up</td><td>Sensory evaluation of fruits/Shelf-life prediction</td><td></td></tr><tr><td>59</td><td>TraceGains (USA)</td><td>TraceGains offers a comprehensive suite of tools that streamlines supply chain management, &amp; product development in the food industry.</td><td></td></tr><tr><td>60</td><td>traceregister</td><td>ORIGIN by trace register™ is a Global Seafood Traceability Platform that helps you make smarter decisions</td><td></td></tr><tr><td>61</td><td>Trustwell USA 2006 – Update in 2023</td><td>Traceability/Recall management/Supplier management</td><td></td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>N<sub>o</sub></th><th>Platform</th><th>Applications/Features/Services</th><th>Website/Brief description/Strengths &amp; limitations</th></tr></thead><tbody><tr><td>62</td><td>Wiliot USA (2017)</td><td>Real-time food safety monitoring/Traceability/Supply chain management/Real-time carbon footprint measurement</td><td><strong>Strengths:</strong> Trusted platform in traceability and recall utility.<br><strong>Limitations:</strong> Dependence on upstream data integrity.<br>https://www.wiliot.com/<br>Combining machine learning and AI in the cloud, the Wiliot Platform delivers sensing functionalities like temperature and location. The Wiliot cloud-based machine learning algorithm translates raw sensory information into the corresponding physical domains. <strong>Strengths:</strong> Innovative ambient sticker sensors for packaging. <strong>Limitations:</strong> Deployment scalability, sensor costs, durability in food environments.</td></tr><tr><td>63</td><td>Winnow Vision, UK, USA, Dubai Singapore, Romania, N/A</td><td>Food waste management</td><td>https://www.winnowsolutions.com/product/food-waste-management-software<br>helps restaurants and foodservice providers minimize food waste. AI enabled tools through to basic digital measurement via a tablet, trained to recognize over 600 different food items with a sensor camera.<br><strong>Strengths:</strong> Proven in foodservice sectors, can recognize many food items, reduce waste. <strong>Limitations:</strong> Primarily for kitchens, not full supply chain; lighting/camera constraints.</td></tr></tbody></table>

# 5. The integration of information and blockchain technologies to food value chain

## 5.1. Big data in food quality management

With regards to food safety, food security and agriculture, big data has been one of the main focuses of research since 2013. Up to that time, the research interests were focused on food safety from a different perspective, investigating, among others-emerging diseases, food contaminants and mild, ecological processing technologies. Since 2013, the capital investment for the promotion of research into big food data has been significantly increased. A critical role in this shift, apart from the technological evolution, was also played by the involvement of China, as the Chinese government decided to pay more attention to food safety, a decision with high international impact (Tao et al., 2021).

The literature concerning AI technologies in food safety from 2012 to 2023 was analysed by CiteSpace to describe technology transition and identify future trends (Liu et al., 2023). Besides discriminating the different stages and progress made on this field in that period (from the initial application of AI algorithms to the thriving of machine learning coupled with BD and to the application of deep learning), this review also describes the research hotspots separating them in different clusters. Along with remote sensing, deep learning and artificial neural networks, BD is one of the hottest topics relevant to AI in food sector research.

BD contributes to food safety assessment by providing real-time monitoring, predictive capabilities, supply chain transparency, and insights from consumer feedback. By exploiting the power of BD analytics, stakeholders could make timely and informed decisions, improve food safety practices and public health, minimising, at the same time, the produced waste (Rejeb et al., 2022).

Despite the advantages that BD offers to various stakeholders in the food supply chain and food services, there are still several barriers that inhibit its widespread application. The digitalisation and automation of industry procedures, using newly developed equipment that can produce big data, which can be managed through a cloud-based platform, is still challenging for most of the enterprises, excluding large corporations (Konur et al., 2021). The required primary investment needed, along with the uncertainty for the economic benefits, are among the major challenges relevant to these technologies. Also, the integration and the collaboration of different partners involved in the supply chain, as well as the lack of relevant legislation and official protocols and standards, constitute important barriers. Although the limitation related to the lack

of appropriate infrastructure has been solved, as many companies provide such services nowadays, the challenge of ensuring data quality still exists. Many companies are now able to generate a large amount of complex and heterogeneous data, however, it becomes challenging to precisely measure the completeness and accuracy of the data, enhancing the risk of false discoveries and eventually decisions. Additionally, when data are frequently modified and shared among multiple collaborators, the maintenance of their integrity and consistency is difficult to ensure (Raj et al., 2020).

## 5.2. Cloud computing

Following the rise of big data in food quality management, cloud computing has become a key enabler of digital, smart food safety systems. Cloud-based platforms support real-time data collection, monitoring, traceability, analytics, and collaboration across the food supply chain, making them essential for implementing AI-driven quality assurance strategies (Dey et al., 2022; Wei et al., 2021; Xing et al., 2016). The synergy between AI and cloud computing has further advanced the capabilities of both technologies. AI enhances cloud platforms by automating complex analytical tasks and enabling predictive modelling, while cloud infrastructure provides the scalability needed to process and deploy AI solutions effectively (Gill et al., 2019). These combined technologies facilitate compliance, auditing, supplier management, and education while reducing manual processes and improving data accessibility. A wide range of cloud-based software solutions now serve food quality management functions, including traceability, compliance, waste reduction, supply chain optimization, and sensory analysis. In Table 1, several companies providing cloud-based Food Safety and Quality Management Systems Powered by AI and Traceability Tools is listed. Recent technological advances have also enabled the integration of sensor-based tools into cloud environments. Innovations in computer vision, spectroscopy, and imaging technologies now allow real-time, online assessment of food quality (e.g. ImpactVision, Intello Labs, P&P Optica). Additionally, newer platforms are combining genomic sequencing and AI to support advanced food safety monitoring (e.g. Clear Labs, SafeTraces).

It is important to note that many of these platforms are multifunctional, capable of supporting diverse operations across production, distribution, and retail. Their adoption continues to grow as the food industry moves towards more transparent, data-driven quality management frameworks.
### 5.3. Blockchain technologies and their relevance for the agro-food value chain

In the past few years, the use of blockchain technology has experienced exponential growth. This technology was originally used for cryptocurrencies as an immutable way to record data. Its proven efficiency encouraged several other industries to adopt it. Blockchains are digital ledgers that ensure tamper-evident and tamper-resistant transactions in a decentralised manner, without a central authority. They enable a community of users to record unchangeable transactions in a shared ledger. Once published, transactions cannot be modified (Kuhn & Yaga, 2019). Due to their immutable nature, blockchain implementation in the food industry is growing. The blockchain technology allows traceability of food products along the Food Supply Chain (FSC, especially when the rising frequency of safety issues demands even more efficient traceability (Dabbene et al., 2014; Marchese & Tomarchio, 2021). Tracking requires decentralisation of the data among the different food industry actors.

At its core, a blockchain functions as a distributed and secure database. In the realm of blockchain technology, data management takes a decentralised approach, avoiding reliance on a central authority. Transactions are stored and added to a chain of "nodes". The data is recorded periodically in blocks, interconnected through cryptographic hash functions, forming a secure chain of references.

The creation of a valid new block hinges on consensus among all participating nodes regarding the broadcasted information to be included. Users, identified by public key addresses, authorise transactions using their private keys. Attempts to manipulate the blockchain require control of over 50% of the network, making it highly resistant to tampering or censorship. The building blocks of the system are organised into blocks, creating an unchangeable history. The order of transactions within a block is determined by a randomly selected node following a consensus algorithm. Light client nodes store only block hashes for efficiency. Attempting to manipulate history in a single node leads to inconsistencies and invalid states.

Transactions in a blockchain can refer to traditional database updates or the transfer of tokens. Smart contracts, snippets of code deployed at every node, add a layer of business logic triggered by transactional events. Despite the name, smart contracts go beyond contractual agreements, offering diverse functionalities. In essence, a blockchain is a system where transactions, signatures, and smart contracts work together to ensure trust and transparency on a global stage.

To confirm extensive adoption of the system, blockchain interoperability is a prerequisite. If this is not realised, in the medium to long term, the market may be monopolised by one or two dominant architectures (Tyagi, 2023). This will create several problems, as the current blockchain platforms present a completely different architecture that is fragmented and presents different solutions. Today, market participants are categorised into infrastructure, application and service providers (101 blockchains, 2019). However, the interoperability and standardisation of the system remain partial, which significantly limits the development/application of blockchain technology (Tyagi, 2023).

Blockchain could efficiently support tracking agricultural products from farm-to-fork (Fig. 2). This ability to track and trace means that by using blockchain technology, non-integrated processing and production methods can be accounted for. Thus, the use of blockchain technology can help strengthen accountability, facilitate global trade by ensuring "compliance with international rules and agreements" and ensure the application of appropriate charges, enforcement of Intellectual Property Rights, etc. (Tripoli & Schmidhuber, 2019). In other words, blockchain provides solutions to complications, such as the accountability and traceability of agro-foods, solutions that can be used for the benefit of

the public interest (by public bodies, control mechanisms, etc.). In this way, blockchain technology can assist international trade by simplifying the application of rules along the global food value chain.

A second major application of blockchain is in trade finance. The accessibility of financial tools could be a critical barrier to the entry of new as well as small and medium-sized enterprises (SMEs) into the commercial value chain. Many SMEs face significant legal, financial, and regulatory issues and cannot compete with more reputable players who enjoy an obvious advantage (Tyagi, 2023).

Blockchain can also help solve issues of counterfeiting, corruption and bureaucracy, which constitute major challenges for global supply chains (Bajpai & Myers, 2020). The complication of the procedures and often the behaviour of officials participate and reinforce this corrupt behaviour. The disposal of documents in a digital ledger could confirm that documents are digitally accessible, with the direct effect of simplifying and automating the process (Tyagi, 2023).

In this respect, below are referred to several pilot blockchain projects (Table 2), implemented at different stages across from farm-to-plate in the global food value chain, that may serve as an example for future applications:

Today, blockchain technology in the agricultural sector, as mentioned and analysed in Tables 2 and is essentially relevant to the product tracking, trade, and traceability. Indeed, a wide range of cloud-based software solutions now serve food quality management functions, including traceability, compliance, waste reduction, supply chain optimization, and sensory analysis. Examples include platforms such as IBM Food Trust, SafetyChain, AWS IoT, and Winnow, many of which are summarised in Table 2. These systems are available as web or mobile applications and offer modular functionalities suitable for various stakeholders across the food sector.

To our knowledge, in an already implemented pilot application, blockchain technology has never been implemented in tandem with AI and sensors, to support not only traceability and transaction authenticity, but also ensuring quality and safety of food products across the supply chain. Soon, we strongly believe things may change. Yet, in the literature cited, research in the field of sensors appears to have progressed significantly (See Ropodi et al. (2016) for a comprehensive review of sensor applications). Sensors are now specialised for the function we need them for, are much smaller in size, cheaper, and their use in combination with AI leads to making important decisions in relation to the state of the product at the given moment in time. In addition, in recent years, an important step has been achieved to connect the scientific community with the industry (food or not) to bridge the gap that exists, and all the knowledge may be transferred to practical applications.

In the 2030s, fulfilling the principles of Industry 4.0 and within the spirit of Industry 5.0, major corporations in the food industry will deploy sensor-based AI systems that facilitate dynamic decision-making based on real-time information. For instance, if an IR sensor continuously measures food (or at specified intervals determined by the human's creator), and this measurement is subsequently processed through algorithms and models to classify the microbiological status of the food, it implies that a dynamic electronic label could provide real-time information regarding the food's microbiological condition. The expiration date of the food will be dynamically adjusted based on the handling practices employed during manufacturing and distribution. This information will be uploaded to a platform and registered via blockchain technology, permitting access to designated participants of the chain. Access would be granted to (i) food business operators within the company responsible for processing and distributing the food product, enabling efficient tracing and recall in the event of a malfunction in the processing chain, and (ii) consumers who can scan the product's QR
<figure><img src="image_5.png" /><figcaption>Fig. 2. Blockchain technologies in food production and distribution.</figcaption></figure>
code to view comprehensive information, including its dynamic expiration date, via an application, minimising in this way both food waste and loss.

In the future, products will feature intelligent labels that will contain essential, immutable and traceable information for the user. The smart labels will be dynamic, featuring QR codes that, when scanned, provide specific information about the item, including a dynamic expiration date that varies based on handling conditions. These intelligent labels, via an application, will connect to other devices such as smartphones and smart refrigerators, thereby notifying consumers of food items nearing their expiration date, thus aiding in the reduction of household food waste, transforming the internet of things into the internet of foods. During the DiTECT (2020) EU-Chinese project (www.Ditect.eu), Blockchain technologies together with QR, have been demonstrated and implemented in China https://www.youtube.com/watch?v=OE6OfZNmnP0&t=145s & and in Greece, https://www.youtube.com/watch?v=T3bwYRgCbfM. The reason for limited use among the SME is out of the scope of this review, although in Tables 1 and 2, it was indicated that the use of Blockchain is in progress.

Blockchain technology, in tandem with AI, will benefit consumers and contribute positively to: (i) food companies by enabling efficient tracking and tracing of products, addressing counterfeiting, corruption, and bureaucratic challenges, as well as enhancing trade finance; (ii) society by facilitating the entry of SMEs into the commercial value chain, thereby increasing competitiveness against established entities, and assisting authorities in managing accountability, traceability, and fraud related to agri-food products; and (iii) the environment by minimising food waste throughout the supply chain from farm to fork.

# 6. Conclusions

## 6.1. Perspectives and impact of the new generation tools

The new generation of monitoring tools is fast, efficient and non-invasive and could be easily implemented on, in and at the site (Milzi et al., 2025). By leveraging integrated technologies, there is potential to reach mass-scale production to further drive down costs by several multiples (Esteve & Barrett, 2020). Their wider use will contribute to the EU Farm to Fork strategy (European Commission, 2020a, 2025) as well as to digital transformation (European Commission, 2020b), and to Food2030 priorities (European Commission, 2018) and will result in.

* rapid and cost-effective assessment of food quality, safety and authenticity (McVey et al., 2021)

* increased data availability, interoperability and use, and improved capacity for enhanced traceability and transparency along the food supply chains (Badia-Melis et al., 2015)

* transforming food systems for health, sustainability and inclusion by improving food safety and traceability and fighting food fraud (McVey et al., 2021)

* unlocking the potential of new technologies and other innovative approaches (e.g., business models) to be adopted by farmers and food businesses (especially small-scale farmers and SMEs), as well as policymakers

* well-informed decision-making by farmers, food businesses and policymakers to improve climate, environmental, economic and social sustainability along the supply chains

* improve functioning and effectiveness of the control systems and the legislative framework (European Food Safety Authority [EFSA], 2022).

* innovative governance, and digital solutions in support of the Green Deal (European Commission, 2019).

* strengthening the competitiveness of the food industry both in the domain of sensor technologies and control solutions with respect to economically sustainable industrial processes (Baierle et al., 2022).

* increase consumer confidence in the food industry, which improves in the long term the competitiveness of these industries.

## 6.2. Wider scientific, economic and societal impact

Digital tools will help stakeholders in the food industry to increase quality, safety and transparency in their activities, boosting their products trust and acceptance, while engaging consumers in sustainable food consumption and the lowest ecological and carbon footprint. The wide use of the innovative solutions by the food sector, competent authorities and consumers is expected to have multidimensional impacts on science, economy and society. Supporting job growth and competitiveness in the food industry by increasing transparency in the selective food value chain consumers' trust, increasing sales, reducing waste, creating new ICT-based companies and transparent fresh food supply chains, and reducing unemployment in food industry; and Improving citizens' quality of life by increasing food safety and security, protecting consumers' health, improving living conditions in cities, protecting biodiversity and ecosystem services and safeguarding land and cultural heritage.

Market growth and an increase in competitiveness will stimulate the creation of high-added-value technology jobs in the sector. The expected profiles will include highly skilled experts, consultants, and scientists from the disciplines involved. New employment will be accompanied by the creation of new value chains and new SMEs, possibly spin-offs of the research partners. This will contribute to addressing major societal problems derived from the currently high unemployment in Europe, particularly among highly skilled people, who were left behind in earlier stages of the decade-long economic crisis or the recent COVID-19 side effects on the economy.

Blockchain technology is helping farmers and food suppliers to prepare for and better manage sudden disruptions and risks in food supply chains. It helps farmers to track contaminated produce and identify hotspots (Ellahi et al., 2024). It also improves the transparency and efficiency of business relationships, which in turn enables food companies to reduce food waste. This can lead to greater profitability, as the system can pinpoint where a product was contaminated in a matter of seconds. This can lead to greater profitability, as the system can pinpoint where a product was contaminated in a matter of seconds.

## 6.3. Epitome

This review summarizes the available information and the current implementations of spectroscopy-based sensors in tandem with AI tools and Information Technologies, such as cloud computing, Internet of Things, big data and Blockchain to assure food quality and safety from farm to fork. In the complexity of the food systems, i.e., managing safety and quality across a multi-stage supply chain from farm to fork, the key challenges are (i) Food security and safety i.e., Ensuring sufficient and safe food for a growing global population under environmental constraints (b) consumer expectations regarding their demands for food that is fresh, natural, healthy, and sustainably produced (c) the limitations of adapting technology and resources, i.e., to rapid technological changes and diminishing natural resources, vs the strong impact of the utilization of traditional methods which are slow, constly, destructive and provide retrospective results.

Under this condition, we strongly believe that Spectroscopy-Based Techniques, Integration with AI and IT i.e., coupling sensors with AI, IoT, cloud computing, and blockchain, offers smarter, continuous
<table><caption>**Table 2**
Blockchain in action: Lessons from global pilot projects that shape the future of food traceability.</caption><thead><tr><th>No</th><th>Company</th><th>Aim/Key functions</th><th>Process/Application type</th><th>Outcomes/Issues</th><th>Reference/Source</th></tr></thead><tbody><tr><td>1</td><td>Agritrack,</td><td>A Greek-based start-up in the field of agriculture technology, designed and implemented a blockchain automated solution for milk traceability</td><td>The blockchain automated solution enables unprecedented visibility and security across the milk value chain, from collection to distribution and processing. More than 200 milk farms (including cattle, sheep and goat) were leveraging the solution to improve their operations and digitally transform their supply chain</td><td>The current solution was adopted by Hellenic Agricultural Organization (the responsible public body for milk traceability by the Greek Ministry of Rural Development and Food) and is planned to be implemented throughout the Greek territory.</td><td>HAO-DIMITRA, 2020; https://agritrack.io/milk-traceability-automation/;</td></tr><tr><td>2</td><td>Albert Heijn Dutch Supermarket</td><td>Their blockchain tests commenced in 2018. In collaboration with Refresco, they integrated orange juice onto the blockchain</td><td>The project's purpose was to elucidate the complete transparency of the orange juice's journey from Brazilian farms to the consumer's kitchen. Samples of orange juice are collected during travel to verify quality (Kickstart AI)</td><td>The outcomes are subsequently recorded on a blockchain. Consumers can utilise a QR code to view the precise route and quality of their orange juice</td><td>https://www.somo.nl/tran sparency-in-the-orange-juice-supply-chain-from-albert-h eijn-and-refresco/</td></tr><tr><td>3</td><td>Axio Zen Canada</td><td>A Vancouver-based start-up established the 'blockchain-based non-fungible asset - Honu, to raise awareness about marine conservation</td><td>The company decided to design 'Crypto Kitty' like a sea turtle, as the latter species are endangered or in critical condition</td><td>"Honu" raised approximately $25,000 in an online auction. The proceeds of the auction were donated to the Sea Shepherd Conservation Society.</td><td>Howson (2020)</td></tr><tr><td>4</td><td>Barclays,</td><td>A blockchain-based trade finance deal for Irish dairy cooperative Ornau, for the exportation of agricultural goods to the Seychelles Trading Company.</td><td>On this blockchain, the Society for Worldwide Interbank Financial Telecommunication System issued the credit, and the funds were released through traditional channels.</td><td>The system did not offer a complete digital solution, only the financial touchpoints of the value chain were partially executed on the blockchain for this pilot project.</td><td>McDaniel and Norberg (2019)</td></tr><tr><td>5</td><td>Bumble Bee Foods (USA)</td><td>Bumble Bee Foods commenced trials of recording yellowfin tuna on a blockchain to enhance traceability and transparency, thereby mitigating mislabeling and fraud.</td><td>Bumble Bee Foods is an American enterprise that provides canned salmon, tuna, various seafood, and poultry products. Upon scanning a QR code, consumers obtain information regarding the tuna's origin, the community responsible for its capture, the amount of the catch, and the process by which it achieved fair trade certification. Bumble Bee Foods employs a private blockchain created by SAP.</td><td>Distributed ledger technology enables all participants in a supply chain to access real-time data. This data is unchangeable, traceable, and verifiable. This ultimately guarantees confidence in the safety of the canned tuna for consumption. Since its inception, it has garnered substantial acclaim from retailers.</td><td>https://thebumblebeecomp any.com/bumble-bee-foods-and-sap-create-blockch ain-to-track-fresh-fish-from-o cean-to-table/</td></tr><tr><td>6</td><td>Carrefour</td><td>In March 2018, a blockchain initiative to trace its chickens, tomatoes, eggs, milk, salmon and cheese</td><td>Apart from ensuring additional transparency and safety for consumers, the blockchain project also aimed to act as a showcase for suppliers. If consumers appreciated the food from a particular supplier, it might go back to that supplier</td><td>Consumers can scan a QR code on the product packaging to access detailed information about the product's origin and journey</td><td>Forbes https://www.forbes. com/sites/sap/2024/04/19/ artificial-intelligence-in-ret ail-6-use-cases-and-e xamples/</td></tr><tr><td>7</td><td>CORE</td><td>To optimize the process and remove inefficiencies in the procedure of trading flowers from Kenya to the Netherlands.</td><td>A blockchain-like solution was designed and implemented as a smart contract. The process immediately created efficiency in the value chain by reducing the entire process by several days.</td><td>Two notable improvements were observed; the ability to transfer a digital phytosanitary certificate, which contains additional information from customs, and the access of customs officers to this electronic platform to review the digital files before the flowers were transported.</td><td>Grainger et al. (2018)</td></tr><tr><td>8</td><td>JD (China)</td><td>A blockchain platform to track beef imports from Australia</td><td>The Chinese e-commerce giant partnered with InterAgri to make the production process of Australian Angus beef fully traceable on a blockchain. The private blockchain solution enables Chinese citizens to know that they can trust the quality of the imported product</td><td>Consumers can trace each piece of steak back to the farm in Australia. In addition, consumers will receive a lot of additional information. This includes details of the animal, its rearing, the processing of the meat and transportation.</td><td>https://jdcorporateblog.co m/leveraging-ai-to-drive- innovation-reduce-costs-a nd-create-jobs-insights-from- jd-coms-chief-economist/</td></tr><tr><td>9</td><td>Maersk and IBM</td><td>'TradeLens', a blockchain-based platform, to simplify tracking along the food supply chain</td><td>It is used by Highland Foods and Maersk to track and trace meat and processed foods originated from 16 countries all over the world</td><td>The system is used to receive real-time updates through automated data synchronization for more efficient management with the goal of faster 10custom clearance through a "point-to-point installation</td><td>https://www.maersk.com/n ews/articles/2021/07/27/h ow-blockchain-techn ology-is-beefing-up. Accessed July 29, 2025</td></tr><tr><td>11</td><td>NESTLE</td><td>Nestlé unveiled a new blockchain proof of concept to trace products from their New Zealand source to Nestlé's plants in the Middle East.</td><td>The project was scheduled to launch at the end of 2019. Nestlé employed a public blockchain to provide consumers with complete transparency regarding the movement of items within the</td><td>Benjamin Ware, global head of responsible sourcing at Nestlé, stated: "This open blockchain technology will enable individuals worldwide to evaluate our</td><td>https://www.ledgerinsights. com/nestle-blockchain-foo d-traceability-zoegas-co ffe-ibm/</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 2 (continued)**</caption><thead><tr><th>No</th><th>Company</th><th>Aim/Key functions</th><th>Process/Application type</th><th>Outcomes/Issues</th><th>Reference/Source</th></tr></thead><tbody><tr><td>12</td><td>North & Central American Free Trade Agreement</td><td>NAFTA/CAFTA were designed and implemented a blockchain proof of concept platform to enter all the information required by US custom offices to standardize the procedure for filers</td><td>supply chain. It is the inaugural significant food and beverage corporation to implement a public, permissionless blockchain for product traceability.<br>NAFTA/CAFTA was a public-private joint project based on a consortium led by the US Customs and Border Protection and the Department of Homeland Security. The pilot project was executed to import the information required to complete the forms by the US Customs via blockchain.</td><td>responsible sourcing data and statistics." Nestlé initially sources dairy products from New Zealand but subsequently incorporates palm oil from the Americas.<br>Notable benefits include the standardization of the process of filing, the ability to digitally submission of certificates, the elimination of paperwork and the ensuing speed of filing.</td><td>U.S. Customs and Border Protection, 2020</td></tr><tr><td>13</td><td>Plataza (Montenegro)</td><td>To thwart counterfeiting in wine bottles.</td><td>Plataza is a vineyard in Montenegro that annually yields 22 million kg of grapes and distributes over 16 million bottled products across more than 40 countries worldwide. In 2018, the winery collaborated with OriginTrail and TagItSmart to monitor over 15,000 distinct wine bottles to thwart counterfeiting. Documenting distinctive wine bottles on a blockchain guarantees buyers the wine's safety for consumption. Plantaza has utilised a QR code and photochromic ink to guarantee the veracity and originality of origin for each bottle.</td><td>Wine counterfeiting constitutes a significant issue. It is estimated that up to 50% of wine is counterfeit or misrepresented to mislead buyers. A multitude of these wines include various hazardous ingredients that may adversely affect your health</td><td>https://medium.com/datasets/7-companies-protecting-your-food-with-blockchain-f0e44dd1b01d</td></tr><tr><td>14</td><td>Walmart (partnered with IMB Food trust)</td><td>To enhance tracking and traceability of pork and sliced mangoes.</td><td>Each block contained information regarding 'farm origination, processing data, fertilizers, soil quality, batch numbers, storage temperature and shipping details' for pork and sliced mangoes. The information was uploaded on an e-certificate and the product carried a QR code that the user could then scan and look all the detailed information through an app.</td><td>Each product carried with a record of its journey from the farm to the consumer's table. In a recall simulation experiment, Walmart found that the detection time for an average batch of product was "6 days, 18 h and 26 min" below standard procedure, while using blockchain technology, sliced mangoes and pork could be accurately identified in 2.2 s.<br>Currently in the pilot stage, the initiative will use tokens to determine the viability of charity projects.</td><td>Kamath, 2018; Tyagi, 2023</td></tr><tr><td>15</td><td>WWF ConsenSys</td><td>To track tuna from catch to consumer</td><td>The World Wildlife Fund (WWF) has partnered with blockchain company ConsenSys to track tuna from catch to consumer, helping to prevent illegal and unsustainable fishing practices.</td><td></td><td>https://www.ledgerinsights.com/consensys-wwf-blockchain-charities-impactio/</td></tr></tbody></table>

monitoring, and by the authorities and/or food industry mandates the use of Process Analytical Technology (PAT) to enhance safety and quality control, in tandem with digital Innovations e.g., QR codes and portable devices will enable real-time tracking and consumer engagement. These smart labels, through an app, will be connected to other devices such as smartphones and "smart refrigerators", where they can additionally inform the consumer, as well as the food companies and authorities, with important information to secure food quality and safety in the whole food supply chain. Despite promising research, there is limited literature on the practical implementation and market adoption of these technologies. The agri-food sector faces a dual challenge of ensuring food security and safety while meeting evolving consumer and environmental demands. Traditional methods are no longer sufficient. Spectroscopy-based sensors, enhanced by AI and digital technologies, offer a transformative.

## Author contributions

Conceptualization: N. Chorianopoulos, F. Mohareb, G. Nychas; Data curation: L.Fengou, A. Lytou, N.Chorianopoulos, S.Kinoshita, P.Dong, Y. Zhang; Formal analysis: A. Lytou, P.Dong; Funding acquisition: G. Nychas, C.Tassou, Y.Zhang; Investigation: N. Chorianopoulos, L. Fengou, A. Lytou, P. Dong, Y. Zhang, C. Tassou and G. Nychas; Methodology: G. Nychas, N.Chorianopoulos, F. Mohareb; Project administration: G. Nychas, Y. Zhang; Resources: G. Nychas, Y. Zhang, N.

Chorianopoulos; Software: n/a; Supervision; F.Mohareb, G.Nychas, P. Dong; Validation: C. Tassou, F. Mohareb; Visualisation: F. Mohareb, S. Kinoshita, A. Lytou, P.Dong, C. Tassou; Writing Original draft: L. Fengou, A. Lytou; Writing - review and Editing: N. Chorianopoulos, G. Nychas, C. Tassou, F. Mohareb.

## Conflicts of interest

"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work has been co-funded by the EU project FoodGuard (101136542) and DiTECT (861915-2)

## Data availability

No data was used for the research described in the article.

## References

101 Blockchains. (2019). Blockchain vendors: An ultimate guide. https://101blockchain.s.com/blockchain-vendors/. (Accessed 28 July 2025).
Abdurrahman, E. E. M., & Ferrari, G. (2025). Digital Twin applications in the food industry: A review. *Frontiers in Sustainable Food Systems, 9*, Article 1538375. https://doi.org/10.3389/fsufs.2025.1538375

Adade, S. Y. S. S., Lin, H., Johnson, N. A. N., Nunekpeku, X., Aheto, J. H., Ekumah, J. N., ... Chen, Q. (2025). Advanced food contaminant detection through multi-source data fusion: Strategies, applications, and future perspectives. *Trends in Food Science & Technology, 156*, Article 104851. https://doi.org/10.1016/j.tifs.2024.104851

Badia-Melis, R., Mishra, P., & Ruiz-García, L. (2015). Food traceability: New trends and recent advances. *A review. Food Control, 57*, 393-401.

Baierle, I. C., da Silva, F. T., de Faria Correia, R. G., Schaefer, J. L., Da Costa, M. B., Benitez, G. B., & Benitez Nara, E. O. (2022). Competitiveness of food industry in the era of digital transformation towards agriculture 4.0. *Sustainability, 14*(18), Article 11779. https://doi.org/10.3390/su141811779

Baily, M. N., & Bosworth, B. P. (2014). US manufacturing: Understanding its past and its potential future. *The Journal of Economic Perspectives, 28*(1), 3-26. https://doi.org/10.1257/jep.28.1.3

Bajpai, R., & Myers, C. (2020). *Enhancing government effectiveness and transparency: The fight against corruption*. World Bank, World Bank group. http://documents.worldbank.org/curated/en/235541600116631094.

Barata, J., & Kayser, I. (2023). Industry 5.0-past, present, and near future. *Procedia Computer Science, 219*, 778-788. https://doi.org/10.1016/j.procs.2023.01.351

Béc, K. B., Grabska, J., & Huck, C. W. (2022). Miniaturised NIR spectroscopy in food analysis and quality control: Promises, challenges, and perspectives. *Foods, 11*(10), 1465. https://doi.org/10.3390/foods11101465

Bhattacharya, P., Obaidat, M. S., Sanghavi, S., Sakariya, V., Tanwar, S., & Hsiao, K. F. (2022). Internet-of-explainable-digital-twins: A case study of versatile corn production ecosystem. In *2022 international conference on communications, Computing, Cybersecurity, and Informatics (CCCI)* (pp. 1-5). IEEE. https://doi.org/10.1109/CCCI55352.2022.9926502.

Cantarelli, M.Á., Moldes, C. A., Marchevsky, E. J., Azcarate, S. M., & Camiña, J. M. (2020). Low-cost analytic method for the identification of cinnamon adulteration. *Microchemical Journal, 159*, Article 105513. https://doi.org/10.1016/j.microc.2020.105513

Cavallini, N., Pennisi, F., Giraudo, A., Pezzolato, M., Esposito, G., Gavoci, G., ... Bozzetta, E. (2022). Chemometric differentiation of sole and plaice fish fillets using three near-infrared instruments. *Foods, 11*(11), 1643. https://doi.org/10.3390/foods11111643

Chen, J., Liu, H., Li, J., & Wang, Y. (2022). A rapid and effective method for species identification of edible boletes: FT-NIR spectroscopy combined with ResNet. *Journal of Food Composition and Analysis, 112*, Article 104698. https://doi.org/10.1016/j.jfca.2022.104698

Chen, K., Wang, X.x., & Song, H. Y. (2015). Food safety regulatory systems in Europe and China: A study of how co-regulation can improve regulatory effectiveness. *Journal of Integrative Agriculture, 14*(11), 2203-2217. https://doi.org/10.1016/S2095-3119(15)61113-3

da Silva Medeiros, M. L., Cruz-Tirado, J. P., Lima, A. F., de Souza Netto, J. M., Ribeiro, A. P. B., Basseglio, D., ... Barbin, D. F. (2022). Assessment oil composition and species discrimination of brassicas seeds based on hyperspectral imaging and portable near infrared (NIR) spectroscopy tools and chemometrics. *Journal of Food Composition and Analysis, 107*, Article 104403. https://doi.org/10.1016/j.jfca.2022.104403

Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. *Biosystems Engineering, 120*, 65-80. https://doi.org/10.1016/j.biosystemseng.2013.09.006

Dey, S., Saha, S., Singh, A. K., & McDonald-Maier, K. (2022). SmartNoshWaste: Using blockchain, machine learning, cloud computing and QR code to reduce food waste in decentralized web 3.0 enabled smart cities. *Smart Cities, 5*(1), 162-176. https://doi.org/10.3390/smartcities5010011

Dimopoulou, M., Kefaloniiti, V., Tsakanikas, P., Papanikolaou, S., & Nychas, G. J. E. (2021). Assessing the biofilm formation capacity of the wine spoilage yeast Brettanomyces bruxellensis using FTIR spectroscopy. *Microorganisms, 9*(3), 587. https://doi.org/10.3390/microorganisms9030587

DiTECT. (2020). Digital technologies as an enabler of continuous transformation of food safety system. https://ditect.eu/. (Accessed 4 October 2025).

Dourou, D., Grounta, A., Argyri, A. A., Froutis, G., Tsakanikas, P., Nychas, G. J. E., ... Tassou, C. C. (2021). Rapid microbial assessment of chicken liver inoculated or not with salmonella using FTIR spectroscopy and machine learning. *Frontiers in Microbiology, 11*, Article 623788. https://doi.org/10.3389/fmicb.2020.623788

Edwards, K., Manley, M., Hoffman, L. C., & Williams, P. J. (2021). Non-destructive spectroscopic and imaging techniques for the detection of processed meat fraud. *Foods, 10*(2), 448. https://doi.org/10.3390/foods10020448

EFSA. (2004). Regulation (EC) no 852/2004 of the European parliament and of the council of 29 April 2004 on the hygiene of foodstuffs. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32004R0852#d1e1057-1-1. (Accessed 11 October 2025).

EFSA. (2005). Commission regulation (EC) no 2073/2005 of 15 November 2005 on microbiological criteria for foodstuffs. http://data.europa.eu/eli/reg/2005/2073/oj. (Accessed 11 October 2025).

EFSA. (2022). EFSA consolidated annual activity report 2021. Publications Office of the European Union. https://data.europa.eu/doi/10.2805/226427.

EFSA. (2023). Commission regulation (EU) 2023/915 of 25 April 2023 on maximum levels for certain contaminants in food and repealing regulation (EC) no 1881/2006. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:02023R0915-20250701. (Accessed 11 October 2025).

Ellahi, R. M., Wood, L. C., & Bekhit, A. E. D. A. (2024). Blockchain-driven food supply chains: A systematic review for unexplored opportunities. *Applied Sciences, 14*(19), 8944. https://doi.org/10.3390/app14198944

Estelles-Lopez, L., Ropodi, A., Pavlidis, D., Fotopoulou, J., Gkousari, C., Peyrodie, A., ... Mohareb, F. (2017). An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling. *Food Research International, 99*, 206-215. https://doi.org/10.1016/j.foodres.2017.05.013

Esteve, M. J., & Barrett, D. M. (2020). New tools for non-invasive monitoring in food quality and safety. *Trends in Food Science & Technology, 96*, 1-5.

European Commission. (2014). Call for factories of the future: Process optimisation of manufacturing assets. http://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/2180-fof-01-2014.html. (Accessed 20 April 2014).

European Commission. (2019). The european green deal striving to be the first climate-neutral continent. https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal_en. (Accessed 11 October 2025).

European Commission. (2020a). A farm to fork strategy for a fair, healthy and environmentally-friendly food system. https://food.ec.europa.eu/horizontal-topics/farm-fork-strategy_en. (Accessed 11 October 2025).

European Commission. (2025). A vision for agriculture and food, shaping together an attractive farming and agrifood sector for future generations. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52025DC0075. (Accessed 11 October 2025).

European Commission: Directorate-General for Communications Networks, Content and Technology. (2020b). *Shaping Europe's digital future*. Publications Office of the European Union. (2023). EU AI act: First regulation on artificial intelligence. https://www.europarl.europa.eu/topics/en/article/20230601ST093804/eu-ai-act-first-regulation-on-artificial-intelligence. (Accessed 11 October 2025).

European Union. (2024). The european union summary report on antimicrobial resistance in zoonotic and indicator bacteria from humans, animals and food in 2021-2022. *EFSA Journal, 22*(2), Article e8583. https://doi.org/10.2903/j.efsa.2024.8583

FAO. (2011). Global food losses and food waste - Extent, causes and prevention. Rome. https://www.fao.org/4/mb060e/mb060e00.pdf. (Accessed 4 October 2025).

Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., ... Garraghan, P. (2019). Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: Evolution, vision, trends and open challenges. *Internet of Things, 8*, Article 100118. https://doi.org/10.1016/j.iot.2019.10018

Goodacre, R., Broadhurst, D., Smilde, A. K., Kristal, B. S., Baker, J. D., Beger, R., ... Wulfert, F. (2007). Proposed minimum reporting standards for data analysis in metabolomics. *Metabolomics, 3*(3), 231-241. https://doi.org/10.1007/s11306-007-00813

Grainger, A., Huiden, R., Rukanova, B., & Tan, Y. H. (2018). What is the cost of customs and borders across the supply chain? ... and how to mitigate the cost through better coordination and data sharing. *World Customs Journal, 12*(2), 3-30. https://doi.org/10.55596/001c.115725

Guo, H., Bao, Z., Zhang, S., Ran, Y., Ning, R., Li, Y., ... He, L. (2022). A novel NIR-based strategy for rapid freshness assessment of preserved eggs. *Food Analytical Methods, 15*(5), 1457-1469. https://doi.org/10.1007/s12161-021-02218-7

Guo, Z., & Jayan, H. (2023). Fast nondestructive detection technology and equipment for food quality and safety. *Foods, 12*(20), 3744. https://doi.org/10.3390/foods12203744

Guo, D., Wang, L., Du, Y., Wu, Z., Zhang, W., Zhi, Q., & Ma, R. (2024). Online optimization of adjustable settings for agricultural machinery assisted by digital twin. *Computers and Electronics in Agriculture, 227*, Article 109504. https://doi.org/10.1016/j.compag.2024.109504

HAO-DIMITRA. (2020). Digitalisation of the milk supply chain as a weapon to combat fraud. https://www.elgo.gr/images/MARILENA_FRESKOU/TEVXOS_30/04-05_GA_LA_PDF. (Accessed 29 July 2025).

Hassoun, A. (2024c). *Food Industry 4.0*. Elsevier. https://doi.org/10.1016/C2022-01211-3

Hassoun, A. (2025a). Food sustainability 4.0: Harnessing fourth industrial revolution technologies for sustainable food systems. *Discover Food, 5*(1), 171. https://doi.org/10.1007/s44187-025-00461-z

Hassoun, A., Alhaj Abdullah, N., Aït-Kaddour, A., Ghellam, M., Beşir, A., Zannou, O., ... Regenstein, J. M. (2024a). Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies. *Critical Reviews in Food Science and Nutrition, 64*(3), 873-889. https://doi.org/10.1080/10408398.2022.1100303

Hassoun, A., Jagtap, S., Garcia-Garcia, G., Trollman, H., Patiroe, M., Lorenzo, J. M., ... Câmera, J. S. (2023a). Food quality 4.0: From traditional approaches to digitalised automated analysis. *Journal of Food Engineering, 337*, Article 111216. https://doi.org/10.1016/j.jfoodeng.2022.111216

Hassoun, A., Jagtap, S., Trollman, H., Garcia-Garcia, G., Duong, L. N., Saxena, P., ... Aït-Kaddour, A. (2024b). From food industry 4.0 to food industry 5.0: Identifying technological enablers and potential future applications in the food sector. *Comprehensive Reviews in Food Science and Food Safety, 23*(6), Article e370040. https://doi.org/10.1111/1541-4337.70040

Hassoun, A., Kamiloglu, S., Garcia-Garcia, G., Parra-López, C., Trollman, H., Jagtap, S., ... Esatbeyoglu, T. (2023b). Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on traceability 4.0. *Food Chemistry, 409*, Article 135303. https://doi.org/10.1016/j.foodchem.2022.135303

Hassoun, A., Mhlanga, D., Rejeb, A., Bhat, Z., Buheji, M., & Bigliardi, B. (2025b). The role of industry 4.0 in global food security: A promising pathway to ending hunger. *Smart
Agricultural Technology, Article 100974. https://doi.org/10.1016/j.
etec.2025.100974

Heffer, S., Anastasiadi, M., Nychas, G. J., & Mohareb, F. (2025). Fusion vs. isolation: Evaluating the performance of multi-sensor integration for meat spoilage prediction. *Foods*, 14(9), 1613. https://doi.org/10.3390/foods14091613

Howson, P. (2020). Building trust and equity in marine conservation and fisheries supply chain management with blockchain. *Marine Policy*, 115, Article 103873. https://doi.org/10.1016/j.marpol.2020.103873

International Organization for Standardization (ISO). (2019). ISO 23247-1: Automation systems and integration - Digital twin manufacturing framework - Part 1: Overview and general principles. https://cdn.standards.iteh.ai/samples/75066/ec0a1c59176e488887873ada6b7ecd9/ISO-23247-1-2021.pdf. (Accessed 29 July 2025).

Khan, M. H., Saleem, Z., Ahmad, M., Sohaib, A., Ayaz, H., Mazzara, M., & Raza, R. A. (2021). Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants. *Neural Computing & Applications*, 33(21), 14507-14521. https://doi.org/10.1007/s00521-021-06094-4

Konur, S., Lan, Y., Thakker, D., Morkyani, G., Polovina, N., & Sharp, J. (2021). Towards design and implementation of industry 4.0 for food manufacturing. *Neural Computing & Applications*, 35(33), 23753-23765. https://doi.org/10.1007/s00521-021-05726-z

Kuhn, R., Yaga, D., & Voas, J. (2019). Rethinking distributed ledger technology. *Computer*, 52(2), 68-72. https://doi.org/10.1109/MC.2019.2898162

Li, L. (2018). China's manufacturing locus in 2025: With a comparison of 'Made-in-China 2025' and 'Industry 4.0'. *Technological Forecasting and Social Change*, 135, 66-74.

Liu, Z., Wang, S., Zhang, Y., Feng, Y., Liu, J., & Zhu, H. (2023). Artificial intelligence in food safety: A decade review and bibliometric analysis. *Foods*, 12(6), 1242. https://doi.org/10.3390/foods12061242

Ludovica Milzi, L., Diana, R., & Panunzi, B. (2025). Visual food sensing: The state-of-the-art through strategies, tools, analytes, and actual applications. *Journal of Food Composition and Analysis*, 139, Article 107096. https://doi.org/10.1016/j.jfca.2024.107096

Marchese, A., & Tomarchio, O. (2021). An agri-food supply chain traceability management system based on hyperledger fabric blockchain. *ICEIS*, 2, 648-658. https://doi.org/10.5220/0010447606480658

McDaniel, C. A., & Norberg, H. C. (2019). Can blockchain technology facilitate international trade? *Mercatus Research Paper*. https://doi.org/10.2139/ssrn.3377708

McVey, C., Elliott, C. T., Cannavan, A., Kelly, S. D., Petchkongkaew, A., & Haughey, S. A. (2021). Portable spectroscopy for high throughput food authenticity screening: Advancements in technology and integration into digital traceability systems. *Trends in Food Science & Technology*, 118, 777-790. https://doi.org/10.1016/j.tifs.2021.11.003

Mishra, P., Roger, J. M., Rutledge, D. N., & Woltering, E. (2020). SPORT pre-processing can improve near-infrared biology prediction models for fresh fruits and agro-materials. *Postharvest Biology and Technology*, 168, Article 111271. https://doi.org/10.1016/j.postharvbio.2020.111271

Ndlovu, P. F., Magwaza, L. S., Tesfay, S. Z., & Mphalele, R. R. (2021). Rapid spectroscopic method for quantifying gluton concentration as a potential biomarker to test adulteration of green banana flour. *Specchiromica Acta Part A: Molecular and Biomolecular Spectroscopy*, 262, Article 120081. https://doi.org/10.1016/j.saa.2021.120081

New Food. (2025). Understanding near-infrared (NIR) spectroscopy in food testing. https://www.newfoodmagazine.com/article/249425/near-infrared-spectroscopy-food-analysis. (Accessed 5 October 2025).

Nychas, G. J. E., Panagou, E. Z., & Mohareb, F. (2016). Novel approaches for food safety management and communication. *Current Opinion in Food Science*, 12, 13-20. https://doi.org/10.1016/j.cofsc.2016.06.005

Nychas, G. J., Sims, E., Tsakanikas, P., & Mohareb, F. (2021). Data science in the food industry. *Annual Review of Biomedical Data Science*, 4(1), 341-367. https://doi.org/10.1146/annurev-biodatasci-020221-123602

Parra-Lopez, C., Trollman, H., Ben Abdallah, S., Jagtap, S., Garcia-Garcia, G., Gupta, S., Hassoun, A., Aït-Kaddour, A., Makmuang, S., & Carmona-Torres, C. (2025). Digital technologies for water use and management in agriculture: Recent applications and future outlook. *Agricultural Water Management*, 309, Article 109347.

Power, A., & Cozzolino, D. (2020). How fishy is your fish? Authentication, provenance and traceability in fish and seafood by means of vibrational spectroscopy. *Applied Sciences*, 10(12), 4150. https://doi.org/10.3390/app10124150

Qian, C., Murphy, S. I., Orsi, R. H., & Wiedmann, M. (2023). How can AI help improve food safety? *Annual Review of Food Science and Technology*, 14(1), 517-538. https://doi.org/10.1146/annurev-food-060721-013815

Quelal-Vásquez, M. A., Lerma-García, M. J., Pérez-Esteve, É., Talens, P., & Barat, J. M. (2020). Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods. *Comprehensive Reviews in Food Science and Food Safety*, 19(2), 448-478. https://doi.org/10.1111/1541-4337.12522

Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A. B. L., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-

country comparative perspective. *International Journal of Production Economics*, 224, Article 107546. https://doi.org/10.1016/j.ijpe.2019.107546

Raju, R., Bridges, G. E., & Bhadra, S. (2020). Wireless passive sensors for food quality monitoring: Improving the safety of food products. *IEEE Antennas and Propagation Magazine*, 62(5), 76-89. https://doi.org/10.1109/MAP.20003216

Ramachandran, R. P., Clément, A., & Erkinbaev, C. (2025). Miniaturised spectroscopy and AI-driven probes in food industry automation. *Food Research International*, 214, Article 116646. https://doi.org/10.1016/j.foodres.2025.116646

Rejeb, A., Keogh, J. G., & Rejeb, K. (2022). Big data in the food supply chain: A literature review. *Journal of Digital Information Management*, 4(1), 33-47. https://doi.org/10.1007/s42488-021-00064-0

Robert, C., Jessep, W., Sutton, J. J., Hicks, T. M., Loeffen, M., Farouk, M., ... Gordon, K. C. (2021). Evaluating low-mid and high-level fusion strategies for combining raman and infrared spectroscopy for quality assessment of red meat. *Food Chemistry*, 361, Article 130154. https://doi.org/10.1016/j.foodchem.2021.130154

Ropodi, A. I., Panagou, E. Z., & Nychas, G. J. (2016). Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. *Trends in Food Science & Technology*, 50, 11-25. https://doi.org/10.1016/j.tifs.2016.01.011

Shin, S., Lee, Y., Kim, S., Choi, S., Kim, J. G., & Lee, K. (2021). Rapid and non-destructive spectroscopic method for classifying beef freshness using a deep spectral network fused with myoglobin information. *Food Chemistry*, 352, Article 129329. https://doi.org/10.1016/j.foodchem.2021.129329

Song, J., & Fu, Y. (2024). Research and application of digital twin control system for grain drying. In *2024 4th international conference on electronic information engineering and computer science (EIECS)* (pp. 514-518). https://doi.org/10.1109/EIECS63941.2024.10800468

Sun, X., Li, J., Shen, Y., & Li, W. (2021). Non-destructive detection of insect foreign bodies in finishing tea product based on terahertz spectrum and image. *Frontiers in Nutrition*, 8, Article 757491. https://doi.org/10.3389/fnut.2021.757491

Tao, Q., Ding, H., Wang, H., & Cui, X. (2021). Application research: Big data in food industry. *Foods*, 10(9), 2203. https://doi.org/10.3390/foods10092203

Torrione, P., Collins, L. M., & Morton Jr, K. D. (2014). Application analysis, chemometrics, and machine learning in laser spectroscopy. In *Laser spectroscopy for sensing* (pp. 125-164). Woodhead Publishing. https://doi.org/10.1533/9780857098733.1.125.

Tripoli, M., & Schmidhuber, J. (2019). *How can blockchain's general architecture enhance trade facilitation in agricultural supply chains? Food and Agriculture Organization of the United Nations. Trade Policy Briefs: Trade and Agriculture Innovation No. 33.*

Tsakanikas, P., Karnavas, A., Panagou, E. Z., & Nychas, G. J. (2020). A machine learning workflow for raw food spectroscopic classification in a future industry. *Scientific Reports*, 10(1), Article 11212. https://doi.org/10.1038/s41598-020-68156-2

Tyagi, K. (2023). A global blockchain-based agro-food value chain to facilitate trade and sustainable blocks of healthy lives and food for all. *Humanities and Social Sciences Communications*, 10(1). https://doi.org/10.1057/s41599-023-01658-2

U.S. Customs and Border Protection. (2020). NAFTA/CAFTA proof of concept report - Pub # (OPA) 0912-0619. https://www.cbp.gov/document/technical-documentation/naftacrafta-proof-concept-report. (Accessed 29 July 2025).

Verboven, P., Defraeye, T., Datta, A. K., & Nicolai, B. (2020). Digital twins of food process operations: the next step for food process models? *Current Opinion in Food Science*, 35, 79-87. https://doi.org/10.1016/j.cofsc.2020.03.002

Villegas-Ch, Govea, W. J., Santiago Moncayo-Moncayo, P., Maldonado Navarro, A., & Chavez-Pirca, C. (2025). Optimisation of the traceability of perishable products through light blockchain and IoT in the food industry. In *IEEE access* (Vol. 13, pp. 67599-67615). https://doi.org/10.1109/ACCESS.2025.3561596

Wang, Y. J., Li, T. H., Li, L. Q., Ning, J. M., & Zhang, Z. Z. (2020). Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models. *Specchiromica Acta Part A: Molecular and Biomolecular Spectroscopy*, 237, Article 118403. https://doi.org/10.1016/j.saa.2020.118403

Wei, J., Liu, Y., Wang, L., & Sun, X. (2021). Research on evaluation of manufacturing cloud service oriented to environmental benefits of supply chain. *Environmental Science and Pollution Research*, 28(42), 59473-59485. https://doi.org/10.1007/s11356-021-13427-2

Wold, J. P. (2016). On-line and non-destructive measurement of core temperature in heat treated fish cakes by NIR hyperspectral imaging. *Innovative Food Science & Emerging Technologies*, 33, 431-437. https://doi.org/10.1016/j.ifset.2015.12.012

Xing, K., Qian, W., & Zaman, A. U. (2016). Development of a cloud-based platform for footprint assessment in green supply chain management. *Journal of Cleaner Production*, 139, 191-203. https://doi.org/10.1016/j.jclepro.2016.08.042

Xu, Y., Yang, W., Wu, X., Wang, Y., & Zhang, J. (2022). ResNet model automatically extracts and identifies FT-NIR features for geographical traceability of Polygonatum kingianum. *Foods*, 11(22), 3568. https://doi.org/10.3390/foods11223568

Zhang, J., Feng, X., Wu, Q., Yang, G., Tao, M., Yang, Y., & He, Y. (2022). Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning. *Plant Methods*, 18(1), 49. https://doi.org/10.1186/s13007-022-00882-2