Smart traceability for food safety

Chemical safety screening of products - better proactive

The increasing pressure to ensure product safety in a global market comes up against the current practice of targeting only known hazardous compounds in product safety analysis. However, product safety refers not only to known but also to unknown or hidden hazards that are very important to know and avoid. Shortcomings and limitations of currently used technologies seem to cause an obvious discrepancy between intended and actual consumer protection. Products are not as safe as claimed by stakeholders. An existing but overlooked proactive safety screening with a prioritization strategy is brought into focus as it offers a unique solution. It can handle the complexity of a product with thousands of compounds of unknown identity and unknown toxicity and can figure out the important hazardous compounds, both known and unknown. Using hardly any sample preparation and the effect detection at an early position in the workflow is a game changer not to overlook hazardous compounds. All analytical technologies are needed, but the key is the re-arrangement of the instrument order, i.e. firstly hazard-related screening (effect first) and secondly, focus on identification of prioritized hazardous compounds. Such a proactive safety screening revealed previously unknown hazardous compounds in products on the market claimed to be safe. The highly sustainable, affordable, and all-in-one 2LabsToGo-Eco with easy-to-use planar bioassays empowers stakeholders to implement proactive safety screening and dynamic risk management. The transition to greater efficacy in consumer protection needs incentives and the critical review aims to stimulate a debate.

A safety risk assessment method based on conditionally constrained game theory and adaptive ensemble learning: Application to wheat flour and rice

Food safety risk control and comprehensive assessment are crucial measures to ensure food safety. However, existing food safety risk assessment methods face challenges, such as unreasonable weight distribution of hazard factors and poor adaptability. Therefore, a safety risk assessment model based on conditionally constrained game theory and adaptive ensemble learning is proposed in this paper. Firstly, new constraints are established on the traditional game theory combination weighting method and solved using the augmented Lagrange multiplier method to obtain the optimal linear combination coefficients and actual composite risk values of the samples, which are taken together with the hazard factor detection data as inputs to the adaptive ensemble learning model. Then, an adaptive ensemble learning model is constructed, which prefers the base learner based on the combined measure of stability and accuracy, and predicts the composite risk value by using robust weighted random forest as the meta-learner. Finally, the model's validity was verified using wheat flour and rice hazard factor detection data. The experimental results indicate that the model's fit on the two datasets is 0.996 and 0.991, respectively, demonstrating strong generalization ability and high prediction accuracy. Meanwhile, unqualified products in wheat flour and rice can be effectively identified through risk thresholds, which helps to provide early warning of potential safety risks.

Identifying freshness of various chilled pork cuts using rapid imaging analysis

BACKGROUND

Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time-consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small-scale traders. Therefore, developing a user-friendly, rapid and economical method is of particular importance.

RESULTS

This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively.

CONCLUSION

Our study provides a feasible and practical non-destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry.

Biosensor in smart food traceability system for food safety and security

The burden of food contamination and food wastage has significantly contributed to the increased prevalence of foodborne disease and food insecurity all over the world. Due to this, there is an urgent need to develop a smarter food traceability system. Recent advancements in biosensors that are easy-to-use, rapid yet selective, sensitive, and cost-effective have shown great promise to meet the critical demand for onsite and immediate diagnosis and treatment of food safety and quality control (i.e. point-of-care technology). This review article focuses on the recent development of different biosensors for food safety and quality monitoring. In general, the application of biosensors in agriculture (i.e. pre-harvest stage) for early detection and routine control of plant infections or stress is discussed. Afterward, a more detailed advancement of biosensors in the past five years within the food supply chain (i.e. post-harvest stage) to detect different types of food contaminants and smart food packaging is highlighted. A section that discusses perspectives for the development of biosensors in the future is also mentioned.

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

A General Strategy for Food Traceability and Authentication Based on Assembly-Tunable Fluorescence Sensor Arrays

Food traceability and authentication systems play an important role in ensuring food quality and safety. Current techniques mainly rely on direct measurement by instrumental analysis, which is usually designed for one or a group of specific foods, not available for various food categories. To develop a general strategy for food identification and discrimination, a novel method based on fluorescence sensor arrays is proposed, composed of supramolecular assemblies regulated by non-covalent interactions as an information conversion system. The stimuli-responsiveness and tunability of supramolecular assemblies provided an excellent platform for interacting with various molecules in different foods. In this work, five sensor arrays constructed by supramolecular assemblies composed of pyrene derivatives and perylene derivatives are designed and prepared. Assembly behavior and sensing mechanisms are investigated systematically by spectroscopy techniques. The traceability and authentication effects on several kinds of food from different origins or grades are evaluated and verified by linear discriminant analysis (LDA). It is confirmed that the cross-reactive signals from different sensor units encompassing all molecular interactions can generate a unique fingerprint pattern for each food and can be used for traceability and authentication toward universal food categories with 100% accuracy.

Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring

Monitoring and evaluating food quality, especially meat quality, has received a growing interest to ensure human health and decrease waste of raw materials. Standard analytical approaches used for meat spoilage assessment suffer from time consumption, being labor-intensive, operation complexity, and destructiveness. To overcome shortfalls of these traditional methods and monitor spoilage microorganisms or related metabolites of meat products across the supply chain, emerging analysis devices/systems with higher sensitivity, better portability, on-line/in-line, non-destructive and cost-effective property are urgently needed. Herein, we first overview the basic concepts, causes, and critical monitoring indicators associated with meat spoilage. Then, the conventional detection methods for meat spoilage are outlined objectively in their strengths and weaknesses. In addition, we place the focus on the recent research advances of emerging non-destructive devices and systems for assessing meat spoilage. These novel strategies demonstrate their powerful potential in the real-time evaluation of meat spoilage.

Definition, detection, and tracking of nanowaste in foods: Challenges and perspectives

Commercial applications of nanotechnology in the food industry are rapidly increasing. Accordingly, there is a simultaneous increase in the amount and diversity of nanowaste, which arise as byproducts in the production, use, disposal, or recycling processes of nanomaterials utilized in the food industry. The potential risks of this nanowaste to human health and the environment are alarming. It is of crucial significance to establish analytical methods and monitoring systems for nanowaste to ensure food safety. This review provides comprehensive information on nanowaste in foods as well as comparative material on existing and new analytical methods for the detection of nanowaste. The article is specifically focused on nanowaste in food systems. Moreover, the current techniques, challenges as well as potential use of new and progressive methods are underlined, further highlighting advances in technology, collaborative efforts, as well as future perspectives for effective nanowaste detection and tracking. Such detection and tracking of nanowaste are required in order to effectively manage this type ofwasted in foods. Although there are devices that utilize spectroscopy, spectrometry, microscopy/imaging, chromatography, separation/fractionation, light scattering, diffraction, optical, adsorption, diffusion, and centrifugation methods for this purpose, there are challenges to be overcome in relation to nanowaste as well as food matrix and method characteristics. New technologies such as radio-frequency identification, Internet of things, blockchain, data analytics, and machine learning are promising. However, the cooperation of international organizations, food sector, research, and political organizations is needed for effectively managing nanowaste. Future research efforts should be focused on addressing knowledge gaps and potential strategies for optimizing nanowaste detection and tracking processes.

Microfluidics in smart food safety

The evolution of food safety practices is crucial in addressing the challenges posed by a growing global population and increasingly complex food supply chains. Traditional methods are often labor-intensive, time-consuming, and susceptible to human error. This chapter explores the transformative potential of integrating microfluidics into smart food safety protocols. Microfluidics, involving the manipulation of small fluid volumes within microscale channels, offers a sophisticated platform for developing miniaturized devices capable of complex tasks. Combined with sensors, actuators, big data analytics, artificial intelligence, and the Internet of Things, smart microfluidic systems enable real-time data acquisition, analysis, and decision-making. These systems enhance control, automation, and adaptability, making them ideal for detecting contaminants, pathogens, and chemical residues in food products. The chapter covers the fundamentals of microfluidics, its integration with smart technologies, and its applications in food safety, addressing the challenges and future directions in this field.

Modernization of digital food safety control

Foodborne illness remains a pressing global issue due to the complexities of modern food supply chains and the vast array of potential contaminants that can arise at every stage of food processing from farm to fork. Traditional food safety control systems are increasingly challenged to identify these intricate hazards. The U.S. Food and Drug Administration's (FDA) New Era of Smarter Food Safety represents a revolutionary shift in food safety methodology by leveraging cutting-edge digital technologies. Digital food safety control systems employ modern solutions to monitor food quality by efficiently detecting in real time a wide range of contaminants across diverse food matrices within a short timeframe. These systems also utilize digital tools for data analysis, providing highly predictive assessments of food safety risks. In addition, digital food safety systems can deliver a secure and reliable food supply chain with comprehensive traceability, safeguarding public health through innovative technological approaches. By utilizing new digital food safety methods, food safety authorities and businesses can establish an efficient regulatory framework that genuinely ensures food safety. These cutting-edge approaches, when applied throughout the food chain, enable the delivery of safe, contaminant-free food products to consumers.

Can digital twin efforts shape microorganism-based alternative food?

With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing food production in a sustainable manner for future generations is critical. Microorganisms are potential alternative food sources that can help close the gap in food production. For the development of more efficient and yield-enhancing products, it is necessary to have a better understanding on the underlying regulatory molecular pathways of microbial growth. Nevertheless, as microbes are regulated at multiomics scales, current research focusing on single omics (genomics, proteomics, or metabolomics) independently is inadequate for optimizing growth and product output. Here, we discuss digital twin (DT) approaches that integrate systems biology and artificial intelligence in analyzing multiomics datasets to yield a microbial replica model for in silico testing before production. DT models can thus provide a holistic understanding of microbial growth, metabolite biosynthesis mechanisms, as well as identifying crucial production bottlenecks. Our argument, therefore, is to support the development of novel DT models that can potentially revolutionize microorganism-based alternative food production efficiency.

Controlling of foodborne pathogen biofilms on stainless steel by bacteriophages: A systematic review and meta-analysis

This study investigates the potential of using bacteriophages to control foodborne pathogen biofilms on stainless steel surfaces in the food industry. Biofilm-forming bacteria can attach to stainless steel surfaces, rendering them difficult to eradicate even after a thorough cleaning and sanitizing procedures. Bacteriophages have been proposed as a possible solution, as they can penetrate biofilms and destroy bacterial cells within, reducing the number of viable bacteria and preventing the growth and spread of biofilms. This systematic review and meta-analysis evaluates the potential of bacteriophages against different biofilm-forming foodborne bacteria, including Cronobacter sakazakii, Escherichia coli, Staphylococcus aureus, Pseudomonas fluorescens, Pseudomonas aeruginosa and Listeria monocytogenes. Bacteriophage treatment generally causes a significant average reduction of 38 % in biofilm formation of foodborne pathogens on stainless steel. Subgroup analyses revealed that phages are more efficient in long-duration treatment. Also, applying a cocktail of phages is 1.26-fold more effective than applying individual phages. Phages at concentrations exceeding 107 PFU/ml are significantly more efficacious in eradicating bacteria within a biofilm. The antibacterial phage activity decreases substantially by 3.54-fold when applied at 4 °C compared to temperatures above 25 °C. This analysis suggests that bacteriophages can be a promising solution for controlling biofilms in the food industry.

(Non)targeted Chemical Analysis and Risk Assessment of Organic Contaminants in Darkibor Kale Grown at Rural and Urban Farms

This study investigated the presence and human hazards associated with pesticides and other anthropogenic chemicals identified in kale grown in urban and rural environments. Pesticides and related compounds (i.e., surfactants and metabolites) in kale samples were evaluated using a nontargeted data acquisition for targeted analysis method which utilized a pesticide mixture containing >1,000 compounds for suspect screening and quantification. We modeled population-level exposures and assessed noncancer hazards to DEET, piperonyl butoxide, prometon, secbumeton, terbumeton, and spinosyn A using nationally representative estimates of kale consumption across life stages in the US. Our findings indicate even sensitive populations (e.g., pregnant women and children) are not likely to experience hazards from these select compounds were they to consume kale from this study. However, a strictly nontargeted chemical analytical approach identified a total of 1,822 features across all samples, and principal component analysis revealed that the kale chemical composition may have been impacted by agricultural growing practices and environmental factors. Confidence level 2 compounds that were ≥5 times more abundant in the urban samples than in rural samples (p < 0.05) included chemicals categorized as "flavoring and nutrients" and "surfactants" in the EPA's Chemicals and Products Database. Using the US-EPA's Cheminformatics Hazard Module, we identified that many of the nontarget compounds have predicted toxicity scores of "very high" for several end points related to human health. These aspects would have been overlooked using traditional targeted analysis methods, although more information is needed to ascertain whether the compounds identified through nontargeted analysis are of environmental or human health concern. As such, our approach enabled the identification of potentially hazardous compounds that, based on their hazard assessment score, merit follow-up investigations.

Fatty Acid Profiling as a Tool for Fostering the Traceability of the Halophyte Plant Salicornia ramosissima and Contributing to Its Nutritional Valorization

Salicornia ramosissima, commonly known as glasswort or sea asparagus, is a halophyte plant cultivated for human consumption that is often referred to as a sea vegetable rich in health-promoting n-3 fatty acids (FAs). Yet, the effect of abiotic conditions, such as salinity and temperature, on the FA profile of S. ramosissima remains largely unknown. These factors can potentially shape its nutritional composition and yield unique fatty acid signatures that can reveal its geographical origin. In this context, samples of S. ramosissima were collected from four different locations along the coastline of mainland Portugal and their FAs were profiled through gas chromatography-mass spectrometry. The lipid extracts displayed a high content of essential FAs, such as 18:2n-6 and 18:3n-3. In addition to an epoxide fatty acid exclusively identified in samples from the Mondego estuary, the relative abundance of FAs varied between origin sites, revealing that FA profiles can be used as site-specific lipid fingerprints. This study highlights the role of abiotic conditions on the nutritional profile of S. ramosissima and establishes FA profiling as a potential avenue to trace the geographic origin of this halophyte plant. Overall, the present approach can make origin certification possible, safeguard quality, and enhance consumers' trust in novel foods.

An explainable unsupervised risk early warning framework based on the empirical cumulative distribution function: Application to dairy safety

Efficient food safety risk assessment significantly affects food safety supervision. However, food detection data of different types and batches show different feature distributions, resulting in unstable detection results of most risk assessment models, lack of interpretability of risk classification, and insufficient risk traceability. This study aims to explore an efficient food safety risk assessment model that takes into account robustness, interpretability and traceability. Therefore, the Explainable unsupervised risk Warning Framework based on the Empirical cumulative Distribution function (EWFED) was proposed. Firstly, the detection data's underlying distribution is estimated as non-parametric by calculating each testing indicator's empirical cumulative distribution. Next, the tail probabilities of each testing indicator are estimated based on these distributions and summarized to obtain the sample risk value. Finally, the "3σ Rule" is used to achieve explainable risk classification of qualified samples, and the reasons for unqualified samples are tracked according to the risk score of each testing indicator. The experiments of the EWFED model on two types of dairy product detection data in actual application scenarios have verified its effectiveness, achieving interpretable risk division and risk tracing of unqualified samples. Therefore, this study provides a more robust and systematic food safety risk assessment method to promote precise management and control of food safety risks effectively.

A comprehensive exploration of tartrazine detection in food products: Leveraging fluorescence nanomaterials and electrochemical sensors: Recent progress and future trends

Azo dyes are widely used as food coloring agents because of their affordability and stability. Examples include brilliant blue, carmoisine, sunset yellow, allura red, and tartrazine (Tar), etc. Notably, Tar is often utilized in hazardous food goods. They are frequently flavoured and combined with food items, raising the likelihood and danger of exposure. Therefore, detecting Tar in food is crucial to prevent health risks. Fluorescence nanomaterials and electrochemical sensors, known for their high sensitivity, affordability, simplicity, and speed, have been widely adopted by researchers for Tar detection. This comprehensive paper delves into the detection of Tar in food products. It extensively covers the utilization of advanced carbon-based nanomaterials, including CDs, doped CDs, and functionalized CDs, for sensitive Tar detection. Additionally, the paper explores the application of electrochemical sensors. The paper concludes by addressing current challenges and prospects, emphasizing efforts to enhance sensitivity, and selectivity for improved food safety.

Evaluating the Use of Vibrational Spectroscopy to Detect the Level of Adulteration of Cricket Powder in Plant Flours: The Effect of the Matrix

Edible insects have been recognised as an alternative food or feed ingredient due to their protein value for both humans and domestic animals. The objective of this study was to evaluate the ability of both near- (NIR) and mid-infrared (MIR) spectroscopy to identify and quantify the level of adulteration of cricket powder added into two plant proteins: chickpea and flaxseed meal flour. Cricket flour (CKF) was added to either commercial chickpea (CPF) or flaxseed meal flour (FxMF) at different ratios of 95:5% w/w, 90:10% w/w, 85:15% w/w, 80:20% w/w, 75:25% w/w, 70:30% w/w, 65:35% w/w, 60:40% w/w, or 50:50% w/w. The mixture samples were analysed using an attenuated total reflectance (ATR) MIR instrument and a Fourier transform (FT) NIR instrument. The partial least squares (PLS) cross-validation statistics based on the MIR spectra showed that the coefficient of determination (R2CV) and the standard error in cross-validation (SECV) were 0.94 and 6.68%, 0.91 and 8.04%, and 0.92 and 4.33% for the ALL, CPF vs. CKF, and FxMF vs. CKF mixtures, respectively. The results based on NIR showed that the cross-validation statistics R2CV and SECV were 0.95 and 3.16%, 0.98 and 1.74%, and 0.94 and 3.27% using all the samples analyzed together (ALL), the CPF vs. CKF mixture, and the FxMF vs. CKF mixture, respectively. The results of this study showed the effect of the matrix (type of flour) on the PLS-DA data in both the classification results and the PLS loadings used by the models. The different combination of flours (mixtures) showed differences in the absorbance values at specific wavenumbers in the NIR range that can be used to classify the presence of CKF. Research in this field is valuable in advancing the application of vibrational spectroscopy as routine tools in food analysis and quality control.

Photodynamic Inactivation of Foodborne Bacteria: Screening of 32 Potential Photosensitizers

The development of novel antimicrobial technologies for the food industry represents an important strategy to improve food safety. Antimicrobial photodynamic disinfection (aPDD) is a method that can inactivate microbes without the use of harsh chemicals. aPDD involves the administration of a non-toxic, light-sensitive substance, known as a photosensitizer, followed by exposure to visible light at a specific wavelength. The objective of this study was to screen the antimicrobial photodynamic efficacy of 32 food-safe pigments tested as candidate photosensitizers (PSs) against pathogenic and food-spoilage bacterial suspensions as well as biofilms grown on relevant food contact surfaces. This screening evaluated the minimum bactericidal concentration (MBC), minimum biofilm eradication concentration (MBEC), and colony forming unit (CFU) reduction against Salmonella enterica, methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas fragi, and Brochothrix thermosphacta. Based on multiple characteristics, including solubility and the ability to reduce the biofilms by at least 3 log10 CFU/sample, 4 out of the 32 PSs were selected for further optimization against S. enterica and MRSA, including sunset yellow, curcumin, riboflavin-5'-phosphate (R-5-P), and erythrosin B. Optimized factors included the PS concentration, irradiance, and time of light exposure. Finally, 0.1% w/v R-5-P, irradiated with a 445 nm LED at 55.5 J/cm2, yielded a "max kill" (upwards of 3 to 7 log10 CFU/sample) against S. enterica and MRSA biofilms grown on metallic food contact surfaces, proving its potential for industrial applications. Overall, the aPDD method shows substantial promise as an alternative to existing disinfection technologies used in the food processing industry.

The Application of Artificial Intelligence and Big Data in the Food Industry

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

How can blockchain technology promote food safety in agricultural market?-an evolutionary game analysis

The governance of agricultural food safety issues is closely linked to social interests. To promote food safety supervision in the Chinese agricultural markets under the background of blockchain application, this paper develops a partnership comprising vendors, consumers, and the government. Using the theory of evolutionary game combined with the actual situation of China, the evolutionary process simulations of three participants prove that the tripartite subjects can realize a stable state under the specific relationship. Impact investigation results of typical influential factors indicate the following: (1) The behavior of vendors depends on the government's supervision and consumers' reporting attitude. Limiting the penalty amount for vendors to 66.7% of speculative gains can shorten the processing time for vendors to comply with the law. (2) Consumers play a vital role in food safety supervision of the agricultural market. The penalty for consumers should be limited to 1/3 of the reward amount. (3) The government's incentive-oriented and punishment-inhibited policies can promote blockchain technology in supervision. Punishment-inhibited and key influencing parameters can cooperate in obtaining the maximum regulatory benefits. The results of this study have certain reference values for promoting policy formulation and implementing blockchain technology in agricultural food safety supervision.

Global trends and research hotspots on HACCP and modern quality management systems in the food industry

HACCP (Hazard Analysis and Critical Control Points) and modern quality management systems have a significant impact on public health in the food industry. These systems ensure that food products are safe for consumption by identifying and managing potential hazards at every stage of the production process. To stimulate ongoing studies in both developing and underexplored areas of inquiry, this research synthesizes and organizes the contributions made in this field. It examines more than 40 years of studies from Scopus data base on HACCP and modern quality management systems in the food industry using the VOSviewer software version 1.6.18 (Leiden University, The Netherlands) and bibliometrix R-package. This represents, to the authors' knowledge, the first bibliometric analysis undergone in this direction. The graphical framework demonstrates the highest developments in research and the literature review investigates barriers and opportunities of implementing HACCP in food industry organizations. Findings indicate that until the beginning of the 1990s, there was not a large number of scientific production in the field of HACCP and modern quality management systems in the food industry. The USA were the most prolific affiliation terms of scientific production until 2012, when studies from Italy, the United Kingdom, China and Greece intensified. Currently, the most prolific country in terms of publications is Italy. In terms of global cooperation, the United Kingdom, The United States and The Netherlands represent most active nations on this topic Motor themes that reflect the main interest of the researchers include food diseases, quality control, hazards or food supply. The study also provides future research directions regarding food quality and safety management. These should be focused on improving the safety, quality, and sustainability of food products, while also adapting to changing consumer demands, emerging risks, and regulatory requirements.

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.

An Ethereum-Based Distributed Application for Enhancing Food Supply Chain Traceability

In today’s era, humanity has been overwhelmed by technological revolutions that have changed and will continue to change how business operations are performed, directly or indirectly. At the same time, the processes within the supply chain are quite complex, and as technology and processes evolve, they become more and more challenging. Traceability has become a critical issue in the food industry to ensure safety, quality, and compliance with regulations. The adoption of blockchain technology in the food supply chain has gained significant attention as a potential solution to improve traceability. This paper presents the development of a distributed application for table olives’ traceability on the Ethereum network. The paper also presents a methodological framework, which can help anyone aiming to implement an Ethereum decentralized application and demonstrates the practical use of the developed application by a Greek table olives producer. The application significantly improved the producer’s product traceability by providing a secure, transparent, and efficient solution for tracking and tracing the products in the supply chain. The app reduced the time, increased the accuracy and reliability of data, improved supply chain efficiency, and helped the producer comply with international regulations and standards.

Alizarin: Prospects and sustainability for food safety and quality monitoring applications

Active and intelligent food packaging has emerged to ensure food safety, quality, or spoilage monitoring and extend the shelf life of food. The development of intelligent packaging has accelerated significantly in recent years with a focus on monitoring changes in the quality of packaged products in real-time throughout the food supply chain. As one of the popular natural colorants, alizarin has attracted much consideration due to its excellent functional properties and quality to color change under varying pH. Alizarin is an efficient and cost-effective biomaterial with numerous biological features such as antioxidant, antibacterial, non-cytotoxic, and antitumor. This review focuses on an in-depth summary and prospects for alizarin as a natural and safe colorant that has the potential to be incorporated into intelligent packaging to track the freshness of packaged foodstuffs. The use of alizarin as an intelligent packaging agent shows huge potential for the application of food packaging and brings it one step closer to real-time monitoring of food quality throughout the supply chain. Finally, various limitations and future requirements are discussed to underscore the importance of developing alizarin-based intelligent functional food packaging systems.

Innovative processes in smart packaging. A systematic review

Smart packaging provides one possible solution that could reduce greenhouse gas emissions. In comparison with traditional packaging, which aims to extend the product's useful life and to facilitate transport and marketing, smart packaging allows increased efficiency, for example by ensuring authenticity and traceability from the product's origin, preventing fraud and theft, and improving security. Consequently, it may help to reduce pollution, food losses, and waste associated with the food supply chain. However, some questions must be answered to fully understand the advantages and limitations of its use. What are the most suitable smart packaging technologies for use in agro-industrial subsectors such as meat, dairy, fruits, and vegetables, bakery, and pastry? What are the opportunities from a perspective of life extension, process optimization, traceability, product quality, and safety? What are the future challenges? An up-to-date, systematic review was conducted of literature relevant to the application of indicator technologies, sensors, and data carriers in smart packaging, to answer these questions. © 2022 Society of Chemical Industry.

A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks

The unexpected emergence of the COVID-19 pandemic has changed how grocery shopping is done. The grocery retail stores need to ensure hygiene, quality, and safety concerns in-store shopping by providing "no-touch" smart packaging solutions for agri-food products. The benefit of smart packaging is to inform consumers about the freshness level of a packaged product without having direct contact. This paper proposes a data-driven decision support system that uses smart packaging as a smart product-service system to manage the sustainable grocery store supply chain during outbreaks to prevent food waste. The proposed model dynamically updates the price of a packaged perishable product depending on freshness level while reducing food waste and the number of rejected customers and maximising profit by increasing the inventory turnover rate of grocery stores. The model was tested on a hypothetical but realistic case study of a single product. The results of this study showed that stock capacities, freshness discount rate, freshness period, and quantity discounts significantly affect the performance of a grocery store supply chain during outbreaks.

Dry surface biofilms in the food processing industry: An overview on surface characteristics, adhesion and biofilm formation, detection of biofilms, and dry sanitization methods

Bacterial biofilm formation in low moisture food processing (LMF) plants is related to matters of food safety, production efficiency, economic loss, and reduced consumer trust. Dry surfaces may appear dry to the naked eye, however, it is common to find a coverage of thin liquid films and microdroplets, known as microscopic surface wetness (MSW). The MSW may favor dry surface biofilm (DSB) formation. DSB formation is similar in other industries, it occurs through the processes of adhesion, production of extracellular polymeric substances, development of microcolonies and maturation, it is mediated by a quorum sensing (QS) system and is followed by dispersal, leading to disaggregation. Species that survive on dry surfaces develop tolerance to different stresses. DSB are recalcitrant and contribute to higher resistance to sanitation, becoming potential sources of contamination, related to the spoilage of processed products and foodborne disease outbreaks. In LMF industries, sanitization is performed using physical methods without the presence of water. Although alternative dry sanitizing methods can be efficiently used, additional studies are still required to develop and assess the effect of emerging technologies, and to propose possible combinations with traditional methods to enhance their effects on the sanitization process. Overall, more information about the different technologies can help to find the most appropriate method/s, contributing to the development of new sanitization protocols. Thus, this review aimed to identify the main characteristics and challenges of biofilm management in low moisture food industries, and summarizes the mechanisms of action of different dry sanitizing methods (alcohol, hot air, UV-C light, pulsed light, gaseous ozone, and cold plasma) and their effects on microbial metabolism.

Comparative evaluation of miniaturized and conventional NIR spectrophotometer for estimation of fatty acids in cheeses

The miniaturization of near-infrared spectrometers has been growing rapidly. Several designs are now available, but there is a lack of understanding of how spectral data from these designs are affected by complex matrices and what are the limitations when compared to established systems. This study compares a popular miniaturized NIR device based on Hadamard-transform spectrometer (named miniaturized NIR) with a system based on dispersive spectrometer (named handheld-NIR) to assess: 1) their predictive performance; 2) the effect of a complex matrix on the performance, and 3) ability to discriminate multiples compounds in that matrix. The devices were challenged with a wide range of cheese types (n = 36) from different species (cow, goat, ewe and buffalo), brands (n = 30), countries of origin (n = 9) and with a broad range of cheese matrices (soft, fresh, semi-hard, hard and aged) to predict fat composition. Spectra were collected non-invasively with no sample preparation. Three wavelength ranges from handheld NIR were compared to miniaturized NIR based on two modelling approaches were used: a linear (Partial Least Square - PLS) and a non-linear (Support Vector Machine - SVM). The important wavelengths for each model were identified and used to assess the ability of the spectral data to differentiate among fatty acids. The highest prediction performance was observed for saturated fatty acids (C4.0, C14.0, C15.0 C16.0, total SCF and total SFA) with the RPD_EXT-VAL for the external validation dataset presenting values higher than 3 and the coefficient of determination for the external validation dataset (R²_EXT-VAL) higher than 0.89, mostly for SVM models. The sum of fatty acids also shows good prediction performance with RPD_EXT-VAL higher than 3 and R²_EXT-VAL higher than 0.89. Models with RPD_EXT-VAL between 2 and 3 includes: C6.0; C17.0; C18.0; C10.1; C16.1; C17.1; iso.C15.0; iso.C.16; iso.C17; C18.1.c11; C18.1.c9; anteiso C17; total MUFA; and total BCFA. The cheese matrix affected the linearity between spectral data and fatty acids concentration requiring a more complex model (SVM), but this effect was not enhanced by the instrument type. It was shown that the spectral information allows discrimination among fatty acids and this ability was not affected by the type of instrument. These findings demonstrated that the miniaturized NIR can be directly applied to a cheese matrix to monitor fatty acid composition with results equivalent to an optical-based design.

Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Concern about food safety has become a hot topic, and numerous researchers have come up with various effective solutions. To ensure the safety of food and avoid financial loss, it is important to improve the safety of food information in addition to the quality of food. Additionally, protecting the privacy and security of food can increase food harvests from a technological perspective, reduce industrial pollution, mitigate environmental impacts, and obtain healthier and safer food. Therefore, food traceability is one of the most effective methods available. Collecting and analyzing key information on food traceability, as well as related technology needs, can improve the efficiency of the traceability chain and provide important insights for managers. Technology solutions, such as the Internet of Things (IoT), Artificial Intelligence (AI), Privacy Preservation (PP), and Blockchain (BC), are proposed for food monitoring, traceability, and analysis of collected data, as well as intelligent decision-making, to support the selection of the best solution. However, research on the integration of these technologies is still lacking, especially in the integration of PP with food traceability. To this end, the study provides a systematic review of the use of PP technology in food traceability and identifies the security needs at each stage of food traceability in terms of data flow and technology. Then, the work related to food safety traceability is fully discussed, particularly with regard to the benefits of PP integration. Finally, current developments in the limitations of food traceability are discussed, and some possible suggestions for the adoption of integrated technologies are made.

Hydrophobic Wafer-Scale High-Reproducibility SERS Sensor Based on Silicon Nanorods Arrays Decorated with Au Nanoparticles for Pesticide Residue Detection

High sensitivity and reproducibility are highly desirable to a SERS sensor in diverse detection applications. Moreover, it is a great challenge to determine how to promote the target molecules to be more concentrated on the hotspots of the SERS substrate by engineering a surface with switching interfacial wettability. Along these lines, wafer-scale uniformly hydrophobic silicon nanorods arrays (SiNRs) decorated with Au nanoparticles were designed as the SERS substrate. Typically, the SERS substrate was fabricated by enforcing the polystyrene (PS) sphere self-assembly, as well as the plasma etching and the magnetron sputtering techniques. Consequently, the SERS substrate was treated by soaking within a n-dodecyl mercaptan (NDM) solution at different times in order to obtain adjustable wettabilities. By leveraging the electromagnetic enhancement resulted from the Au nanostructures and enrichment effect induced by the hydrophobicity, the SERS substrate is endowed with efficient SERS capabilities. During the detection of malachite green (MG), an ultralow relative standard deviation (RSD) 4.04-6.14% is achieved and the characteristic signal of 1172 cm-1 can be detected as low as 1 ng/mL. The proposed SiNRs' structure presents outstanding SERS activity with sensitivity and reproducibility rendering thus an ideal candidate for potential application in analytical detection fields.

Biopreservation of beer: Potential and constraints

The biopreservation of beer, using only antimicrobial agents of natural origin to ensure microbiological stability, is of great scientific and commercial interest. This review article highlights progress in the biological preservation of beer. It describes the antimicrobial properties of beer components and microbiological spoilage risks. It discusses novel biological methods for enhancing beer stability, using natural antimicrobials from microorganisms, plants, and animals to preserve beer, including legal restrictions. The future of beer preservation will involve the skilled knowledge-based exploitation of naturally occurring components in beer, supplementation with generally regarded as safe antimicrobial additives, and mild physical treatments.

How Can We Provide Additively Manufactured Parts with a Fingerprint? A Review of Tagging Strategies in Additive Manufacturing

Additive manufacturing (AM) is rapidly evolving from "rapid prototyping" to "industrial production". AM enables the fabrication of bespoke components with complicated geometries in the high-performance areas of aerospace, defence and biomedicine. Providing AM parts with a tagging feature that allows them to be identified like a fingerprint can be crucial for logistics, certification and anti-counterfeiting purposes. Whereas the implementation of an overarching strategy for the complete traceability of AM components downstream from designer to end user is, by nature, a cross-disciplinary task that involves legal, digital and technological issues, materials engineers are on the front line of research to understand what kind of tag is preferred for each kind of object and how existing materials and 3D printing hardware should be synergistically modified to create such tag. This review provides a critical analysis of the main requirements and properties of tagging features for authentication and identification of AM parts, of the strategies that have been put in place so far, and of the future challenges that are emerging to make these systems efficient and suitable for digitalisation. It is envisaged that this literature survey will help scientists and developers answer the challenging question: "How can we embed a tagging feature in an AM part?".

Artificial intelligence, big data, and blockchain in food safety

Food safety plays an essential role in our daily lives, and it becomes serious with the development of worldwide trade. To tackle the food safety issues, many advanced technologies have been developed to monitor the process of the food industry (FI) to ensure food safety, including the process of food production, processing, transporting, storage, and retailing. These technologies are often referred to as artificial intelligence (AI), big data, and blockchain, which have been widely applied in many research areas. In this review, we introduce these technologies and their applications in the food safety domain. Firstly, basic concepts of these technologies are presented. Then, applications for food safety from a data perspective based on these technologies are analyzed. Finally, future challenges of the applications of AI, big data, and blockchain are discussed.

Microbial Food Safety in China: Past, Present, and Future

Food safety is a major public health issue worldwide, especially in heavily populated countries such as China. As in other countries, the predominant food safety issues in China are foodborne diseases caused by microbial pathogens. Hence, this review provides a systematic overview on microbial food safety in the past, present, and future in China. Management of microbial food safety in China is generally divided into three stages: Stage I before 2000, Stage II from 2000 to 2009, and Stage III from 2010 to present. At Stage I, China's main food concern gradually shifted from food security to food safety. At Stage II, foodborne pathogen surveillance was initiated and gradually became a focus of microbial food safety marked by the establishment of national food contamination monitoring system in 2000 and the promulgation of China Food Safety Law in 2009, although chemical food safety was considered a priority issue during this stage. At Stage III, microbial food safety was recognized as a high priority supported by many national food safety policies such as the launch of a national foodborne disease molecular tracing network in 2013 and the revision of China Food Safety Law in 2015. Advancement in food safety education and research support by central and local governments has also made significant contributions to tackling and solving microbial food safety problems. Management in the future should be focused on active involvement of food industries in mitigating microbial risks by introducing ISO 22000, regulatory enforcement to oversee compliances to standards and rules, and application of molecular tools for fast detection and source tracking to support decision-making. Future research efforts may include, but are not limited to, exploitation of interaction mechanisms among pathogenic bacteria, food and gut microbiota, smart traceability of microbial hazards, and development of novel antimicrobial strategies.

Chemical Contamination Pathways and the Food Safety Implications along the Various Stages of Food Production: A Review

Historically, chemicals exceeding maximum allowable exposure levels have been disastrous to underdeveloped countries. The global food industry is primarily affected by toxic chemical substances because of natural and anthropogenic factors. Food safety is therefore threatened due to contamination by chemicals throughout the various stages of food production. Persistent Organic Pollutants (POPs) in the form of pesticides and other chemical substances such as Polychlorinated Biphenyls (PCBs) have a widely documented negative impact due to their long-lasting effect on the environment. This present review focuses on the chemical contamination pathways along the various stages of food production until the food reaches the consumer. The contamination of food can stem from various sources such as the agricultural sector and pollution from industrialized regions through the air, water, and soil. Therefore, it is imperative to control the application of chemicals during food packaging, the application of pesticides, and antibiotics in the food industry to prevent undesired residues on foodstuffs. Ultimately, the protection of consumers from food-related chemical toxicity depends on stringent efforts from regulatory authorities both in developed and underdeveloped nations.