Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Revolutionizing the food industry: The transformative power of artificial intelligence-a review

Artificial Intelligence (AI) is revolutionizing the food industry by optimizing processes, improving food quality and safety, and fostering innovation. This review examines AI's applications in food science, including supply chain management, production, sensory science, and personalized nutrition. It discusses techniques like knowledge-based expert systems, fuzzy logic, artificial neural networks, and machine learning, highlighting their roles in predictive maintenance, quality control, product development, and waste management. The integration of AI with sophisticated sensors enhances real-time monitoring and decision-making in food safety and packaging. However, challenges such as ethical concerns, data security, transparency, and high costs persist. AI is poised to advance sustainability by optimizing resource use, enhance food security through predictive analytics of crop yields, and drive innovation in personalized nutrition and supply chain automation, ensuring tailored products and efficient delivery. This paper underscores AI's transformative potential in the food industry while addressing the obstacles to its widespread adoption.

The impact of high-intensity interval training on women's health: A bibliometric and visualization analysis

BACKGROUND

High-intensity interval training (HIIT) can significantly improve health indicators such as cardiopulmonary function, metabolic efficiency, and muscle strength in a short period. However, due to significant physiological and metabolic differences between males and females, the effects of HIIT vary between genders. Therefore, exploring the specific impacts of HIIT on women's health is crucial. Although there is a considerable amount of individual research on the impact of HIIT on women's health, a systematic bibliometric analysis is still lacking.

METHODS

Publications related to HIIT in women's health were retrieved from the Web of Science Core Collection database, and tools like Microsoft Office Excel 2021, VOSviewer, and Citespace were used to create visualized tables and views.

RESULTS

The study included 808 publications distributed across 1234 institutions in 61 countries, authored by 3789 researchers. The United States, Australia, and Canada lead in this domain. Researchers like Astorino TA and Gibala MJ are notably influential in this field. The research has been prominently published in specific academic journals and widely cited by high-impact journals. Highly cited and bursting documents primarily discuss the effects of HIIT on metabolic adaptation, muscle adaptation, cardiovascular health, insulin sensitivity, and exercise performance. Frequent keywords include "aerobic exercise," "sprint interval training," "resistance training," "obesity," "body composition," "aging," and "insulin resistance." Keyword burst analysis reveals that early studies focused primarily on basic concepts and training models, which then expanded to specific physiological responses, applications in particular populations, and impacts on specific diseases.

CONCLUSION

This field has emerged as a research hotspot with international characteristics and extensive academic productivity. Journals and cited journals hold high academic influence, with highly cited and bursty references laying a solid theoretical and practical foundation for the field. In the rapid development of the past decade, research hotspots and frontier directions such as metabolic adaptation, muscle adaptation, cardiovascular health, exercise performance, and personalized training plans have been formed.

Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence

In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.

Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis

PURPOSE

To analyze the hotspots and trends in artificial intelligence (AI) research in the field of cataracts.

METHODS

The Science Citation Index Expanded of the Web of Science Core Collection was used to collect the research literature related to AI in the field of cataracts, which was analyzed for valuable information such as years, countries/regions, journals, institutions, citations, and keywords. Visualized co-occurrence network graphs were generated through the library online analysis platform, VOSviewer, and CiteSpace tools.

RESULTS

A total of 222 relevant research articles from 41 countries were selected. Since 2019, the number of related articles has increased significantly every year. China (n = 82, 24.92%), the United States (n = 55, 16.72%) and India (n = 26, 7.90%) were the three countries with the most publications, accounting for 49.54% of the total. The Journal of Cataract and Refractive Surgery (n = 13, 5.86%) and Translational Vision Science & Technology (n = 10, 4.50%) had the most publications. Sun Yat-sen University (n = 25, 11.26%), the Chinese Academy of Sciences (n = 17, 7.66%), and Capital Medical University (n = 16, 7.21%) are the three institutions with the highest number of publications. We discovered through keyword analysis that cataract, diagnosis, imaging, classification, intraocular lens, and formula are the main topics of current study.

CONCLUSIONS

This study revealed the hot spots and potential trends of AI in terms of cataract diagnosis and intraocular lens power calculation. AI will become more prevalent in the field of ophthalmology in the future.

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.

Virtualization and digital twins of the food supply chain for enhanced food safety

Meeting food safety requirements without jeopardizing quality attributes or sustainability involves adopting a holistic perspective of food products, their manufacturing processes and their storage and distribution practices. The virtualization of the food supply chain offers opportunities to evaluate, simulate, and predict challenges and mishaps potentially contributing to present and future food safety risks. Food systems virtualization poses several requirements: (1) a comprehensive framework composed of instrumental, digital, and computational methods to evaluate internal and external factors that impact food safety; (2) nondestructive and real-time sensing methods, such as spectroscopic-based techniques, to facilitate mapping and tracking food safety and quality indicators; (3) a dynamic platform supported by the Internet of Things (IoT) interconnectivity to integrate information, perform online data analysis and exchange information on product history, outbreaks, exposure to risky situations, etc.; and (4) comprehensive and complementary mathematical modeling techniques (including but not limited to chemical reactions and microbial inactivation and growth kinetics) based on extensive data sets to make realistic simulations and predictions possible. Despite current limitations in data integration and technical skills for virtualization to reach its full potential, its increasing adoption as an interactive and dynamic tool for food systems evaluation can improve resource utilization and rational design of products, processes and logistics for enhanced food safety. Virtualization offers affordable and reliable options to assist stakeholders in decision-making and personnel training. This chapter focuses on definitions and requirements for developing and applying virtual food systems, including digital twins, and their role and future trends in enhancing food safety.

Thermaland non-thermal pasteurization of citrus fruits: A bibliometrics analysis

Thermal and non-thermal pasteurization (TNP) process of food is not new to food technology, disparities in the merits and demerits of the two pasteurizations necessitate their uses concurrently. Bibliometric analysis of the subject matter is expedient to analyses of database for published publications. Especially to provide times, state-of-the art innovations and prospects of the techniques. In addressing these lacunas, we utilized VOSview visualization to establish connections among crucial elements within a dataset of 495 research publications gathered from Web of Science. This approach facilitated the identification of links and collaboration networks among key factors in the research landscape. Analysis of publications indicate thermal pasteurization is an age long practices, while non-thermal pasteurization is gaining more acceptance. This study exposed ranking differences in scholar's collaboration, citations of scholars, impactful institution and most published countries. United State, China, United Kingdom have largest publications of research in TNP among the top 10 countries. Coupling network and Sankey illustration showed new area of research where new researchers and scholars can begin new phase of findings.

Based on CiteSpace Insights into Illicium verum Hook. f. Current Hotspots and Emerging Trends and China Resources Distribution

Illicium verum Hook. f. is a globally significant spice, which is recognized in China as a food-medicine homolog and extensively utilized across the pharmaceutical, food, and spice industries. China boasts the world's leading resources of I. verum, yet its comprehensive utilization remains relatively underexplored. Through a resource survey of I. verum and the application of bibliometric visualization using CiteSpace, this study analyzed 324 papers published in the Web of Science Core Collection (WOSCC) from 1962 to 2023 and 353 core documents from China's three major databases (CNKI, Wanfang Database, and VIP Database). I. verum from Guangxi province towards various southern provinces in China, with autumn fruits exhibited superior quality and market value over their spring fruits. Literature in WOSCC emerged earlier, with a research emphasis on food science technology and pharmacology pharmacy domains. WOSCC research on I. verum could be divided into two phases: an embryonic period (1962-2001) and a growth period (2002-2023), showing an overall upward trend in publication. The three major Chinese databases contain a larger number of publications, with a focus on the food sector, which could be categorized into three stages: an embryonic period (1990-1999), a growth period (2000-2010), and a stable period (2011-2023), with an overall downward trend in publication. Both Chinese and international research hotspots converge on the medical applications of I. verum, with antioxidant bioactivity research emerging as a prevailing trend. This study delineated the resource distribution of I. verum across China and identified the research hotspots and trends both in China and internationally. The findings are beneficial for guiding researchers in swiftly establishing their research focus and furnishing decision-makers with a comprehensive reference for industry information.

Causal Artificial Intelligence Models of Food Quality Data

Research background

The aim of this study is to emphasize the importance of artificial intelligence (AI) and causality modelling of food quality and analysis with 'big data'. AI with structural causal modelling (SCM), based on Bayesian networks and deep learning, enables the integration of theoretical field knowledge in food technology with process production, physicochemical analytics and consumer organoleptic assessments. Food products have complex nature and data are highly dimensional, with intricate interrelations (correlations) that are difficult to relate to consumer sensory perception of food quality. Standard regression modelling techniques such as multiple ordinary least squares (OLS) and partial least squares (PLS) are effectively applied for the prediction by linear interpolations of observed data under cross-sectional stationary conditions. Upgrading linear regression models by machine learning (ML) accounts for nonlinear relations and reveals functional patterns, but is prone to confounding and failed predictions under unobserved nonstationary conditions. Confounding of data variables is the main obstacle to applications of the regression models in food innovations under previously untrained conditions. Hence, this manuscript focuses on applying causal graphical models with Bayesian networks to infer causal relationships and intervention effects between process variables and consumer sensory assessment of food quality.

Experimental approach

This study is based on the data available in the literature on the process of wheat bread baking quality, consumer sensory quality assessments of fermented milk products, and professional wine tasting data. The data for wheat baking quality were regularized by the least absolute shrinkage and selection operator (LASSO elastic net). Bayesian statistics was applied for the evaluation of the model joint probability function for inferring the network structure and parameters. The obtained SCMs are presented as directed acyclic graphs (DAG). D-separation criteria were applied to block confounding effects in estimating direct and total causal effects of process variables and consumer perception on food quality. Probability distributions of causal effects of the intervention of individual process variables on quality are presented as partial dependency plots determined by Bayesian neural networks. In the case of wine quality causality, the total causal effects determined by SCMs are positively validated by the double machine learning (DML) algorithm.

Results and conclusions

The data set of 45 continuous variables corresponding to different chemical, physical and biochemical variables of wheat properties from seven Croatian cultivars during two years of controlled cultivation were analysed. LASSO regularization of the data set yielded the ten key predictors, accounting for 98 % variance of the baking quality data. Based on the key variables, the quality predictive random forest model with 75 % cross-validation accuracy was derived. Causal analysis between the quality and key predictors was based on the Bayesian model shown as a DAG graph. Protein content shows the most important direct causal effect with the corresponding path coefficient of 0.71, and THMM (total high-molecular-mass glutenin subunits) content was an indirect cause with a path coefficient of 0.42, and protein total average causal effect (ACE) was 0.65. The large data set of the quality of fermented milk products included binary consumer sensory data (taste, odour, turbidity), continuous physical variables (temperature, fat, pH, colour) and three grade classes of products by consumer quality assessment. A random forest model was derived for the prediction of the quality classification with an out-of-bag (OOB) error of 0.28 %. The Bayesian network model predicts that the direct causes of the taste classification are temperature, colour and fat content, while the direct causes of the quality classification are temperature, turbidity, odour and fat content. The key quality grade ACE of temperature -0.04 grade/°C and 0.3 quality grade/fat content were estimated. The temperature ACE dependency shows a nonlinear type as negative saturation with the 'breaking' point at 60 °C, while for fat ACE had a positive linear trend. Causal quality analysis of red and white wine was based on the large data set of eleven continuous variables of physical and chemical properties and quality assessments classified in ten classes, from 1 to 10. Each classification was obtained in triplicate by a panel of professional wine tasters. A non-structural double machine learning (DML) algorithm was applied for total ACE quality assessment. The alcohol content of red and white wine had the key positive ACE relative factor of 0.35 quality/alcohol, while volatile acidity had the key negative ACE of -0.2 quality/acidity. The obtained ACE predictions by the unstructured DML algorithm are in close agreement with the ACE obtained by the structural SCM.

Novelty and scientific contribution

Novel methodologies and results for the application of causal artificial intelligence models in the analysis of consumer assessment of the quality of food products are presented. The application of Bayesian network structural causal models (SCM) enables the d-separation of pronounced effects of confounding between parameters in noncausal regression models. Based on the SCM, inference of ACE provides substantiated and validated research hypotheses for new products and support for decisions of potential interventions for improvement in product design, new process introduction, process control, management and marketing.

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

Advancing food safety risk assessment in China: development of new approach methodologies (NAMs)

Novel techniques and methodologies are being developed to advance food safety risk assessment into the next-generation. Considering the shortcomings of traditional animal testing, new approach methodologies (NAMs) will be the main tools for the next-generation risk assessment (NGRA), using non-animal methodologies such as in vitro and in silico approaches. The United States Environmental Protection Agency and the European Food Safety Authority have established work plans to encourage the development and application of NAMs in NGRA. Currently, NAMs are more commonly used in research than in regulatory risk assessment. China is also developing NAMs for NGRA but without a comprehensive review of the current work. This review summarizes major NAM-related research articles from China and highlights the China National Center for Food Safety Risk Assessment (CFSA) as the primary institution leading the implementation of NAMs in NGRA in China. The projects of CFSA on NAMs such as the Food Toxicology Program and the strategies for implementing NAMs in NGRA are outlined. Key issues and recommendations, such as discipline development and team building, are also presented to promote NAMs development in China and worldwide.

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications

Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.

Farm Animal Welfare Is a Field of Interest in China: A Bibliometric Analysis Based on CiteSpace

Farm animal welfare research conducted in China is not commonly accessed or known outside of China, which may lead to the assumption that farm animal welfare receives relatively little attention in China. Therefore, a bibliometric analysis was conducted on the existing Chinese farm animal welfare literature to provide robust evidence to refute this assumption. A total of 1312 peer-reviewed Chinese studies on farm animal welfare published between March 1992 and June 2023 were retrieved from the Web of Science (WoS) and the China National Knowledge Infrastructure (CNKI) database. CiteSpace software was used to analyze and visualize the number, species, authors, institutions, journals, and keywords of the papers. In China, farm animal welfare research has gone through the processes of an early stage (1992-2001), rapid-growth stage (2002-2007), and mature stage (2008-present), and the scale of research continues to grow. Notably, swine and chickens have received priority attention in this area. A Matthew effect was observed for authors and institutions, with relatively little collaboration among authors and institutions. Most of the papers were published in a small number of journals, with an apparent agglomeration characteristic. The research hotspots, summarized as "feed and diet", "environmental impacts and control", "integrated rearing management", "injury and disease", "behavior and technologies for behavior monitoring", "genetic analysis", "welfare during transport and slaughter", "welfare-friendly animal product consumption", "attitudes toward farm animal welfare", and "healthy breeding". The keywords "computer vision", "recognition", "temperature", "precision livestock farming", "laying hen", and "behavior", represent the major research frontiers in the field, which could indicate potential areas of significant future research. The findings of the present bibliometric analysis confirm the fact that farm animal welfare is a field of interest in China. Farm animal welfare research in China tends to be pragmatic, with a strong emphasis on enhancing growth and production performance, as well as product quality, rather than solely concentrating on improving farm animal welfare. This paper provides insightful references that researchers can use to identify and understand the current status and future direction of the farm animal welfare field in China.