Application Research: Big Data in Food Industry

Unveiling Digital Transformation: A Catalyst for Enhancing Food Security and Achieving Sustainable Development Goals at the European Union Level

The digital revolution is reshaping various aspects of society, including having a profound impact on food security and the advancement of Sustainable Development Goals (SDGs). This study investigates the relationship between digital transformation, quantified through the components of the Digital Economy and Society Index (DESI), and SDGs related to food (SDG1, SDG2, SDG3, and SDG10), along with the overall SDG Index score. The data used for investigation are sourced from reports issued by the European Commission concerning DESI, as well as the SDG reports for the period from 2017 to 2022. The paper elucidates how different components of digitalization, such as connectivity, digital skills, internet usage, and digital public services, influence the attainment of food security objectives and broader sustainable development targets using structural equation modeling and cluster analysis. The findings underscore the pivotal role of digital technologies in enhancing poverty alleviation, health and well-being, and, in particular, mitigating inequality. This study contributes to understanding the complex relationship between digital transformation and food security, offering insights for policymakers, practitioners, and stakeholders aiming to leverage technology for advancing SDGs and fostering a more equitable and sustainable future.

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework

BACKGROUND

The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence.

METHOD

This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases.

RESULTS

Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area.

CONCLUSION

AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.

Online Reputation for Food Sector Exporters in the B2B Context: The Importance of Sustainability

Food exporting companies play a fundamental role in the development of international trade. However, the conceptualization and measurement of their online corporate reputation has not been extensively studied in Business to Business markets, unlike in Business to Consumer contexts. The aim of this research is to identify the variables that determine the online reputation of food companies operating in B2B markets, and to analyze their relationship with the volume of food exports. For this purpose, a three-stage quantitative and qualitative study has been developed, based on in-depth interviews with experts from export organizations and managers of Peruvian food exporting companies, the estimation of an Advanced Sentiment Analysis, the construction of a Total Online Reputation Index (TOR), and the development of regression analysis. The study has identified 13 variables that affect the online reputation of food exporting companies and indicates that the presence of sustainability content on their website and the number of pages visited positively affect the volume of food exports. Moreover, the TOR could have a significant explanatory capacity with respect to company exports. These results constitute a reference guide for both companies in the sector and official export agencies, highlighting the critical aspects to promote their exports.

Machine Learning Approaches for Predicting Fatty Acid Classes in Popular US Snacks Using NHANES Data

In the US, people frequently snack between meals, consuming calorie-dense foods including baked goods (cakes), sweets, and desserts (ice cream) high in lipids, salt, and sugar. Monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) are reasonably healthy; however, excessive consumption of food high in saturated fatty acid (SFA) has been related to an elevated risk of cardiovascular diseases. The National Health and Nutrition Survey (NHANES) uses a 24 h recall to collect information on people's food habits in the US. The complexity of the NHANES data necessitates using machine learning (ML) methods, a branch of data science that uses algorithms to collect large, unstructured, and structured data sets and identify correlations between the data variables. This study focused on determining the ability of ML regression models including artificial neural networks (ANNs), decision trees (DTs), k-nearest neighbors (KNNs), and support vector machines (SVMs) to assess the variability in total fat content concerning the classes (SFA, MUFA, and PUFA) of US-consumed snacks between 2017 and 2018. KNNs and DTs predicted SFA, MUFA, and PUFA with mean squared error (MSE) of 0.707, 0.489, 0.612, and 1.172, 0.846, 0.738, respectively. SVMs failed to predict the fatty acids accurately; however, ANNs performed satisfactorily. Using ensemble methods, DTs (10.635, 5.120, 7.075) showed higher error values for MSE than linear regression (LiR) (9.086, 3.698, 5.820) for SFA, MUFA, and PUFA prediction, respectively. R2 score ranged between -0.541 to 0.983 and 0.390 to 0.751 for models one and two, respectively. Extreme gradient boost (XGR), Light gradient boost (LightGBM), and random forest (RF) performed better than LiR, with RF having the lowest score for MSE in predicting all the fatty acid classes.

Digital twin-based fault tolerance approach for Cyber-Physical Production System

Cyber-Physical Production Systems (CPPSs) as distributed Systems of Systems (SoS) are at the center of attention from different industries. CPPSs face different categories of errors. These errors will cause failures of the entire production chain. To handle this concern, production systems should be converted into fault-tolerant production systems. To present such systems, a fault tolerance approach was developed to help possible faults prediction and detection of faults causes in this study. Also, the increasing complexity and uncertainty of CPPS call for Digital Twin (DT)-based fault tolerance approach. The proposes approach uses an extraction module to extract the faults signatures efficiently. Based on all extracted faults, appropriate responses could be generated through reliable faults patterns prediction. This method is provided using Fault Tree Analyzer (FTA), Zero-suppressed Decision Diagram (ZDD), and Support Vector Machine-Adaptive Neuro-Fuzzy Inference System (SVM-ANFIS) structure. The results based on digital twin-based CPPS of the food production system as a use case show that the proposed approach can predict reliable faults signatures to prevent failures and make a much reliable production system. Also, this method can guarantee that CPPS is up and running with optimal levels at all times.

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.

Special Issue “Innovations in the Food System: Exploring the Future of Food”

The Food and Agriculture Organization of the United Nations (FAO) in 2018 provided a definition of “food systems” highlighting that they “encompass the entire range of actors and their interlinked value-adding activities involved in the production, aggregation, processing, distribution, consumption, and disposal of food products that originate from agriculture, forestry or fisheries, and food industries, and the broader economic, societal and natural environments in which they are embedded” [...]