Analysis of food safety based on machine learning: A comprehensive review and future prospects.

Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied.

Ion mobility spectrometry (IMS) is an analytical separation and diagnostic technique that is simple and sensitive and a rapid response and low-priced technique for detecting trace levels of chemical compounds in different matrices. Chemical agents and environmental contaminants are successfully detected by IMS and have been recently considered to employ in food safety. In addition, IMS uses stand-alone or coupled analytical diagnostic tools with chromatographic and spectroscopic methods. Scientific publications show that IMS has been applied 21% in the pharmaceutical industry, 9% in environmental studies and 13% in quality control and food safety. Nevertheless, applications of IMS in food safety and quality analysis have not been adequately explored. This review presents the IMS-related analysis and focuses on the application of IMS in food safety and quality. This review presents the important topics including detection of traces of chemicals, rate of food spoilage and freshness, food adulteration and authenticity as well as natural toxins, pesticides, herbicides, fungicides, veterinary, and growth promoter drug residues. Further, persistent organic pollutants (POPs), acrylamide, polycyclic aromatic hydrocarbon (PAH), biogenic amines, nitrosamine, furfural, phenolic compounds, heavy metals, food packaging materials, melamine, and food additives were also examined for the first time. Therefore, it is logical to predict that the application of the IMS technique in food safety, food quality, and contaminant analysis will be impressively increased in the future. Highlights Current status of IMS for residues and contaminant detection in food safety. To assess all the detected contaminants in food safety, for the first time. Identified IMS-related parameters and chemical compounds in food safety control.

Recently, the global food supply chain has become increasingly complex, and its scalability has grown. From farm to fork, the performance of food‐producing systems is influenced by significant changes in the environment, population and economy. These changes may cause an increase in food fraud and safety hazards and hence, harm human health. Adopting artificial intelligence (AI) technology in the food supply chain is one strategy to reduce these hazards. Although the use of AI has been rising in numerous industries, such as precision nutrition, self‐driving cars, precision agriculture, precision medicine and food safety, much of what AI systems do is a black box due to its poor explainability. This study covers numerous use cases of food fraud risk prediction using explainable artificial intelligence (XAI) techniques, such as LIME, SHAP and WIT. We aimed to interpret the predictions of a machine learning model with the aid of these technologies. The case study was performed on a food fraud dataset using adulteration/fraud notifications retrieved from the Rapid Alert System for Food and Feed system and economically motivated adulteration database. A deep learning model was built based on this dataset and XAI tools have been investigated on the proposed deep learning model. Both features and shortcomings of the current XAI tools in the food fraud area have been presented.

Ensuring food safety in a world facing escalating demand and complex supply chains is a pressing challenge. Despite increasing awareness, obstacles such as information distribution, financial limitations, and insufficient infrastructure impede food safety efforts. Blockchain technology presents a promising solution by improving transparency and traceability in supply chains, which are essential for tackling food safety issues. This study explores the integration of blockchain into food safety frameworks, emphasising its compatibility and potential to transform food production and distribution. Drawing on literature, it identifies key challenges to blockchain adoption, including regulatory frameworks and interoperability issues, and proposes strategies such as government intervention and standardisation to overcome them. Ultimately, blockchain holds immense promise in revolutionizing food safety practices, ensuring safe and nutritious food for all.

Ensuring food safety and quality remains a major challenge amid growing global food demands and complex supply chains. Fluorescence spectroscopy, owing to its speed and sensitivity, is a promising non-destructive method for food quality assessment, but its effectiveness is often hindered by the complexity and scarcity of spectral data. This study investigates the application of machine learning (ML) and deep learning (DL) techniques to overcome these limitations and presents novel methods for extracting chemical and physical insights, including improved feature selection tailored to continuous spectral ranges, to enhance the analysis of one-dimensional (1D) and two-dimensional (2D) fluorescence data. Two case studies are presented: (1) classification of multiple mycotoxin contamination in corn using 1D fluorescence spectra; and (2) prediction of extra virgin olive oil (EVOO) quality degradation using 2D excitation-emission matrices through DL models with domain adaptation and transfer learning. The results highlight the potential of ML/DL to improve spectral data interpretation, address limitations of small datasets, and uncover underlying physico-chemical insights, ultimately enabling faster, more cost-effective, and sustainable food quality monitoring.

In 2017, IBM announced a collaboration with a few major food producers and retailers, including inter alia Dole, Nestle, Tyson Foods, Kroger, Unilever, and Walmart, to leverage disruptive technologies such as distributed ledger technologies (DLTs, colloquially known as “blockchain”) to address imminent governance challenges along the global food supply chain. Walmart has further required its upstream suppliers of leafy greens to use the cloud- and blockchain-based “IBM Food Trust” platform by September 2019. Similarly, the World Food Programme (WFP) of the United Nations launched the “Building Block” program since 2017. Using iris-scanning technologies and blockchains, this program helped Syrian refugees verify their identities and directly deduct what they spend from the amount of aid they receive from the WFP. Initiatives as such have the potential to help retailers and consumers to pinpoint sources of contamination at times of outbreaks or provide production details and quality certifications (e.g. product origin, farm history, processing and shipping information, and fair trade or safety/sustainability standards). Blockchains can also be combined with smart contract systems or other AI techniques to increase efficiency, simplify transactions, ensure compliance and security, and promote trade facilitation across borders. While the far-reaching ramifications of blockchain technologies in the financial area (such as fintech and cryptocurrency issues) have been documented in media, literature, and political arenas in recent years, the opportunities as well as challenges posed by blockchain to food safety, traceability, and sustainable development have not been fully examined. The benefit of applying blockchain technologies in the global food supply chain seems salient: transforming paper-based documents into blockchain-enabled identity can, generating a high level of transparency and data integrity, enabling smaller farmers to bypass middlemen in crops trading and cash transfers, and providing efficient and cost-effective way to manage the production system. However, blockchainizing the food supply chain may pose legal and policy challenges to both developed and developing (especially underdeveloped) countries in different ways, which may in turn undermine the overall legitimacy and accountability of such techno-regulatory mechanism. This paper therefore aims to explore the potential of blockchain technologies in revolutionizing the global food supply chain in terms of food safety, traceability, and sustainable development. More specifically, this paper will examine concrete cases in which blockchains have effectively transformed how we conventionally think about food safety, certification, and traceability (which has by and large been manual and paper-based, and therefore a labor-intense and time-consuming). At the same time, when all participants in the global supply chain are being connected and required to upload their data to the cloud-based system and generate a transparent, traceable, immutable, and shared record of production details, quality specifications and origin facts, sustainability and fair trade certifications, and storage, import/export, and logistics information, the forms and substances of conventional food law (as well as data protection law, anti-trust law, and trade law) may need to be re-conceptualized. In this light, this paper will also endeavor to locate the possible barriers and challenges to “blockchainizing” food law at national and international levels, and offer recommendations for leveraging such technology in an effective, efficient, and responsible manner.

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection’s long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. Samples were taken from a cohort of 142 COVID-19, 48 Post-COVID-19, and 38 control patients, comprising 111 identified metabolites. Traditional analysis methods, like PCA and PLS-DA, were compared with ML techniques, particularly eXtreme Gradient Boosting (XGBoost) enhanced by SHAP (SHapley Additive exPlanations) values for explainability. XGBoost, combined with SHAP, outperformed traditional methods, demonstrating superior predictive performance and providing new insights into the metabolic basis of the disease’s progression and aftermath. The analysis revealed metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. Key metabolic signatures in Post-COVID-19 include taurine, glutamine, alpha-Ketoglutaric acid, and LysoPC a C16:0. This study highlights the potential of integrating ML and XAI for a fine-grained description in metabolomics research, offering a more detailed understanding of metabolic anomalies in COVID-19 and Post-COVID-19 conditions.

Cultural revolution

<p class="MsoNormal" style="margin: 0in 0in 10pt; line-height: 16.8667px; font-size: 11pt; font-family: Calibri, sans-serif; color: rgb(0, 0, 0);"><span style="font-size: 12pt; line-height: 18.4px; font-family: &quot;Times New Roman&quot;, serif;">The cotton supply chain is one of the most complex supply chains in the world, and stakeholders and consumers alike are demanding methods of measuring and reducing the environmental impact of textile products and the social responsibility of textile firms (Lindskog &amp; Roth, 2011). Graduates entering the workplace need to know and understand the complexity of the global supply chain and how a fiber, like cotton, interact within the complex supply chain to meet consumer’s demanding needs. Today’s professionals will be tasked with ensuring consumers’ needs are met while mitigating the environmental impact of the global apparel complex and demonstrating their social responsibility efforts. Therefore, the purpose of this project is to provide students with an opportunity to explore and understand cotton sourcing and cotton supply chain transparency through a leading textile focal company. This project, funded through a competitive grant from the <i>Importer Support Program</i> of the Cotton Board and Cotton Incorporated, was developed for students to 1) gain knowledge about cotton fiber; 2) understand how the quality of cotton fiber may impact the final product; 3) understand cotton industry and sustainability practices in different countries including U.S. and China; 3) select and apply the global supply chain framework to appreciate the complex interrelationships among supply chain partners; 4) explore global apparel supply chain transparency; and 5) make recommendations for appropriate future sourcing plans for the case company to achieve its sustainability goals.<o:p></o:p></span>

Food safety compliance is a critical matter of public health and regulatory oversight, which requires strict monitoring to protect against contamination, ensure quality, and maintain standards across the industry. Conventional food safety monitoring methods typically depend on manual inspections and rule-based systems, which can be labor-intensive, time-consuming, susceptible to human error, and ineffective in identifying complex or evolving anomalies. This research investigates the effectiveness of AI-driven anomaly detection systems in food safety compliance monitoring, focusing on their capacity to improve real-time monitoring, enhance detection accuracy, and risks of non-compliance. It evaluates the performance of AI-based anomaly detection vs. traditional monitoring approaches using key metrics including detection accuracy, response time, and false-positive rates. Additionally, it also discusses the challenges of adopting AI in food safety applications, such as data quality limitations, model interpretability and regulatory restrictions. Through an analysis of case study comparisons and industry observations, the findings highlight that AI-driven technologies such as machine learning, deep learning, and computer vision significantly enhance food safety compliance through automation of contamination detection, optimizing processing parameters, and supply chain traceability. Different applications of AI mentioned in this research study have demonstrated success in reducing food spoilage, improving shelf-life prediction and quality control, as evidenced by improved accuracy of contamination detection and compliance with regulations. Nevertheless, challenges remain in terms of data availability, model interpretability, regulatory restrictions, and the high cost of AI adoption. Through a thorough analysis, this research addresses the indicators and practical uses of AI automation in food safety compliance monitoring and its potential impact on existing food safety standards.

PurposeTo delve into the integration of global logistics and supply chain networks amidst the digital transformation era. This study aims to investigate the potential role of China’s Belt and Road Initiative (BRI) in facilitating the integration of global flows encompassing both tangible goods and intangibles. Additionally, the study seeks to incorporate third-party logistics activities into a comprehensive global logistics and supply chain integration framework.Design/methodology/approachPrior research is synthesised into a global logistics and supply chain integration framework. A case study was undertaken on Yuan Tong (YTO) express group to investigate the framework, employing qualitative data analysis techniques. The study specifically examined the context of the BRI to enhance comprehension of its impact on global supply chains. Information was collected in particular to two types of supply chain flows, the physical flow of goods, and intangible information and cash flows.FindingsThe proposed framework aligns well with the case study, leading to the identification of global logistics and supply chain integration enablers. The results demonstrate a range of ways BRI promotes global logistics and supply chain integration.Research limitations/implicationsThe case study, with multiple examples, focuses on how third-party logistics firms can embrace global logistics and supply chain integration in line with BRI. The case study approach limits generalisation, further applications in different contexts are required to validate the findings.Originality/valueThe framework holds promise for aiding practitioners and researchers in gaining deeper insights into the role of the BRI in global logistics and supply chain integration within the digital era. The identified enablers underscore the importance of emphasising key factors necessary for success in navigating digital transformation within global supply chains.

From the Chief Executive and <scp>IFST</scp> News

Food safety is being transformed by artificial intelligence (AI), which is boosting contamination detection, real time monitoring and transparency of food supply chain. AI based techniques like machine learning, deep learning and computer vision help to detect chemical, microbial and physical contaminants in food more accurately and efficiently. These advancements have led processes to be automated, minimize the impact of human error and facilitate better decision taking. Other innovations include rapid, automated detection and traceability using AI driven spectroscopy, sensor based monitoring and block chain integration. Challenges in adopting AI, however, include fragmented and proprietary data, lack of model interpretability, the sheer implementation costs, and regulatory hurdles. Implementing AI has cost and technical challenges for small and medium sized businesses. Also, the AI models must be explainable and FMV compliant to provide the necessary transparency and reliability. Future research will consist of building upon the AI models developed in this thesis, incorporation of AI with IoT and edge computing for real time monitoring as well as setting up of ethical and regulatory frameworks. Trust in AI driven food safety will be developed with standardized AI regulations, unbiased predictions, and data privacy protections. Although AI presents some hurdles, it has the power to contribute in building a much safer, more efficient and transparent global food supply chain.

Building trust in food safety certification

Against the backdrop of intensified globalization and complex supply chains, food quality management faces three major challenges: insufficient real-time monitoring capabilities, information silos and traceability difficulties, and weak data collaboration and risk prediction capabilities. This article focuses on the practical applications and impact of distributed technologies such as blockchain, Internet of Things, and federated learning in food quality and safety management. This article explores how to break down information barriers while ensuring data security, achieve real-time data monitoring and collaboration, and effectively address key challenges such as traceability, intelligent production optimization, and quality control in the process of food production, processing, and transportation. The synergy of these technologies creates a comprehensive framework for data flow: IoT ensures accurate data collection, FL enables efficient analysis and risk prediction under privacy protection, and BC ensures transparent and reliable data sharing. This article also analyzes the collaborative and complementary relationships between these technologies, analyzing their challenges and potential solutions. This comprehensive review systematically examines the technological synergies and complementarities among blockchain (BC), Internet of Things (IoT), and federated learning (FL), revealing how their integration creates emergent capabilities that address fundamental trade-offs in food safety management. By establishing a sound mechanism for information sharing and synergy, the integration and application of these technologies will help build an intelligent, efficient, and trustworthy modern food safety management system, providing innovative ideas and development directions for food safety governance.