Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study

Efficient detection of foodborne pathogens via SERS and deep learning: An ADMIN-optimized NAS-Unet approach

Amid the increasing global challenge of foodborne diseases, there is an urgent need for rapid and precise pathogen detection methods. This study innovatively integrates surface-enhanced Raman Spectroscopy (SERS) with deep learning technology to develop an efficient tool for the detection of foodborne pathogens. Utilizing an automated design of mixed networks (ADMIN) strategy, coupled with neural architecture search (NAS) technology, we optimized convolutional neural networks (CNNs) architectures, significantly enhancing SERS data analysis capabilities. This research introduces the U-Net architecture and attention mechanisms, which improve not only classification accuracy but also the model's ability to identify critical spectral features. Compared to traditional detection methods, our approach demonstrates significant advantages in accuracy. In testing samples from 22 foodborne pathogens, the optimized NAS-Unet model achieved an average precision of 92.77 %, surpassing current technologies. Additionally, we explored how different network depths affect classification performance and validated the model's generalization capabilities on the Bacteria-ID dataset, laying the groundwork for practical applications. Our study provides an innovative detection approach for the food safety sector and opens new avenues for applying deep learning technologies in microbiology. Looking ahead, we aim to further explore diverse network modules to enhance model generalization and promote the application of these technologies in real-world food safety testing, playing a crucial role in the fight against foodborne diseases.

Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Utilization of AI - reshaping the future of food safety, agriculture and food security - a critical review

Artificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there is an increased interest in embracing applications associated with Artificial Intelligence to positively contribute to food safety. These applications such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools have been established to contribute to several domains of food safety such as early warning of outbreaks, risk prediction, detection and identification of food associated pathogens. Simultaneously, the ambition toward establishing a sustainable food system has motivated the adoption of cutting-edge technologies such as Artificial Intelligence to strengthen food security. Given the myriad challenges confronting stakeholders in their endeavors to safeguard food security, Artificial Intelligence emerges as a promising tool capable of crafting holistic management strategies for food security. This entails maximizing crop yields, mitigating losses, and trimming operational expenses. AI models present notable benefits in efficiency, precision, uniformity, automation, pattern identification, accessibility, and scalability for food security endeavors. The escalation in the global trend for adopting alternative protein sources such as edible insects and microalgae as a sustainable food source reflects a growing recognition of the need for sustainable and resilient food systems to address the challenges of population growth, environmental degradation, and food insecurity. Artificial Intelligence offers a range of capabilities to enhance food safety in the production and consumption of alternative proteins like microalgae and edible insects, contributing to a sustainable and secure food system.

From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases

The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools.

Frontiers of machine learning in smart food safety

Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks. Despite advances in both theory and practice, application of ML to smart food safety still suffers from issues such as data availability, model reliability, and transparency. Solving these problems can help realize the full potential of ML in food safety. Development of ML in smart food safety is also driven by social and industry impacts. The improvement and implementation of legal policies brings both opportunities and challenges. The future of smart food safety lies in the strategic implementation of ML technologies, navigating social and industry impacts, and adapting to regulatory changes in the AI era.

The potential application of artificial intelligence in veterinary clinical practice and biomedical research

Artificial intelligence (AI) is a fast-paced technological advancement in terms of its application to various fields of science and technology. In particular, AI has the potential to play various roles in veterinary clinical practice, enhancing the way veterinary care is delivered, improving outcomes for animals and ultimately humans. Also, in recent years, the emergence of AI has led to a new direction in biomedical research, especially in translational research with great potential, promising to revolutionize science. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, we highlighted and discussed the potential impact of various aspects of AI in veterinary clinical practice and biomedical research, proposing this technology as a key tool for addressing pressing global health challenges across various domains.

A data-driven approach for prioritising microbial and chemical hazards associated with dairy products using open-source databases

This study presents a data-driven approach for classifying food safety alerts related to chemical and microbial contaminants in dairy products using the Rapid Alert System for Food and Feed (RASFF) and the World Health Organization (WHO)'s Global Environmental Monitoring System (GEMS) food contaminants databases. This research aimed to prioritise microbial and chemical hazards based on their presence and severity through exploratory data analysis and to classify the severity of chemical hazards using machine learning (ML) approaches. It identified Listeria monocytogenes, Escherichia coli, Salmonella, Pseudomonas spp., Staphylococcus spp., Bacillus cereus, Clostridium spp., and Cronobacter sakazakii as the microbial hazards of priority in dairy products. The study also prioritised the top ten chemical hazards based on their presence and severity. These hazards include nitrate, nitrite, ergocornine, 3-MCPD ester, lead, arsenic, ochratoxin A, cadmium, mercury, and aflatoxin (G1, B1, G2, B2, G5 and M1). Using ML techniques, the accuracy rate of classifying food safety alerts as either 'serious' or 'non-serious' was up to 98 %. Additionally, the study identified Reference dose (RfD), substance amount, notification type, product, and substance as the most important features affecting the ML models' performance. These ML models (decision trees, random forests, k-nearest neighbors, linear discriminant analysis, and support vector machines) were also validated on an external dataset of RASFF alerts related to chemical contaminants in dairy products. They achieved an accuracy of up to 95.1 %. The study's findings demonstrate the models' robustness and ability to classify food safety alerts related to chemical contaminants in dairy products, even on new data. These results can enhance the development of more effective machine-learning models for classifying food safety alerts related to chemical contaminants in dairy products, highlighting the importance of developing accurate and efficient classification models for timely intervention.

Mushroom poisoning outbreaks in Guizhou Province, China: a prediction study using SARIMA and Prophet models

Mushroom poisoning is a public health concern worldwide that not only harms the physical and mental health of those who are poisoned but also increases the medical and financial burden on families and society. The present study aimed to describe and analyze the current situations and factors influencing mushroom poisoning outbreaks in Guizhou province, Southwest China, between January 2012 and June 2022, and to predict the future trends of its occurrence. Our study provides a basis for the rational formulation of prevention and control and medical resource allocation policies for mushroom poisoning. The epidemiological characteristics and factors influencing mushroom poisoning incidence were analyzed using descriptive epidemiological methods and the chi-squared test, respectively. Then, future occurrence trends were predicted using the SARIMA and Prophet models. In total, 1577 mushroom poisoning incidents were recorded in Guizhou Province, with 7347 exposures, 5497 cases, 3654 hospitalizations, and 93 fatalities. The mortality rate was 4.45% in 1 ~ 6 years higher than other age groups. There were notable geographic and seasonal characteristics, with the number of occurrences much higher in rural areas (1198) than in cities (379), and poisoning cases were more common during the rainy season (June to September). The mortality rate of household poisoning cases was 1.86%, with the most deaths occurring in households. Statistically significant differences were observed in the incidence across various cities, periods, and poisoning locations (P < 0.05). Both models had advantages and disadvantages for prediction. Nevertheless, the SARIMA model had better overall prediction results than the Prophet model (R > 0.9, the residual plot of the prediction results was randomly distributed, and RMSESARIMA < RMSEProphet). However, the prediction result plot of the Prophet model was more explanatory than the SARIMA model and could visualize overall and seasonal trends. Both models predicted that the prevalence of mushroom poisoning would continue to increase in the future; however, the number of fatalities is generally declining. Seasonal patterns indicated that a high number of deaths from gooseberry mushroom poisoning occurred in October. The epidemiological trends of mushroom poisoning remain severe, and health education on related knowledge must be strengthened in rural areas, with June to October as the key prevention and control phase. Further, medical treatment of mushroom poisoning cases with clinical symptoms should pay attention to inquiries to check whether the mushroom is similar in appearance to the Amanita, particularly in October.

Review of visual analytics methods for food safety risks

With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.

Predicting Foodborne Disease Outbreaks with Food Safety Certifications: Econometric and Machine Learning Analyses

Since the late 1990s, food safety certification has emerged as a prominent and influential regulatory mechanism in both the private and public spheres of the contemporary agri-food system. Food safety standards protect consumers from foodborne illnesses and help producers avoid the massive economic losses associated with food safety breaches. We empirically examine the relationship between foodborne disease outbreaks and certification adoption by utilizing the data on food safety certification adoption in the United States and Europe from 2015 through 2020. In our regression models, food safety certification along with select economic variables such as gross domestic product are used to explain the number of illnesses caused by foodborne disease outbreaks. For the United States at the state level, we found that certifications to SQF, PrimusGFS, BRC, or FSSC 22000 are negatively associated with the number of foodborne illnesses. For the case of Europe at the country level, certifications to ISO 22000 or FSSC 22000 are negatively associated with the number of foodborne illnesses. We then proceed to use machine learning techniques to examine how well we can use food safety certification data to predict foodborne disease outbreaks. Applying several algorithms (ordinary least squares, multinomial, decision tree, and random forest) to the U.S. data, we found that our models with food safety certification adoption can predict the number of U.S. foodborne illnesses or deaths with a relatively high degree of precision (testing accuracy at around 70% or better). Feature importance analysis allows us to inspect the relative importance of each explanatory variable (or feature) for making accurate predictions of the illness or death numbers. Through ranking the importance of explanatory variables, our study reveals that certification information could be the second most important variable (after gross domestic product) contributing to explain foodborne disease outbreaks.