A review article recently published in the journal Applied food researchextensively explored significant advances in food safety by applying advanced technologies, primarily machine learning (ML), to pathogen detection. Researchers sought to leverage the transformative potential of ML by improving the speed and accuracy of real-time identification of foodborne pathogens. This could revolutionize food safety practices and ultimately improve public health.
Technological advances in pathogen detection
ML has transformed various fields, including food safety, education, and healthcare, by enabling rapid and accurate analysis of large data sets. Traditional pathogen detection methods, such as culture-based techniques and polymerase chain reaction (PCR), often have limitations such as long processing times and lower sensitivity.
In contrast, ML uses artificial intelligence (AI) biosensing and deep learning models to accelerate pathogen identification, thereby significantly reducing detection times and improving accuracy. This integration also improves tracking, traceability and transparency of the food supply chain. These technological advances are crucial to combating the high incidence of foodborne illnesses which, according to the World Health Organization (WHO), affect more than 600 million people each year.
Review Objectives and Methodology
In this article, the authors reviewed recent advances and applications of ML in real-time detection of foodborne pathogens and risk assessment. They conducted a literature review using various scientific databases, including IEEE Xplore, PubMed, Scopus, Google Scholar, and Web of Science. The search included combinations of keywords such as “machine learning,” “pathogen detection,” “food safety,” “foodborne illness,” and “predictive models.”
The study mainly focused on the research paper published between 2013 and 2023 to ensure the inclusion of the latest advancements in ML. The selected studies were assessed for their relevance, quality and contributions to food security.
The methodology used a systematic approach to identify, select and analyze relevant articles. Inclusion criteria included peer-reviewed studies that used ML algorithms for pathogen detection in food safety. The chosen publications were assessed using a standardized data extraction form that captured details such as authors, year of publication, study objectives, ML algorithms used, main findings and challenges.
The analysis focused on several key areas, including types of ML algorithms (unsupervised, supervised, reinforcement learning) and specific food safety applications (predictive modeling, contamination detection). Researchers also examined performance metrics such as accuracy, precision, and recall to identify trends in ML methods and their effectiveness in detecting pathogens.
Main conclusions and perspectives
The study showed that ML algorithms significantly improved the detection of foodborne pathogens by enabling rapid analysis of complex data sets. These algorithms can process data from a variety of sources, such as genomic sequencing and spectroscopy, enabling the identification of distinct spectral signatures linked to different pathogens. As a result, ML facilitates rapid and accurate identification of pathogens like Escherichia coli, Pseudomonas aeruginosaAnd Magnaporthe oryzae.
One of the main advantages of ML is its predictive ability. It can predict outbreaks and contamination events by analyzing past contamination incidents, environmental conditions and real-time sensor data. This capability enables preventative actions, making predictive power crucial for ecological monitoring and the development of effective monitoring systems.
For example, AI biosensing frameworks trained on lab-grown bacteria can detect pathogens like Escherichia coli in liquid foods and water within hours, achieving accuracy rates between 80% and 100%. Additionally, deep learning models, particularly convolutional neural networks (CNN), have demonstrated high accuracy in identifying pathogens through image-based detection, thereby reducing human errors in the process identification.
The integration of ML with other technologies, such as the Internet of Things (IoT) and blockchain, promises to further improve food safety management. IoT devices can provide real-time data on environmental conditions, while blockchain ensures traceability and transparency in food supply chains. Combining these technologies with ML can improve real-time monitoring and overall food safety.
Applications
ML models can automate the detection of pathogens in food, reducing the need for lengthy cultivation and incubation processes. This automation not only speeds detection, but also increases accuracy, enabling the identification of potential sources of contamination and the prediction of foodborne illness outbreaks.
Beyond pathogen detection, ML can also be used in environmental monitoring to assess the risk of foodborne illnesses and predict potential outbreaks. By analyzing data from microbiological tests, imaging and other sources, ML models provide valuable information about the presence of pathogens in food and water, helping to inform preventive measures and improve food safety management practices.
Conclusion
The review summarized that ML has significant potential to improve food safety through improved detection of pathogens. Its ability to process complex data sets and provide real-time risk assessments makes it an important tool in the fight against foodborne illnesses.
Additionally, integrating ML with emerging technologies such as IoT and blockchain improves its efficiency. However, challenges remain, including data quality, model interpretability, and regulatory compliance, which must be addressed to fully realize the benefits of ML. Future work should focus on improving data quality and developing transparent ML models while ensuring rigorous validation to meet regulatory standards.
Journal reference
Onyeaka, H. and and, al. Advancing food safety: the role of machine learning in pathogen detection. Applied food research, 20244/2, 100532. DOI: 10.1016/j.afres.2024.100532, https://www.sciencedirect.com/science/article/pii/S2772502224001422
