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The increasing globalisation and complexity of food supply chains have made developing new tools for maintaining and monitoring food safety more critical. A contaminated raw food or food product can quickly be transported long distances and distributed to many locations or used in numerous processed food products. This increases the risk of food safety incidents, affecting more people and causing significant economic losses.
As an example of a real-world food safety event, in 2022, more than 80% of sick people who were interviewed by public health officials reported eating at Wendy’s restaurants in several states before they became unwell.
Although many of those interviewed had consumed food containing romaine lettuce, the specific cause of the outbreak could not be confirmed.
This example demonstrates the need for rapid techniques to identify food contamination to protect people’s health and prevent economic loss by businesses. Although not all widely used, several big data techniques can improve food safety.
Data analytics in the food industry involves the systematic analysis of vast amounts of data to drive decision-making, optimise processes, and enhance overall efficiency. By leveraging advanced analytics, businesses can gain insights into consumer behavior, streamline supply chain operations, and improve food safety. Through the use of predictive analytics, companies forecast demand and manage inventory more effectively, reducing waste and ensuring product availability. Additionally, data analytics helps in monitoring and maintaining quality control by analysing data from various stages of production and distribution.
Big data is increasingly pivotal in enhancing efficiency, safety, and innovation in the food and beverage industry. Food safety databases, enriched with data from IoT devices and sensors, enable real-time monitoring and swift responses to contamination threats, ensuring higher standards of public health. Additionally, analysing online information reveals consumer trends and preferences, allowing businesses to detect patterns and adapt their strategies accordingly. This data-driven approach not only fosters a more responsive supply chain, it also promotes sustainable practices across the industry.
Artificial intelligence (AI) is revolutionising the food industry by driving efficiency, enhancing food safety, and fostering innovation. AI-powered systems optimise supply chains by predicting demand and reducing waste through precise inventory management. In food safety, AI algorithms analyze data from various sources to detect contamination risks and ensure compliance with health regulations. Moreover, AI is transforming food production with advanced robotics and automation, leading to higher productivity and consistency in food quality. This integration of AI not only streamlines operations and enhances food safety but also promotes sustainability and improves customer satisfaction.
Advances in whole genome sequencing (WGS) and computing make it possible to identify food-borne pathogens accurately and rapidly. It can also differentiate between different strains so that it’s possible to tell if infections in other places – as in the example above – are from the same source.
WGS rapidly takes over from older analytical techniques due to its greater speed and accuracy. There are many types of instruments using different methods. Due to the equipment costs, it’s mainly found in central government laboratories, universities and businesses providing analytical services. However, the technology is evolving rapidly, reducing processing times and lowering costs.
WGS generates vast amounts of data from reading DNA sequences and requires specialist analytical tools to identify which microorganisms the DNA belongs to. It also requires a database of reference genomes to compare the DNA in the samples with. The Global Microbial Identifier (GMI) project, a consortium of experts from 55 countries, is building a global reference source of genomic information on all known microorganisms – bacteria, viruses, parasites and fungi. This is available to food safety professionals and scientists to identify microorganisms in their samples.
IBM has created the Consortium for sequencing the food supply chain to create WGS tools that detect anomalies in foods that indicate food safety hazards. A project with Cornell University is characterising the genetic features of raw milk throughout the milk supply chain and developing tools to detect any anomalies that could be caused by microbiological contamination during production and transport along the supply chain.
Large amounts of data are needed for risk assessment and decision-making by food safety authorities. The World Health Organization’s food safety collaborative platform (FOSCOLLAB) integrates multiple data sources essential for preventing and managing disease outbreaks into a single platform, including
These and other FAO and WHO databases provide the information needed by food safety professionals and authorities to assess the risks of food contamination by ingredients used and contaminants to consumers of food products.
RFID technology for tracking shipments along the supply chain has brought many advantages for food businesses. It reduces the labour in handling goods on shipping and delivery, during processing, production and warehouse management.
RFID tags can hold far more data on the goods than barcodes, including production data, batch, dates, product variables, weights and sizes. Data can be automatically read and written to the tags using wireless devices, giving complete visibility of processes and improving stock management.
With more sophisticated connected sensors, the system could also collect environmental data as goods are moved through the supply chain, such as temperature – even during cooking – and humidity, dust, dirt, microbes or food spoilage chemicals. The EU MUSE-Tech project developed sensors for the real-time monitoring of temperature, gases and volatile compounds for quality control in food processing.
As this data is gathered on cloud storage systems, it could be analysed with big data analytics tools to extract hidden trends and relationships between environmental or production variables and food quality to indicate ways to reduce the risk of food spoilage and contamination.
In a food-borne disease outbreak, rapid tracing and identification of the contaminated products are essential to minimise the health and economic effects and to identify products to take samples from for analysis.
Food sales data, which food retail companies automatically collect, can be used to identify food products likely to be the source of contamination. A model of the likelihood of particular foods being consumed at different locations can be constructed using the likelihood-based approach and mapped against the sites of food-borne disease outbreaks. This can help retailers quickly identify contaminated products in their retail outlets.
People who have visited restaurants are more likely to report an illness on an online review site than the relevant food safety authority. This was noticed by the staff of the New York City Department of Health and Mental Hygiene (DOHMH), which then worked with Columbia University to develop software to analyse online reviews posted on Yelp.
The project identified 468 food-borne illness complaints, of which only 15 had been reported to the local authority via the dedicated phoneline for reporting illnesses. Follow-up interviews with some people posting complaints identified outbreaks requiring further investigation and three restaurants with multiple food safety violations.
This shows that information posted online by the public on widely accessible social media platforms can be harvested to provide a more efficient way to detect food-borne disease outbreaks and poor hygiene standards in food outlets than waiting for people to report incidents directly to the authorities.
The increasing digitisation in all aspects of business and society opens opportunities to monitor, analyse and improve processes, safety and health.
In the food sector, digital and analytical technologies are creating a traceable safer food supply system from the producer to the consumer. The examples described above show that the intelligent use of data, data sharing and collaboration are essential to develop these rapidly developing fields so that all can benefit from making a smarter, safer food system.
Just as big data is used in the food sector, revolutionising how food is produced, distributed, and consumed, its power is also being harnessed across various other industries to drive innovation, optimise operations, and enhance decision-making.
In healthcare, it aids in patient care and medical research; it enhances risk management and fraud detection in finance. Retailers use big data for personalised marketing and inventory management, while manufacturers employ it for predictive maintenance and supply chain optimisation. In the technology sector, big data fuels advancements in AI and machine learning. Additionally, big data is pivotal in transportation and logistics for route optimisation, in energy for smart grid management, and in agriculture for precision farming.
Technology and data-driven digital pest monitoring for effective, smart pest management solutions