Novel Technologies Impacting Food Safety
Several exciting new technologies are combining to help improve food safety. They include electronic tracing of foods from the farm to the supermarket, the utilization of “smart” sensors transmitting data into the Internet-of-Things (IoT), utilization of artificial intelligence, and smart packaging. The immense amount of data generated by these technologies can reside in the cloud. While most of these technologies are in their infancy, significant strides are made to make them a reality.
By now everyone knows about the Walmart experiment (with nine other companies) to trace mangos using blockchain that resulted in a trace in seconds rather than days by the traditional method.
Rob Chester, managing director of food at NSG International (New Food Magazine, NSF, January 2019 ) discussed the McKinsey & Company report about blockchain. They reported that while blockchain seems to be an amazing solution “Evidence for practical, scalable use for blockchain is thin on the ground,” “The stuttering blockchain development path is not entirely surprising since it is an infant technology that is relatively unstable, expensive, and complex” and that while blockchain has practical value in terms of niche applications, and move food tracking into the future, it may be an inappropriate and overly complex solution in many applications.
The adaptation of blockchain is contingent on large-scale adaptation by the entire supply chain, including multiple suppliers, intermediaries, transport, warehousing, processing, etc. This is a huge challenge. However, it might be doable under certain situation, especially if the FDA includes it in a mandatory system.
There is no doubt that for high-risk produce such as lettuce, blockchain, or another tracking method will enable companies to trace the source of the product much more rapidly than using the current paper methodology. An electronic system saves time to identify contaminated products and increases the consumer’s trust in the products. The challenge is to make all the stakeholders communicate with each other. Also blockchain is a tool and not the only solution for tracking.
Internet of Things (IoT)
The areas where most progress has been made throughout food production facilities in IoT temperature sensors. Temperature monitoring is critically important for food safety and must meet health code regulations. The temperature needs to be tracked and monitored in a restaurant, grocery store, and foodservice facilities.
For IoT, this means that the software platform must support interactions with distributed sensors and automation. The software can track and interpret temperature, humidity, or movement data and should integrate it in a way that can be incorporated into core business and executed by its staff. IoT can be expanded to monitor functions such as lighting and energy usage, HVAC conditions, customer wait times, open/closed doors, water levels, and fluid flow volume at beverage dispensers.
Some additional top uses for IoT (https://www.infor.com/blog/top-ten-io-t-applications-in-the-food-and-beverage-industry) are listed below:
IoT on the farm
IoT sensors can be utilized to monitor weather conditions, the amount of moisture of the soil, crop maturity, and even the presence of fungus or insects. Moisture sensors in the soil can help optimize irrigation, automating switching on systems residing in a location, and switching it off as needed. Monitoring soil conditions can also help determine when and where fertilization is needed.
IoT in the livestock barn
IoT sensors can be used to monitor herd weight, milk production in dairy cows, and other important parameters. They can automate feeding cycles, controlling the diet of the animals. Drones are added to the list of technological solutions which can be used by farmers to remotely check out and monitor field or building conditions.
IoT in Equipment Maintenance
Remote equipment monitoring can help determine when preventative maintenance is required by detecting early warning signs, allowing the prevention of problems by timely intervention.
IoT on food products
Tagging food products with Quick Response Code (QR code) can allow consumers to scan the QR code that can open a website page with information about the product, e.g., nutritional parameters, ingredients, allergens, usage instructions, advisories & safe handling instructions).
Artificial Intelligence (AI)
AI and machine learning making the machine imitate the human intelligence processes by a computer system. It includes the acquisition of information and rules for using it, and rules to reach conclusions and self-correction based on the information obtained. It can improve food safety; prevent recalls by monitoring data and verify compliance with sanitation protocol. AI software can flag events outside of a system’s usual operating pattern, indicating a possible risk of cross-contamination.
AI makes it possible for computers software to analyze data, learn from experience, and perform many human tasks with superior precision and efficiency. Below is a brief examination of how AI can enhance food safety and quality.
AI to predict recall before it happens
Carletta Ooton, vice president of health and safety, sustainability, security, and compliance at Amazon shared how Amazon detects faulty food products on an average of 50 days before an official recall. Amazon does it by continually monitoring and processing customer feedback, and by scanning millions of emails, phone calls, instant messages, and social media platforms to detect a food safety issue using an AI algorithm.
AI for automated root cause analysis
Root Cause Analysis (RCA) can identify relationships that may not be instinctive to human rationale.
AI automated RCA can uncover the root cause of malfunctions and quality issues before they disrupt production.
In a recent study, a manufacturer solved RCA problems faster with automated RCA leading to a 4.7% increase in production.
Reducing food waste with a learning machine
TOMRA Sorting Food developed an optical learning machine with cameras and near-infrared sensors, to “view food in the same way that consumers do” and sort it based on that perception. The result is fewer hours spent on manual sorting, higher yields and less waste, and better quality.” The machine reduced food waste by 5-10%.
AI to improve taste
Gastrograph uses machine learning and predictive algorithms to model consumer flavor preferences and predicts how well consumers will react to new tastes. The information can be segmented into demographic groups to help companies develop new products that match the preferences of their target audience.
AI for maintenance
AI and machine learning together with artificial neural networks can predict when maintenance is required, reducing time to repair and the cost of the repair.
Cornell University food scientists are developing a milk carton technology that provides wholesalers, retailers, and consumers accurate shelf life information to improve sustainability by reducing food waste. Dr. Wiedmann, a Professor in Food Safety at Cornell cited that milk cartons of the future would likely have a QR code that would offer specific information about the milk, such as the originating farm, the fluid milk processing plant and possible microbial influences, as well as a separate indicator that records carton temperature and time. Retailers and consumers could scan both the QR code symbol; an app would then quickly calculate how much longer the milk will last. The research will use predictive modeling to show how much shelf life remains.
Many fascinating technologies are becoming available to the food industry. All have the potential to be game changers. However, most are still in their infancy. As a result, their adaptation curve is very slow.