ARTIFICIAL INTELLIGENCE, ROBOTICS AND FOOD SAFETY

Introduction
 

Artificial Intelligence (AI) or machine learning makes it possible for computers to analyze data and learn from experience how to perform tasks that were in the past reserved to humans with precision and accuracy. The FDA announced that it plans to utilize AI as part of the “New Era of Smarter Food Safety.”

Innovative food companies started using AI and robotics as alternative ways to produce and serve foods. Efficiencies can be maximized by integrating AI into the manufacturing processes.

 

Data from various sensors, scanners, x-rays can be analyzed in real-time to adjust processes in real-time. Some suggest that “electronic nose” can “smell” or detect food pathogens or spoilage organisms in raw foods. Sensors such as temperature sensors, humidity, and other environmental factors can be used together with AI to alert the system of changes and make the necessary corrections. Such systems can be instrumental in compliance with FSMA regulations.

 

The utilization of AI can improve food safety by monitoring the system compliance with established parameters and flag unusual operating patterns, indicting risk.

 

Will AI and robots replace humans in food manufacturing? We are seeing a trend of more and more applications of process automation utilizing AI, robotic process automation in food manufacturing, and food service.

 

Definitions

 Artificial Intelligence (AI) is the development of computerized systems that can execute tasks that usually require human intelligence (e.g., visual perception, speech recognition, and decision-making). The term AI and machine learning are synonymous terms.

 

Robotics Process Automation (RPA) uses AI to automate processes without a physical robot. Typically RPA is programmed to fill out forms, validate invoices, copy and paste data. It is ideal for repetitive tasks that can be automated with software programs.

 

Internet of Things (IoT) is computing devices or sensors capable of gathering and transferring data over the internet, without human interaction, allowing devices the ability to monitor, interact, and communicate with each other. IoT improves food safety since critical data like storage temperature can be accessed on-demand helping companies to prevent delay in responding, thereby eliminating problems before they become health risks.

 

RPA is used together with IoT sensors that feed data to AI tools.

 

How can AI help in food safety?

 In “Food Industry Executive” presents some applications of AI in the food industry: 

 

Maintenance of Records- optimizing the time of maintenance can be achieved by combining AI with IoT to collect sensor information from the equipment and the company maintenance records.

 

Identify recalls before they happen- A significant reason to adopt AI is to predict failures. This means identifying issues such as a truck storage area exceeding allowable operating temperature before it arrives at its destination and applying appropriate action.

 

Carletta Ooton, vice president of health and safety, sustainability, security, and compliance at Amazon, 

reported about a system being developed to predict food recalls. The system continually monitors a variety of platforms (customer feedback, emails, instant messages, phone calls, and social media) to detect food safety problems and block faulty products on an average of 50 days before the official recall, based on collective customer feedback. While such system has several drawbacks (food poisoning can take several days to evolve, and users tend to blame what they eat last, people miss-identify the causative food), it can in some cases point in the right direction.

 

Sorting Foods-Fresh produce sorting (by size, color, appearance, or foreign matter) is a time-consuming task. TOMRA sorting food is the leading provider of sensor-based food sorting machines for the food industry. The systems use various technologies, including cameras and near-infrared (NIR) sensors, x-rays, fluorescent lighting, and lasers.  The technology saves time over manual sorting with higher yields, less waste, and better quality.

 

Cleaning Equipment- Systems called Self-Optimizing-Clean-in-Place are being developed, using ultrasonic sensing and optical fluorescence imaging to measure food residue and microbial debris in a piece of equipment and then optimize the cleaning process. The system can reduce cleaning time and the required resources. By limiting human intervention the system limits cross-contamination. This system is more flexible scheduling cleaning based on operational use. This automation further ensures that CIP (clean in place) systems optimize the cleaning resources.

 

Personal hygiene monitoring-In China KanKan developed an Artificial Intelligence-powered application to improve personal hygiene among food workers. The AI system uses cameras to monitor workers, employing facial-recognition and object-recognition software to determine whether workers are wearing hats and masks as required by food safety regulations. Images of violations are displayed. The company claims that the accuracy of this technology is more than 96%.

 

Use of Intelligent robots

Intelligent robots can do many repetitive tasks; work in harsh environments such as freezers, and lift heavy packages. They are very dependable, reliable, and obedient and can work 24/7. Another benefit is that they can be sterile, thereby not introducing any contamination into the products. PR News Wire estimated that the global food robotics market would grow at a compound annual growth rate of 12.7% between 2019 and 2025 and reach $3,107.4 million by 2025.

 

The growth in the use of robotics can be attributed to increased food safety regulations, demand for improved productivity and cost reductions, the pressure to reduce labor cost, and the production of low-cost robots.

 

It is predicted (https://www.foodqualityandsafety.com/article/is-robotics-process-automation-an-up-and-comer-for-food-safety/?singlepage=1) that 72 % of food companies will use robots to reduce costs, increase compliance, improve productivity, and shorten transaction times.