By the end of 2018, the U.S. food and beverage industry will produce enough goods to generate $16.2 billion in revenue. In the long term, this figure is second only to China, the world's largest food and beverage producer by revenue and also the world's most populous country with nearly 1.4 billion people.
The smooth operation of the vast U.S. food and beverage industry requires complex processing, production, packaging, and distribution processes. Things don't always go as planned; manufacturers may find themselves facing product recalls, foodborne illness outbreaks, and even consumer complaints. Industry 4.0 offers a smart and efficient approach, aided by machine vision, to ensure the highest levels of food inspection and compliance with food safety standards.
Machine vision definition
Machine vision has only recently been recognized as a widely used tool in the food production industry; in fact, it was first used for food quality inspection in the 1980s. These early iterations of machine vision consisted of nothing more than a single photographic camera used to inspect muffin production lines to ensure that oversized products did not enter the machine. While the process was rudimentary, it proved effective.
Today's machine vision systems are far more complex. The Association for Automated Imaging (AIA) categorizes machine vision as a combination of hardware and image analysis software that assists the device in its work by capturing images. Modern machine vision systems typically incorporate some degree of sophisticated artificial intelligence, enabling them to analyze patterns and extract data from objects within their field of view. The acquired data is then compared to any existing data extracted from the system's database—managed by humans rather than automatically. Once the data has been reviewed, the system draws conclusions about the captured item.
The entire process, from start to finish, takes less than a second. However, in such a short time, the system collects a wealth of useful information about the project. Data on the food's color, ripeness, degree of spoilage, and internal temperature are obtained in the blink of an eye. It may even obtain information undetectable by the human eye, such as machine vision analyzing the food's internal components using different wavelengths. It can also be used for packaging defect detection, preventing material waste, mislabeling, and costly food recalls.
Food Recall
According to data from the U.S. Food and Drug Administration (FDA) and the USDA Food Safety and Inspection Service, there were 456 food recalls in 2017. These included multiple food safety violations/recalls of the same product. Undeclared food allergens (particularly in dairy products) were the leading cause of recalls. Listeria was the second most common cause of recalls, often affecting popular breakfast foods. There were also 24 Salmonella-based recalls, and 2.4 million pounds of ready-to-eat breaded chicken products contained undeclared milk content, putting people with dairy allergies at risk.
Although the total number of recalls has decreased compared to 2015 and 2016, recalls remain a major issue in food production. In fact, as an industry, food production lags far behind other production line industries in its utilization of automation, despite the fact that increased use of machine vision can significantly improve health and safety standards.
Industry challenges
The food production industry is doing well. It must remain highly efficient while producing food that meets consumer demand and federal health and safety regulations. Furthermore, producers need to balance the costs of traditionally low-margin operations with the higher safety and quality requirements across the food production value chain.
Implementing machine vision may require a higher initial investment than many manufacturers, but it can improve processing efficiency and reduce costly errors, negligence, and recalls (some of which can lead to more expensive litigation), making up for the investment cost within a shorter vision period.
Furthermore, consumer costs must be considered. With consumer health and safety at risk, food inspection processes must have a zero-tolerance policy for errors. A single misstep by a brand or product can erode customer trust. Mislabeled food or overlooked signs of infection in meat or poultry can lead to loss of life. This year alone, the FDA has linked 44 deaths to Salmonella outbreaks.
