Machine Vision in the Food and Beverage Industry: Mostly Feast, But Some Famine
| By: Winn Hardin, Contributing Editor
Food and beverage producers face continuous pressure to verify product quality, ensure safe and accurate packaging, and deliver consumables that are completely traceable through the supply chain. Machine vision has been helping the industry achieve these goals for the better part of two decades. But as government regulations tighten and consumers demand more transparency about the contents of their sustenance, adoption of vision and imaging systems in food inspection is on the rise — despite a few segments that show hesitance toward the technology.
Even though the U.S. Food Safety Modernization Act (FSMA) took effect in 2011, some food processors and packagers are still finalizing solutions to meet the law's product tracking and tracing requirements. "FSMA has forced the food industry to have better recording and reporting systems of their processes, so more food and beverage manufacturers are using 2D barcode reading to track and serialize data," says Billy Evers, Global Account Manager for the food and beverage industry at Cognex (Natick, Massachusetts).
But a more pressing need is driving the adoption of both barcode and vision technologies in food processing facilities. "Right now as a society, we're at an all-time high for food allergies," Evers says. "There's a heightened awareness in the industry about determining proper labels for allergen-based contaminants."
Incorrect or incomplete allergen labeling could lead to customer illness, costly recalls, and damage to the food producer's brand. While some manufacturers are using barcode readers for label verification, many of them "have legacy artwork that's been in existence for 60 or 70 years and don't want to mess up their brand by putting a 2D code on their packaging," Evers says.
In such cases, companies will use optical character recognition (OCR) and verification (OCV) of existing alphanumeric characters on the label, or pattern matching to track fonts or check for the absence/presence of certain words. Food producers also are using barcode readers and vision systems to comply with a 2016 U.S. law mandating the labeling of food that contains genetically modified ingredients, or GMOs.
Sometimes, the demand for barcode scanning comes from within the supply chain itself. Evers cites the example of one food company pushing its suppliers to guarantee that their barcodes are accessible from almost every portion of the pallets containing them so that workers aren't wasting time twisting individual boxes in order to scan them at distribution centers or back-of-store warehouses.
Like other industries relying on machine vision for inspection, food and beverage makers want systems that do more with less. For the past decade, many beverage filling facilities have been manufacturing PET plastic bottles on site rather than relying on a converter to make, palletize, and ship them. Pressco Technology Inc. (Cleveland, Ohio) has developed vision systems that conduct inspection up-and-down the line to include not only the preforms blown into the PET bottles but also the fill levels, caps, and labels on the filled containers.
"The advantage of doing all of this with one control is that you don't have to train operators on or buy spare parts for three or four different inspection systems," says Tom O'Brien, Vice President of Marketing, Sales, and New Business Development at Pressco.
O'Brien points to two competing challenges in the plastic bottling industry that can benefit from machine vision inspection. One is the lightweighting of PET containers and closures to reduce cost and provide a more sustainable package. "As you make things lighter, you use less plastic and have a greater opportunity for defects to occur," he says.
Secondly, with the use of post-consumer, re-ground material to make new beverage bottles, vision systems can inspect for contaminants such as dirt that can enter the production process as the recycled PET is melted and extruded into pellets.
To accommodate customers' requests for more intelligence in their machine vision products, Pressco provides correlation of defects in the blow molder for mold, spindle, and transfer arms, and in the filler for filling valves and capping heads. "If you get a repetitive defect coming from one of those machines, the machine vision system identifies which component is producing the defect to pinpoint that machine's component so the customer can take corrective action," O'Brien says.
Imaging opaque plastics like high-density polyethylene (HDPE) and polypropylene presents another challenge, as these materials require x-ray, gamma ray, or high-frequency units to measure fill lines. "We have primarily been a machine vision–based company, but we're selectively developing those technologies because of the market demand," O'Brien says.
Pedal to the Metal
On the metals side of its business over the last two years, Pressco has fielded a high volume of requests for its Decospector360 product, which inspects the entire outside surface of a decorated beverage can. "This is something can makers have wanted and needed for many years because the process of decorating a beverage can is volatile and unstable," says Michael Coy, Marketing Manager at Pressco.
Decospector360 features multiple cameras, sophisticated software algorithms, and a proprietary lighting design that illuminates a wide range of labels, colors, and can styles and heights. The system accurately inspects every can on the line, which typically runs about 2,000 units per minute.
"To be able to inspect 360 degrees around the outside of that decorated can and look at the label for any print quality issues and color defects, and to do it that fast, is extremely challenging," Coy says. "Our system solves that problem to the degree that the world's largest can manufacturers are installing the technology."
According to Coy, prior to the release of Decospector 360, can makers relied on inspectors to eyeball the production line. If plant personnel saw a suspicious defect such as ink missing from cans, they would have to flag entire pallets of cans that already completed the production process to be reinspected.
This process, known as hold for inspection (HFI), "is probably one of the most expensive and time-consuming for any can manufacturer," Coy says. "You have to store the pallets someplace and pay someone to look at those cans and decide if they're going to scrap them or ship them, and the can maker also runs the risk of making their customer angry."
In fact, brand protection is a key driver for automated can inspection. "Visual brand identity is very important to beverage manufacturers," Coy says. "The cans have to be perfect. Our system provides a degree of assurance that the cans are being produced, printed, and sent to the filling companies with a quality that matches the brand owner's expectations."
To Protect and Serve … Safe Food
When a food product recall occurs, it's more than a company's brand or reputation at risk. A North Carolina meat processing company recently issued a recall of more than 4,900 pounds of ground beef because it contained shredded pieces of Styrofoam packaging.
Upon reading about the recall, Steve Dehlin, Senior Sales Engineer with machine vision integrator Integro Technologies in Salisbury, North Carolina, reached out to the meat processor. "I have contacted numerous people in quality and plant management positions and told them that we can help prevent future recalls using machine vision technology, specifically using hyperspectral imaging," Dehlin recalls. "In fact, we are reaching out to a number of food manufacturers to solve this problem before it impacts consumer health and becomes both a financial and PR issue for the companies."
Multispectral and hyperspectral imaging of meat products has been well documented. In 2009, the U.S. Department of Agriculture's Agricultural Research Service successfully used hyperspectral imaging to inspect contaminated chicken carcasses in a commercial poultry plant. And machine vision companies like Integro also have installed numerous hyperspectral imaging systems that use RGB to check color differences in the meat and infrared wavelengths to inspect for contaminants below the surface.
Despite the evidence, meat processors are reluctant to employ the technology. "The food industry is very cost sensitive, and while machine vision greatly reduces quality-control risk, it takes planning, design, installation, and training, which may be the reason for their hesitancy," Dehlin says. "With meat or any food coming down the line at high speeds, the product has natural variation and color change. Customized machine vision inspection systems are ideal applications to detect quality issues."
Often the reluctance comes from a lack of knowledge about hyperspectral imaging among plant engineers at the meat processing facilities. Other segments of the food industry can benefit from the technology as well. For example, a 2016 salmonella outbreak in cantaloupe likely could have been prevented if hyperspectral imaging had been used to detect pathogens, according to Dehlin.
Dehlin expects that the U.S. Food and Drug Administration eventually will require spectral analysis of a food product sample to test for pathogens, but the push to adopt multispectral and hyperspectral imaging technology on a broader scale will likely come from food conglomerates like Walmart. Opportunities for machine vision in the food industry are ripe for the picking. To encourage continued adoption of machine vision technologies, system integrators have one more food metaphor to rely on: The proof is in the pudding.