Machine vision in food and beverage industry seeing growth, challenges
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. 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 2-D barcode reading to track and serialize data," said Billy Evers, global account manager for the food and beverage industry at Cognex.
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 said. "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 2-D code on their packaging," Evers said.
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. 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," said Tom O’Brien, vice president of marketing, sales, and new business development at Pressco.
O’Brien pointed 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 said.
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 said.
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 said.
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, 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 said. "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 said. "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.
Winn Hardin is contributing editor for AIA. This article originally appeared on Vision Online. AIA is a part of the Association for Advancing Automation (A3). A3 is a CFE Media content partner. Edited by Chris Vavra, production editor, CFE Media, firstname.lastname@example.org.