As regulatory bodies in the U.S. and around the globe prepare to accept cell-based food products into their markets, a new report, Food Safety Aspects of Cell-Based Food, sheds light on potential hazards posed by this new class of foods.
Published jointly by the Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO), the 146-page report includes country case studies from Israel, Qatar, Singapore, as well as findings and recommendations from a global technical panel of experts from academia and industry.
The panel identified 53 potential hazards that may be introduced during the Cell Sourcing, Cell Harvesting, Food Production and Food Processing Phases. All of which exist within traditionally produced foods, apart from:
The potential for expression of novel toxins, toxic metabolites, or allergens or a change in expression of toxins, toxic metabolites, or allergens as a result of genomic instability, genetic or phenotypic Instability, and / or induced through physical or biochemical stimuli during cell culture.
Physiochemical transformation of food components (i.e., altered protein structure, reactive species formation) due to food processing or storage. The panel notes that while this same hazard is present in conventionally produced food, cell-based foods may contain new inputs (e.g., scaffolds and residues) and processing ingredients that must be tested.
“Many hazards are already well-known, and they exist in conventionally produced food,” the reports reads. “For example, microbiological contamination can occur at any stages of any food production process, including those involved in producing cell-based food. The experts concluded, however, that most cases of microbial contamination during the cell growth and production stages would inhibit cell growth. If the cells have grown and reached product expectations for harvest, then occurrence of such contamination would be extremely rare during the production process, but it could occur post-harvest, as is the case with many other food products. Various existing prerequisite programmes such as good manufacturing and hygiene practices, as well as food safety management systems such as Hazard Identification and Critical Control Points (HACCP), are applicable to ensure food safety for cell-based food.”
The reports calls for additional research and funding to determine if cell-based foods could provide healthy, nutritious, and sustainable food for future generations, while reducing environmental impacts by using less land and water, emitting fewer greenhouse gases, reducing agriculture-related pollution, improving farm animal welfare and reducing the risk of zoonotic diseases that can spread from animals to humans.
Additional topics touched upon by the Technical Panel include the challenge of high production costs, the need for uniform terminology, potential regulatory frameworks for cell-based foods, and a need to further investigate consumer perception and acceptance (including taste and affordability).
Superheated Steam (SHS) may offer dry facilities a new, effective option to destroy pathogens on produce harvesting, processing, and packing tools. SHS differs from the visible, wet steam vapor emitted by a tea kettle in that it is invisible and acts like a hot gas at super-high temperatures. Applied to surfaces, SHS has been shown to kill pathogens without leaving moisture or condensation. However, little is known about its performance on a pilot scale.
She also plans to address other considerations, including cost, range of applications, wear and tear on equipment, changes to ambient relative humidity and worker safety.
“It’s a really tough problem to solve,” Dr. Snyder said. “We wanted to bring some practical assessments to our academic research to better understand whether these tools would be useful to the industry.”
Joining her as co-principal investigator is V.M. Balasubramaniam, Ph.D., with Ohio State University, who brings expertise in food and agricultural engineering. “He’s an important collaborator because the project is at the intersection of produce safety and process engineering,” said Dr. Snyder, whose background is microbial food safety. “This is an interdisciplinary approach to developing novel sanitation technology.”
The researchers are using portable pilot-scale roll-along and backpack units fabricated by a collaborating manufacturer. As part of the project, they reviewed Occupational Safety and Health Administration guidelines and developed worker safety and operator compliance training.
Initial trials looked at thermal distribution across stainless steel coupons—or discs—at ambient temperatures using three different coupon thicknesses and three different nozzle distances. Temperatures at the contact point ranged from 170 to 320 degrees C (338 to 608 degrees F), depending on nozzle distance. The researchers plan to conduct similar tests with concrete coupons as well as ones made of materials used in picking bags.
Although SHS doesn’t use large amounts of water, it is unknown whether prolonged use of the technology could change ambient relative humidity in enclosed spaces, depending on size and ventilation. As part of the project, Dr. Snyder said they will look at whether those relative humidity changes could potentially lead to condensation with extended SHS use. They also plan to characterize how rapidly temperature dissipates across surfaces.
One of the project’s objectives is to better understand how much the industry would be willing to pay for SHS technology. To that end, Dr. Snyder is conducting an online survey that proposes different scenarios.
Because SHS doesn’t use large amounts of water, the technology could offer the produce industry potential water savings in addition to more sanitation options.
Food processing bottlenecks are a persistent problem in the industry. Whether due to labor shortages, shifts in consumer demand leading to product or formulation changes, or inefficient facility design, new technologies are coming to market that can help companies identify and overcome these challenges.
Overcoming Labor Shortages
A 2023 survey from Food Processing found that 40% of respondents plan to add new employees to their workforces this year. The survey highlighted the challenges companies continue to face due to labor shortages, including the inability to find enough workers and difficulty in retaining skilled employees. These problems collectively can cause food processing bottlenecks, particularly as companies seek to scale output to meet growing demand and enhance training to build employees’ skills.
Taiwan’s Taipei Medical University is using virtual reality (VR) at its food safety training center, and the technology shows promise as a faster and more engaging training tool. A professor familiar with the implementation of the technology noted that it can reduce safety-related errors and improve knowledge retention, as workers appreciate engaging in the more immersive learning style of VR compared to watching videos and reading textbook chapters.
That means it could also encourage workers to acquire new skills. That’s vital since the Food Processing survey noted that 55% of respondents have ramped up their in-house technical training in response worker shortages.
Incorporating new technologies, such as VR, may improve onboarding while also helping companies attract younger employees seeking companies that keep pace with modern times.
Digital Twins Increase Visibility
Digital twins are highly realistic models that allow companies to run various scenarios in a controlled environment before trying them in real life. They can help managers identify potential bottlenecks prior to implementing a new product or process, and can help solve existing bottlenecks by showing why an assembly line at a particular plant is less efficient than one at a nearby facility operated by the same company.
Digital twins can also help companies reduce waste. In one example, Accenture and Mars collaborated on a digital twin that enabled real-time monitoring of a filling process. That approach prevented too much product from going into a package.
When it comes to food safety, digital twins can identify insufficient food safety measures, highlighting where and how to make improvements.
One company even developed a digital twin for flavor formulations and customer response predictions. Nearly 90% of new food products remain on the market for only one year. The digital twin developed by Foodpairing was designed to predict “winning” formulations to increase the likelihood of success before introducing new food products.
Robots Supplement Human Efforts
There are limits to how fast humans can work safely and maintain stringent quality control. That’s why many food processing businesses have begun using robots to increase efficiency.
In one case, a food processor used cobots to decrease the six-hour time frame required to transition an assembly line to a different product. These robots also allowed the company to move to 24/7 production, which aligned with customer demand.
FlexCRAFT, a Dutch research program centered on using dual-arm robots for greenhouse management, food processing and packaging, has built machines with grippers that can handle various product shapes and packages.
The food processing application centers on deploying robots to separate, remove or cut various chicken parts. The team is currently developing machines to handle variations in size or type to ensure the robots can work with wings as well as thighs. Future initiatives include incorporating active learning and task planning to further improve the technology.
Food processing bottlenecks can keep companies from meeting goals and remaining competitive in a demanding marketplace. However, technology increasingly provides feasible solutions to help overcome these obstacles.
Leaders tasked with approving technological implementations should assess the most significant issues in their companies. From there, they can investigate which technologies have the most appropriate capabilities to reduce or solve those problems.
The food processing and manufacturing industry is one of many in the United States that is continuing to struggle with attracting and retaining workers. The situation is one that processors have found themselves in for years, but amidst an ongoing pandemic, the problem of labor shortage has rapidly reached a critical mass.
To fill understaffed processing lines, companies have employed a wide range of tactics—boosting wages, dishing out bonuses, announcing better work schedules, and bolstering benefits packages. While these recruiting tactics will get bodies on plant floors, they alone aren’t enough to keep things running smoothly. (And, noticeably, there’s been less chatter around measures that aren’t as public facing.) Inevitably, some employees will continue to fall ill with COVID-19 and need to isolate for periods of time, requiring job function shuffling and the need for temporary workers. Likewise, turnover is predicted to remain high industry-wide as companies continue to compete for a slim labor market. With this tumult will come continued product delays, supply chain disruptions, and the very real risk of critical food safety slipping.
For years now, food processors and manufacturers have looked to color-coding as a method for ensuring quality and preventing product contamination and cross-contamination. Conceptually, the process is simple. By assigning different colors to plant zones, assembly process steps, shift teams, or potential allergens and hazards, workers are able to use the correct, conveniently color-coded tools and products in the way they were intended. The plans are customized by facility but are always framed by four basic models mentioned here. When implemented correctly—and inclusively—a color-coding plan can bring so many benefits to a facility, especially in this moment.
Benefit #1: It’s Easily Recognizable
The point of a color-coding plan is to streamline and systemize food safety and hygiene procedures to minimize risks to the safety of products and team members in a facility. With that in mind, most color-coding plans comprise just a handful of colors, and oftentimes, workers in a plant will only ever interact with one or two. Once a team member learns, “I work in this zone, and I will always use blue tools here; or, I work in this part of the assembly process, which will always use red tools”; it’s pretty easy to remember that guidance.
With the availability of high-quality, hygienic tools in full-color options these days, it is pretty effortless to spot a tool that’s out of place. Additionally, many plants will choose to color-code wearables and PPE such as gowns, masks, and gloves so that it’s immediately obvious when a team member isn’t where they should be. Facility signage also comes into play as it’s a best practice for color-coding to always place descriptive plan signage in sight. Some facilities even put color-coding plans on individual ID tags to ensure it is always at the fingertips of team members.
Benefit #2: It’s Easily Understood
The success of a color-coding plan hinges on marrying design simplicity (meaning as few colors as possible with the most logical categorization), with a robust rollout (where every functional item is color-coded). When these needs are met, the plan is easy to understand and follow. It can help multilingual teams as the language barrier is minimized with a focus on colors vs. terminology, and as these plans are growing in popularity, a new employee with experience in the industry has likely worked with a plant operating under some form of a color-coding plan.
Most importantly, now, a color-coding plan can allow for new employees or temporary workers to get up-to-speed quickly. When turnover and hiring are happening more frequently and training team leaders are strapped for time, this is a game-changer as people can be on-boarded quickly without compromising quality and safety.
Benefit #3: It Doesn’t Rely On One Team Member To Train
It’s never a good idea to have important procedural safety standards of a facility live in just one person’s head. It is especially risky at a time when employees are falling ill and needing to isolate themselves on an ongoing basis.
One of the things that makes a color-coding plan successful is that everyone who works in a facility is involved. The plan only succeeds if everyone understands their unique role in the equation, and because of that structure and expectation, everyone is aware of how the plan should be working in practice. This means training new employees doesn’t only involve a small handful of individuals, allowing the responsibility of onboarding to be shared.
Benefit #4: It Can Boost Morale—Really
It’s no secret that many companies are facing dips in team morale these days. Between an ongoing pandemic and persistent turnover, new stressors are added every workday. This can impact not only job satisfaction for employees while at work but also present a safety risk, as food safety culture truly relies on every person in any given facility.
A color-coding plan sets the tone of teamwork and serves as a reminder of the importance of every individual in the larger goal of keeping every other person and the product safe. That reminder of personal responsibility and impact can go a long way when baseline tensions are up, and workflow disruptions are the norm.
If there’s anything the past couple of years has taught the industry, it is to expect the unexpected and, in turn, use whatever devices you have to make the best out of the current situation. A color-coding plan can help you do just that by serving as one of the best tools at your disposal in this moment.
The food allergen testing industry has garnered considerable traction across North America, especially due to the high volume of processed food and beverages consumed daily. Allergens are becoming a significant cause for concern in the present food processing industry worldwide. Food allergies, which refer to abnormal reactions or hypersensitivity produced by the body’s immune system, are considered a major food safety challenge in recent years and are placing an immense burden on both personal and public health.
In 2019, the most common reason behind recalls issued by the USDA FSIS and the FDA was undeclared allergens. In light of this growing pressure, food producers are taking various steps to ensure complete transparency regarding the presence of allergenic ingredients, as well as to mitigate risk from, or possibly even prevent contact with, unintended allergens. One of these steps is food allergen testing.
Allergen detection tests are a key aspect of allergen management systems in food processing plants and are executed at nearly every step of the process. These tests can be carried out on work surfaces, as well as the products, to detect any cross contamination or allergen presence, and to test the effectiveness of a food processing unit’s cleaning measures.
There has been a surge in awareness among consumers about food allergies and tackling the risk of illnesses that may arise from consuming any ingredient. One of the key reasons for a higher awareness is efforts to educate the public. In Canada, for example, May has been designated “Food Allergy Awareness Month”. It is estimated that more than 3 million people in Canada are affected by food allergies.
The size of the global food allergen testing market is anticipated to gain significant momentum over the coming years, with consistent expansion of the dairy, processed food and confectionary segments.
Understanding the Prevailing Trends in Food Allergen Testing Industry
Food allergies risen nearly 50% in the last 10 years, with a staggering 700% increase observed in hospitalizations due to anaphylaxis. Studies also suggest that food allergies are a growing health concern, with more than 250 million people worldwide estimated to be affected.
Although more than 170 foods have been identified as causing food allergies in sensitive consumers, the USDA and the FDA have identified eight major allergenic foods, based on the 2004 FALCPA (the Food Allergen Labeling and Consumer Protection Act). These include eggs, milk, shellfish, fish, peanuts, tree nuts, soybean, and wheat, which are responsible for 90% of allergic reactions caused due to food consumption. In April 2021, the FASTER (Food Allergy Safety, Treatment, Education, and Research) Act was signed into law, which categorized sesame as the ninth major food allergen.
This ever-increasing prevalence of allergy-inducing foods has presented lucrative opportunities for the food allergen testing industry in recent years since food processing business operators are placing a strong emphasis on ensuring transparency in their products’ ingredient lists. By testing for allergens in food products, organizations can accurately mention each ingredient, and thereby allow people with specific food allergies to avoid consuming them.
Several allergen detection methods are used in the food processing industry, including mass spectrometry, DNA-based polymerase chain reaction (PCR) as well as ELISA (enzyme-linked immunosorbent assay), to name a few. The FDA, for instance, created a food allergen detection assay, called xMAP, designed to simultaneously identify 16 allergens, including sesame, within a single analysis, along with the ability to expand for the targeting of additional food allergens. Such industry advancements are improving the monitoring process for undeclared allergen presence in the food supply chain and enabling timely intervention upon detection.
Furthermore, initiatives, such as the Voluntary Incidental Trace Allergen Labelling (VITAL), created and managed by the Allergen Bureau, are also shedding light on the importance of allergen testing in food production. The VITAL program is designed to support allergen management with the help of a scientific process for risk assessment, in order to comply with food safety systems like the HACCP (Hazard Analysis and Critical Control Point), with allergen analysis playing a key role in its application.
ELISA Gains Prominence as Ideal Tool for Food Allergen Testing
In life sciences, the detection and quantification of various antibodies or antigens in a cost-effective and timely manner is of utmost importance. Detection of select protein expression on a cell surface, identification of immune responses in individuals, or execution of quality control testing—all these assessments require a dedicated tool.
ELISA is one such tool proving to be instrumental for both diagnostics as well as research). Described as an immunological assay, ELISA is used commonly for the measurement of antibodies or antigens in biological samples, including glycoproteins or proteins.
While its utility continues to grow, ELISA-based testing has historically demonstrated excellent sensitivity in food allergen testing applications, in some cases down to ppm (parts per million). It has a distinct advantage over other allergen detection methods like PCR, owing to the ability to adapt to certain foods like milk and oils, where its counterparts tend to struggle. The FDA is one of the major promoters of ELISA for allergen testing in food production, involving the testing of food samples using two different ELISA kits, prior to confirming results.
Many major entities are also taking heed of the growing interest in the use of ELISA for food allergen diagnostics. A notable example of this is laboratory analyses test kits and systems supplier, Eurofins, which introduced its SENSISpec Soy Total protein ELISA kit in September 2020. The enzyme immunoassay, designed for quantitative identification of soy protein in swab and food samples, has been developed by Eurofins Immunolab to measure residues of processed protein in various food products, including instant meals, chocolate, baby food, ice cream, cereals, sausage, and cookies, among others.
In essence, food allergens continue to prevail as high-risk factors for the food production industry. Unlike other pathogens like bacteria, allergenic proteins are heat resistant and stable, and cannot easily be removed once present in the food supply chain. In this situation, diagnostic allergen testing, complete segregation of allergenic substances, and accurate food allergen labeling are emerging as the ideal courses of action for allergen management in the modern food production ecosystem, with advanced technologies like molecular-based food allergy diagnostics expected to take up a prominent role over the years ahead.
Across industries, new innovations in robotics technologies are helping to speed up day-to-day work and improve product quality. Robots can be especially effective for businesses in the food processing industry, where a growing labor shortage poses trouble for processors.
While a number of critical industry tasks were difficult to fully or partially automate in the past, new robotics technology is helping to increase the number of potential applications for robots in the industry.
Consistency, Accuracy, and Speed
Food processing robots offer a few major advantages over conventional food processing workflows. Robots can perform a task repeatedly over the course of a work day or shift, typically with minimal deviation in precision. Unlike human workers, robots don’t get tired, and their pace of work tends to stay consistent. This combination of accuracy and speed has been found to increase site throughput while ensuring packaged products are up to company standards.
Food processors that adopt robots also see major gains in item consistency—more often, packaged products contain the same amount of food, weigh the same, and are packaged in the same manner.
Automated packaging systems can sometimes be a poor fit for certain food commodities, especially for products like delicate fruits and vegetables.
Experimentation, however, often leads to custom solutions that can handle these unique challenges. After experimentation with new weighing and packaging robots in the cannabis industry, for example, processors were able to accelerate the packaging process and create more consistently packaged items.
In the food processing industry, this can come in the form of robots with soft silicon grippers and attachments, which help companies package delicate products.
Workers in a factory sorting food by hand, could be assisted by new robot technology. (Unsplash image)
The use of robots can help control cross-contamination in food processing plants.
With any human labor force comes the risk of cross-contamination. Workers assigned to packaging foods can easily transport pathogens from product to product or from one area of the facility to another. This is especially true in sites that process raw meat products. Even when following proper site hygiene practices, it’s possible for workers to unintentionally transport pathogens and other contaminants from one workcell to another.
Because work in food processing facilities is often shoulder-to-shoulder, it’s also easy for contaminants to spread from one worker to another once a particular cell has been contaminated.
Robots that are fixed in place and handle all the aspects of a particular packaging job can help localize potential contamination, making it easier for processors to minimize cross-contamination and keep food safe.
Robots can still contribute to cross contamination if not properly cleaned, but an additional set of robots could solve this problem, too. For example, one a provider of robots for the food processing industry has developed a set of robots capable of washing down an entire workcell.
These robots, working in pairs, activate at the end of each operating cycle and use high-powered jets of water to wash down the workcell, the packaging robots used there, and themselves.
Collaborative Robotics (Cobots)
One major recent innovation in robots has a new focus on tech that is collaborative.
These new robots, unlike conventional robotics, aren’t always built to fully automate a particular task. Instead, they are built to interact and work collaboratively alongside humans where necessary.
Artificial intelligence-based machine vision technology helps them navigate factory floors safely or assist in tasks like assembly and machine tending. Safety features like force limiters and padded joints help prevent injuries that can occur while working in close proximity to conventional robots.
These features also enable them to work in tight spaces without the use of safety cages that conventional robots sometimes require. In factories and food processing plants, they can provide assistance and speed up existing workflows.
For example, an article in Asia Pacific Food Industry cites one case study from a Swedish food processor, Orkla Foods. The company integrated cobots into a production line packaging vanilla cream, freeing up the human workers who had been responsible for the task. Before the cobots were introduced, workers had to bag and manually pack the vanilla cream into cartons.
Even with cobots, human workers are still necessary for tasks that require judgment, creativity, and problem-solving skills. Cobots can take over tasks that don’t lend themselves well to automation. These tasks tend to be tedious, dull, or even dangerous due to the repetitive motions workers need to make.
Even if a task can’t be fully automated, cobots can still help improve efficiency and boost accuracy. These robots provide the most significant benefits for businesses that need flexibility and agility in production.
Cobots are often lightweight and easy to reprogram on-the-fly, allowing workers to quickly move them from task to task as needed. In many cases, an entire fleet of cobots can be repositioned and reprogrammed in half a day, allowing a business to reconfigure its robots to handle entirely new tasks without additional capital investment.
This flexibility can also make cobots a better fit for personalized products than other systems. As product specifications change, a cobot can be easily programmed and reprogrammed to handle the differences.
The use of these robots can also help prevent cross contamination, like more conventional robotics.
Sector-Specific Applications
A handful of sectors within the food processing industry can also benefit from niche robotics designed to automate certain specific tasks.
Danish robotics manufacturer Varo, for example, developed a line of cake decorating and filling robots. These robots are designed with technology that allows them to determine which cake will be decorated next, minimizing the amount of human involvement needed to operate.
While these robots won’t be useful for every manufacturer, they are a good example of how many sectors within the industry stand to benefit from robots that can automate niche tasks.
Using Food Processing Robots to Improve Product Quality and Consistency
Robots help automate tasks that are dull, dirty or dangerous. In doing so, they typically provide businesses with significant upgrades to process accuracy, speed, and consistency.
New technology—like machine vision and collaborative robotics technology—is helping to expand the use cases of robots in the food processing industry. These robots can often improve product quality more effectively than process changes alone, and may help manage a labor gap that could persist well into the future.
It is an exciting time to be in the food industry. Consumers are ever more aware of what they are eating and more demanding of quality. And the vital need to reduce global food waste is transforming how we produce and consume food. This is driving innovation all the way along the supply chain, from gate to plate.
One of the biggest areas of opportunity for the industry to increase automation and improve food safety is in the processing plant. The challenges processors have faced in the last 12 months have accelerated the focus on optimizing resources and the drive for more adoption of new technology.
Foreign material contamination is a growing issue in the meat industry and new types of detection systems are emerging to help address this challenge. As Casey Gallimore, director of regulatory and scientific affairs at the North American Meat Institute, highlighted in a recent webinar, 2019 was a record year for the number of recalls related to foreign object contamination, which totaled 27% of all FSIS recalls in that year.
“There are a number of potential reasons why recalls due to foreign object contamination have increased over the years: Greater regulatory focus, more discerning consumers, [and] more automation in plants. But one important reason for this trend is that we have a lot of new technology to help detect more, [but] we are not necessarily using it to its full potential,” said Gallimore. “As an industry, we have a strong track record of working together to provide industry-wide solutions to industry-wide problems. And I believe that education is key to understanding how different detection systems—often used together—can increase the safety and quality of our food.”
Types of Detection Systems
Processors use many different detection systems to find foreign materials in their products. Equipment such as x-rays and metal detectors, which have been used for many years, are not effective against many of today’s contaminants: Plastics, rubber, cardboard and glass. And even the most well trained inspectors are affected by fatigue, distraction, discomfort and many other factors. A multi-hurdle approach is imperative, and new technologies like vision systems need to be considered.
Vision systems, such as cameras, multi-spectral, and hyperspectral imaging systems can find objects, such as low-density plastics, that may have been missed by other detection methods. Yet, depending on the system, their performance and capabilities can vary widely.
Camera-based systems are the most similar to the human eye. These systems are good for distinguishing objects of varying size and shape, albeit in two-dimensions rather than three. But they become less effective in situations with low contrast between the background and the object being detected. Clear plastics are a good example of this.
Multi-spectral systems are able to see more colors, including wavelengths outside of the visible spectrum. However, multispectral systems are set up to use only specific wavelengths, which are selected based on the materials that the system is expected to detect. That means that multispectral systems can identify some chemical as well as visual properties of materials, based on those specific wavelengths. It also means that other materials, which the system has not been designed to find, will likely not be detected by a multispectral system.
Another relatively new type of vision system uses hyperspectral imaging. These systems use chemistry to detect differences in the materials being inspected and therefore recognize a broad range of different contaminants. They are especially good at seeing objects that cameras or human inspectors may miss and at identifying the specific contaminant that’s been detected. The same system can assess quality metrics such as composition and identify product flaws such as woody breast in chicken. Hyperspectral systems also gather tremendous amounts of chemistry data about the products they are monitoring and can use artificial intelligence and machine learning to get a more holistic picture of what is happening in the plant over time, and how to prevent future contamination issues. This might include identifying issues with a specific supplier, training or other process challenges on one line (or in one shift), or machinery in the plant that is causing ongoing contamination problems.
Many processors are considering implementing new inspection systems, and are struggling to understand how to compare the expected performance of different systems. One relatively simple methodology that can be used to evaluate system performance is, despite its simplicity, called a “Confusion Matrix”.
The Confusion Matrix
A confusion matrix is often used in machine learning. It compares the expected outcome of an event with the actual outcome in order to understand the reliability of a test.
Figure 1 shows four possible outcomes for any kind of test.
Actual (True Condition)
Predicted
(Measured Outcome)
Positive (P)
Negative (N)
Positive Detection
True Positives (TP)
False Positives (FP)
Negative Detection
False Negatives (FN)
True Negatives (TN)
P = TP + FN
N = FP + TN
Figure 1. Confusion Matrix
But what does a confusion matrix tell us, and how can it help us assess a detection system?
The matrix shows us that a detection system may incorrectly register a positive or negative detection event—known as a ‘False Positive’ or ‘False Negative’.
As an example, say we are testing for a disease such as COVID-19. We want to know how often our system will give us a True Positive (detecting COVID when it *IS* present) versus a False Positive (detecting COVID when it *IS NOT* present).
Let’s apply this to processing. If you are using an x-ray to detect foreign objects, a small piece of plastic or wood would pass through unnoticed. This is a False Negative. By contrast, a system that uses hyperspectral imaging would easily identify that same piece of plastic or wood, because it has a different chemical signature from the product you’re processing. This is a True Positive.
A high rate of false negatives—failing to identify existing foreign materials—can mean contaminated product ends up in the hands of consumers.
The other side of the coin is false positives, meaning that the detector believes foreign material to be present when in fact it is not. A high rate of False Positives can lead to significant and unnecessary product wastage, or in time lost investigating an incident that didn’t actually occur (see Figure 2).
Figure 2. Balance of True Positives and False Positives
The secret to a good detection system lies in carefully balancing the rates of true positives and false positives by adjusting the sensitivity of a system.
This is where testing comes in. By adjusting a system and testing under different conditions, and then plotting these outcomes on the confusion matrix, you get an accurate picture of the system’s performance.
Effectiveness of a Detector
Detection is not just the act of seeing. It is the act of making a decision based on what you have seen, by understanding whether something of importance has occurred. Many factors influence the effectiveness of any detection system.
Resolution. This is the smallest size of object that can possibly be detected. For example, when you look at a photograph, the resolution affects how closely you can zoom in on an image before it becomes blurry.
Signal to noise ratio. This measures the electronic “noise” of the detector and compares it with the “background noise” that may interfere with the signals received by the detector. Too much background noise makes it harder to identify a foreign object.
Speed of acquisition. This measures how fast the detector can process the signals it receives. Motion limits what you can see. As line speeds increase, this impacts what detectors are able to pick up.
Material being detected. The type of material being detected and its properties will have a significant impact on the likelihood of detection. As previously mentioned, for example, x-rays are unlikely to detect low-density materials such as cardboard, resulting in a high number of False Negatives.
Presentation or location of material being detected. Materials that are underneath another object, that are presented on an angle, are too similar to the product being inspected, or are partially obstructed may be more difficult for some detectors to find. This also presents a risk of False Negatives.
Complexity of the product under inspection. Product composition and appearance vary. For example, just like the human eye, finding a small object on a uniformly illuminated and uniform color background like a white kitchen floor is much easier than finding the same small object on a complex background like industrial carpet. Coarsely ground meat might be more difficult to detect than uniform back fat layers, for example.
Environment. Conditions such as temperature and humidity will have a significant effect on detection.
Detection Curves
To understand system performance even better, we can use a detection curve. This plots out the likelihood of detection against different variables (e.g., object size) and allows us to objectively compare how these different factors impact the performance of each system.
Figure 3 shows how this looks when plotted as a curve, with object size on the x-axis (horizontal) and the probability of detection (a True Positive from the Confusion Matrix) on the y-axis (vertical). It shows three examples of possible detection curves, depending on the detector being used.
Figure 3. Examples of detection curves for different detectors. Probability of detection of an object increases as the size of the object increases.
A detection curve tells you both the smallest and largest object that a detector will find and the probability that it will be found.
In the example presented by Figure 3, Detector 3 can see essentially 100% of large and very large objects, as can Detector 2. But Detector 3 is also more likely than the other two systems in the example to see microscopic objects. Based on this detection curve it would likely be the best option if the goal were to detect as many foreign objects as possible, of all sizes.
Of course, the performance of a detector is determined by multiple measures, not just size,
Detection capability can be improved for most detection systems, but typically comes at a significant cost: Increasing sensitivity will increase the number of false positives, resulting in increased product rejection. This is why looking at the detection curve together with the false-positive/false-negative rates for any detection system gives us a clear picture of its performance and is invaluable for food processing plants when selecting a system.
Using the confusion matrix and a detection curve, processors can compare different detection systems on an apples-to-apples basis. They can easily see whether a system can identify small, tiny or microscopic objects and, crucially, how often it will identify them.
Every detection method—X -ray, metal detection, vision systems, manual inspection—presents a trade-off between actual (correct) detection, rejection of good product (false positive) and missed detections (false negative). This simple way to compare differences means processors can make the right decision for the specific needs of their plant, based on easily gathered information. For all of us data geeks out there, that sounds like the Holy Grail.
For food processors, efficiency can be a major asset. Cutting production times and improving kitchen throughput is one of the best ways to reduce costs and boost profits. In recent years, new management strategies and a range of technologies—like Industry 4.0—has transformed how business owners manage their facilities, including food processing plants. This means there is a range of new, efficiency-improving tools available for businesses that want to streamline plant processes and better manage their operations. The strategies and investments are some of the best possible ways for food processors to improve their plant’s efficiency.
1. Take Advantage of Industry 4.0 Technology
Over the past few years, the digital transformation of industries has resulted in a wide range of products, platforms and devices that can help streamline facility operations and workflows.
Industrial Internet of Things (IIoT) sensors, for example, are Internet-connected sensors that collect a wide range of real-time data from site processes. This data can help food processors improve their bottom lines in a few different ways—like by providing better data on food safety or providing real-time quality control.
For example, IIoT sensors can be used to keep an eye on equipment performance and machine health. An air pressure sensor, installed at the right place in an HVAC duct, can provide valuable notice on blockages and damaged filters. When air pressure drops dramatically, it is typically a sign of some kind of blockage in the HVAC system. This advanced notice can help you fix the HVAC system quicker, potentially saving money and preventing dust or other contaminants from reducing facility air quality.
These IIoT systems also make it much easier to collect information about a facility. This information can help unlock insights about workflows, processes and site layouts, allowing changes that make a facility even more efficient.
For example, you may be able to gather hard data on how an individual product or product line influences machine timing—or how production of a particular item may slow down throughput or make workers less efficient. This information can help you adjust site processes, simplifying the workflow for products that put more strain on your facility, or cutting those products entirely in favor of simpler-to-produce items.
2. Use Efficient Equipment and Materials
Equipment choice can have a major impact on the overall efficiency of a facility. Even small choices—like the lightbulbs used or HVAC filters installed—can add up over time, reducing a facility’s energy bill and contributing to a more comfortable working environment.
Filter choice, for example, is especially important at plants that process a significant amount of wastewater or similar fluids. Good filtration is necessary to remove dangerous chemicals and contaminants from wastewater, but not all filter materials are made equal. Some perform much better than others—and this cost efficiency can have a major impact on a long enough timescale.
EPDM, for example, is an FDA-approved food-grade rubber and a common gasket material for equipment used in industrial kitchens and other food processing plants. It is also a common filter material. However, EPDM filters have a tendency to swell and suffer from performance issues over time. They may require more regular maintenance, which could negatively impact the productivity of a filtration system. PTFE membranes, in contrast, don’t have the same drawbacks.
Making simple adjustments—finding the right kind of filter or LED bulb— can help reduce maintenance costs and improve facility energy efficiency. Often, these changes can happen without major adjustments to the underlying equipment or workflows that keep the factory moving. These upgrades are a great place to start if you want to see how smaller tweaks and adjustments impact facility efficiency before moving on to more major changes.
3. Find Ways to Conserve Water
Similarly, food processing plants can save significantly by finding ways to reduce the amount of water they consume. Water is often seen as a free commodity in food processing plants—but consumption of water can become a significant expense at scale. Equipment, practices and machinery that help reduce water usage can be a way to cut down on costs while making the plant a little more eco-friendly.
Simple changes can make a notable difference without requiring new equipment. For example, some plants may be able to begin cleaning floors and equipment with sweeping or mopping rather than hoses. Mobile sweepers can cover large areas, like parking lots, that can’t be swept with manual labor alone. In one example, Bartter Industries, a New South Wales-based poultry product manufacturer, was able to reduce its water consumption by 10,000 liters a day (approximately 2,640 gallons) by switching from hosing to mopping and sweeping.
More extensive equipment and facility upgrades can yield more significant results.
Increasing the efficiency of water usage may also help future-proof a plant. Industrial water and sewage rates have risen significantly over the past two decades. Water insecurity and droughts may drive these prices higher in the near future.
Adopting similar technology and practices at your facility can provide a valuable competitive advantage now and help in the future when water reuse and stringent water conservation policies are more common.
4. Upgrade Your Maintenance Plan
Scheduled maintenance is one of the most commonly used maintenance approaches. Having such a plan in place can help reduce sudden, unexpected machine failure—helping avoid major downtime and reducing spending on replacement parts for facility machinery.
There are, however, major limitations to the scheduled maintenance model. Every time a machine is opened for maintenance, technicians may unintentionally expose sensitive electronics and internal components to dust, oil, fluids and other contaminants. Regular checks also won’t catch everything. If an issue arises and causes machine failure between scheduled checks, workers and supervisors will have no advanced notice of that machine’s failure, potentially leading to damage or injury.
New Industry 4.0 tech, however, means you can do even better than scheduled maintenance. Predictive maintenance is a maintenance approach that uses data collected from IIoT devices to improve maintenance checks and provide advanced notice on potential failure.
With this approach, IIoT sensors installed in and around machinery capture real-time data on how individual machines are behaving. If one begins to function unusually—exceeding safe temperature ranges, vibrating excessively or emitting strange sounds—the sensors can capture this behavior and alert a supervisor.
This maintenance method can help any facility cut down on maintenance checks and reduce the risk of sudden downtime due to damaged equipment.
Improve Food Processing Efficiency with These Strategies
Improvements to efficiency can be a major advantage for food processors. These strategies and investments are some of the best ways to improve a plant’s efficiency. Simple adjustments to materials, equipment, and workflows—or more serious investments in technology like predictive maintenance platforms—can make a significant difference in a facility’s productivity and resource usage.
The year 2020 brought with it continued court filings within the food safety litigation space, and it should come as no surprise the pandemic presented its own set of unique challenges. We’ve seen disruptions to the food and beverage supply chain, noteworthy changes with recalls, and continued developments in litigation specific to product labeling. These challenges have impacted everyone involved in the industry and laid the groundwork for what’s to come in 2021.
The most notable impact the food industry has faced as a result of the pandemic has been the massive disruption of the food supply chain. Grocers and other retail food providers have seen an immense spike in demand, whereas foodservice locations, such as restaurants, universities, and hotels, have seen the exact opposite. This disruption to the supply chain has required regulatory agencies to take notice and implement temporary policies to support these businesses and consumers alike. Employees across the food industry supply chain, including agriculture and food processing, have further been classified as essential, leading federal agencies to issue guidance to these employers to help them assess COVID-19 control plans and protect their employee’s health. Further, safety concerns and bumps in unemployment compensation have imposed additional strains on worker retention and attendance.
Another interesting facet of the pandemic’s impact on the industry has been its influence in the product recall space. Believe it or not, companies have strayed from pulling their products off the shelf even if it subjects them to potential liability. Why is this? Because as mentioned earlier, the demand for food in the retail space has increased so much, it has become a necessary choice to avoid food shortages across the United States. Don’t worry, if a product possesses a health or safety threat, companies are still recalling those to protect consumers and address safety concerns, but voluntary non-health or safety related recalls may have become a thing of the past. For example, rather than recall a box of cereal or other dry good for not meeting a fill-line requirement, providers may elect to risk a false-advertising lawsuit to meet the recent shift in retail food demand.
Since 2012, there have been more than 200 class action lawsuits filed related to the labeling on food products. This past year, we observed a continuation of this trend. Class action lawsuits were filed addressing the authenticity of “all-natural” products or claims based on the “origin” of a product, while we witnessed a sharp decline in slack-fill lawsuits. Consumers are becoming increasingly aware of the ingredients in food products and are continuing to demand transparency from companies to disclose how their products are made. There has been a particular increase in claims related to the definition of vanilla—is it pure? Is it natural? The same goes for citric acid, a product that can be made naturally or synthetically. There has been continued debate within the industry about citric acid in its use within other products where some citric acid is naturally occurring either from citrus fruit, tomatoes or other fruits with citric acid. If all-natural citric acid is added into tomato paste to help with the taste, can the tomato paste still be classified as being all-natural, even if the use of citric acid is displayed on the label?
To help combat the discrepancies around all-natural products, the USDA is currently working on developing an official definition of “all-natural,” which upon its completion is anticipated to have a major impact on the labeling industry and the number of false-advertising class actions. This definitional development comes at a crucial time especially as plant-based protein continues to rise in popularity.
The next wave of claims are being filed related to plant-based protein products. These claims include trademark and First Amendment issues. For example, when is a burger, a burger? Everyone assumes a burger means a hamburger, traditionally deriving from beef, and there has been an increase in debate around when the sale of plant-based products infringe on the rights of ranchers selling traditional beef products. Can food created in a petri-dish claim the same title as products created through traditional harvesting methods? What about other genetically modified products? These issues will likely spawn additional litigation in the coming year.
Looking ahead towards 2021, we can fully anticipate cases addressing food labeling issues to continue. Historically many of these claims were filed in Northern California with one federal court there earning the moniker of the “Food Court”. Recent years have seen increased filings in New York and Illinois, but the coming year may see a decrease in cases filed in New York as a result of recent court decisions relating to pre-emption and a recent opinion of a federal appellate court disallowing the settlement of class claims on an injunction-only basis. California may also see changes in their total cases as food producers curtail product sales in California to avoid the ambit of Prop 65.1
2021 will continue to bear witness to the effects of the COVID-19 pandemic. The supply chain will continue to adjust to the varying demands of the public as they navigate safety regulations, and companies will maintain an “only-recall-if-absolutely-necessary” mindset. Many of the adjustments that businesses, consumers and regulators have had to make in light of the pandemic may also lead to long-term or permanent shifts. In fact, the Consumer Brands Association has identified a few select areas ready for change, such as the maintenance of flexibility in food labeling to ease the transfer process of products between foodservice and food retail providers. We just might find 2021 to be one of the most industry-defining years in the food safety litigation space.
Reference
California Office of Environmental Health Hazard Assessment. (n.d.). Proposition 65. Accessed December 17, 2020. Retrieved from https://oehha.ca.gov/proposition-65
The COVID-19 pandemic propelled food processors to scrutinize various aspects of their existing employee hygiene and environmental safety programs in an effort to protect facility workers’ health. Implementation of measures such as social distancing, illness screening, workspace barriers, additional personal protective equipment (PPE) and enhanced cleaning measures have aided the industry in reducing employee sickness and unplanned shutdowns.1 Of these actions, effective cleaning protocols in non-production areas, under the scope of facility janitorial programs, have been brought to heightened attention as a critical preventative measure for surface contamination of SARS-CoV-2.1 Through incorporation of the fundamental principles of sanitation programs utilized for food production zones, processors can elevate the effectiveness of their janitorial cleaning programs in non-production areas.
Scope of Janitorial Program
Food processing facilities should evaluate, using a risk-based assessment, all non-production areas that employees occupy on a routine basis, for inclusion into the janitorial cleaning program. Examples of areas that are routinely subject to high employee traffic and regular congregation include, but are not limited to, locker rooms, restrooms, break rooms, cafeterias, hallways, conference rooms and offices.
Additionally, specific surfaces within each of the identified non-production areas for inclusion into the program should also be evaluated in the risk-based assessment. Surfaces within these identified areas that are frequently touched, and present a greater likelihood of contamination to employees, would be considered higher-risk, and thus, command more focus during routine janitorial cleaning activities. Examples of such surfaces may include the following: Door handles, tables, desks, chairs, toilet and faucet handles, vending machines, phones, computers and other electronic devices.
Janitorial Best-Practice Examples
Sanitation Standard Operating Procedures
Sanitation standard operating procedures (SSOPs), or written cleaning instructions, should be developed for all janitorial cleaning tasks of selected employee and welfare areas, in a similar manner as those for production area equipment and infrastructure. These documents should contain pertinent information to effectively perform the desired janitorial tasks, such as the following: The individual(s) responsible for the task, appropriate chemicals, personal protective equipment (PPE) and other safety measures, frequency of cleaning, steps of cleaning execution and verification measures.
Chemical Selection & Use
Selection of chemicals for cleaning of employee and welfare areas is critically important in ensuring biological agents are effectively removed from surfaces during janitorial activities. Much like in production areas, the facility janitorial cleaning program should utilize an appropriate detergent suitable for removing residual surface soils as a base of the program. Inadequate removal of soils, such as grease or food debris in break rooms, will inhibit the effective removal of adverse biological agents.2 Additionally, the program should include an application of sanitizer or disinfectant to the target surface effective in neutralizing SARS-CoV-2.3
Cleaning Process & Frequency
An effective cleaning process for routine janitorial tasks can be modeled after the established Seven Steps of Sanitation commonly utilized in food production zones.4 Typical steps in this process applicable for janitorial cleaning should include: area preparation and dry cleaning, wiping surfaces with fresh water, application and wiping with detergent, removal of detergent with fresh water wiping, inspection verification activities and application of sanitizer or disinfectant to target surfaces for required dwell time (subsequent wiping of chemical after dwell time may be required). The frequency of cleaning and additional sanitizing activities should be validated and take into consideration times of employees breaks, level of non-production area occupancy and extent of employee contact with higher-risk surfaces. Additionally, individuals who performed the required cleaning tasks should ensure appropriate PPE is worn, not only to protect from chemicals utilized, but from biological agents that may be present on surfaces.
Master Sanitation Schedule
A master sanitation schedule, or MSS, encompassing janitorial cleaning activities that occur on a non-daily basis should be maintained either separately, or included in an existing sanitation schedule.
Misting frequently touched surfaces with an additional disinfectant chemical approved to inactivate SARS-Cov-2. Image courtesy of OSI Group.
Examples of non-routine janitorial tasks may include:
Emptying and cleaning of personnel storage lockers
Cleaning of difficult-to-access surfaces for daily cleaning, such as ceilings, walls and around vending machines
Misting of frequently touched surfaces, or entire rooms, with an additional disinfectant chemical approved to inactivate SARS-Cov-2
The appropriate frequencies of these non-routine tasks should be validated through a risk-based assessment and continually verified to ensure effectiveness.
Employee Training
All employees who are required to perform routine and non-routine janitorial tasks should be fully trained and records maintained. This should not only include adequate training knowledge of required practices and documentation, but also chemical selection and handling specific to janitorial activities. Retention of knowledge should be verified and included in existing facility training programs. Routine auditing of the cleaning practices by facility personnel will ensure continued acceptable outcomes of the program.
Documentation
Completion of all janitorial cleaning activities should be documented and records maintained following similar practices for sanitation in production areas. As a best practice, documentation, such as checklists, should be made visible to employees who utilize the welfare areas as a means to convey facility hygiene practices and ease potential health concerns.
Validation & Verification of Cleaning Effectiveness
To ensure an established janitorial cleaning program for non-production areas is effective in achieving appropriate hygiene outcomes, the facility must validate and routinely verify the process. Validating the effectiveness of janitorial programs can be undertaken in much the same manner as performed for the traditional sanitation process in food production zones. A combination of visual inspection, environmental sampling and other methods should be utilized both during the validation and subsequent routine verification process. Specific to the COVID-19 pandemic, several contract laboratories offer surface environmental testing for SARS-CoV-2 (via RT-qPCR) that should be incorporated into janitorial validation and verification protocols.2,5 Routine absence of the virus will assist in demonstrating effectiveness of the facility janitorial cleaning program.
Conclusion
With the increased scrutiny of employee welfare during the COVID-19 pandemic, maintaining effective facility hygiene remains a critical goal of food processing facilities. Through incorporation of current sanitation best practices utilized in food production zones, facilities can elevate the outcomes of their janitorial cleaning programs, ensuring effective hygiene.
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