Tag Archives: contamination

Olga Pawluczyk, P&P Optica
FST Soapbox

Assessing Detection Systems to Make Food Safer

By Olga Pawluczyk
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Olga Pawluczyk, P&P Optica

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).

True Positives and False Positives
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.

Detection curves
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.

Deane Falcone, CropOne
FST Soapbox

E. Coli on the Rise: Lettuce Explain

By Deane Falcone, Ph.D.
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Deane Falcone, CropOne

The CDC estimates that 48 million people in the United States become sick with a foodborne illness each year. Some of the most common of these illnesses include norovirus, Salmonella, and E. coli. Each can result in a range of symptoms, from mild discomfort to serious, life-threatening illnesses. Although the coronavirus pandemic has worked to create a sense of heightened public health awareness, one of these common, yet preventable, foodborne illnesses—E. coli—is still on the rise.

What Is E. coli and How Common Are Infections?

According to the CDC, Escherichia coli (E. coli) are a large and diverse group of bacteria found in the environment, foods, and intestines of people and animals. Most strains of the bacteria are harmless, but certain ones can make you sick, causing diarrhea, urinary tract infections, respiratory illness and pneumonia, or other illnesses.

When it comes to understanding the scale of the problem, upwards of 70,000 Americans are estimated to fall ill because of E. coli each year, thousands of whom require hospitalization. E. coli outbreaks have been occurring with regularity, and the number of cases are increasing instead of slowing down, in frequency. In November 2020 alone, there were three ongoing E.coli outbreaks in the United States, accounting for 56 infections, 23 hospitalizations, and one death. At least one of these outbreaks stemmed from a common target for the bacteria: Romaine lettuce. When it comes to E. coli-contaminated foods, fresh leafy greens such as romaine or spinach are the most common vehicles for E. coli that can pose serious risks to human health.

Leafy Greens: An Ideal Target

Leafy greens are an easy target for E. coli for a number of reasons, the first being their popularity. The public recognition of the health value of consuming greater amounts of fresh leafy greens has correspondingly increased the production area of such produce to meet consumer demand. Crop production over wider areas makes tracking of contamination in the field more difficult and the greater consumption increases chances of eating contaminated leafy greens. This type of produce also grows low to the ground, increasing chances of exposing the edible, leafy portions of the lettuce to contaminated water. Finally, other vegetables are often cooked prior to consumption, killing the bacteria, whereas romaine and other leafy greens are often consumed raw.

Once this type of produce is exposed to contaminants, several characteristics of leaf surfaces make removal of bacteria such as E. coli difficult. Studies have shown that, at the microscopic level, the “roughness” or shape of the leaf surface can influence the degree to which bacteria adheres to leaves. Bacteria have specific protein fibers on their surface that are involved in the attachment of the bacteria to the leaf surface and this has been shown to be dependent on the surface roughness of the leaf. Other factors include the “pores” on leaf surfaces—stomata—through which plants take up carbon dioxide and release oxygen and water vapor. Pathogenic E. coli has been observed to enter such stomatal pores and therefore is often very resistant to removal by washing. Moreover, the density of stomata within leaves can vary between different varieties of lettuce or spinach and so affects the degree of E. coli attachment. Additional factors such as leaf age, damage and amount of contaminating bacteria also affect how effectively bacteria adhere to the leaves, making washing difficult.

Are E. Coli Outbreaks Avoidable?

Unfortunately, E. coli outbreaks will likely remain prevalent because of the challenge of interrogating all irrigation water for large and widespread production fields. Once microbial contaminants are present on fresh leafy produce, their complete removal by washing cannot be guaranteed, and it is very difficult to monitor every plot of crops continuously. However, there is a solution to this problem: Controlled environment agriculture (CEA). CEA is an broad term used for many varieties of indoor plant cultivation and can be defined as a method of cultivating plants in an enclosed environment, using technology to ensure optimal growing conditions.

Because outbreaks caused by E. coli-contaminated produce are most often due to produce coming into contact with contaminated irrigation water, indoor growing provides an ideal solution with zero reliance on irrigation water. It also offers a sealed environment with virtually no risk of contamination from animal excrement or other pathogen sources. Indoor farming also makes additional features possible that enhance safety including the use of purified water and handling done only by staff wearing protective clothing (for the plants) including lab coats, hair nets, and gloves. No ungloved hand ever comes into contact with the produce either during growth or in packaging. These standards are nearly impossible to achieve in a traditional farm setting.

Using hydroponic technology enables farming in a clean and contaminant-free, indoor environment. Applying best hygienic practices with this growing model provides safe and clean growth in a sealed, controlled environment, with virtually no risk of illness-causing pathogens.

At this point, not everyone can access food coming from a clean, indoor facility. At the consumer level the best way to avoid E. coli infection remains simply being diligent when it comes to washing. Even if produce is labeled “triple-washed,” if it was grown outdoors, the consumer should always wash it again. Or better yet, look for indoor, hydroponically-grown produce to further mitigate the risk.

Although these outbreaks will continue, as they do, we suspect more consumers will embrace indoor-grown produce and this emerging form of agriculture as a safer alternative. As consumers increasingly understand the advantages of indoor growing, such as enhanced quality and longer shelf life, the popularity of this growth method will increase. Eventually, a greater quantity of the most commonly-infected produce will come from these controlled environments, gradually producing an overall safer and healthier mass product.

Nicole Lang, igus
Retail Food Safety Forum

Robots Serve Up Safety in Restaurants

By Nicole Lang
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Nicole Lang, igus

Perhaps the top takeaway from the worldwide COVID-19 pandemic is that people the world over realize how easily viruses can spread. Even with social distancing, masks and zealous, frequent handwashing, everyone has learned contagions can cycle through the atmosphere and put a person at risk of serious, and sometimes deadly, health complications. In reality, there are no safe spaces when proper protocols are not followed.

The primary culprit in transmission of norovirus, according to the CDC, is contaminated food. “The virus can easily contaminate food because it is very tiny and spreads easily,” the CDC says in a fact sheet for food workers posted on its website. “It only takes a very small amount of virus to make someone sick.”

The CDC numbers are alarming. The agency reports about 20 million people get sick from norovirus each year, most from close contact with infected people or by eating contaminated food. Norovirus is the leading cause of disease outbreaks from contaminated food in the United States, and infected food workers cause about 70% of reported norovirus outbreaks from contaminated food.

The solution to reducing the transmission of unhealthy particles could be starting to take shape through automation. While robots have been used for the past few years in food manufacturing and processing, new solutions take food handling to a new level. Robots are no longer in the back of the house in the food industry, isolated in packaging and manufacturing plants. They are now front and center. The next time you see a salad prepared for you at a favorite haunt, you may be watching a robot.

“The global pandemic has altered the way that we eat,” said Justin Rooney, of Dexai Robotics, a company that developed a food service robotic device. Reducing human contact with food via hands-free ordering and autonomous food serving capabilities has the potential to reduce the spread of pathogens and viruses, and could help keep food fresh for a longer period of time.

Painful Pandemic

Increased use of automation in the foodservice industry might be one of the salvations of the COVID-19 pandemic. In an industry searching for good news, that might be the silver lining in an otherwise gloomful crisis.

Job losses in the restaurant industry have been brutal. By the end of November, nearly 110,000 restaurants in the United States had closed. A report by the National Restaurant Association said restaurants lost three times more jobs than any other industry since the beginning of the pandemic. In December, reports said nearly 17% of U.S. restaurants had closed. Some restaurants clung to life by offering outdoor dining, but as winter set in, that option evaporated. Some governors even demanded restaurant closures as the pandemic escalated in late fall.

Restaurants have faced a chronic labor shortage for years. Despite layoffs during the pandemic, many former foodservice employees are electing to leave the industry.

Teenagers, for instance, and some older workers are staying away for health and safety reasons. Some former workers are also finding out that they can make more money on unemployment benefits than by returning to work. Restaurant chains have hiked wages, but filling positions still remains a challenge.

Automated Solutions

Restaurants began dancing with the idea of robots nearly 50 years ago. The trend started slowly, with customers ordering food directly through kiosks. As of 2011, McDonald’s installed nearly 7,000 touchscreen kiosks to handle cashiering responsibilities at restaurants throughout Europe.

As technology has advanced, so has the presence of robots in restaurants. In 2019 Seattle-based Picnic unveiled a robot that can prepare 300 pizzas in an hour. In January, Nala Robotics announced it would open the world’s first “intelligent” restaurant. The robotic kitchen can create dishes from any cuisine in the world. The kitchen, which is expected to open in April in Naperville, Illinois, will have the capability to create an endless variety of cuisine without potential contamination from human contact.

Dexai designed a new robotic unit that allows for hands-free ordering that can be placed through any device with an Internet connection. The robot also includes a new subsystem for utensils, which are stored in a food bin to keep them temperature controlled. This ensures that robot is compliant with ServSafe regulations. The company is working on improving robot system’s reliability, robustness, safety and user friendliness. The robot has two areas to hold tools, a kitchen display system, bowl passing arm, an enclosure for electronics and two refrigeration units. It has the unique ability to swap utensils to comply with food service standards and prevent contamination as a result of allergens, for example.

Why Automation

Many industries have been impacted by advancements in automation, and the foodservice industry is no different. While initially expensive, the benefits over time can provide to be worth the investment.

One of the most significant advantages, particularly important in the post-COVID era, is better quality control. Automated units can detect issues much earlier in the supply chain, and address those issues.

Automation can also help improve worker safety by executing some of the more repetitive and dangerous tasks. Robots can also boost efficiency (i.e., a robot used for making pizza that can press out dough five times faster than humans and place them into ovens) and eliminate the risk of injury. Robots are also being used to make coffee, manage orders and billing, and prepare the food. Robots can also collect data that will help foodservice owners regarding output, quantity, speed and other factors.

“Alfred’s actions are powered by artificial intelligence,” according to Rooney. “Each time Alfred performs an action, the associated data gets fed into a machine learning model. Consequently, each individual Alfred learns from the accumulated success and failures of every other Alfred that has existed.” Dexai plans to teach the robot to operate other commonly found pieces of kitchen equipment such as grills, fryers, espresso machines, ice cream cabinets and smoothie makers.

Unrelenting Trend

Automated solutions might have come along too late to save many restaurants, but the path forward is clear. While they are not yet everywhere, robots are now in play at significant number of restaurants, and there is no turning back. Any way you slice it, robots in restaurants, clearly, is an idea whose time has come.

El Abuelito Cheese

Recall Alert: Listeria Outbreak Linked to Hispanic-Style Fresh and Soft Cheeses

By Food Safety Tech Staff
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El Abuelito Cheese

–UPDATE March 9, 2021 — Today the FDA confirmed that the recalled cheeses were also distributed to Rhode Island. “States with confirmed distribution now include: AL, CT, FL, GA, IA, IL, IN, KS, KY, MA, MD, MI, MN, MO, MS, NC, NJ, NY, NE, OH, PA, RI, SC, TN, VA, and WI.”

–UPDATE February 24, 2021 — FDA has expanded its warning related to El Abuelito Cheese to include all cheese branded by the company “until more information is known”.

—END UPDATE—

A multistate outbreak of Listeria monocytogenes has been linked to Hispanic-style fresh and soft cheeses produced by El Abuelito Cheese, Inc. As a result, the company has recalled all Questo Fresco products with sell by dates through March 28 (032821).

Join Food Safety Tech on April 15 for the complimentary Food Safety Hazards Series: Listeria Detection, Mitigation, Control & Regulation“As the FDA stated, about this outbreak investigation, the Connecticut Department of Public Health collected product samples of El Abuelito-brand Hispanic-style fresh and soft cheeses from a store where a sick person bought cheeses. Sample analysis showed the presence of Listeria monocytogenes in samples of El Abuelito Queso Fresco sold in 10 oz packages, marked as Lot A027 with an expiration date of 02/26/2021,” the company stated in an announcement posted on FDA’s website. “Samples are currently undergoing Whole Genome Sequencing (WGS) analysis to determine if the Listeria monocytogenes found in these samples is a match to the outbreak strain. At this time, there is not enough evidence to determine if this outbreak is linked to El Abuelito Queso Fresco.”.

The recalled products were distributed to Connecticut, Maryland, New Jersey, North Carolina, New York, Pennsylvania and Virginia. Thus far seven people, all of whom have been hospitalized, have fallen ill.

FDA recommends that consumers, restaurants and retailers do not consume, sell or serve any of the recalled cheeses. The agency also states that anyone who purchased of received the recalled products use “extra vigilance in cleaning and sanitizing any surfaces and containers that may have come in contact with these products to reduce the risk of cross-contamination.”

Mitzi Baum, Stop Foodborne Illness
Food Safety Culture Club

Our Petition to USDA: The Time for Change Is Now

By Mitzi Baum
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Mitzi Baum, Stop Foodborne Illness

On January 25, 2021 Stop Foodborne Illness (STOP), in collaboration with Center for Science in the Public Interest, Consumer Reports, Consumer Federation of America and five STOP constituent advocates filed a petition with USDA Food Safety Inspection Service (FSIS) to reform and modernize poultry inspections. The goal of these reforms is to reduce the incidence of Salmonella and Campylobacter contamination in raw poultry thus drastically decreasing foodborne illnesses due to these pathogens.

According to the CDC, in 2019, these two pathogens combined were responsible for more than 70% of foodborne illnesses in the United States. As Mike Taylor, former FDA Deputy Commissioner for Foods and Veterinary Medicine, shares in his
Op-Ed, the time for change is now as the current regulatory framework is inadequate and has not delivered the desired results of reducing Salmonella and Campylobacter outbreaks.

Today, the USDA’s mark of inspection is stamped on poultry, although birds may exceed the performance standards; there are no clear consequences for establishments that do not meet the current guidelines. Without science-based standards or penalties for non-compliance, the burden of this problem falls upon consumers.

At STOP, we share the voices of consumers whose lives have been altered due to preventable problems such as this. Our constituent advocates share their journeys through severe foodborne illness to share the WHY of food safety. Real people, real lives are impacted when we do not demand action. STOP board member, Amanda Craten, shares her son Noah’s story:

“My toddler suddenly came down with a fever and diarrhea, but it wasn’t until weeks later that I learned that his symptoms, which nearly killed him, were caused by a multi-drug resistant strain of Salmonella.

After being admitted to the hospital, his doctors found abscesses in the front of his brain caused by infection and they were creating pressure on his brain. He underwent surgery and weeks of antibiotic treatments.

My 18-month son was seriously injured and permanently disabled as a result of Salmonella-contaminated chicken.” – Amanda Craten.

Unfortunately, Noah’s story is not rare, which is why Amanda supports this petition for change and has provided a powerful video about Noah’s foodborne disease journey and his life now.

Because there are too many stories like Noah’s, STOP and its partner consumer advocacy organizations want to work with FSIS and industry to:

  1. Develop real benchmarks that focus on reduction of known, harmful pathogens in poultry
  2. Modernize standards to reflect current science
  3. Implement on-farm control measures
  4. Re-envision the standards to focus on the risk to public health

As a new administration begins, capitalizing on this opportunity to modernize poultry inspection that can benefit consumers and the food industry makes sense. STOP and its partners are hopeful that leadership at USDA/FSIS will take this opportunity to create consequential and relevant change. Ultimately, this transformation will reduce the incidence of foodborne illness due to contamination of poultry and increase consumer confidence in the USDA’s mark of inspection. Please comment on this petition.

Have you been impacted by foodborne illness? Tell STOP Foodborne Illness about it.

U.S. House of Representatives Seap

House Subcommittee Releases Report on Dangerous Levels of Toxic Heavy Metals in Baby Food

By Food Safety Tech Staff
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U.S. House of Representatives Seap

Last week a report released by Congress cited dangerous levels of toxic heavy metals in several brands of baby food. Back in November 2019, the Subcommittee on Economic and Consumer Policy asked for internal documents and test results from baby food manufacturers Nurture, Inc. (Happy Family Organics), Beech-Nut Nutrition Company, Hain Celestial Group, Inc., Gerber, Campbell Soup Company, Walmart, Inc., and Sprout Foods. According to the staff report, Nurture, Beech-Nut, Hain and Gerber responded to the requests, while Walmart, Campbell and Sprout Organic Foods did not.

The findings indicate that significant levels of arsenic, lead, cadmium and mercury were found in the baby foods of the four manufacturers who responded to the Subcommittee’s requests (Nurture, Beech-Nut, Hain and Gerber). It also stated the alarming point that, “Internal company standards permit dangerously high levels of toxic heavy metals, and documents revealed that the manufacturers have often sold foods that exceeded those levels.”

The Subcommittee voiced “grave concerns” that the baby food made by Walmart, Sprout Organic Foods and Campbell was “obscuring the presence of even higher levels of toxic heavy metals in their baby food products than their competitors’ products” due to their lack of cooperation.

In addition, the report states that the Trump administration “ignored a secret industry presentation to federal regulators revealing increased risks of toxic heavy metals in baby foods” in August 2019.

“To this day, baby foods containing toxic heavy metals bear no label or warning to parents. Manufacturers are free to test only ingredients, or, for the vast majority of baby foods, to conduct no testing at all,” the report stated (infant rice cereal is the only baby food held to a stringent standard regarding the presence of inorganic arsenic).

As a result of the findings, the Subcommittee has made several recommendations:

  • FDA should require baby food manufacturers to test their finished products for toxic heavy metals.
  • FDA should require manufacturers to report toxic heavy metals on food labels.
  • Manufacturers should find substitutes for ingredients that are high in toxic heavy metals or phase out the ingredients that are high in toxic heavy metals.
  • FDA should set maximum levels of toxic heavy metals allowed in baby foods.
  • Parents should avoid baby foods that contain ingredients that test high in toxic heavy metals.

The 59-page report, “Baby Foods Are Tainted with Dangerous Levels of Arsenic, Lead, Cadmium, and Mercury”, is available on the U.S. House of Representatives’ website.

FDA

FDA to Test Yuma-Grown Romaine Lettuce for E. Coli and Salmonella

By Food Safety Tech Staff
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FDA

Today the FDA announced a new plan to collect samples of romaine lettuce as part of its ongoing surveillance after the spring 2018 multistate outbreak of E. coli O157:H7. The samples, which will be tested for Shiga toxin-producing Escherichia coli (STEC) and Salmonella, will be collected from commercial coolers in Yuma County, Arizona during the current harvest season.

FDA plans to collect and test about 500 samples (each of which will consist of 10 subsamples), beginning in February and continuing through the end of the harvest season. In order to reduce the time between sample collection and reporting results, an independent lab close to the collection sites in Arizona will be testing the samples. FDA expects to receive test results within 24 hours.

“Helping to ensure the safety of leafy greens continues to be a priority of the FDA. This assignment adds to other work underway in collaboration with stakeholders in the Yuma agricultural region to implement actions identified in the Leafy Greens Action Plan, including a multi-year study to assess the environmental factors that impact the presence of foodborne pathogens in this region. Consistent with the action plan, the agency will engage with industry on conducting root cause analyses for any positive samples found during this assignment. Root cause analyses are important in that they seek to identify potential sources and routes of contamination, inform what preventive measures are needed, and help prevent outbreaks of foodborne illness,” FDA stated in a release.

COVID-19 precautions will be taken during the sampling plan. Agency investigators will preannounce visits and wear PPE while conducting the work.

FDA

FDA Issues Update on E. Coli Outbreak Involving Leafy Greens

By Food Safety Tech Staff
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FDA

FDA has completed its investigation of the multistate outbreak of E. coli 0151:H7 that occurred last fall and was linked to leafy greens. The FDA and CDC found the outbreak was caused by an E. coli strain that was genetically related to the strain found in the fall 2019 outbreak involving romaine lettuce (Salinas, California). Despite conducting environmental sampling at dozens of ranches in the area, the FDA was unable to identify a single site as the source of the outbreak. However, the analysis did confirm “a positive match to the outbreak strain in a sample of cattle feces,” which was located uphill from where the leafy greens identified in the agency’s traceback investigation were grown, according to an FDA release.

Although the FDA’s investigation has ended, the agency will be reviewing the findings and release a report in the “near future” with recommendations. “In the meantime, as recommended in our Leafy Greens Action Plan, the FDA continues to recommend growers assess and mitigate risk associated with adjacent and nearby land use practices, particularly as it relates to the presence of livestock, which are a persistent reservoir of E. coli O157:H7 and other STEC,” FDA stated in the update.

Karen Everstine, Decernis
Food Fraud Quick Bites

Food Authenticity: 2020 in Review

By Karen Everstine, Ph.D.
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Karen Everstine, Decernis

It is fair to say that 2020 was a challenging year with wide-ranging effects, including significant effects on our ongoing efforts to ensure food integrity and prevent fraud in the food system. COVID-19 caused major supply chain disruptions for foods and many other consumer products. It also highlighted challenges in effective tracking and standardization of food fraud-related data.

Let’s take a look at some of the notable food fraud occurrences in 2020:

  • Organic Products. The Spanish Guardia Civil investigated an organized crime group that sold pistachios with pesticide residues that were fraudulently labeled as organic, reportedly yielding €6 million in profit. USDA reported fraudulent organic certificates for products including winter squash, leafy greens, collagen peptides powder, blackberries, and avocados. Counterfeit wines with fraudulent DOG, PGI, and organic labels were discovered in Italy.
  • Herbs and Spices. Quite a few reports came out of India and Pakistan about adulteration and fraud in the local spice market. One of the most egregious involved the use of animal dung along with various other substances in the production of fraudulent chili powder, coriander powder, turmeric powder, and garam masala spice mix. Greece issued a notification for a turmeric recall following the detection of lead, chromium, and mercury in a sample of the product. Belgium recalled chili pepper for containing an “unauthorized coloring agent.” Reports of research conducted at Queen’s University Belfast also indicated that 25% of sage samples purchased from e-commerce or independent channels in the U.K. were adulterated with other leafy material.
  • Dairy Products. India and Pakistan have also reported quite a few incidents of fraud in local markets involving dairy products. These have included reports of counterfeit ghee and fraudulent ghee manufactured with animal fats as well as milk adulterated with a variety of fraudulent substances. The Czech Republic issued a report about Edam cheese that contained vegetable fat instead of milk fat.
  • Honey. Greece issued multiple alerts for honey containing sugar syrups and, in one case, caramel colors. Turkey reported a surveillance test that identified foreign sugars in honeycomb.
  • Meat and Fish. This European report concluded that the vulnerability to fraud in animal production networks was particularly high during to the COVID-19 pandemic due to the “most widely spread effects in terms of production, logistics, and demand.” Thousands of pounds of seafood were destroyed in Cambodia because they contained a gelatin-like substance. Fraudulent USDA marks of inspection were discovered on chicken imported to the United States from China. Soy protein far exceeding levels that could be expected from cross contamination were identified in sausage in the Czech Republic. In Colombia, a supplier of food for school children was accused of selling donkey and horse meat as beef. Decades of fraud involving halal beef was recently reported in in Malaysia.
  • Alcoholic Beverages. To date, our system has captured more than 30 separate incidents of fraud involving wine or other alcoholic beverages in 2020. Many of these involved illegally produced products, some of which contained toxic substances such as methanol. There were also multiple reports of counterfeit wines and whisky. Wines were also adulterated with sugar, flavors, colors and water.

We have currently captured about 70% of the number of incidents for 2020 as compared to 2019, although there are always lags in reporting and data capture, so we expect that number to rise over the coming weeks. These numbers do not appear to bear out predictions about the higher risk of food fraud cited by many groups resulting from the effects of COVID-19. This is likely due in part to reduced surveillance and reporting due to the effects of COVID lockdowns on regulatory and auditing programs. However, as noted in a recent article, we should take seriously food fraud reports that occur against this “backdrop of reduced regulatory oversight during the COVID-19 pandemic.” If public reports are just the tip of the iceburg, 2020 numbers that are close to those reported in 2019 may indeed indicate that the iceburg is actually larger.

Unfortunately, tracking food fraud reports and inferring trends is a difficult task. There is currently no globally standardized system for collection and reporting information on food fraud occurrences, or even standardized definitions for food fraud and the ways in which it happens. Media reports of fraud are challenging to verify and there can be many media reports related to one individual incident, which complicates tracking (especially by automated systems). Reports from official sources are not without their own challenges. Government agencies have varying priorities for their surveillance and testing programs, and these priorities have a direct effect on the data that is reported. Therefore, increases in reports for a particular commodity do not necessarily indicate a trend, they may just reflect an ongoing regulatory priority a particular country. Official sources are also not standardized with respect to how they report food safety or fraud incidents. Two RASFF notifications in 2008 following the discovery of melamine adulteration in milk illustrate this point (see Figure 1). In the first notification for a “milk drink” product, the hazard category was listed as “adulteration/fraud.” However, in the second notification for “chocolate and strawberry flavor body pen sets,” the hazard category was listed as “industrial contaminants,” even though the analytical result was higher.1

RASFF

RASFF, melamine detection
Figure 1. RASFF notifications for the detection of melamine in two products.1

What does all of this mean for ensuring food authenticity into 2021? We need to continue efforts to align terminology, track food fraud risk data, and ensure transparency and evaluation of the data that is reported. Alignment and standardization of food fraud reporting would go a long way to improving our understanding of how much food fraud occurs and where. Renewed efforts by global authorities to strengthen food authenticity protections are important. Finally, consumers and industry must continue to demand and ensure authenticity in our food supply. While most food fraud may not have immediate health consequences for consumers, reduced controls can lead to systemic problems and have devastating effects.

Reference

  1. Everstine, K., Popping, B., and Gendel, S.M. (2021). Food fraud mitigation: strategic approaches and tools. In R.S. Hellberg, K. Everstine, & S. Sklare (Eds.) Food Fraud – A Global Threat With Public Health and Economic Consequences (pp. 23-44). Elsevier. doi: 10.1016/B978-0-12-817242-1.00015-4
Emily Newton, Revolutionized Magazine
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How Can Preventive Maintenance Save Food Processors Money?

By Emily Newton
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Emily Newton, Revolutionized Magazine

The right preventive maintenance approach can improve food safety while saving money. With the right plan, food processing professionals can prevent serious machine failure, decrease maintenance costs and get a better sense of which machines may be more trouble than they’re worth.

However, not every preventive maintenance plan is guaranteed to help processors cut costs. Investing in the right strategy and tools will be necessary for a business that wants to save money with effective maintenance.

How an Effective Preventive Maintenance Approach Can Save Money

To start, the food safety benefits of a preventive maintenance program can help food processors avoid significant troubles down the line. Contamination and recalls will cost time and money.

They can also damage the professional relationships that businesses have with buyers. Recalls are extraordinarily expensive for food and beverage companies, costing an average of $10 million per recall, according to one joint study from the Food Marketing Institute and the Consumer Brands Association (formerly the Grocery Manufacturers Association).

Preventive maintenance can also extend machines’ life spans, giving a company more time before they’ll need to completely replace or rebuild a piece of equipment. Over time, this will help a business prevent machine failure or injuries resulting from improper machine behavior or function. In some cases, it can also mean cheaper repairs and less downtime.

Improving Records With the Right Plan

An effective preventive maintenance plan also generates a significant and detailed archive of maintenance records.

If a plan is implemented correctly, technicians will create a record every time they inspect, repair or otherwise maintain a particular machine. These records will be an invaluable asset in the event of an in-house or third-party audit, as they can help prove that machines have been properly lubricated, calibrated and otherwise maintained.

If a food processing business needs to resell a particular piece of equipment, they’ll also have a full service record that can help them establish the machine’s value.

Over time, the records will also give a highly accurate sense of how expensive the machines really are across an entire business. If the staff records repairs performed, tools used and resources and time spent, professionals can quickly tabulate each machine’s cost concerning man-hours or resources needed. These logs can help single out machinery that may be more trouble than it’s worth and plan future buying decisions.

With a digital system, like a computerized maintenance management system (CMMS), managers can automate most of the administrative work that goes into a preventive maintenance plan.

Modern CMMS tech also provides a few additional benefits beyond streamlining recordkeeping. For example, if a business is up against a major maintenance backlog or trying to balance limited resources against necessary repairs and checkups, a CMMS can help optimize their use of resources. As a result, they can make the most of the time, money and tools they have.

Common Preventive Maintenance Pitfalls

Typically, an effective preventive maintenance plan starts with a catalog of facility equipment. This catalog includes basic information on every piece of equipment in the facility — such as location, name, serial number and vendor, as well as information on how frequently the machine should be inspected or maintained.

Keeping spotty or incomplete records can make a preventative maintenance plan both less effective and more expensive. For example, a partial service record may give an improper idea of how well-maintained certain equipment is. Missing machine information may also confuse service technicians, making it harder for them to properly inspect or maintain a machine.

Too-frequent maintenance checks can also become a problem over time. Every time a maintenance technician opens up a machine, they can potentially expose sensitive electronics to dust, humidity or facility contaminants, or risk damage to machine components.

A maintenance check also means some downtime, as it’s usually not safe or practical to inspect a running machine.

Using the wrong maintenance methods can also sometimes decrease a machine’s life span. For example, certain cleaning agents can damage door gaskets over time. This can eventually cause equipment like a freeze dryer to be unable to create a proper seal.

The equipment manufacturer and technicians can usually help a company know what kind of maintenance will work best and how often they should inspect or tune up a machine.

Going Beyond Preventive Maintenance

Preventive maintenance is the standard approach in most industries, but it’s no longer the cutting-edge of maintenance practices. New developments in the tech world, like new Industrial Internet of Things (IIoT) sensors and real-time artificial intelligence (AI) analysis, have enabled a new form of maintenance called predictive maintenance.

With predictive maintenance, a food processing plant can outfit their machines with an array of special sensors. These sensors track information like vibration, lubrication levels, temperature and even noise. A digital maintenance system will record that information, establishing baselines and data about normal operating levels.

Once the baseline is established, the predictive technology can use fluctuations or extreme variables to predict improper operation or machine failure. If some machine variable exceeds safe operating thresholds, the predictive maintenance system can alert facility supervisors — or, depending on what kind of control the system has, shut down a machine altogether.

The predictive approach can catch issues that may arise in-between checks in a preventive schedule. This can help reduce the frequency of maintenance checks — possibly preventing further machine damage and saving the business money on technician labor.

The data a predictive maintenance system collects can also help optimize equipment for maximum efficiency.

Implementing a predictive maintenance plan will require a bit of a tech investment, however.

Food Processors Can Save Money With the Right Maintenance Approach

Preventive maintenance isn’t just essential for food safety — done well, it can also be a major cost-saving measure for food processors.

Good recordkeeping, a regular maintenance schedule and new technology can all help a business decrease maintenance and equipment costs. For processors that want to invest more in their maintenance plans, a predictive approach can provide even better results.