On Monday Sabra Dipping Company, LLC and the FDA announced a voluntary recall of the company’s Classic Hummus due to potential Salmonella contamination. The discovery was made when the FDA conducted a routine screen of one tub. Sabra has recalled about 2100 cases of its 10 oz Classic Hummus (1 SKU), which was produced on February 10 and has a “Best Before” date of April 26. The product was distributed to 16 states, but according to the company announcement posted on FDA’s website, since the hummus is more than halfway through its shelf life, “it’s unlikely you’ll find this product on the shelf.”
Thus far no illnesses have been reported in connection with this recall.
Find records of fraud such as those discussed in this column and more in the Food Fraud Database. Image credit: Susanne Kuehne
Tax officials in the Irish city of Cork seized almost 25,000 liters of counterfeit wine, the equivalent of 33,000 bottles. The wine is valued at more than $360,000, which also results in a significant loss in alcohol tax revenue. Investigators are looking into whether this is the largest seizure of counterfeit wine in the past five years. The container passed through the terminal in Cork from the Netherlands and was discovered during an official operation that targets illicit alcohol sales.
Over the last several months, we have been exploring the details of several critical factors that are impacting the food and beverage manufacturing sector in terms of sustainability, including:
Two additional factors that food manufacturers now have to manage regarding sustainable practices are transportation and regulatory restrictions. Each can be discussed as a separate topic, but they are intertwined, as there have always been regulations regarding food transportation, and obviously food has always needed to be transported. Now that sustainability is an important topic in all areas of food manufacturing, it makes sense to discuss these two subjects both individually and collectively.
Transportation and Regulatory Joint Concerns
Ensuring that all areas of food transportation incorporate sustainable practices is a critical component of achieving sustainability in food manufacturing. To this point, however, these types of practices have not fully been implemented or even designed. This area is still evolving. From a straight transportation point of view, governments globally have been imposing restrictions for decades. These restrictions vary from country to country, province to province, region to region, and so on, and this causes confusion when inter- or intra-region transportation of food is required. There are also several regulatory differences based on mode of transportation. Land, air and sea transportation can and should have different regulations.
Another ingredient that should be added to this product mix of sustainability, transportation and regulations is food safety and the integrity of the food materials being transported whether it is ingredients, work-in-process foods or finished products. Various modes of transportation can affect the chemical composition, physical appearance, nutritional value and quality and safety of food. It could be straightforward to start implementing restrictions, regulations and new methods of how to package, manufacture and transport food to satisfy the growing trend of sustainable food behaviors. However, what cannot get lost in this is the issue of food safety and integrity.
Sustainability More than Recycling and Litter
When discussing regulations around transportation and food, many people immediately think of littering, of some uncaring individual throwing a soda pop can out of a car window. Littering regulations, laws, fines, penalties and public service campaigns have been in place globally for more than 50 years. The next time you go outside, take a look around at how effective those have been. Sustainability goes far beyond the issue of litter. One area that works hand in hand with transportation of food is climate change. Governments have been evaluating the current practices and have begun implementing changes designed to positively affect climate change. Some examples include:
23 American states and Washington, D.C. limit idling by some or all vehicles.
The California Air Resources Board adopted the TRU Airborne Toxic Control Measure in 2004 to reduce diesel particulate matter pollutant emissions.
In 2020, the International Maritime Organization will implement a new regulation for a 0.50% global sulfur cap for marine fuels.
The food and beverage industry is actively embracing other changes that affect sustainability. Electric trucks fit well with the F&B distribution hub model, with clean, quiet, short-run deliveries. Fuel usage during transportation is being considered from every angle. Local and regional food systems, where farmers and processors sell and distribute their food to consumers within a given area, use less fossil fuel for transportation because the distance from farm to consumer is shorter. This shorter distance also can help to reduce CO2 emissions.
Change Starts with Money
During many conversations I have had with my wife about a variety of subjects, especially those that can be considered controversial, one of us always raises the same question which is: “When in doubt, what is it all about?” And most of the time, the answer is money. Regulations around sustainability in food manufacturing are being driven by demands made by the consumer. The purchasers of the finished food product dictate almost every aspect of that product to the manufacturer because, let’s face it, if the consumer doesn’t like it, they won’t buy it. And if they don’t buy it, what will eventually happen to the manufacturer? That’s right—it goes out of business.
Now there is a good definition of sustainability or at least of what is not sustainable. From the transportation side of things, manufacturers in almost all cases pay the freight of shipping their food products to the members of the value chain. This obviously affects the costs of goods sold, which is a direct component of the bottom line and the profitability of the business. And with margins typically low in food and beverage manufacturing, transportation costs are always on the minds of the executives. So as the drive for sustainable transportation practices rolls into food manufacturing, you can bet that in addition to meeting sustainable practices, they will fit into the financial plans of the organization as well.
Sustainability: Just Another Component in a Long Line of Disruptors in Food Manufacturing
Years ago, when the topic of disruption in food manufacturing came up, many would mention things like a customer changing an order, an ingredient not arriving on time, or a packaging line going down for an hour. Today, these occurrences are just part of the day-to-day process and reality of food manufacturing. They are going to happen, and disruptions are the things that will make a food manufacturer have to change their business model and force them to change their philosophy and begin to evaluate their business practices and systems to adjust to the world in which they operate.
Sustainability is another one of those disruptions that will impact the process of food transportation long term. Sustainability will be an area that eventually forces manufacturers to operate within new regulatory parameters imposed on how they produce and ship their food. Through these changes, manufacturers will have to ensure that food meets the current and future safety regulations while maintaining profitability. That is where real sustainability will be measured. Changes to business, movements like sustainability are adding to the disruption of the food industry at unprecedented rates of speed. In order to survive and thrive, and to meet these disruptions head on and be sustainable themselves, global food manufacturers must be able to innovate and adapt their business models.
Find records of fraud such as those discussed in this column and more in the Food Fraud Database. Image credit: Susanne Kuehne
A multinational criminal smuggling ring was involved in the import of mislabeled siluriformes fish, including several species of catfish, into the United States. Import of such fish is prohibited to ensure the safety of the food supply in the United States. The smuggled catfish was labeled and listed on the import paperwork as other types of fish, which was discovered during a customs inspection. Subsequent seizures of shipping containers and warehouses led to the discovery of large amounts of mislabeled fish. The defendants face steep prison sentences.
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.
Since 2019 the FDA’s crosscutting work has implemented artificial intelligence (AI) as part of the its New Era of Smarter Food Safety initiative. This new application of available data sources can strengthen the agency’s public health mission with the goal using AI to improve capabilities to quickly and efficiently identify products that may pose a threat to public health by impeding their entry into the U.S. market.
On February 8 the FDA reported the initiation of their succeeding phase for AI activity with the Imported Seafood Pilot program. Running from February 1 through July 31, 2021, the pilot will allow FDA to study and evaluate the utility of AI in support of import targeting, ultimately assisting with the implementation of an AI model to target high-risk seafood products—a critical strategy, as the United States imports nearly 94% of its seafood, according to the FDA.
Where in the past, reliance on human intervention and/or trend analysis drove scrutiny of seafood shipments such as field exams, label exams or laboratory analysis of samples, with the use of AI technologies, FDA surveillance and regulatory efforts might be improved. The use of Artificial intelligence will allow for processing large amount of data at a faster rate and accuracy giving the capability for revamping FDA regulatory compliance and facilitate importers knowledge of compliance carrying through correct activity. FDA compliance officers would also get actionable insights faster, ensuring that operations can keep up with emerging compliance requirements.
Predictive Risk-based Evaluation for Dynamic Imports Compliance (PREDICT) is the current electronic tracking system that FDA uses to evaluate risk using a database screening system. It combs through every distribution line of imported food and ranks risk based on human inputs of historical data classifying foods as higher or lower risk. Higher-risk foods get more scrutiny at ports of entry. It is worth noting that AI is not intended to replace those noticeable PREDICT trends, but rather augment them. AI will be part of a wider toolset for regulators who want to figure out how and why certain trends happen so that they can make informed decisions.
AI’s focus in this regard is to strengthen food safety through the use of machine learning and identification of complex patterns in large data sets to order to detect and predict risk. AI combined with PREDICT has the potential to be the tool that expedites the clearance of lower risk seafood shipments, and identifies those that are higher risk.
The unleashing of data through this sophisticated mechanism can expedite sample collection, review and analysis with a focus on prevention and action-oriented information.
American consumers want safe food, whether it is domestically produced or imported from abroad. FDA needs to transform its computing and technology infrastructure to close the gap between rapid advances in product and process technology solutions to ensure that advances translate into meaningful results for these consumers.
There is a lot we humans can learn from data generated by machine learning and because of that learning curve, FDA is not expecting to see a reduction of FDA import enforcement action during the pilot program. Inputs will need to be adjusted, as well as performance and targets for violative seafood shipments, and the building of smart machines capable of performing tasks that typically require human interaction, optimizing workplans, planning and logistics will be prioritized.
In the future, AI will assist FDA in making regulatory decisions about which facilities must be inspected, what foods are most likely to make people sick, and other risk prioritization factors. As times and technologies change, FDA is changing with them, but its objective remains in protecting public health. There is much promise in AI, but developing a food safety algorithm takes time. FDA’s pilot program focusing on AI’s capabilities to strengthen the safety of U.S. seafood imports is a strong next step in predictive analytics in support of FDA’s New Era of Smarter Food Safety.
Find records of fraud such as those discussed in this column and more in the Food Fraud Database. Image credit: Susanne Kuehne
Since only 417 Masters of Wine exist globally (and their palates and noses)—and they are amazing in identifying wines by grape varietal or blend, type, vintage and location—it is a good idea to have some automated backup when it comes to wine fraud detection. Aside from other analytical methods, nuclear magnetic resonance (NMR) spectroscopy can be used in the authentication of wine. The new proton measurement 1H NMR Method with easier sample preparation is recommended for the investigation of wine fraud, to detect for example the addition of water or sugar. NMR spectroscopy measures several compounds of a wine at once and therefore is able to detect a fingerprint of a wine, such as the geographic origin or grape varietal.
The COVID-19 pandemic has brought challenges to all industries, and many restaurants have been forced to close their doors permanently. Restaurant owners have struggled due to COVID-19 restrictions that have drastically cut the number of customers they can serve—whether as a result of an indoor dining ban or capacity limits. Those that have been allowed to re-open are being stretched to meet new guidelines to keep guests safe and comfortable while dining. Not only do restaurant owners need to make sure their restaurants are COVID-safe, but they also need to ensure they are providing the quality service and meals their customers have come to know and love. The Internet of Things (IoT) can not only ease the burden of implementing new protocols while also ensuring a clean and safe environment for both employees and patrons, but also help restaurants enhance efficiency.
The following are some points on how the IoT can help restaurants not only survive, but thrive amid the pandemic.
Monitoring Cleaning
Easy-to-deploy IoT-enabled devices provide several benefits to QSRs, including the monitoring of employee hand washing stations, dishwashing water temperatures, sanitizer solution concentrations and customer bathroom usage frequency to ensure constant compliance with cleanliness standards.
By placing sensors on tables and work lines, restaurant owners can collect valuable data and insights in real time. For example, the sensors can share information about how often tables are being cleaned. This information will help owners trust that tables are being cleaned thoroughly in between each use.
Sensors can also be placed on washbasins to monitor employee hand washing. Sensors on the sinks will not only confirm that employees’ hands have been washed, but they will also share exactly how long employees washed their hands. That way, owners can have peace of mind knowing employees’ hands and restaurant surfaces are properly sanitized before customers sit down to eat. With door sensors monitoring customer bathrooms, store owners can ensure adequate cleaning is allocated based on frequency of usage.
Rodent Detection
Owners can also have peace of mind knowing their restaurant is rodent free by using IoT monitored sensors. Rodents are especially dangerous to be found lurking in restaurants because they carry diseases and can cause electrical fires. Devices can be placed throughout the restaurant to detect any motion that occurs. When the devices detect a motion, restaurant owners will receive notifications and will be immediately aware of any rodents that may have snuck into the restaurant.
These sensors give restaurant owners a chance to proactively address a rodent issue before it causes damage to their business.
Routine Monitoring
In addition to monitoring sanitation and detecting motion, restaurant owners can leverage the IoT many other ways. For example, IoT devices can be placed on trash bins to alert when they are full and ready to be taken out. They can also be placed near pipes to detect a leak. Sensors can also be placed on all refrigerators to detect temperature. With accurate updates on refrigerators’ temperatures, restaurant owners can easily monitor and ensure that food is stored at the appropriate temperature around the clock—and be immediately alerted if a power issue causes temperatures to change.
IoT devices can offer restaurant owners insights to help them change their operations and behavior for the better. While everyone is eager to go back to “normal” and want our favorite restaurants to re-open as soon as possible, it is important that restaurant owners have the tools needed to reopen safely—and create efficiencies that can help recoup lost income due to COVID-19 restrictions. Restaurant owners looking to receive real-time, accurate data and insights to help run their restaurants more efficiently and ensure a safe and comfortable experience for customers can turn to the IoT to achieve their goals.
Next month, Food Safety Tech will host the first event in its Food Safety Hazards Series, “Listeria Detection, Mitigation, Control & Regulation” on April 15. The virtual event features Sanjay Gummalla, Ph.D., senior vice president of scientific & regulatory affairs at AFFI; April Bishop, senior director of food safety at TreeHouse Foods; and Douglas Marshall, Ph.D., chief scientific officer at Eurofins. These experts will address Listeria from the perspective of food manufacturing and preventing the introduction of the pathogen; risk based and practical approaches to address the presence of Listeria in food production and achieve key publish health goals relative to the pathogen; how to implement a strong Listeria control program; and the testing challenges from a lab perspective.
The event begins at 12 pm ET on Thursday, April 15.
Presentations are as follows:
Listeria Control and New Approaches to Addressing Risks, by Sanjay Gummalla
Managing Food Safety and Sanitation in the Digital Age, by April Bishop
Listeria Testing: Choosing the Right Method and Target, by Doug Marshall
The presentations will be followed by a panel discussion and a live Q&A with attendees.
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.
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