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.

Angel Suarez, EAS Consulting Group
FST Soapbox

Regulatory Cross Cutting with Artificial Intelligence and Imported Seafood

By Angel M. Suarez
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Angel Suarez, EAS Consulting Group

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.

Susanne Kuehne, Decernis
Food Fraud Quick Bites

The Automated Nose of a Master of Wine

By Susanne Kuehne
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Susanne Kuehne, Decernis
Wine fraud
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.

Resource

  1. Solovyev, P.A., et. al. (January 27, 2021) “NMR spectroscopy in wine authentication: An official control perspective”. Comprehensive Reviews in Food Science and Food Safety. Wiley Online Library.
Hussain Suleman, Sigfox
Retail Food Safety Forum

How to Use the IoT to Keep Your Restaurant Clean and Safe

By Hussain Suleman
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Hussain Suleman, Sigfox

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.

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.

Susanne Kuehne, Decernis
Food Fraud Quick Bites

Going Against The (Organic) Grains

By Susanne Kuehne
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Susanne Kuehne, Decernis
Organic Grain
Find records of fraud such as those discussed in this column and more in the Food Fraud Database. Image credit: Susanne Kuehne

Organic fraud cases are on the increase and often, the criminals get away with these crimes. The USDA-OIG catching a multi-million dollar case like this one represents just the tip of the iceberg. Over the course of five years, the operator of a grain and seed business misrepresented his products as organic, claiming premium prices and defrauding clients of millions of dollars. Transactions for money and documents crossed multiple state lines in the United States through several related business entities. The business owner was sentenced to several years in prison and hefty fines for wire fraud, money laundering, and for defrauding the National Organic Program.

Resource

  1. United States Attorney’s Office District of South Dakota (February 25 2021). “Florida Man Sentenced for Conspiracy to Commit Wire Fraud and Money Laundering”. The United States Department of Justice.

 

Emily Newton, Revolutionized Magazine
FST Soapbox

How to Improve Food Processing Efficiency

By Emily Newton
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Emily Newton, Revolutionized Magazine

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.

Many major food production companies—including Pepsi-Cola and Coca-Cola—are already in the process of investing major amounts of money in water reuse and conservation technology.

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.

Susanne Kuehne, Decernis
Food Fraud Quick Bites

All Bison, No Bull

By Susanne Kuehne
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Susanne Kuehne, Decernis
Bison
Find records of fraud such as those discussed in this column and more in the Food Fraud Database. Image credit: Susanne Kuehne

Bison and other game meats have become increasingly popular over the course of the past years, and these products have enjoyed an increase in pricing as a result. Bison, deer and beef meats have very similar appearances; in addition, bison and domestic cattle can cross-breed and therefore the meat cannot be distinguished by DNA barcoding alone. To ensure that bison meat was not mixed with other red meat species, a specific polymerase chain reaction method (PCR-SFLP) was used in a recently published study. Out of 45 commercial bison meat samples, three samples showed other meat species, which were not identified on the label.

Resource

  1. Scales, Z.M., et al. (February 3, 2021). “Use of DNA Barcoding Combined with PCR-SFLP to Authenticate Species in Bison Meat Products”. MDPI.

 

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.

Melody Ge
Women in Food Safety

Find Your Passion and Confidence Will Come

By Melody Ge
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Melody Ge

Deborah Coviello, founder and CEO of Illumination Partners, LLC and host of The Drop in CEO Podcast, recently shared her journey with the Women in Food Safety Group. Deb believes in self-confidence and that everyone is a talented individual.

Two years ago, Deb decided to start her own business, which focuses on elevating people, after 10 years of working as a food safety professional for a top flavor company. When I spoke with Deb about her experiences, she admitted that at times she felt vulnerable, stressed and unsatisfied after working for corporations for more than 30 years. She was no longer happy with herself, and even her coworkers could see it. However, she got past it and has found the passion to share her experiences and stories to help anyone who is struggling in a similar situation.

When formulating the business plan for her new firm, Deb started testing her ideas with consulting professionals, discussing how to elevate their self-confidence. It wasn’t until her third client that she had built the confidence to say that she could provide a service that people needed. “I have to constantly tell myself that I can do this!” Deb said. “It is that food safety professional combined with elevating leadership—men and women—which is the purpose for my work, and I enjoy doing so much. ” Hearing Deb’s client feedback is what drives her mission and business forward. “I love helping people find their confidence, because that’s what I was once going through,” she explained.

Deb thinks it is more important to help food safety professionals sustain improvement after working with them, versus simply passing on pure technical knowledge. “I elevate people, while at the same time I elevate the business,” she said.

The topic of a glass ceiling is discussed in so many interviews, and Deb would like to challenge that idea. “Maybe in the past, there were challenges to female leadership and we claimed it as a glass ceiling that we had to break through.” Deb continued, “However, I would definitely challenge that! I do not think women need to own that narrative anymore! If you believe there’s a glass ceiling, then you will have a glass ceiling. If you do not believe it, it is not there.”

Deb Coviello
“I think women need to find the network for support and realize that the only glass ceiling may now be just themselves.” – Deborah Coviello, CEO of Illumination Partners, LLC and host of The Drop in CEO Podcast

Deb has one final piece of advice for women who are in an executive position or seeking a position at the table: “Advocate for yourself!” Females have not always been good at speaking up for themselves. However, there are many means to achieve your goals—seek advocates through your network and peers. “Do not ever think people can read your mind,” she added.

She ended the conversation with a personal story that still inspires her today. At one point in her career, she asked her boss at the time what could she change or do to move ahead. Her boss said, “Change nothing, just be yourself!” Deb started gaining the trust and confidence in herself from that point forward. She has become relaxed and more herself—and this is a disposition that gives her energy and helps sustain her passion.

Melody Ge: How did you get into the food safety industry and reach where you are today?

Deborah Coviello: I graduated from college as a biomedical engineer. I knew that I really loved manufacturing because I always loved the creation aspect, going from nothing to something. But along the way, I found out that I enjoyed the area of quality, so I pursued it for several years in multiple industries. One of the things I liked about quality assurance was taking a standard to meet customer expectations. I pursued the ASQ Six Sigma Black Belt to ensure continuous improvement, and I got to learn how to solve problems in business, become more effective and reduce customer complaints. After having my third child, my husband was also changing his business, so I had to return to work sooner than I had planned. I really enjoyed learning about the chemical industry and my skills in quality had evolved, so I landed in the flavors industry, where I started my food safety journey. This is where things started changing for me. I worked for an international flavors company that services the food industry. I became the head of quality for North America for Givaudan, which is the number one global flavors company. It was the dream job that I always wanted. However, it came with a price. Not only did I have to protect the brand by ensuring we delivered a consistent, quality product to our customers; I was also responsible for food safety. At Givaudan, I had a lot of “a-ha” moments realizing that I was in a high-profile, high-risk role. I started taking my job and my voice to the next level because it was such a serious position—I had consumers and professionals around me who depended on me. Food safety was a top priority of mine—not just the compliance aspect but also any area of quality. So that was what took up my time in my previous career before I started my own business in the area of quality, food safety and continuous improvement. Maintaining product safety and brand reputation is a mission that holds true today in my own business.

Ge: What’s your perspective on being a female CEO?

Coviello: Well, there still aren’t a lot of us, although there are more and more female business owners. From 2014–2019, female-owned businesses grew at a rate of about 21% versus all businesses, which grew at about 9%.

I am constantly in search of fellow female CEOs to network with, and share thoughts and challenges. You mentioned a glass ceiling and the need for more women at the table. I appreciate all the female professionals who preceded us, and those who faced adversity to gain the right, trust and respect for all the opportunities that we have now. To give an example of my mother, she was a microbiologist and probably did not have all the opportunities that she would have wanted to in her time. I now have infinite possibilities. I have never felt disadvantaged. I was always able to turn a situation that did not serve me well into a new opportunity. I could take the accountability and move to a different role. I never let people, or the environment, stand in my way. I never felt the fear of any challenges of being a female CEO. I do not feel like I have any extra advantages either. I am simply a person who has a belief in myself and I have established a service through which I can help others.

Ge: Looking back, would you make any decisions differently?

Coviello: Oh yeah, I am much smarter and more confident now. In the past I didn’t believe in my abilities as much as I do now. I used to ask someone’s opinion to validate who I was or how good my performance was. The time was not right for me to start my own business. But looking back, I should have believed more in my capabilities and myself. I probably could have started my business a long time ago. In a way, I was forced into doing it at the end of my last job, and now I realize I am doing the work I was meant to do. I had a great career, and I will continue to have a great career. However, I do feel like I am playing catch-up now. Don’t get me wrong—I have a beautiful life and a very supportive husband. But when I look at successful business people who have 15 years of business experience, I am trying to play catch up. I feel like I have to work a lot faster, which involves business development, marketing, the Podcast, and I am also getting ready to write a book. I am trying to accelerate everything, which maybe I should have been a little bit more methodical if I had enough time. So, I definitely would have started business sooner to achieve all that I want to have an impact on. I would also like to give one piece of advice: Never doubt what you have already accomplished. When you reflect back, you have achieved so much and you should celebrate those successes. There are no failures, [they are] simply steps towards achieving the success you were meant to achieve. When you project yourself forward, ask your self what “is” possible vs. “not” possible. Having a positive mindset and belief in yourself is your guiding light.