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)
True Positives (TP)
False Positives (FP)
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).
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.
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.
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.
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.
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.
COVID-19 has put a spotlight on the importance of proper handwashing and overall hygiene. In addition to focusing on worker and operational safety, it has also pushed food manufacturers and processors to pay more attention to the location of high-touch areas and how they should be cleaned, sanitized, disinfected and sterilized. During last week’s Food Safety Consortium episode on sanitation, there was discussion about the need to have the right sanitation plan and properly trained people in place. “When it comes to food safety, who are the most important people in the plant? It’s the sanitation crew and employees. They are on the frontlines, ” said Shawn Stevens, founder of Food Industry Counsel, LLC. “If they don’t do their job or are not given the tools to do their jobs, that’s where the failures occur. We need to empower them. We have to invest in sanitation and not be complacent.”
Investing in a sanitation plan is where it all begins, said Elise Forward, president of Forward Food Solutions. Within the plan, companies need to include items such as PPE and sanitation equipment, along with what resources will be needed and what chemicals will be required. “What would it look like in our manufacturing facilities if we had a plan for the pandemic?” asked Forward. “There was so much scrambling: ‘How do we do this and what do we do’. We need to plan for these events.” Forward, along with David Shelep, microbiologist and consultant for Paramount Sciences and Bill Leverich, president of Microbiologics, Inc., offered a strong overview of the right components of a sanitation plan and the common products and technologies used in the process (quaternary ammoniums, sodium hypochlorite, ethyl alcohol, peracetic acid, hydrogen peroxide, and chlorine dioxide). They also provided insight on some of the products and technologies that are being explored in the face of COVID-19—UV-C and hypochlorous acid, which has applications in cleaning biofilms, hand sanitizing, fogging, and surface application (i.e., electrostatic spraying, mopping).
“Cleaning and sanitizing is setting up your production team(s) for success.” – Elise Forward, Forward Food Solutions
Beyond sanitation methods, companies need to invest in employee training and be committed to their safety. This means giving employees sick days and not incentivizing them to come to work when they are sick.
Rob Mommsen, senior director, global quality assurance and food safety for Sabra Dipping Company, shared a candid perspective on how Sabra developed an effective and validated Listeria environmental monitoring program (LEMP) following an FDA inspection that led to a swab-a-thon, findings of resident Listeria in the plant, and a huge product recall as a result of the Listeria contamination in the plant (Mommsen stated that Listeria was never found in product samples). “We had to severely alter the way we cleaned our plant,” he said. And the company did, with a number of changes that included taking the plant apart and cleaning it; removing all high pressure water nozzles; changing areas in the plant from low care to high care; keeping movable equipment to certain areas in the plant; changing employee and equipment traffic patterns; and retraining staff on GMPs. The company also changed its microbiological strategy, conducting daily swabbing in certain zones, increasing testing on samples, and implementing a weekly environmental meeting that was attended by senior and department managers. “Fast forward” to 2019: FDA conducted an unannounced audit and noted that Sabra’s environmental monitoring program was one of the best they’ve seen and that the company’s culture was clearly driven by food safety, according to Mommsen.
Fast forward again to 2020 and the pandemic: With work-from-home orders in place and other frontline workers staying home for various reasons, the company saw a change GMP adherence, employee training and the frequency of environmental monitoring, said Mommsen. So Sabra had some work to do once again to re-right the ship, and Mommsen presented it as a lessons learned for folks in the food industry: In addition to employee safety, food safety must be the number one priority, and having the support of senior management is critical; the turnaround time for environmental swabs is also critical and an effective LEMP should consist of both conventional testing as well as rapid detection technology; and an environmental monitoring program requires persistence—it is not self sustaining and there are no shortcuts.
Starting in late March, based on travel restrictions and the risk of COVID-19 infection transmission, GFSI released direction to the food industry on the possibility of recertification extensions. The extensions enabled a one-time, six-month grace period to prevent certification loss.
In June GFSI updated guidance to allow up to half of the recertification process to be completed off-site using remote technology, while requiring completion of an audit’s on-site inspection within 28 to 30 days. In exceptional circumstances, a certification program could allow a maximum of 90 days for the on-site audit portion. As these “blended” audits began, fewer facilities sought extensions.
The remote portion of an audit, which includes program and record review as well as interviews, may increase audit time compared to pre-COVID audits, as all involved adjust to the use of technology and accessible electronic formats for records and programs.
After COVID-19, it is conceivable to predict that a portion of the audit could remain virtual. However, in food production, auditing requires the use of sight, touch and smell, not yet replicated without human observation. And, while COVID-19 has forced an audit evolution by pushing “virtual” adoption based on business needs, remote capabilities will still require a significant investment in technology, time and re-education of the industry. In the meantime, expect audit schedules to be disrupted for the next 9 to 12 months.
As the industry seeks to adapt for the future, we will likely see an acceleration in terms of digitized quality management systems. In the short term, manufacturers are putting their energy and focus into keeping employees safe, maintaining production and meeting customer commitments.
Several leading food safety groups have issued guidance on best practices for blended audits and the use of technology. And while the answer to “Are blended audits are here to stay?” appears to be “yes” for the immediate future, audits are expected to evolve over time. Although certain sections within audits are better adapted to remote capabilities, facilities will continue to use on-site auditors until new technologies enable them to do otherwise.
The USDA estimates that foodborne illnesses cost more than $15.6 billion each year. However, biological contamination isn’t the only risk to the safety and quality of food. Food safety can also be compromised by foreign objects at virtually any stage in the production process, from contaminants in raw materials to metal shavings from the wear of equipment on the line, and even from human error. While the risk of foreign object contamination may seem easy to avoid, in 2019 alone the USDA reported 34 food recalls, impacting 17 million pounds of food due to ‘extraneous material’ which can include metal, plastic and even glass.
When FSMA went into effect, the focus shifted to preventing food safety problems, necessitating that food processors implement preventive controls to shift the focus from recovery and quarantine to proactive risk mitigation. Food producers developed Hazard Analysis and Critical Control Point (HACCP) plans focused on identifying potential areas of risk and placement of appropriate inspection equipment at these key locations within the processing line.
Metal detection is the most common detection technology used to find ferrous, non-ferrous, and stainless steel foreign objects in food. In order to increase levels of food safety and better protect brand reputation, food processors need detection technologies that can find increasingly smaller metal foreign objects. Leading retailers are echoing that need and more often stipulate specific detection performance in their codes of practice, which processors must meet in order to sell them product.
As food processors face increased consumer demand and continued price-per-unit pressures, they must meet the challenges of greater throughput demands while concurrently driving out waste to ensure maximum operational efficiencies.
Challenges Inherent in Meat Metal Detection
While some food products are easier to inspect, such as dry, inert products like pasta or grains, metal foreign object detection in meat is particularly challenging. This is due to the high moisture and salt content common in ready-to-eat, frozen and processed, often spicy, meat products that have high “product effect.” Bloody whole muscle cuts can also create high product effect.
The conductive properties of meat can mimic a foreign object and cause metal detectors to incorrectly signal the presence of a physical contaminant even when it is nonexistent. Food metal detectors must be intelligent enough to ignore these signals and recognize them as product effect to avoid false rejection. Otherwise, they can signal metal when it is not present, thus rejecting good product and thereby increasing costs through scrap or re-work.
Equipping for Success
When evaluating metal detection technologies, food processors should request a product test, which allows the processor to see how various options perform for their application. The gold standard is for the food processor to send in samples of their product and provide information about the processing environment so that the companies under consideration can as closely as possible simulate the manufacturing environment. These tests are typically provided at no charge, but care should be taken upfront to fully understand the comprehensiveness of the testing methodologies and reporting.
Among the options to explore are new technologies such as multiscan metal detection, which enables meat processors to achieve a new level of food safety and quality. This technology utilizes five user-adjustable frequencies at once, essentially doing the work of five metal detectors back-to-back in the production line and yielding the highest probability of detecting metal foreign objects in food. When running, multiscan technology allows inspectors to view all the selected frequencies in real time and pull up a report of the last 20 rejects to see what caused them, allowing them to quickly make appropriate adjustments to the production line.
Such innovations are designed for ease of use and to meet even the most rigorous retailer codes of practice. Brands, their retail and wholesale customers, and consumers all benefit from carefully considered, application-specific, food safety inspection.
The food processing industry is necessarily highly regulated. Implementing the right food safety program needs to be a top priority to ensure consumer safety and brand protection. Innovative new approaches address these safety concerns for regulatory requirements and at the same time are designed to support increased productivity and operational efficiency.
One of the worst suspected alcoholic beverage poisoning incidents has claimed dozens of lives in Mexico. A possible cause may be tainted liquor from illegal bootleg sources; the suspicion is pointing to methanol as a contaminant, which can lead to blindness and even death. Due to the coronavirus crisis, some Mexican states banned alcohol production and sales, which may have promoted the sales of illicit alcoholic beverages. An Euromonitor report mentions that about 25% of alcohol beverages in developing markets are illicit and may endanger consumers’ health and lives.
Register to attend the complimentary webinar: New Technology’s Impact on Pest Management in a FSMA Regulated World | March 5, 2020 | 12 pm ETMillions of pounds of food are lost every year due to pest activity. A lot of those lost food products could have been prevented through a quality sanitation program. One of the best ways to protect your facility from the potential damage and pathogen spread caused pests like rodents is to maintain a quality sanitation program.
Every sanitation program should take into consideration conditions that are conducive to attracting and supporting unwanted visitors. As rodents are incredibly agile and intelligent creatures, one of the best ways to keep them out of a facility is to give them no reason to be interested in coming in. This means eliminating access to each of their basic needs: Food, water and harborage—in any amount. Remember, they are small, scrappy creatures and only need crumbs and droplets of water to survive. Once you change your perspective from that of a human being to that of a rodent you may be surprised by the bountiful conditions that are at your feet.
As technologies become more and more advanced, the best pest technicians are often those willing to use the latest and greatest, most advanced tools on the market to provide superior service. However, if your technician is not carrying this one basic item in their toolkit, there is a good chance you’re not getting the quality of service you deserve. Any guesses at what that tool might be?
Register now for the complimentary webinar: New Technology’s Impact on Pest Management in the FSMA Regulated World | March 5, 2020 | 12 pm ETIf you guessed flashlight, you’re correct. Whether or not your technician carries a flashlight with them when they perform the inspection speaks volumes about the quality of service you are getting. A flashlight tells you two important things. First, that your technician is not just checking traps, but performing an investigation. He or she is looking for conditions that attract and foster pests. Second, a flashlight sends the message that your technician is willing to inspect dark or difficult-to-reach places. This is the type of technician willing to get on hands and knees to check under equipment. They will climb and crouch in order to reach the places pests are likely hiding. They value a pest-free environment more than their own convenience.
In short, the most important work your pest technician can provide is a thorough investigation to help prevent pest problems before they occur. If your pest technician is not performing an investigation each time they enter your facility, you’re not getting the value you need and should expect from your service provider.
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