Loma Systems has launched a new addition to its X5 X-ray inspection series: the X5 SideShoot, designed specifically for the inspection of taller products.
Using dynamic detection, the X5 SideShoot carries out a series of product inspections looking for unwanted contaminants; it can also be used to estimate fill level, check for missing content and determine whether a package is damaged. It can inspect product packaging up to 265mm in height, including plastic bottles, glass jars with metal lids, aluminum cans, plastic tubs and pots, tetra pack cartons, cardboard cans and tubes.
The X-ray device features a line speed of up to 50m/min, and can detect a broad range of contaminants, including ferrous, non-ferrous and stainless steel (~1.5-2.0mm St/St) metal, glass, bone, ceramic/stone, dense plastics and product clumps.
IP66-rated for low-pressure washdown, the X5 SideShoot includes a 15″ color touchscreen, multilevel password access to store data-logged events for traceability, improved digital detection to reduce electrical interference for image optimization, and energy-saving automatic idle capabilities.
“The New X5 SideShoot has been specifically designed and engineered to help our customers deliver new packaging technologies, successfully inspect products that are taller versus width, promote sustainability and enhance food safety,” said Toby Kemp, LOMA’s Director of Marketing & Innovation.
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.
During the production process, physical hazards can contaminate food products, making them unfit for human consumption. According to the USDA’s Food Safety and Inspection Service (FSIS), the leading cause of food recalls is foreign material contamination. This includes 20 of the top 50, and three of the top five, largest food recalls issued in 2019.
As methods for detecting foreign materials in food have improved over time, you might think that associated recalls should be declining. To the contrary, USDA FSIS and FDA recalls due to foreign material seem to be increasing. During the entire calendar year of 2018, 28 of the 382 food recalls (7.3%) in the USDA’s recall case archive were for foreign material contamination. Through 2019, this figure increased to approximately 50 of the 337 food recalls (14.8%). Each of these recalls may have had a significant negative impact on those brands and their customers, which makes foreign material detection a crucial component of any food safety system.
The FDA notes, “hard or sharp foreign materials found in food may cause traumatic injury, including laceration and perforation of tissues of the mouth, tongue, throat, stomach and intestine, as well as damage to the teeth and gums”. Metal, plastic and glass are by far the most common types of foreign materials. There are many ways foreign materials can be introduced into a product, including raw materials, employee error, maintenance and cleaning procedures, and equipment malfunction or breakage during the manufacturing and packaging processes.
The increasing use of automation and machinery to perform tasks that were once done by hand are likely driving increases in foreign matter contamination. In addition, improved manufacturer capabilities to detect particles in food could be triggering these recalls, as most of the recalls have been voluntary by the manufacturer.
To prevent foreign material recalls, it is key to first prevent foreign materials in food production facilities. A proper food safety/ HACCP plan should be introduced to prevent these contaminants from ending up in the finished food product through prevention, detection and investigation.
Food manufacturers also have a variety of options when it comes to the detection of foreign objects from entering food on production lines. In addition to metal detectors, x-ray systems, optical sorting and camera-based systems, novel methods such as infrared multi-wavelength imaging and nuclear magnetic resonance are in development to resolve the problem of detection of similar foreign materials in a complex background. Such systems are commonly identified as CCPs (Critical Control Points)/preventive controls within our food safety plans.
But what factors should you focus on when deciding between different inspection systems? Product type, flow characteristics, particle size, density and blended components are important factors in foreign material detection. Typically, food manufacturers use metal and/or x-ray inspection for foreign material detection in food production as their CCP/preventive control. While both technologies are commonly used, there are reasons why x-ray inspection is becoming more popular. Foreign objects can vary in size and material, so a detection method like an x-ray that is based on density often provides the best performance.
Regardless of which detection system you choose, keep in mind that FSMA gives FDA the power to scientifically evaluate food safety programs and preventive controls implemented in a food production facility, so validation and verification are crucial elements of any detection system.
It is also important to remember that a key element of any validation system is the equipment validation process. This process ensures that your equipment operates properly and is appropriate for its intended use. This process consists of three steps: Installation qualification, operational qualification and performance qualification.
Installation qualification is the first step of the equipment validation process, designed to ensure that the instrument is properly installed, in a suitable environment free from interference. This process takes into consideration the necessary electrical requirements such as voltage and frequency ratings, as well as other factors related with the environment, such as temperature and humidity. These requirements are generally established by the manufacturer and can be found within the installation manual.
The second step is operational qualification. This ensures that the equipment will operate according to its technical specification. In order to achieve this, the general functions of the equipment must be tested within the specified range limits. Therefore, this step focuses on the overall functionality of the instrument.
The third and last step is the performance qualification, which is focused on providing documented evidence through specific tests that the instrument will performs according to the routine specifications. These requirements could be established by internal and industry standards.
Following these three steps will allow you to provide documented evidence that the equipment will perform adequately within the work environment and for the intended process. After completion of the equipment validation process, monitoring and verification procedures must be established to guarantee the correct operation of the instrument, as well procedures to address deviations and recordkeeping. This will help you effectively control the hazards identified within our operation.
There can be massive consequences if products contaminated with foreign material are purchased and consumed by the public. That’s why the development and implementation of a strong food safety/ HACCP plan, coupled with the selection and validation of your detection equipment, are so important. These steps are each key elements in protecting your customers and your brand.
As much as food manufacturers take precautions to avoid all types of contaminants, there can still come a moment when you realize that your best efforts have failed. Maybe you find a broken blade or a missing wire during a sanitation break, but the product has already gone through your inline inspection machines—and nothing was detected.
This is the freak-out moment that no plant manager or quality assurance manager wants to have. Knowing that there’s possible contamination of your food product (and not knowing where that contaminant might be) creates a hailstorm of possibilities that your plant works hard to avoid. And you’re probably wondering how this could have happened in the first place.
Understanding How Contaminants Get Past Detection
To prevent physical contamination from occurring, it’s important to understand the reasons why it happens. In-house inspection systems often fail to detect contaminants for the following reasons:
The equipment isn’t calibrated to detect contaminants to a small enough degree, or the contaminants are materials that aren’t easily detected by the in-house machinery (glass, rubber, plastic, etc.)
The machines aren’t constantly monitored
The speed of the production line doesn’t allow for detecting small particles
Metal detectors are the most commonly used inline inspection devices in food manufacturing, and they depend on an interference in the signal to indicate there is metal contamination in the product.
Despite the fact that technology has progressed to deliver fewer false positives, the machines can still be deceived by moisture, high salt contents and dense products that could provide interference in the signal. When that continues to occur, it’s common for manufacturers to recalibrate the machine to get fewer false positives—but that also decreases its effectiveness.
Another limitation of the metal detector is that, as the name indicates, it can only find metal. That means contaminants like plastic, glass, rubber and bone won’t be found through a metal detector, but will hopefully be discovered through some other means before the product is shipped out.
Oftentimes, contamination or suspected physical contamination is discovered when a product, such as cheese or yogurt, goes through a filtration system, or when a piece of machinery is inspected during a sanitation break.
If the machinery is found to be missing a part, such as a bolt or a rubber gasket, the manufacturer then has to backtrack to the machinery’s last inspection and determine how much, if any, of the product manufactured during that time has been contaminated.
What To Do When Contamination Occurs
Once a food manufacturer discovers that it may have a physical contamination problem, it must make a decision on how to handle the situation. Options come down to four basic choices, each of which comes with its own risks and benefits.
Option 1: Dispose of the full production run
The one advantage of disposing of a full production run is that it entirely eliminates the possibility of the contaminated product reaching consumers.
However, this is an expensive solution, as the manufacturer has to pay for the cost of disposal in a certified landfill and absorbs the cost of packaging, labor and ingredients. It also presents the risk of lost revenue by having a product temporarily out of stock.
Option 2: Shut down your production lines for re-inspection/re-work
Running the product through inline inspections a second time may result in finding the physical contaminant, but there’s also a risk that the contaminant won’t be found—and now the company has lost money through overtime pay and lost productivity.
If the inspection equipment was not sensitive enough to find the contaminant the first time around, it may not find it the second time, which puts the manufacturer back at square one. The advantage to this method is that the manufacturer maintains complete accountability and control over the process, although it may not yield the desired results.
Option 3: Risk it and ship the product to retailers
There’s always a chance that a missing bolt didn’t make its way into the product. Sometimes, if a metal detector goes off and the manufacturer can’t find any contaminants upon closer examination, they will choose to ship the product and take their chances.
The advantage for them is that, on the front end, this is the least expensive option—or it could be the costliest choice of all if a consumer finds a physical contaminant in their food. In fact, the average cost of a food recall is estimated at $10 million; lawsuits may push that cost even higher and result in a business being closed for good.
Option 4: Use third-party X-ray inspection
X-ray inspection is the most effective way to find physical contaminants. In addition to metal, X-ray systems can find glass, plastic, stone, bone, rubber/gasket material, product clumps, container defects, wood and missing components at 0.8 mm or smaller.
When a food manufacturer has a contamination issue, it can have the bracketed product inspected by a third-party X-ray inspection company and only dispose the affected food, allowing the rest of the product to be distributed. This option allows the manufacturer to maintain inventory and keep food deliveries on schedule while still eliminating the problem of contamination.
X-ray inspection can find what other forms of inspection cannot, because it’s based on the density of the product, as well as the density of the physical contaminant. When X-ray beams are directed through a food product, the rays lose some of their energy, but will lose even more energy in areas that have a physical contaminant. So when those images are interpreted on a monitor, the areas that have a physical contaminant in them will show up as a darker shade of gray.
This allows the workers monitoring machines to immediately identify any foreign particles that are in the food, regardless of the type of material.
Detection is Key to Avoiding Contamination Issues
Handling contamination properly is vital to every food manufacturing company. It affects the bottom line and the future of the company, and just one case of a physical contaminant reaching the consumer is enough to sideline food companies of any size. As X-ray technology continues to evolve, it remains an effective and efficient form of food inspection.
Educating plant managers and quality managers on what to do if inline inspection machines fail to detect contaminants should include information on how X-ray technology can be a food company’s first line of defense. While physical contaminants can’t always be avoided, they can be detected—and the future of your company may depend on it.
FlexXray will be exhibiting at the 2016 Food Safety Consortium in Schaumburg, IL. At the booth representing FlexXray will be CEO Kevin Fritzmeyer and Project Manager John Hower. They will be discussing their food inspection process and capabilities of foreign material detection.
FlexXray is the leader in Inspection & Recovery Services dedicated to serving food companies. The company X-rays food products for various types of foreign material and contaminants, which it can see down to 0.8 mm or even smaller. Metal, plastic, gasket material, glass, stones and bone are a few of the items our customers ask us to inspect for.
FlexXray provides quick turn IN/OUT service, your truckload of product is inspected, contaminants removed and returned in only 8-12 hours. The company has introduced a new audit program for our customers to conform to the new HACCP and FSMA regulations. It is meant to help catch and prevent problems before recalls occur.
Our goal is to work with food companies to inspect their finished product for foreign material versus their other option, throwing it away. We strive to provide your company a cost-effective option in the event that you have an incident.
A new X-ray detector technology features a 0.4-mm high-sensitivity detector that enables the integration of a 100-W X-ray generator. The technology, provided by Mettler Toledo, offers improved detection levels with a 20% power reduction under standard operating conditions.
The Safeline X-ray system includes software that “lends itself especially to ‘difficult’ or ‘busy’ images which contain varied density distribution, and is especially valuable for inspecting multi-textured foods and products that have a tendency to move around inside the packaging, such as boxes of cereal or bags of mixed nuts. In fact, detection sensitivity is unaffected by any type of packaging thus improving false rejection rates,” according to a company press release. The system enables the removal of contaminants before products leave a factory.
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