Skippy Foods, LLC issued a voluntary recall of certain peanut butter jars due to concerns of metal fragment contamination, which may have originated from a piece of manufacturing equipment. The recall affects 9,353 cases (161,692 pounds) of product: Skippy Reduced Fat Creamy Peanut Butter (40 oz ), Skippy Reduced Fat Chunky Peanut Butter (16.3 oz), and Skippy Creamy Peanut Butter Blended with Plant Protein (14 oz). The products have various “Best If Used By” Dates ranging from May 4–10, 2023.
The issue was uncovered by the manufacturing facility’s internal detection systems. No other sizes or varieties of Skippy brand peanut butter or spreads are affected by this recall. In addition, no consumer complaints have been associated with this recall thus far.
Rare and old whisky can be a significant investment, except for when they are fraudulent. One report found that about one-third of rare whisky (and wine) may be fake, leading to $1.5 billion in losses in Europe alone. A handheld device is now able to detect fraudulent products rapidly. The analysis method involves electrodes that analyze characteristic groups of molecules that can be found in the real product. The device then checks the results against real whisky samples from a database.
An especially perfidious type of edible oil fraud is the dissolution of inedible plastic material, such as polypropylene or polyethylene packaging material, in hot cooking oil during the frying process. This is supposed to prolong the shelf life and the crispness of deep-fried snack food, not surprisingly with serious health implications. Attenuated total reflectance fourier-transform infrared spectroscopy (ATR-FTIR) in combination with principal component analysis (PCA) provides a straightforward method to analyze samples directly with minimal preparation, to detect polymers in palm cooking oil, as done in this study.
The food allergen testing industry has garnered considerable traction across North America, especially due to the high volume of processed food and beverages consumed daily. Allergens are becoming a significant cause for concern in the present food processing industry worldwide. Food allergies, which refer to abnormal reactions or hypersensitivity produced by the body’s immune system, are considered a major food safety challenge in recent years and are placing an immense burden on both personal and public health.
In 2019, the most common reason behind recalls issued by the USDA FSIS and the FDA was undeclared allergens. In light of this growing pressure, food producers are taking various steps to ensure complete transparency regarding the presence of allergenic ingredients, as well as to mitigate risk from, or possibly even prevent contact with, unintended allergens. One of these steps is food allergen testing.
Allergen detection tests are a key aspect of allergen management systems in food processing plants and are executed at nearly every step of the process. These tests can be carried out on work surfaces, as well as the products, to detect any cross contamination or allergen presence, and to test the effectiveness of a food processing unit’s cleaning measures.
There has been a surge in awareness among consumers about food allergies and tackling the risk of illnesses that may arise from consuming any ingredient. One of the key reasons for a higher awareness is efforts to educate the public. In Canada, for example, May has been designated “Food Allergy Awareness Month”. It is estimated that more than 3 million people in Canada are affected by food allergies.
The size of the global food allergen testing market is anticipated to gain significant momentum over the coming years, with consistent expansion of the dairy, processed food and confectionary segments.
Understanding the Prevailing Trends in Food Allergen Testing Industry
Food allergies risen nearly 50% in the last 10 years, with a staggering 700% increase observed in hospitalizations due to anaphylaxis. Studies also suggest that food allergies are a growing health concern, with more than 250 million people worldwide estimated to be affected.
Although more than 170 foods have been identified as causing food allergies in sensitive consumers, the USDA and the FDA have identified eight major allergenic foods, based on the 2004 FALCPA (the Food Allergen Labeling and Consumer Protection Act). These include eggs, milk, shellfish, fish, peanuts, tree nuts, soybean, and wheat, which are responsible for 90% of allergic reactions caused due to food consumption. In April 2021, the FASTER (Food Allergy Safety, Treatment, Education, and Research) Act was signed into law, which categorized sesame as the ninth major food allergen.
This ever-increasing prevalence of allergy-inducing foods has presented lucrative opportunities for the food allergen testing industry in recent years since food processing business operators are placing a strong emphasis on ensuring transparency in their products’ ingredient lists. By testing for allergens in food products, organizations can accurately mention each ingredient, and thereby allow people with specific food allergies to avoid consuming them.
Several allergen detection methods are used in the food processing industry, including mass spectrometry, DNA-based polymerase chain reaction (PCR) as well as ELISA (enzyme-linked immunosorbent assay), to name a few. The FDA, for instance, created a food allergen detection assay, called xMAP, designed to simultaneously identify 16 allergens, including sesame, within a single analysis, along with the ability to expand for the targeting of additional food allergens. Such industry advancements are improving the monitoring process for undeclared allergen presence in the food supply chain and enabling timely intervention upon detection.
Furthermore, initiatives, such as the Voluntary Incidental Trace Allergen Labelling (VITAL), created and managed by the Allergen Bureau, are also shedding light on the importance of allergen testing in food production. The VITAL program is designed to support allergen management with the help of a scientific process for risk assessment, in order to comply with food safety systems like the HACCP (Hazard Analysis and Critical Control Point), with allergen analysis playing a key role in its application.
ELISA Gains Prominence as Ideal Tool for Food Allergen Testing
In life sciences, the detection and quantification of various antibodies or antigens in a cost-effective and timely manner is of utmost importance. Detection of select protein expression on a cell surface, identification of immune responses in individuals, or execution of quality control testing—all these assessments require a dedicated tool.
ELISA is one such tool proving to be instrumental for both diagnostics as well as research). Described as an immunological assay, ELISA is used commonly for the measurement of antibodies or antigens in biological samples, including glycoproteins or proteins.
While its utility continues to grow, ELISA-based testing has historically demonstrated excellent sensitivity in food allergen testing applications, in some cases down to ppm (parts per million). It has a distinct advantage over other allergen detection methods like PCR, owing to the ability to adapt to certain foods like milk and oils, where its counterparts tend to struggle. The FDA is one of the major promoters of ELISA for allergen testing in food production, involving the testing of food samples using two different ELISA kits, prior to confirming results.
Many major entities are also taking heed of the growing interest in the use of ELISA for food allergen diagnostics. A notable example of this is laboratory analyses test kits and systems supplier, Eurofins, which introduced its SENSISpec Soy Total protein ELISA kit in September 2020. The enzyme immunoassay, designed for quantitative identification of soy protein in swab and food samples, has been developed by Eurofins Immunolab to measure residues of processed protein in various food products, including instant meals, chocolate, baby food, ice cream, cereals, sausage, and cookies, among others.
In essence, food allergens continue to prevail as high-risk factors for the food production industry. Unlike other pathogens like bacteria, allergenic proteins are heat resistant and stable, and cannot easily be removed once present in the food supply chain. In this situation, diagnostic allergen testing, complete segregation of allergenic substances, and accurate food allergen labeling are emerging as the ideal courses of action for allergen management in the modern food production ecosystem, with advanced technologies like molecular-based food allergy diagnostics expected to take up a prominent role over the years ahead.
The event begins at 11:45 am ET on Thursday, July 15.
Presentations are as follows:
Get with the Program: Modernization of Poultry Inspections in the United States; A panel discussion with Mitzi Baum, STOP Foodborne Illness;
Sarah Sorscher, Center for Science in the Public Interest; Martin Weidman, DMV, Ph.D., Cornell University; and Bruce Stewart-Brown, Perdue Foods
Detect, Deter, Destroy! A Discussion on Salmonella Detection, Mitigation and Control, with Elise Forward, Forward Food Solutions; Dave Pirrung, DCP Consulting; additional speaker TBA
A Case Study on Salmonella, with Rob Mommsen, Sabra Dipping Company
Sponsored TechTalks will be provided by Will Eaton of Meritech, Patrick Casey of BestSanitizer, Adam Esser of Sterilex, and Asif Rahman of Weber Scientific.
Prevention of foreign object contamination is a growing priority for food processors. Multi-hurdle approaches are becoming a more common method to foreign materials detection in processing plants. During this webinar, we’ll look at what a multi-hurdle approach can look like in different environments, along with the emerging set of technologies for automated inspection: Vision systems.
Food safety experts will discuss challenges and tangible best practices in Salmonella detection, mitigation and control, along with critical issues that the food industry faces with regards to the pathogen. This includes the journey and progress of petition to USDA on reforming and modernizing poultry inspections to reduce the incidence of Salmonella and Campylobacter; Salmonella detection, mitigation and control; and a case study on the pathogen involving crisis management.
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.
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.
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