Tag Archives: polymerase chain reaction

Susanne Kuehne, Decernis
Food Fraud Quick Bites

The Straw that Broke the Camel’s Back

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

Due to its health benefits, camel meat is gaining in popularity for consumers but unfortunately also for fraudsters for economic gain. Polymerase chain reaction (PCR) technologies allow quick and accurate detection of specific meat types, including processed and cooked meats. This newly developed PCR lateral flow immunology method found adulteration of camel meat with beef in 10% of the 20 samples that were investigated in this Chinese study.

Resource

  1. Zhao, L., et. al. (July 30, 2020). “Identification of camel species in food products by a polymerase chain reaction-lateral flow immunoassay”. Food Chemistry. Science Direct. Volume 319.
Susanne Kuehne, Decernis
Food Fraud Quick Bites

Marzipan Or Persipan, That’s the Question

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

Both Prunus species produce similar flavor and sensory profiles, but have significantly different costs—the 50% cheaper apricot kernels are sometimes used as an adulterant, replacing almonds in products such as marzipan, almond oil or almond powder. A polymerase chain reaction (PCR) method shows that the DNA barcode of almond shows significant differences from other Prunus species and can therefore be used to detect adulteration of almond products.

Resource

  1. Uncu, A.O. (March 2, 2020). “A trnH-psbA barcode genotyping assay for the detection of common apricot (Prunus armeniaca L.) adulteration in almond (Prunus dulcis Mill.)” Retrieved from Taylor & Francis Online.
Susanne Kuehne, Decernis
Food Fraud Quick Bites

A New Way to Spot a Fake

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

The common cuttlefish (Sepia officinalis) is a popular food source, and it is often adulterated with other cephalopod and sepia species. A new, low cost, real time polymerase chain reaction (PCR) method can be used on fresh, cooked, grilled, frozen and canned preparations of Sepia officinalis, producing quick and highly reliable results. In this study, 25% of the samples were found to be different cephalopod species, and not Sepia officinalis.

Resource

  1. Amaya Velasco, Graciela Ramilo-Fernandez, Carmen G. Sotelo (March 4, 2020) Instituto de Investigaciones Marinas (IIM-CSIC), Eduardo Cabello 6, 36208 Vigo (Pontevedra), Spain: “A Real-Time PCR Method for the Authentication of Common Cuttlefish (Sepia officinalis) in Food Products”. This study is part of the SEATRACES project (www.seatraces.eu).
Raj Rajagopal, 3M Food Safety
In the Food Lab

Pathogen Detection Guidance in 2020

By Raj Rajagopal
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Raj Rajagopal, 3M Food Safety

Food production managers have a critical role in ensuring that the products they make are safe and uncontaminated with dangerous pathogens. Health and wellness are in sharp focus for consumers in every aspect of their lives right now, and food safety is no exception. As food safety becomes a continually greater focus for consumers and regulators, the technologies used to monitor for and detect pathogens in a production plant have become more advanced.

It’s no secret that pathogen testing is performed for numerous reasons: To confirm the adequacy of processing control and to ensure foods and beverages have been properly stored or cooked, to name some. Accomplishing these objectives can be very different, and depending on their situations, processors rely on different tools to provide varying degrees of testing simplicity, speed, cost, efficiency and accuracy. It’s common today to leverage multiple pathogen diagnostics, ranging from traditional culture-based methods to molecular technologies.

And unfortunately, pathogen detection is more than just subjecting finished products to examination. It’s become increasingly clear to the industry that the environment in which food is processed can cross-contaminate products, requiring food manufacturers to be ever-vigilant in cleaning, sanitizing, sampling and testing their sites.

For these reasons and others, it’s important to have an understanding and appreciation for the newer tests and techniques used in the fight against deadly pathogens, and where and how they might be fit for purpose throughout the operation. This article sheds light on the key features of one fast-growing DNA-based technology that detects pathogens and explains how culture methods for index and indicator organisms continue to play crucial roles in executing broad-based pathogen management programs.

LAMP’s Emergence in Molecular Pathogen Detection

Molecular pathogen detection has been a staple technology for food producers since the adoption of polymerase chain reaction (PCR) tests decades ago. However, the USDA FSIS revised its Microbiology Laboratory Guidebook, the official guide to the preferred methods the agency uses when testing samples collected from audits and inspections, last year to include new technologies that utilize loop-mediated isothermal amplification (LAMP) methods for Salmonella and Listeria detection.

LAMP methods differ from traditional PCR-based testing methods in four noteworthy ways.

First, LAMP eliminates the need for thermal cycling. Fundamentally, PCR tests require thermocyclers with the ability to alter the temperature of a sample to facilitate the PCR. The thermocyclers used for real-time PCR tests that allow detection in closed tubes can be expensive and include multiple moving parts that require regular maintenance and calibration. For every food, beverage or environmental surface sample tested, PCR systems will undergo multiple cycles of heating up to 95oC to break open DNA strands and cooling down to 60oC to extend the new DNA chain in every cycle. All of these temperature variations generally require more run time and the enzyme, Taq polymerase, used in PCR can be subjected to interferences from other inhibiting substances that are native to a sample and co-extracted with the DNA.

LAMP amplifies DNA isothermally at a steady and stable temperature range—right around 60oC. The Bst polymerase allows continuous amplification and better tolerates the sample matrix inhibitors known to trip up PCR. The detection schemes used for LAMP detection frees LAMP’s instrumentation from the constraints of numerous moving pieces.

Secondly, it doubles the number of DNA primers. Traditional PCR tests recognize two separate regions of the target genetic material. They rely on two primers to anneal to the subject’s separated DNA strands and copy and amplify that target DNA.

By contrast, LAMP technology uses four to six primers, which can recognize six to eight distinct regions from the sample’s DNA. These primers and polymerase used not only cause the DNA strand to displace, they actually loop the end of the strands together before initiating amplification cycling. This unique looped structure both accelerates the reaction and increases test result sensitivity by allowing for an exponential accumulation of target DNA.

Third of all, it removes steps from the workflow. Before any genetic amplification can happen, technicians must enrich their samples to deliberately grow microorganisms to detectable levels. Technicians using PCR tests have to pre-dispense lysis buffers or reagent mixes and take other careful actions to extract and purify their DNA samples.

Commercialized LAMP assay kits, on the other hand, offer more of a ready-to-use approach as they offer ready to use lysis buffer and simplified workflow to prepare DNA samples. By only requiring two transfer steps, it can significantly reduces the risk of false negatives caused by erroneous laboratory preparation.

Finally, it simplifies multiple test protocols into one. Food safety lab professionals using PCR technology have historically been required to perform different test protocols for each individual pathogen, whether that be Salmonella, Listeria, E. coli O157:H7 or other. Not surprisingly, this can increase the chances of error. Oftentimes, labs are resource-challenged and pressure-packed environments. Having to keep multiple testing steps straight all of the time has proven to be a recipe for trouble.

LAMP brings the benefit of a single assay protocol for testing all pathogens, enabling technicians to use the same protocol for all pathogen tests. This streamlined workflow involving minimal steps simplifies the process and reduces risk of human-caused error.

Index and Indicator Testing

LAMP technology has streamlined and advanced pathogen detection, but it’s impractical and unfeasible for producers to molecularly test every single product they produce and every nook and cranny in their production environments. Here is where an increasing number of companies are utilizing index and indicator tests as part of more comprehensive pathogen environmental programs. Rather than testing for specific pathogenic organisms, these tools give a microbiological warning sign that conditions may be breeding undesirable food safety or quality outcomes.

Index tests are culture-based tests that detect microorganisms whose presence (or detection above a threshold) suggest an increased risk for the presence of an ecologically similar pathogen. Listeria spp. Is the best-known index organism, as its presence can also mark the presence of deadly pathogen Listeria monocytogenes. However, there is considerable skepticism among many in the research community if there are any organisms outside of Listeria spp. that can be given this classification.

Indicator tests, on the other hand, detect the presence of organisms reflecting the general microbiological condition of a food or the environment. The presence of indicator organisms can not provide any information on the potential presence or absence of a specific pathogen or an assessment of potential public health risk, but their levels above acceptable limits can indicate insufficient cleaning and sanitation or operating conditions.

Should indicator test results exceed the established control limits, facilities are expected to take appropriate corrective action and to document the actions taken and results obtained. Utilizing cost-effective, fast indicator tests as benchmark to catch and identify problem areas can suggest that more precise, molecular methods need to be used to verify that the products are uncontaminated.

Process Matters

As discussed, technology plays a large role in pathogen detection, and advances like LAMP molecular detection methods combined with strategic use of index and indicator tests can provide food producers with powerful tools to safeguard their consumers from foodborne illnesses. However, whether a producer is testing environmental samples, ingredients or finished product, a test is only as useful as the comprehensive pathogen management plan around it.

The entire food industry is striving to meet the highest safety standards and the best course of action is to adopt a solution that combines the best technologies available with best practices in terms of processes as well –from sample collection and preparation to monitoring and detection.

Michael Bartholomeusz, TruTag
In the Food Lab

Intelligent Imaging and the Future of Food Safety

By Michael Bartholomeusz, Ph.D.
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Michael Bartholomeusz, TruTag

Traditional approaches to food safety no longer make the grade. It seems that stories of contaminated produce or foodborne illnesses dominate the headlines increasingly often. Some of the current safeguards set in place to protect consumers and ensure that companies are providing the freshest, safest food possible continue to fail across the world. Poorly regulated supply chains and food quality assurance breakdowns often sicken customers and result in recalls or lawsuits that cost money and damage reputations. The question is: What can be done to prevent these types of problems from occurring?

While outdated machinery and human vigilance continue to be the go-to solutions for these problems, cutting-edge intelligent imaging technology promises to eliminate the issues caused by old-fashioned processes that jeopardize consumer safety. This next generation of imaging will increase safety and quality by quickly and accurately detecting problems with food throughout the supply chain.

How Intelligent Imaging Works

In broad terms, intelligent imaging is hyperspectral imaging that uses cutting-edge hardware and software to help users establish better quality assurance markers. The hardware captures the image, and the software processes it to provide actionable data for users by combining the power of conventional spectroscopy with digital imaging.

Conventional machine vision systems generally lack the ability to effectively capture and relay details and nuances to users. Conversely, intelligent imaging technology utilizes superior capabilities in two major areas: Spectral and spatial resolution. Essentially, intelligent imaging systems employ a level of detail far beyond current industry-standard machinery. For example, an RGB camera can see only three colors: Red, green and blue. Hyperspectral imaging can detect between 300 and 600 real colors—that’s 100–200 times more colors than detected by standard RGB cameras.

Intelligent imaging can also be extended into the ultraviolet or infrared spectrum, providing additional details of the chemical and structural composition of food not observable in the visible spectrum. Hyperspectral imaging cameras do this by generating “data cubes.” These are pixels collected within an image that show subtle reflected color differences not observable by humans or conventional cameras. Once generated, these data cubes are classified, labeled and optimized using machine learning to better process information in the future.

Beyond spectral and spatial data, other rudimentary quality assurance systems pose their own distinct limitations. X-rays can be prohibitively expensive and are only focused on catching foreign objects. They are also difficult to calibrate and maintain. Metal detectors are more affordable, but generally only catch metals with strong magnetic fields like iron. Metals including copper and aluminum can slip through, as well as non-metal objects like plastics, wood and feces.

Finally, current quality assurance systems have a weakness that can change day-to-day: Human subjectivity. The people put in charge of monitoring in-line quality and food safety are indeed doing their best. However, the naked eye and human brain can be notoriously inconsistent. Perhaps a tired person at the end of a long shift misses a contaminant, or those working two separate shifts judge quality in slightly different ways, leading to divergent standards unbeknownst to both the food processor and the public.

Hyperspectral imaging can immediately provide tangible benefits for users, especially within the following quality assurance categories in the food supply chain:

Pathogen Detection

Pathogen detection is perhaps the biggest concern for both consumers and the food industry overall. Identifying and eliminating Salmonella, Listeria, and E.coli throughout the supply chain is a necessity. Obviously, failure to detect pathogens seriously compromises consumer safety. It also gravely damages the reputations of food brands while leading to recalls and lawsuits.

Current pathogen detection processes, including polymerase chain reaction (PCR), immunoassays and plating, involve complicated and costly sample preparation techniques that can take days to complete and create bottlenecks in the supply chain. These delays adversely impact operating cycles and increase inventory management costs. This is particularly significant for products with a short shelf life. Intelligent imaging technology provides a quick and accurate alternative, saving time and money while keeping customers healthy.

Characterizing Food Freshness

Consumers expect freshness, quality and consistency in their foods. As supply chains lengthen and become more complicated around the world, food spoilage has more opportunity to occur at any point throughout the production process, manifesting in reduced nutrient content and an overall loss of food freshness. Tainted meat products may also sicken consumers. All of these factors significantly affect market prices.

Sensory evaluation, chromatography and spectroscopy have all been used to assess food freshness. However, many spatial and spectral anomalies are missed by conventional tristimulus filter-based systems and each of these approaches has severe limitations from a reliability, cost or speed perspective. Additionally, none is capable of providing an economical inline measurement of freshness, and financial pressure to reduce costs can result in cut corners when these systems are in place. By harnessing meticulous data and providing real-time analysis, hyperspectral imaging mitigates or erases the above limiting factors by simultaneously evaluating color, moisture (dehydration) levels, fat content and protein levels, providing a reliable standardization of these measures.

Foreign Object Detection

The presence of plastics, metals, stones, allergens, glass, rubber, fecal matter, rodents, insect infestation and other foreign objects is a big quality assurance challenge for food processors. Failure to identify foreign objects can lead to major added costs including recalls, litigation and brand damage. As detailed above, automated options like X-rays and metal detectors can only identify certain foreign objects, leaving the rest to pass through untouched. Using superior spectral and spatial recognition capabilities, intelligent imaging technology can catch these objects and alert the appropriate employees or kickstart automated processes to fix the issue.

Mechanical Damage

Though it may not be put on the same level as pathogen detection, food freshness and foreign object detection, consumers put a premium on food uniformity, demanding high levels of consistency in everything from their apples to their zucchini. This can be especially difficult to ensure with agricultural products, where 10–40% of produce undergoes mechanical damage during processing. Increasingly complicated supply chains and progressively more automated production environments make delivering consistent quality more complicated than ever before.

Historically, machine vision systems and spectroscopy have been implemented to assist with damage detection, including bruising and cuts, in sorting facilities. However, these systems lack the spectral differentiation to effectively evaluate food and agricultural products in the stringent manner customers expect. Methods like spot spectroscopy require over-sampling to ensure that any detected aberrations are representative of the whole item. It’s a time-consuming process.

Intelligent imaging uses superior technology and machine learning to identify mechanical damage that’s not visible to humans or conventional machinery. For example, a potato may appear fine on the outside, but have extensive bruising beneath its skin. Hyperspectral imaging can find this bruising and decide whether the potato is too compromised to sell or within the parameters of acceptability.

Intelligent imaging can “see” what humans and older technology simply cannot. With the ability to be deployed at a number of locations within the food supply chain, it’s an adaptable technology with far-reaching applications. From drones measuring crop health in the field to inline or end-of-line positioning in processing facilities, there is the potential to take this beyond factory floors.

In the world of quality assurance, where a misdiagnosis can literally result in death, the additional spectral and spatial information provided by hyperspectral imaging can be utilized by food processors to provide important details regarding chemical and structural composition previously not discernible with rudimentary systems. When companies begin using intelligent imaging, it will yield important insights and add value as the food industry searches for reliable solutions to its most serious challenges. Intelligent imaging removes the subjectivity from food quality assurance, turning it into an objective endeavor.