Tag Archives: next generation sequencing

Sasan Amini, Clear Labs

2020 Expectations: More Artificial Intelligence and Machine Learning, Technology Advances in Food Safety Testing

By Maria Fontanazza
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Sasan Amini, Clear Labs

2018 and 2019 were the years of the “blockchain buzz”. As we enter the new decade, we can expect a stronger focus on how technology and data advances will generate more actionable use for the food industry. Food Safety Tech has highlighted many perspectives from subject matter experts in the industry, and 2020 will be no different. Our first Q&A of the year features Sasan Amini, CEO of Clear Labs, as he shares his thoughts on tech improvements and the continued rise consumer expectations for transparency.

Food Safety Tech: As we look to the year ahead, where do you see artificial intelligence, machine learning and blockchain advancing in the food industry?

Sasan Amini: AI, ML, and blockchain are making headway in the food industry through advances in supply chain management, food sorting and anomaly detection, and tracing the origin of foodborne outbreaks. On the regulatory side, FDA’s focus on its New Era of Smarter Food Safety will most likely catalyze the adoption of the above mentioned technologies. On the private side, a few of the companies leading the charge on these advancements are IBM and Google, working in partnership with food manufacturers and retailers across the world.

Along those same lines, another area that we expect to grow is the use of AI and ML in tandem with robotics—and the value of new troves of data that they collect, analyze and distribute. For example, robotics for the use of environmental monitoring of potential contaminants, sorting techniques and sterilization are valuable because they ensure that end products have been through thorough testing—and they give us even more information about the lifecycle of that food than ever before.

At the end of the day, data is only valuable when you can transform it into actionable insights in real-time with real-world applications, and we expect to see more and more of this type of data usage in the year ahead.

FST: Where do you think food safety testing technologies will stand out? What advancements can the industry expect?

Amini: In 2020, technology is going to begin to connect itself along the entire supply chain, bringing together disparate pieces and equipping supply chain professionals with action-oriented data. From testing advances that improve speed, accuracy and depth of information to modular software solutions to promote transparency, the food safety industry is finally finding its footing in a data-driven sea of technological and regulatory advances.

Right now, legacy testing solutions are limited in their ability to lead food safety and quality professionals to the source of problems, providing insights on tracking recurring issues, hence having a faster response time, and being able to anticipate problems before they occur based on a more data heavy and objective risk assessment tools. This leaves the industry in a reactive position for managing and controlling their pathogen problems.

Availability of higher resolution food safety technologies that provide deeper and more accurate information and puts them in context for food safety and quality professionals provides the food industry a unique opportunity to resolve the incidents in a timely fashion with higher rigour and confidence. This is very in-line with the “Smarter Tools and Approaches” that FDA described in their new approach to food safety.

FST: How are evolving consumer preferences changing how food companies must do business from a strategic as well as transparency perspective?

Amini: Consumers are continuing to get savvier about what’s in their food and where it comes from. Research suggests that about one in five U.S. adults believe they are food allergic, while only 1 in 20 are estimated to have physician-diagnosed food allergies. This discrepancy is important for food companies to consider when making decisions about transparency into their products. Although the research on food allergies continues to evolve, what’s important to note today is that consumers want to know the details. Radical transparency can be a differentiator in a competitive market, especially for consumers looking for answers to improve their health and nutrition.

Consumers are also increasingly interested in personalization, due in part to the rise in new digital health and testing companies looking to deliver on the promise of personalized nutrition and wellness. Again, more transparency will be key.

FST: Additional comments are welcome.

Amini: Looking ahead, we expect that smaller, multi-use, and hyper-efficient tools with reduced physical footprints will gain market share. NGS is a great example of this, as it allows any lab to gather millions of data points about a single sample without needing to run it multiple times. It moves beyond the binary yes-no response of traditional testing, and lets you get much more done, with far less. Such wealth of information not only increases the confidence about the result, but can also be mined to generate more actionable insights for interventions and root cause analysis.

This “multi-tool” will be driven by a combination of advanced software, robotics, and testing capabilities, creating a food safety system that is entirely connected, driven by data, and powerfully accurate.

Sasan Amini, Clear Labs
FST Soapbox

Beyond the Results: What Can Testing Teach Us?

By Sasan Amini
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Sasan Amini, Clear Labs

The microbiology lab will increasingly be understood as the gravitational center of big data in the food industry. Brands that understand how to leverage the data microbiology labs are producing in ever larger quantities will be in the best position to positively impact their bottom line—and even transform the lab from a cost center to a margin contributor.

The global rapid microbiology testing market continues to grow at a steady pace. The market is projected to reach $5.09 billion by 2023, up from $3.45 billion in 2018. Increased demand for food microbiology testing—and pathogen detection in particular—continues to drive the overall growth of this sector. The volume of food microbiology tests totaled 1.14 billion tests in 2016—up 15% from 2013. In 2018 that number is estimated to have risen to 1.3 billion tests, accounting for nearly half the overall volume of industrial microbiology tests performed worldwide.

The food industry is well aware that food safety testing programs are a necessary and worthwhile investment. Given the enormous human and financial costs of food recalls, a robust food safety testing system is the best insurance policy any food brand can buy.

We are going through a unique transition where food safety tests are evolving from binary tests to data engines that are capable of generating orders of magnitude of more information. This creates a unique opportunity where many applications for big data collected from routine pathogen testing can help go beyond stopping an outbreak. Paired with machine learning and other data platforms, these data have the opportunity to become valuable, actionable insights for the industry.

While some of these applications will have an impact on fundamental research, I expect that big data analytics and bioinformatics will have significant opportunity to push the utilities of these tests from being merely a diagnostic test to a vehicle for driving actions and offering recommendations. Two examples of such transformations include product development and environmental testing.

Food-Safety Testing Data and Product Development

Next-generation-sequencing (NGS) technologies demonstrate a great deal of potential for product development, particularly when it comes to better understanding shelf life and generating more accurate shelf-life estimates.

Storage conditions, packaging, pH, temperature, and water activity can influence food quality and shelf life among other factors. Shelf-life estimates, however, have traditionally been based on rudimentary statistical models incapable of accounting for the complexity of factors that impact food freshness, more specifically not being able to take into consideration the composition and quantity of all microbial communities present on any food sample. These limitations have long been recognized by food scientists and have led them to look for cost-effective alternatives.

By using NGS technologies, scientists can gain a more complete picture of the microbial composition of foods and how those microbial communities are influenced by intrinsic and extrinsic factors.

It’s unlikely that analyzing the microbiome of every food product or unit of product will ever be a cost-effective strategy. However, over time, as individual manufacturers and the industry as a whole analyze more and more samples and generate more data, we should be able to develop increasingly accurate predictive models. The data generation cost and logistics could be significantly streamlined if existing food safety tests evolve to broader vehicles that can create insights on both safety and quality indications of food product simultaneously. By comparing the observed (or expected) microbiome profile of a fresh product with the models we develop, we could greatly improve our estimates of a given product’s remaining shelf life.

This will open a number of new opportunities for food producers and consumers. Better shelf-life estimates will create efficiencies up and down the food supply chain. The impact on product development can hardly be underestimated. As we better understand the precise variables that impact food freshness for particular products, we can devise food production and packaging technologies that enhance food safety and food quality.

As our predictive models improve, an entire market for these models will emerge, much as it has in other industries that rely on machine learning models to draw predictive insights from big data.

Data Visualization for Environmental Monitoring

In the past one to two years, NGS technologies have matured to the point that they can now be leveraged for high-volume pathogen and environmental testing.

Just as it has in other industries, big data coupled with data visualization approaches can play a mainstream role in food safety and quality applications.

Data visualization techniques are not new to food safety programs and have proven particularly useful when analyzing the results of environmental testing. The full potential of data visualizations has yet to be realized, however. Visualizations can be used to better understand harborage sites, identifying patterns that need attention, and visualize how specific strains of a pathogen are migrating through a facility.

Some of this is happening in food production facilities already, but it’s important to note that visualizations are only as useful as the underlying data is accurate. That’s where technologies like NGS come in. NGS provides the option for deeper characterization of pathogenic microorganisms when needed (down to the strain). The depth of information from NGS platforms enables more reliable and detailed characterization of pathogenic strains compared to existing methods.

Beyond basic identification, there are other potential use cases for environmental mapping, including tracking pathogens as they move through the supply chain. It’s my prediction that as the food industry more broadly adopts NGS technologies that unify testing and bioinformatics in a single platform, data visualization techniques will rapidly advance, so long as we keep asking ourselves: What can the data teach us?

The Food Data Revolution and Market Consolidation

Unlike most PCR and immunoassay-based testing techniques, which in most cases can only generate binary answers, NGS platforms generate millions of data points for each sample for up to tens to hundreds of samples. As NGS technologies are adopted and the data we collect increases exponentially, the food safety system will become the data engine upon which new products and technologies are built.

Just as we have seen in any number of industries, companies with access to data and the means to make sense of it will be in the best position to capitalize on new revenue opportunities and economies of scale.

Companies that have adopted NGS technologies for food safety testing will have an obvious advantage in this emerging market. And they won’t have had to radically alter their business model to get there. They’ll be running the same robust programs they have long had in place, but collecting a much larger volume of data in doing so. Companies with a vision of how to best leverage this data will have the greatest edge.

Megan Nichols
FST Soapbox

Technology Tools Improving Food Safety

By Megan Ray Nichols
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Megan Nichols

To cap off a tumultuous year for foodborne illnesses, the end of 2018 saw a rather large E. coli outbreak that affected several different types of lettuce. In all, about 62 people got sick in the United States, with another 29 affected in Canada. The outbreak was traced back to a farm in California thanks to a specific DNA fingerprint in the E. coli. It started in a water reservoir and spread to the nearby crops.

Unfortunately, the event was only one of two separate incidents involving romaine lettuce last year. Another E.coli outbreak was traced back to a source in Arizona. Are these outbreaks more common than we realize? The CDC estimates that 48 million Americans fall ill each year from foodborne pathogens. Of those who get sick, 128,000 have to be hospitalized, and about 3,000 perish.

It’s clear that the industry as a whole needs to buckle down and find more effective solutions, not just for preventing outbreaks but also for mitigating damage when they happen. A new level of safety and management can be achieved with the help of many new, innovative technologies.

The following are some of the technology tools shaping the future of food safety and quality management fields.

Blockchain

As a result of the E. coli outbreak, Walmart implemented blockchain technology to track leafy greens and boost supply chain transparency. The systems and infrastructure is anticipated to be in place by the end of 2019.

Blockchain is a secure, digital ledger. It holds information about various transactions and data, all of which are carried out on the network. It’s called a blockchain because each data set within the network is a chunk or “block,” and they’re all linked to one another—hence the chain portion of the name. What this allows for is complete transparency throughout the supply chain, because you can track goods from their origin all the way to distribution and sale.

Each block is essentially a chunk of information, and when it’s entered into the chain, it cannot be altered, modified or manipulated. It’s simply there for viewing publicly. You cannot alter information contained within a single block without modifying the entire chain—which operates much like a peer-to-peer network and is split across many devices and servers.
This unique form of security establishes trust, accuracy and a clear representation of what’s happening. It allows a company to track contaminated foods along their journey, stopping them before they contaminate other goods or reach customers.

Infrared Heating

Thanks to the rising popularity of ready-to-eat meals, the industry is under pressure to adopt preservation and pasteurization methods. Particularly, they must be able to sanitize foods and package them with minimal exposure and bacteria levels. This practice allows them to stay fresh for longer and protects customers from potential foodborne illness.

Infrared heating is a method of surface pasteurization, and has been used for meats such as ham. Infrared lamps radiate heat at low temperatures, effectively killing surface bacteria and contaminants. The idea is to decontaminate or sanitize the surface of foods before final packaging occurs.

Industrial IoT and Smart Sensors

The food and beverage industry has a rather unique challenge with regard to supply chain operations. Food may be clean and correctly handled at the source with no traces of contamination, but it’s then passed on to a third party, which changes the game. Maybe a refrigerated transport breaks down, and the food within is thawed out. Perhaps a distributor doesn’t appropriately store perishable goods, resulting in serious contamination.

This transportation stage can be more effectively tracked and optimized with the help of modern IoT and smart, connected sensors. RFID tags, for instance, can be embedded in the packaging of foods to track their movements and various stats. Additional sensors can monitor storage temps, travel times, unexpected exposure, package tears and more.

More importantly, they’re often connected to a central data processing system where AI and machine learning platforms or human laborers can identify problematic changes. This setup allows supply chain participants to take action sooner in order to remedy potential problems or even pull contaminated goods out of the supply.

They can also help cut down on fraud or falsified records, which is a growing problem in the industry. Imagine an event where an employee says that a package was handled properly via forms or reporting tools, yet it was exposed to damaging elements. The implications of even simple fraud can be significant. Technology that automatically and consistently reports information—over manual entry—can help eliminate this possibility altogether.

Next-Generation Sequencing

NGS refers to a high-throughput DNA sequencing process that is now available to the food industry as a whole. It’s cheaper, more effective and takes a lot less time to complete, which means DNA and RNA sequencing is more accessible to food companies and suppliers now than it ever has been.

NGS can be used to assess and sequence hundreds of different samples at a time at rates of up to 25 million reads per experiment. What that means is that monitoring teams can accurately identify foodborne pathogens and contamination at the speed of the modern market. It is also a highly capable form of food safety measurement and is quickly replacing older, molecular-based methods like PCR.

Ultimately, NGS will lead to vastly improved testing and measurement processes, which can identify potential issues faster and in higher quantities than traditional methods. The food industry will be all the better and safer for it.

The Market Is Ever Evolving

While these technologies are certainly making a splash—and will shape the future of the food safety industry—they do not exist in a vacuum. There are dozens of other technologies and solutions being explored. It is important to understand that many new technologies could rise to the surface even within the next year.

The good news is that it’s all meant to improve the industry, particularly when it comes to the freshness, quality and health of the goods that consumers eat.

Stephanie Pollard, ClearLabs
In the Food Lab

The Power of Advanced NGS Technology in Routine Pathogen Testing

By Stephanie Pollard
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Stephanie Pollard, ClearLabs

The food industry is beginning to transition into an era of big data and analytics unlike anything the industry has ever experienced. However, while the evolution of big data brings excitement and the buzz of new possibilities, it also comes coupled with an element of confusion due to the lack of tools for interpretation and lack of practical applications of the newly available information.

As we step into this new era and begin to embrace these changes, we need to invest time to educate ourselves on the possibilities before us, then make informed and action-oriented decisions on how to best use big data to move food safety and quality into the next generation.

Stephanie Pollard will be presenting “The Power of Advanced NGS Technology in Routine Pathogen Testing” at the 2018 Food Safety Consortium | November 13–15One of the big questions for big data and analytics in the food safety industry is the exact origins of this new data. Next Generation Sequencing (NGS) is one new and disruptive technology that will contribute significantly to a data explosion in our industry.

NGS-based platforms offer the ability to see what was previously impossible with PCR and other technologies. These technologies generate millions of sequences simultaneously, enabling greater resolution into the microbial ecology of food and environmental surfaces.

This represents a seismic shift in the food safety world. It changes the age-old food microbiology question from: “Is this specific microbe in my sample?” to “what is the microbial makeup of my sample?”

Traditionally, microbiologists have relied on culture-based technologies to measure the microbial composition of foods and inform risk management decisions. While these techniques have been well studied and are standard practices in food safety and quality measures, they only address a small piece of a much bigger microbial puzzle. NGS-based systems allow more complete visibility into this puzzle, enabling more informed risk management decisions.

With these advances, one practical application of NGS in existing food safety management systems is in routine pathogen testing. Routine pathogen testing is a form of risk assessment that typically gives a binary presence/absence result for a target pathogen.

NGS-based platforms can enhance this output by generating more than the standard binary result through a tunable resolution approach. NGS-based platforms can be designed to be as broad, or as specific, as desired to best fit the needs of the end user.

Imagine using an NGS-based platform for your routine pathogen testing needs, but instead of limiting the information you gather to yes/no answers for a target pathogen, you also obtain additional pertinent information, including: Serotype and/or strain identification, resident/transient designation, predictive shelf-life analysis, microbiome analysis, or predictive risk assessment.

By integrating an NGS-based platform into routine pathogen testing, one can begin to build a microbial database of the production facility, which can be used to distinguish resident pathogens and/or spoilage microbes from transient ones. This information can be used to monitor and improve existing or new sanitation practices as well as provide valuable information on ingredient quality and safety.

This data can also feed directly into supplier quality assurance programs and enable more informed decisions regarding building partnerships with suppliers who offer superior products.

Similarly, by analyzing the microbiome of a food matrix, food producers can identify the presence of food spoilage microbes to inform more accurate shelf-life predictions as well as evaluate the efficacy of interventions designed to reduce those microbes from proliferating in your product (e.g. modified packaging strategies, storage conditions, or processing parameters).

Envision a technology that enables all of the aforementioned possibilities while requiring minimal disruption to integrate into existing food safety management systems. NGS-based platforms offer answers to traditional pathogen testing needs for presence/absence information, all the while providing a vast amount of additional information. Envision a future in which we step outside of our age-old approach of assessing the safety of the food that we eat via testing for the presence of a specific pathogen. Envision a future in which we raise our standards for safety and focus on finding whatever is there, without having to know in advance what to look for.

Every year we learn of new advancements that challenge the previously limited view on the different pathogens that survive and proliferate on certain food products and have been overlooked (e.g., Listeria in melons). Advanced NGS technologies allow us to break free of those associations and focus more on truly assessing the safety and quality of our products by providing a deeper understanding of the molecular makeup of our food.

Sequencing pattern, pathogens

Pilot Program Aims to Advance NGS to a Routine Pathogen Testing Platform

By Maria Fontanazza
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Sequencing pattern, pathogens

NGS, or next generation sequencing, is described as the “most updated automated DNA sequencing technology available,” according to Eurofins’ Gregory Siragusa, Ph.D. and Douglas Marshall, Ph.D. Over the past few years, there’s been quite a bit of discussion around the technology and its role in transforming food safety testing.

Clear Labs has been especially vocal about the potential of NGS, as the company has built itself on an NGS platform with capabilities that include GMO testing, pathogen detection and ingredient authenticity. The company just announced a pilot program for its NGS platform that aims to bring the technology into the realm of routine food safety testing. Mahni Ghorashi, co-founder of Clear Labs, recently discussed the program with Food Safety Tech.

Food Safety Tech: Is the platform entering the pilot the same as the technology we talked about in the Q&A,“New Whole Genome Sequencing Test Monitors Threat of Pathogens” a couple of years back?. If so, have there been developments since? If this is a different platform, how long has it been in development and what is the novelty and advantages?

Mahni Ghorashi, Clear Labs
Mahni Ghorashi, co-founder of Clear Labs

Mahni Ghorashi: That’s a good question, and I understand why this could be a little confusing, especially for someone who has followed the development of Clear Labs over the years. (Thank you!).

The current platform being piloted is based on the same fundamental technology we’ve always had, but we have built it out considerably and adapted it for routine food safety testing.

At its core, our platform is based on industry-leading NGS technology paired with IP-protected bioinformatics. It’s always been backed by the world’s largest reference database for genomic food markers and food sample metadata.

Over the last year and a half, we’ve built capabilities into the core platform that allow our system to be deployed at high testing volumes for food safety testing, at scale.

We’ve built in robotics and automation to make this system truly “end-to-end” and to speed the process from start to finish.

We’ve reduced the cost by another order of magnitude, with faster turnaround time and greater accuracy than competing market products.

In short, the latest version of the platform is the first automated system that takes advantage of advanced DNA sequencing, bioinformatics, and robotics.

This pilot represents a new era for Clear Labs and the food safety industry at large. While our tests have always been higher-resolution and higher-accuracy than PCR, we now believe we can compete with the turnaround times and cost of PCR.

FST: What is the duration of the pilot study? What is the goal of the pilot?

Ghorashi: The goal of the pilot study is to demonstrate that NGS is ready to be adopted as the new standard for routine food safety testing. We believe that our pilot study will also help the industry to fully appreciate how NGS technologies will modernize food safety programs, without changing the way food safety is conducted today.

The pilots last for two weeks. Because our platform is for high-volume, routine safety testing, it doesn’t take long to have tested a statistically significant number of samples. We’re able to quickly provide our customers with a report comparing our results to that of their legacy, PCR-based tests.

FST: What feedback have you received about the platform thus far? What is its potential?

Ghorashi: The feedback we’ve gotten has been overwhelmingly positive. We can’t talk specifics until the pilot is complete, but I can tell you in broad terms that our early pilot customers have been overwhelmingly enthusiastic.

The potential is enormous. This NGS platform—the first of its kind—is going to usher in a new era of food safety testing.

Traditional techniques have high rates false negatives and false positives. In 2015, a study from the American Proficiency Institute on about 18,000 testing results from 1999 to 2013 for Salmonella found false negative rates between 2% and 10% and false positive rates between 2% and 6%. Several Food Service Labs claim false positive rates of 5% to 50%.

False positives can create a resource-intensive burden on food companies. Reducing false negatives is important for public health as well as isolating and decontaminating the species within a facility.

The costs savings, but even more important the peace of mind that comes from a near fail-proof system is invaluable to the leading food brand and service labs we’ve been working with.

FST: What are the clearest areas of impact for NGS in food safety?

Ghorashi: The impact of NGS is going to be felt broadly because it will replace existing PCR systems for high-throughput safety testing. Across the food industry, wherever there are PCR systems, we will soon see NGS-based system that will be more comprehensive, accurate, and cost-effective.

And unlike some PCR techniques that can only detect up to five targets on one sample at a time, the targets for NGS platforms are nearly unlimited, with up to 25 million reads per sample, with 200 or more samples processed at the same time. This results in a major difference in the amount of information yielded.

FST: Do you have any additional comments on the pilot program or NGS in general?

Ghorashi: While I can’t talk about specific customers, I should note that our pilot program is already deployed across half of the U.S.’s third-party service labs as well as major food production companies engaged in high-volume, routine safety testing.

The majority of the food safety industry is well aware of how transformative NGS systems can be for both their food safety programs and their bottom line. This pilot will go a long ways toward demonstrating that NGS technology has arrived for primetime in the food safety industry.

We’re still accepting applications for the pilot, and we’re excited to help brands recognize the value of and move forward with this vital progression in testing. After the pilot phase, we’ll be rolling out the full platform at IAFP in July of this year.

We’ll keep you updated!

Mahni Ghorashi, Clear Labs
In the Food Lab

The Food Safety Testing Lab as Profit Center

By Mahni Ghorashi
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Mahni Ghorashi, Clear Labs

It’s not that the industry has been more reluctant than others to embrace change; rather, the forces that will drive the food’s big data revolution have but recently come to bear.

Regulation is now playing a role. FSMA mandates that the industry embrace proactive food safety measures. That means higher testing volumes. Higher testing volumes means more data.

At the same time, new technologies like next-generation sequencing (NGS) are beginning to find wide-scale adoption in food-safety testing. And NGS technologies generate a lot of data—so much so that the food safety lab will soon emerge as the epicenter of the food industry’s big data revolution. As a result, the microbiology lab, a cost center, will soon emerge as one the industry’s most surprising profit centers.

A Familiar Trend

This shift may be unprecedented in food, but plenty of other industries touched by a technological transformation have undergone a similar change, flipping the switch from overhead to revenue generation.

Take the IT department, for instance. The debate about IT departments being a cost or profit center has been ongoing for many years. If data centers had simply kept doing what they have done in the past—data processing, enterprise resource planning, desktop applications, help desk—maintaining an IT department would have remained a cost center.

But things look quite different today. Companies in today’s fast-changing business environment depend on their IT departments to generate value. Now and for the foreseeable future, the IT department is on the hook to provide companies with a strategic advantage and to create new revenue opportunities.

Netflix, for example, recently estimated the value of their recommenders and personalization engines at $1 billion per year by quadrupling their effective catalog and dramatically increasing customer engagement and reducing churn.

Another great example are the call centers of customer support departments. For most of their history, call centers generated incredibly small margins or were outright cost centers.

Now, call centers armed with AI and chatbots are a source of valuable customer insights and are a treasure trove of many brands’ most valuable data. This data can be used to fuel upsells, inform future product development, enhance brand loyalty, and increase market share.

Take Amtrak as a prime example. When the commuter railway implemented natural language chatbots on their booking site, they generated 30% more revenue per booking, saved $1 million in customer service email costs, and experienced an 8X return on investment.

These types of returns are not out of reach for the food industry.

The Food Data Revolution Starts in the Lab

The microbiology lab will be the gravitational center of big data in the food industry. Millions of food samples flow in and out of these labs every hour and more and more samples are being tested each year. In 2016 the global food microbiology market totaled 1.14 billion tests—up 15% from 2013.1

I’d argue that the food-testing lab is the biggest data generator in the entire supply chain. These labs are not only collecting molecular data about raw and processed foods but also important inventory management information like lot numbers, brand names and supplier information, to name a few.

As technologies like NGS come online, the data these labs collect will increase exponentially.
NGS platforms have dramatically reduced turnaround times and achieve higher levels of accuracy and specificity than other sequencing platforms. Unlike most PCR and ELISA-based testing techniques, which can only generate binary answers, NGS platforms generate millions of data points with each run. Two hundred or more samples can be processed simultaneously at up to 25 million reads per sample.
With a single test, labs are able to gather information about a sample’s authenticity (is the food what the label says it is?); provenance (is the food from where it is supposed to be from?); adulterants (are there ingredients that aren’t supposed to be there?); and pathogen risk.

The food industry is well aware that food safety testing programs are already a worthwhile investment. Given the enormous human and financial costs of food recalls, a robust food-safety testing system is the best insurance policy any food brand can buy.

The brands that understand how to leverage the data that microbiology labs produce in ever larger quantities will be in a position to transform the cost of this insurance policy into new revenue streams.

Digitizing the Food Supply Chain

It’s clear that the food lab will generate massive amounts of data in the future, and it’s easy to see that this data will have value, but how, exactly, can food brands turn their data into revenue streams?

The real magic starts to happen when we can combine and correlate the trillions of data points we’re gathering from new forms of testing like NGS, with data already being collected, whether for inventory management, supply chain management, storage and environmental conditions, downstream sales data, or other forms of testing for additives and contaminant like pH, antibiotics, heavy metals and color additives.

When a food brand has all of this data at their fingertips, they can start to feed the data through an artificial intelligence platform that can find patterns and trends in the data. The possibilities are endless, but some insights you could imagine are:

  • When I procure raw ingredient A from supplier B and distributors X, Y, and Z, I consistently record higher-than-average rates of contamination.
  • Over the course of a fiscal year Supplier A’s product, while a higher cost per pound, actually increases my margin because, on average, it confers a greater nutritional value than the supplier B’s product.
  • A rare pathogen strain is emerging from suppliers who used the same manufacturing plant in Arizona.

Based on this information about suppliers, food brands can optimize their supplier relationships, decrease the risk associated with new suppliers, and prevent potential outbreaks from rare or emerging pathogen threats.

But clearly the real promise for revenue generation is in leveraging food data to inform R&D, and creating a tighter food safety testing and product development feedback loop.

The opportunity to develop new products based on insights generated in the microbiology lab are profound. This is where the upside lives.

For instance, brands could correlate shelf life with a particular ingredient or additive to find new ways of storing food longer. We can leverage data collected across a product line or multiple product lines to create new ingredient profiles that find substitutes for or eliminate unhealthy additives like corn syrup.

One of the areas I’m most excited about is personalized nutrition. With microbiome data collected during routine testing, we could develop probiotics and prebiotics that promote healthy gut flora, and eventually are even tailored to the unique genetic profile of individual shoppers. The holistic wellness crowd has always claimed that food is medicine; with predictive bioinformatic models and precise microbiome profiles, we can back up that claim scientifically for the first time.

Insights at Scale

Right now, much of the insight to be gained from unused food safety testing data requires the expertise of highly specialized bioinformaticians. We haven’t yet standardized bioinformatic algorithms and pipelines—that work is foundational to building the food genomics platforms of the future.

In the near future these food genomics platforms will leverage artificial intelligence and machine learning to automate bioinformatic workflows, dramatically increasing our ability to analyze enormous bodies of data and identify macro-level trends. Imagine the insights we could gain when we combine trillions of genomic data points from each phase in the food safety testing process—from routine pathogen testing to environmental monitoring to strain typing.

We’re not there yet, but the technology is not far off. And while the path to adoption will surely have its fair share of twists and turns, it’s clear that the business functions of food safety testing labs and R&D departments will grow to be more closely integrated than ever before.

In this respect the success of any food safety program will depend—as it always has—not just on the technology deployed in labs, but on how food brands operate. In the food industry, where low margins are the norm, brands have long depended on efficiently managed operations and superb leadership to remain competitive. I’m confident that given the quality and depth of its human resources, the food industry will be prove more successful than most in harnessing the power of big data in ways that truly benefit consumers.

The big data revolution in food will begin in the microbiology lab, but it will have its most profound impact at the kitchen table.

References

  1. Ferguson, B. (February/March 2017). “A Look at the Microbiology Testing Market.” Food Safety Magazine. Retrieved from https://www.foodsafetymagazine.com/magazine-archive1/februarymarch-2017/a-look-at-the-microbiology-testing-market/.
Sasan Amini, Clear Labs

NGS in Food Safety: Seeing What Was Never Before Possible

By Sasan Amini
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Sasan Amini, Clear Labs

For the past year, Swedish food provider Dafgård has been using a single test to screen each batch of its food for allergens, missing ingredients, and even the unexpected – an unintended ingredient or pathogen. The company extracts DNA from food samples and sends it to a lab for end-to-end sequencing, processing, and analysis. Whether referring to a meatball at a European Ikea or a pre-made pizza at a local grocery store, Dafgård knows exactly what is in its food and can pinpoint potential trouble spots in its supply chains, immediately take steps to remedy issues, and predict future areas of concern.

The power behind the testing is next-generation sequencing (NGS). NGS platforms, like the one my company Clear Labs has developed, consist of the most modern parallel sequencers available in combination with advanced databases and technologies for rapid DNA analysis. These platforms have reduced the cost of DNA sequencing by orders of magnitude, putting the power to sequence genetic material in the hands of scientists and investigators across a range of research disciplines and industries. They have overtaken traditional, first-generation Sanger sequencing in clinical settings over the past several years and are now poised to supplement and likely replace PCR in food safety testing.

For Dafgård, one of the largest food providers in Europe, the switch to NGS has given it the ability to see what was previously impossible with PCR and other technologies. Although Dafgård still uses PCR in select cases, it has run thousands of NGS-based tests over the past year. One of the biggest improvements has been in understanding the supply chain for the spices in its prepared foods. Supply chains for spices can be long and can result in extra or missing ingredients, some of which can affect consumer health. With the NGS platform, Dafgård can pinpoint ingredients down to the original supplier, getting an unparalleled look into its raw ingredients.

Dafgård hopes to soon switch to an entirely NGS-based platform, which will put the company at the forefront of food safety. Embracing this new technology within the broader food industry has been a decade-long process, one that will accelerate in the coming years, with an increased emphasis on food transparency both among consumers and regulators globally.

Transitioning technology

A decade ago, very few people in food safety were talking about NGS technologies. A 2008 paper in Analytical and Bioanalytical Chemistry1 gave an outlook for food safety technology that included nanotechnology, while a 2009 story in Food Safety Magazine2 discussed spectrometric or laser-based diagnostic technologies. Around the same time, Nature magazine named NGS as its “method of the year” for 2007. A decade later, NGS is taking pathogen characterization and food authentication to the next level.

Over the last 30 years, multiple technology transitions have occurred to improve food safety. In the United States, for example, the Hazard Analysis and Critical Control Points (HACCP) came online in the mid-1990s to reduce illness-causing microbial pathogens on raw products. The move came just a few years after a massive outbreak of E. coli in the U.S. Pacific Northwest caused 400 illness and 4 deaths, and it was clear there was a need for change.

Before HACCP, food inspection was largely on the basis of sight, touch, and smell. It was time to take a more science-based approach to meat and poultry safety. This led to the use of PCR, among other technologies, to better measure and address pathogens in the food industry.

HACCP set the stage for modern-era food testing, and since then, efforts have only intensified to combat food-borne pathogens. In 2011, the Food Safety Modernization Act (FSMA) took effect, shifting the focus from responding to pathogens to preventing them. Data from 20153 showed a 30% drop in foodborne-related bacterial and parasitic infections from 2012 to 2014 compared to the same time period in 1996 to 1998.

But despite these vast improvements, work still remains: According to the CDC, foodborne pathogens in the Unites States alone cause 48 million illnesses and 3,000 fatalities every year. And every year, the food safety industry runs hundreds of millions of tests. These tests can mean the difference between potentially crippling business operations and a thriving business that customers trust. Food recalls cost an average of $10M per incident and jeopardize public health. The best way to stay ahead of the regulatory curve and to protect consumers is to take advantage of the new technological tools we now have at our disposal.

Reducing Errors

About 60% of food safety tests currently use rapid methods, while 40% use traditional culturing. Although highly accurate, culturing can take up to five days for results, while PCR and antigen-based tests can be quicker – -one to two days – but have much lower accuracy. So, what about NGS?

NGS platforms have a turnaround of only one day, and can get to a higher level of accuracy and specificity than other sequencing platforms. And unlike some PCR techniques that can only detect up to 5 targets on one sample at a time, the targets for NGS platforms are nearly unlimited, with up to 25 million reads per sample, with 200 or more samples processed at the same time. This results in a major difference in the amount of information yielded.

For PCR, very small segments of DNA are amplified to compare to potential pathogens. But with NGS tools, all the DNA is tested, cutting it into small fragments, with millions of sequences generated – giving many redundant data points for comparing the genome to potential pathogens. This allows for much deeper resolution to determine the exact strain of a pathogen.

Traditional techniques are also rife with false negatives and false positives. In 2015, a study from the American Proficiency Institute4 on about 18,000 testing results from 1999 to 2013 for Salmonella found false negative rates between 2% and 10% and false positive rates between 2% and 6%. Several Food Service Labs claim false positive rates of 5% to 50%.

False positives can create a resource-intensive burden on food companies. Reducing false negatives is important for public health as well as isolating and decontaminating the species within a facility. Research has shown that with robust data analytics and sample preparation, an NGS platform can bring false negative and positive rates down to close to zero for a pathogen test like Salmonella, Listeria, or E.coli.

Expecting the Unexpected

NGS platforms using targeted-amplicon sequencing, also called DNA “barcoding,” represent the next wave of genomic analysis techniques. These barcoding techniques enable companies to match samples against a particular pathogen, allergen, or ingredient. When deeper identification and characterization of a sample is needed, non-targeted whole genome sequencing (WGS) is the best option.

Using NGS for WGS is much more efficient than PCR, for example, at identifying new strains that enter a facility. Many food manufacturing plants have databases, created through WGS, of resident pathogens and standard decontamination steps to handle those resident pathogens. But what happens if something unknown enters the facility?

By looking at all the genomic information in a given sample and comparing it to the resident pathogen database, NGS can rapidly identify strains the facility might not have even known to look for. Indeed, the beauty of these technologies is that you come to expect to find the unexpected.

That may sound overwhelming – like opening Pandora’s box – but I see it as the opposite: NGS offers an unprecedented opportunity to protect against likely threats in food, create the highest quality private databases, and customize internal reporting based on top-of-the-line science and business practices. Knowledge is power, and NGS technologies puts that power directly in food companies’ hands. Brands that adopt NGS platforms can execute on decisions about what to test for more quickly and inexpensively – all the while providing their customers with the safest food possible.

Perhaps the best analogy for this advancement comes from Magnus Dafgård, owner and executive vice president at Gunnar Dafgård AB: “If you have poor eyesight and need glasses, you could be sitting at home surrounded by dirt and not even know it. Then when you get glasses, you will instantly see the dirt. So, do you throw away the glasses or get rid of the dirt?” NGS platforms provide the clarity to see and address problem directly, giving companies like Dafgård confidence that they are using the most modern, sophisticated food safety technologies available.

As NGS platforms continue to mature in the coming months and years, I look forward to participating in the next jump in food safety – ensuring a safe global food system.

Common Acronyms in Food Genomics and Safety

DNA Barcoding: These short, standardized DNA sequences can identify individual organisms, including those previously undescribed. Traditionally, these sequences can come from PCR or Sanger sequencing. With NGS, the barcoding can be developed in parallel and for all gene variants, producing a deeper level of specificity.

ELISA: Enzyme-linked immunosorbent assay. Developed in 1971, ELISA is a rapid substance detection method that can detect a specific protein, like an allergen, in a cell by binding antibody to a specific antigen and creating a color change. It is less effective in food testing for cooked products, in which the protein molecules may be broken down and the allergens thus no longer detectable.

FSMA: Food Safety Modernization Act. Passed in 2011 in the United States, FSMA requires comprehensive, science-based preventive controls across the food supply. Each section of the FSMA consists of specific procedures to prevent consumers from getting sick due to foodborne illness, such as a section to verify safety standards from foreign supply chains.

HACCP: Hazard analysis and critical control points. A food safety management system, HACCP is a preventative approach to quantifying and reducing risk in the food system. It was developed in the 1950s by the Pillsbury Company, the Natick Research Laboratories, and NASA, but did not become as widespread in its use until 1996, when the U.S. FDA passed a new pathogen reduction rule using HACCP across all meat and poultry raw products.

NGS: Next-generation sequencing. NGS is the most modern, parallel, high-throughput DNA sequencing available. It can sequence 200 to 300 samples at a time and generates up to 25 million reads per a single experiment. This level of information can identify pathogens at the strain level and can be used to perform WGS for samples with unknown pathogens or ingredients.

PCR: Polymerase chain reaction. First described in 1985, PCR is a technique to amplify a segment of DNA and generate copies of a DNA sequence. The DNA sequences generated from PCR must be compared to specific, known pathogens. While it can identify pathogens at the species level, PCR cannot provide the strain of a pathogen due to the limited amount of sequencing information generated.

WGS: Whole genome sequencing. WGS uses NGS platforms to look at the entire DNA of an organism. It is non-targeted, which means it is not necessary to know in advance what is being detected. In WGS, the entire genome is cut it into small regions, with adaptors attached to the fragments to sequence each piece in both directions. The generated sequences are then assembled into single long pieces of the whole genome. WGS produces sequences 30 times the size of the genome, providing redundancy that allows for a deeper analysis.

Citations

  1. Nugen, S. R., & Baeumner, A. J. (2008). Trends and opportunities in food pathogen detection. Analytical and Bioanalytical Chemistry, 391(2), 451-454. doi:10.1007/s00216-008-1886-2
  2. Philpott, C. (2009, April 01). A Summary Profile of Pathogen Detection Technologies. Retrieved September 08, 2017, from https://www.foodsafetymagazine.com/magazine-archive1/aprilmay-2009/a-summary-profile-of-pathogen-detection-technologies/?EMID
  3. Ray, L., Barrett, K., Spinelli, A., Huang, J., & Geissler, A. (2009). Foodborne Disease Active Surveillance Network, FoodNet 2015 Surveillance Report (pp. 1-26, Rep.). CDC. Retrieved September 8, 2017, from https://www.cdc.gov/foodnet/pdfs/FoodNet-Annual-Report-2015-508c.pdf.
  4.  Stombler, R. (2014). Salmonella Detection Rates Continue to Fail (Rep.). American Proficiency Institute.

Minimizing Hazards and Fraud in Milk, IBM Research Partners with Cornell University

By Food Safety Tech Staff
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Americans consume an estimated 600 pounds of milk and milk-based products annually, according to the USDA. In an effort to minimize the hazards in the milk supply and prevent food fraud, IBM Research and Cornell University are joining forces. Combining next-generation sequencing with bioinformatics, the research project will collect genetic data from the microbiome of raw milk samples in a real-world situation at the Cornell University dairy plant and farm in Ithaca, New York.

Specifically, IBM and Cornell will sequence and analyze the DNA and RNA of food microbiomes, which will serve as a raw milk baseline, to develop tools that monitor raw milk and detect abnormalities that could indicate safety hazards and potential fraud. The data collected may also be used to expand existing bioinformatics analytical tools used by the Consortium for Sequencing the Food Supply Chain, a project that was launched by IBM Research and Mars, Inc. at the beginning of 2015.

“As nature’s most perfect food, milk is an excellent model for studying the genetics of food. As a leader in genomics research, the Department of Food Science expects this research collaboration with IBM will lead to exciting opportunities to apply findings to multiple food products in locations worldwide.” – Martin Wiedmann, Gellert Family Professor in Food Safety, Cornell University.

“Characterizing what is ‘normal’ for a food ingredient can better allow the observation of when something goes awry,” said Geraud Dubois, director of the Consortium for Sequencing the Food Supply Chain, IBM Research – Almaden, in a press release. “Detecting unknown anomalies is a challenge in food safety and serious repercussions may arise due to contaminants that may never have been seen in the food supply chain before.”

Cornell University is the first academic institution to join the Consortium for Sequencing the Food Supply Chain.

Sequencing pattern, pathogens

Build Stronger Food Safety Programs With Next-Generation Sequencing

By Akhila Vasan, Mahni Ghorashi
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Sequencing pattern, pathogens

According to a survey by retail consulting firm Daymon Worldwide, 50% of today’s consumers are more concerned about food safety and quality than they were five years ago. Their concerns are not unfounded. Recalls are on the rise, and consumer health is put at risk by undetected cases of food adulteration and contamination.

While consumers are concerned about the quality of the food they eat, buy and sell, the brands responsible for making and selling these products also face serious consequences if their food safety programs don’t safeguard against devastating recalls.

A key cause of recalls, food fraud, or the deliberate and intentional substitution, addition, tampering or misrepresentation of food, food ingredients or food packaging, continues to be an issue for the food safety industry. According to PricewaterhouseCoopers, food fraud is estimated to be a $10–15 billion a year problem.

Some of the more notorious examples include wood shavings in Parmesan cheese, the 2013 horsemeat scandal in the United Kingdom, and Oceana’s landmark 2013 study, which revealed that a whopping 33% of seafood sold in the United States is mislabeled. While international organizations like Interpol have stepped up to tackle food fraud, which is exacerbated by the complexity of globalization, academics estimate that 4% of all food is adulterated in some way.

High-profile outbreaks due to undetected pathogens are also a serious risk for consumers and the food industry alike. The United States’ economy alone loses about $55 billion each year due to food illnesses. The World Health Organization estimates that nearly 1 in 10 people become ill every year from eating contaminated food. In 2016 alone, several high-profile outbreaks rocked the industry, harming consumers and brands alike. From the E. coli O26 outbreak at Chipotle to Salmonella in live poultry to Hepatitis A in raw scallops to the Listeria monocytogenes outbreak at Blue Bell ice cream, the food industry has dealt with many challenges on this front.

What’s Being Done?

Both food fraud and undetected contamination can cause massive, expensive and damaging recalls for brands. Each recall can cost a brand about $10 million in direct costs, and that doesn’t include the cost of brand damage and lost sales.

Frustratingly, more recalls due to food fraud and contamination are happening at a time when regulation and policy is stronger than ever. As the global food system evolves, regulatory agencies around the world are fine-tuning or overhauling their food safety systems, taking a more preventive approach.

At the core of these changes is HACCP, the long implemented and well-understood method of evaluating and controlling food safety hazards. In the United States, while HACCP is still used in some sectors, the move to FSMA is apparent in others. In many ways, 2017 is dubbed the year of FSMA compliance.

There is also the Global Food Safety Initiative (GFSI), a private industry conformance standard for certification, which was established proactively by industry to improve food safety throughout the supply chain. It is important to note that all regulatory drivers, be they public or private, work together to ensure the common goal of delivering safe food for consumers. However, more is needed to ensure that nothing slips through the food safety programs.

Now, bolstered by regulatory efforts, advancements in technology make it easier than ever to update food safety programs to better safeguard against food safety risks and recalls and to explore what’s next in food.

Powering the Food Safety Programs of Tomorrow

Today, food safety programs are being bolstered by new technologies as well, including genomic sequencing techniques like NGS. NGS, which stands for next-generation sequencing, is an automated DNA sequencing technology that generates and analyzes millions of sequences per run, allowing researchers to sequence, re-sequence and compare data at a rate previously not possible.

The traditional methods of polymerase chain reaction (PCR) are quickly being replaced by faster and more accurate solutions. The benefit of NGS over PCR is that PCR is targeted, meaning you have to know what you’re looking for. It is also conducted one target at a time, meaning that each target you wish to test requires a separate run. This is costly and does not scale.

Next-generation sequencing, by contrast, is universal. A single test exposes all potential threats, both expected and unexpected. From bacteria and fungi to the precise composition of ingredients in a given sample, a single NGS test guarantees that hazards cannot slip through your supply chain.  In the not-too-distant future, the cost and speed of NGS will meet and then quickly surpass legacy technologies; you can expect the technology to be adopted with increasing speed the moment it becomes price-competitive with PCR.

Applications of NGS

Even today’s NGS technologies are deployment-ready for applications including food safety and supplier verification. With the bottom line protected, food brands are also able to leverage NGS to build the food chain of tomorrow, and focus funding and resources on research and development.

Safety Testing. Advances in NGS allow retailers and manufacturers to securely identify specific pathogens down to the strain level, test environmental samples, verify authenticity and ultimately reduce the risk of outbreaks or counterfeit incidents.

Compared to legacy PCR methods, brands leveraging NGS are able to test for multiple pathogens with a single test, at a lower cost and higher accuracy. This universality is key to protecting brands against all pathogens, not just the ones for which they know to look.

Supplier Verification. NGS technologies can be used to combat economically motivated food fraud and mislabeling, and verify supplier claims. Undeclared allergens are the number one reason for recalls.

As a result of FSMA, the FDA now requires food facilities to implement preventative controls to avoid food fraud, which today occurs in up to 10% of all food types. Traditional PCR-based tests cannot distinguish between closely related species and have high false-positive rates. NGS offers high-resolution, scalable testing so that you can verify suppliers and authenticate product claims, mitigating risk at every level.

R&D. NGS-based metagenomics analysis can be used in R&D and new product development to build the next-generation of health foods and nutritional products, as well as to perform competitive benchmarking and formulation consistency monitoring.

As the consumer takes more and more control over what goes into their food, brands have the opportunity to differentiate not only on transparency, but on personalization, novel approaches and better consistency.

A Brighter Future for Food Safety

With advances in genomic techniques and analysis, we are now better than ever equipped to safeguard against food safety risks, protect brands from having to issue costly recalls, and even explore the next frontier for food. As the technology gets better, faster and cheaper, we are going to experience a tectonic shift in the way we manage our food safety programs and supply chains at large.

DNA sequencing

Whole Sample Next-Generation DNA Sequencing Method: An Alternative to DNA Barcoding

By Casey Schlenker, Jenna Brooks, Kent Oostra, Ryan McLaughlin
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DNA sequencing

This article discusses a non-targeted method for whole sample next generation DNA sequencing (NGS) that does not rely on DNA barcoding. DNA barcoding requires amplification of a specific gene region, which introduces bias. Our non-targeted method removes this bias by eliminating the amplification step. The applications of this method are broad and we have begun optimizing workflows for numerous materials, both processed and unprocessed. Some of the materials we have been able to successfully identify at the species level are fish tissue, fish meal, unrefined fish oil, unrefined plant-based oils (nuts, seeds, and fruits), specific components of cooked and processed products such as cookies and powders, and processed meats. Non-targeted NGS is also a very powerful tool to comprehensively identify constituents of microbial communities in probiotics and fermented products like kombucha. Additionally, this non-targeted technique is applicable to detection and identification of microbial contamination at various levels of manufacturing including equipment surfaces, processing water and assaying intermediate processing steps. In this communication we briefly review a current issue in the botanicals industry, discuss the methods that have been used in the past to tackle that problem, and present preliminary results from a pilot study we performed to determine the utility of non-targeted NGS in high-throughput identification of botanical raw materials.

The value of the global herbal dietary supplement (botanical) market was estimated to be greater than $90 billion in 2016, with a projected compound annual growth rate of 5-6%. Currently, regulators and manufacturers in this rapidly expanding market seek to confirm the veracity of label claims, investigate fraud, identify adulterants and ensure product quality.1 These products are often dried and ground, making visual identification difficult, time consuming and sometimes impossible.2 It is critical to this market that botanical identification be high-throughput, accurate and cost effective. Historically, various chromatography techniques have been used to meet this need, but those techniques rely on identification of molecules that can vary significantly due to storage conditions, which has led to the use of DNA barcoding as an analytical technique. However, DNA barcoding is not without significant challenges.1

For quite some time, scientists have had the ability to identify biological samples by sequencing their DNA.3 Currently DNA sequencing-based identification methods rely heavily on a technique called DNA barcoding, which functions analogously to the barcodes found on products in a grocery store. DNA barcoding amplifies a distinct small gene region that serves as a unique identifier and “scans” it by DNA sequencing.4 The advantages of this amplification are high sensitivity and simplification of data analysis. However, this amplification is not completely reliable and in practice can create biases and false positives.5 There is also the possibility that the amplification may fail, causing false negatives.6 When using DNA barcoding to identify botanical raw materials, numerous labs have observed notably high levels of apparent contamination.7 While it is certainly likely that some or even many botanical raw material samples contain contamination, it is also possible that the amplification-based method of DNA barcoding is itself contributing to the levels of contamination that are being observed.

We have partnered with Practical Informatics and Pacific Northwest Genomics to develop comprehensive whole sample DNA screening methods that don’t rely on amplification. To achieve this we are utilizing a non-targeted metagenomics workflow. Non-targeted metagenomic analysis is a powerful tool for examining the entire genetic content of a sample, instead of just one particular gene region (if a gene is a word or phrase, then a genome is the entire book, and the metagenome is the library). Unlike DNA barcoding, which requires PCR amplification, non-targeted metagenomic analysis requires no prior knowledge of a sample’s source and does not introduce the biases that plague PCR initiated methods. All of the DNA extracted from a sample is analyzed without targeting any particular gene region, relying instead on complex data analysis to identify the constituents (Figure 1). This is accomplished with the use of advanced molecular biology techniques and sophisticated computational methods, combined with a highly-curated database of species-identifying DNA sequences. Our research and development team has completed several experiments demonstrating the utility of a non-targeted DNA sequencing method.

DNA sequencing
Figure 1. The traditional targeted method, or DNA barcoding uses a PCR amplification step prior to sequencing. Non-target whole sample sequencing skips the amplification step and all present DNA is sequenced and used in analysis.

Our research endeavors to solve the issues of DNA sequence analysis that originate with the PCR step by simply eliminating amplification from our process entirely. PCR amplification as a prelude to DNA sequencing traces to traditional technologies that were lower throughput and required large amounts of material. Current generation high-throughput DNA sequencing technologies do not require large amounts of starting material, and therefore amplification can be avoided. Many DNA barcoding methods require universal primers, which, during PCR, can amplify some products but not others, leading to false negatives. A solution to that issue is to use specific primers, however this is also inherently problematic as a certain foreknowledge of the sample identity is required. What is the advantage to our non-targeted sequencing method? There is no need to direct the analysis to any particular identification before sequencing, decreasing the introduction of bias and false negatives. As an added bonus, we don’t need to know what the sample is prior to analysis—we can tell you what it is rather than you telling us.

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