Tag Archives: analytics

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.

Pathogen

IBM Research Uses Data to Accelerate Source of Contamination During Outbreaks

By Food Safety Tech Staff
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Pathogen

Using electronic retail scanner data from grocery stores, IBM Research scientists may have found a faster way to narrow down the potential source food contamination during an outbreak. Researchers from the firm conducted a study in which they were able to show that, using just 10 medical exam reports of foodborne illness, it is possible to pinpoint an investigation to 12 food products of interest in a only a few hours. A typically investigation ranges from weeks to months.

The study, “From Farm to Fork: How Spatial-Temporal Data can Accelerate Foodborne Illness Investigation in a Global Food Supply Chain”, demonstrated a new way to accelerate an outbreak investigation. Researchers reviewed the spatio-temporal data (i.e., geographic location and potential time of consumption) of hundreds of grocery products, and analyzed each product for shelf life, consumption location and the probability that the product harbored a pathogen. This information was then mapped to the known location of outbreaks.

“When there’s an outbreak of foodborne illness, the biggest challenge facing public health officials is the speed at which they can identify the contaminated food source and alert the public,” said Kun Hu, public health research scientist, IBM Research – Almaden in a press release. Rsearchers created a system to devise a list that ranked products based on likelihood of contamination, which would allow health officials to test the top 12 suspected foods. “While traditional methods like interviews and surveys are still necessary, analyzing big data from retail grocery scanners can significantly narrow down the list of contaminants in hours for further lab testing. Our study shows that big data and analytics can profoundly reduce investigation time and human error and have a huge impact on public health,” said Hu.

The researchers point of out their method isn’t a substitute for proven outbreak investigation tools but rather serves as a faster way to identify contaminated product(s). According to the study, researchers assert that their methodology could significantly reduce the costs associated with foodborne illness, outbreaks and recalls. Thus far IBM Research’s approach has been applied to a Norweigan E. coli outbreak in which there were 17 confirmed cases of infection. Public health officials used the method to devise a list of 10 potential contaminants from the grocery scanner data of more than 2600 products. From there, lab analysis traced the contamination source to batch and lot numbers of sausage.

The study was published in the Association for Computing Machinery’s Sigspatial Journal.