It’s amazing how much innovation has taken place with food safety technology in just the past few years – from radiofrequency electric field (RFEF) processing to Ohmic heating. Now, food safety technology has taken another transformative turn… with next-generation sequencing (NGS). Yes, NGS–the technology that changed the clinical space–is transforming food safety. How is that possible?

 

I sat down with Sasan Amini, the CEO at Clear Labs, to discuss his prognostications for the future of the food safety industry and the role that NGS will play.

 

How Does NGS Impact Routine Pathogen Testing?

When many food safety professionals hear NGS, they immediately think of whole-genome sequencing (WGS), but that’s just one application of NGS. To properly detect and subtype food samples, we do not need to know the entire sequence, a process that is expensive and slow and requires having a pure sample. Instead, we can use targeted sequencing, which looks at specific locations of the genome that are useful for identifying specific pathogens.

 

Many legacy technologies like PCR typically look at one or very few locations of the genome. But NGS has the potential to look at multiple regions. By analyzing more regions, we collect more information about the target organism, and more information means that we can identify and characterize a microorganism more accurately, thus reducing the number of false negatives and false positives.

 

Just think about the value of having more accurate testing. By eliminating false negatives, the food industry can mitigate the risks of recalls, and by eliminating false positives, food manufacturers can curtail the inefficiencies related to chasing ghosts: the cost of diverted or destroyed product, inventory holding costs, intensified sampling, reviewing HACCP plans, and more.

 

Read more about the ROI of NGS here.

 

Beyond genus- and species-level detection, what can NGS do?

 

Legacy technologies like PCR, immunoassay, culture confirmation can provide a binary yes/no answer, but with NGS, we can do so much more thanks to the wealth of data that the technology generates.

 

For example, many companies rely on traditional serology, which is highly subjective, requires highly trained personnel, takes several days for isolation, and involves capital expenditures in the form of instrumentation or a suite of antibodies. With NGS technology, you can detect the serotype (or serotypes) found in a food sample within 24 hours, at the same time that you test for genus or species-level Salmonella.

 

Similarly, many companies rely on ribotyping for Listeria subtyping, which, again, requires several days for isolation and relies on additional instrumentation, as well as specialized technicians. With NGS, you can subtype Listeria directly from an enriched sample and then seamlessly visualize contamination incidents on a map, even follow the flow of related Listeria incidents throughout the processing facility, thereby eliminating the spreadsheets and manual mapping practices that many companies use.

 

How can NGS impact shelf-life assessment?

 

Food waste and spoilage is a big issue. Billions of pounds of food are lost every year. As a result, many food scientists are looking for more accurate alternatives to the legacy experimental and statistical models that they have leveraged historically. Chief among these alternatives is the analysis of the microbial composition of the food samples.

 

We know that spoilage microorganisms like Pseudomonas, Shewanella putrefaciens, and Clostridum estertheticum, to name a few, can affect the color, taste, texture, and odor of food. But legacy tests typically test for only one organism at a time. Furthermore, legacy tests can only look at culturable microorganisms.

 

By using NGS methodologies like shotgun or targeted metagenomics, we can test for all existing microorganisms at one time. Furthermore, by pairing the microbial profile of a food sample with data about storage conditions, pH levels, temperature, packaging, and more, we can better understand how those microbial communities are influenced by intrinsic and extrinsic factors. Finally, funneling all those information into more advanced data models and machine learning allows us to build better risk and shelf-life assessments.

 

Want to learn more?

Click here to learn about Clear Safety, the only NGS platform that was built for food safety testing.

 

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