Why Automating the Lab is Necessary for Food Safety’s Future

by, Mahni Ghorashi

A version of this post appeared in Food Safety News. You can read the full article here.

As next generation sequencing (NGS) replaces traditional DNA methods in food safety testing, laboratories will be limited by how quickly they can prepare samples, not how quickly they can sequence them. In order to relieve these bottlenecks, and to take advantage of the vast amounts of data that NGS produces, labs wills need to automate bioinformatic workflows. This automation will dramatically increase our ability to analyze enormous bodies of data and identify macro-level trends across large volumes of data, and play an increasingly critical role in the evolution of laboratory processes.

Adopting existing robotics systems for food testing is not as straightforward as it might seem, though. In doing so, the food testing industry has had to overcome unique challenges. In contrast to the materials being analyzed in clinical and pharmaceutical settings, food comes in a wide variety of forms: environmental samples, packaged foods, dairy, meat, and produce in solid, liquid, powdered, frozen, cooked, raw and concentrated forms. All of these require different methods of preparation before they can be analyzed.

Another challenge inherent to food analysis is that the compounds in natural products are variably distributed throughout unprepared samples. Complex food items further complicate this issue, as dozens of ingredients can be heterogeneously distributed throughout any given product.

Cryogenic mills or grinders blend samples at low temperatures and are often used to prepare samples of fruits and vegetables being analyzed for volatile pesticides. These mills and grinders produce homogenous samples of small sizes that can be held in a relatively small volumes of solution. This is important because it increases the efficiency of liquid-handling applications, those most commonly automated in today’s food testing labs.

Beyond robotics, another target for automation in the near term time is in artificial intelligence and machine learning to amplify our existing bioinformatic workflows. NGS provides an enormous amount of data, much of which goes unused in food safety applications today. The next revolution in food safety is in automating data-science operations that can leverage this data.

Automating bioinformatic workflows will dramatically increase 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 serotyping.

The first step is to unify all of the various tests performed at each stage of the food safety workflow into a single, universal test. Just having the all this data in a single place represents enormous opportunity.

The holy grail of food safety is to have end-to-end systems in place for gathering supply chain and sequencing data, which can then be appended to and stored on immutable decentralized blockchains. We’re not too far off from that. Recent advances in IoT are making it easier than ever before to gather data at each stage of the supply chain, from farm to table.

The essential technological components exist. In order to build the end-to-end system of the food safety future, we will have to fuse these components and their operation together. That is an enormous technological, infrastructural, and cultural challenge. I’m confident we’ll get there.

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