The techniques that are used to do this have been tried and tested over many decades, however, advanced technology such as genomic sequencing, machine learning and the internet of things have become far more mainstream in recent years. There is now an opportunity to benefit from these advanced techniques within the food manufacturing environment.
These newer techniques can be much faster than traditional ones and importantly can facilitate the monitoring and detection of both culturable and non-culturable bacteria. This overcomes the challenge of traditional techniques which focus on a single bacteria per test. In particular, Next Generation Sequencing (NGS) techniques provide an excellent solution for this.
Over the past few years Creme Global have been a part of the SAFE project, which was led by Professor Seamus Fanning from UCD and in collaboration with a number of industry partners to explore the use of NGS for food safety. There were two aspects to this, the first is whole genome sequencing which allows us to understand in explicit detail specific strains.
See this previous blog for more details. https://www.cremeglobal.com/using-whole-genome-sequencing-wgs-data-to-inform-food-safety-decisions-video
Here we will focus on the second approach which looks at the big picture of the entire community of bacteria or microbiome in an environment.
This was made possible thanks to the use of 16SrDNA sequencing. This approach targets a small region of DNA which is easily identifiable but which varies between different genera. As such it is perfect for identifying and distinguishing between large groups of bacteria. Using this approach we can take a single sample and identify the entire community of bacteria within it and look at their abundances relative to each other. We can then bring data science to bear to analyse how these bacterial populations change over time or to compare the bugs that are present in different areas within a food plant.
Every facility has its own characteristics, something which is manifest in the different bacteria populations that we see in each. As such the old one-size-fits-all approach to microbial safety is no longer really sufficient and using the power of sequencing, big data and data science, every plant should be able to tailor its approach to address the characteristics unique to it.
The other paradigm shift is the capability to move towards predictive microbiology. Using advanced techniques and machine learning, it is possible to make predictions on the bacteria that are present.
NGS generates a rich amount of data and we’ve only scratched the surface of the insights it can give. Creme Global are continuing to work on the application of NGS to food safety. If you’d like to hear more about the work we’re doing in this area please contact us for more details.