Klavs Martin Sørensen

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Research 

I believe strongly that we as food scientists are in the absolute frontlines of the battle when it comes to formulating new and sustainable initiatives for providing a stable, safe and environmentally sane food supply for the future. As an example of my research I am in the process of establishing a new research field, Deep Process Learning, where our traditional chemometrics toolset, Process Analytical Technology and Deep Learning/AI is combined to provide even further insight into how food manufacturing processes can be optimized and become ever more sustainable. This is especially important for circular production systems, where raw materials, for instance water, are cleaned and recycled into the process. While having huge benefits for the environment, such manufacturing methods requires a much more complex insight into the dynamics of the process - and to ensure that we never compromize on safety or quality of the food products. 

Background 

I have more than 20 years of experience in developing measurement equipment for the food industry prior to obtaining my PhD which gives me a unique take on researching food manufacturing applications. I focus on the application of spectral sensors in on-line scenarios for process optimization and sustainable production. I have utilized my background in the industry to formulate new research topics for Quality by Design and Process Analytical Technology - in these fields, I apply my knowledge of multivariate data processing (chemometrics), software development, robotization, automation, electronics and system design.