In conversation with the CTO, Norton Scientific Inc.

Norton and its impact on the Beer and Wine Industries…

In essence, the application that I worked on with an academic collaborator in the UK who now happens to head the Biotechnology _ Biological Sciences Research Council (BBSRC – one of the UK Government’s Higher Education Research Funding Councils) was to develop an in-line method for determining the amount and vitality of biomass (yeast - in the case of brewing) in a fermentor as a way of controlling the brewing process.

Yeast is poisoned by alcohol during the fermentation process requiring periodic downtime while the vat is replenished. Because it is not possible to measure yeast vitality during the fermentation, samples are removed and analysed off-line to enable predictions of batch lifetime to be developed.

The problem with this methodology is that it is highly inaccurate so empirical methods have to be applied to deciding on the number of brew cycles that can be undertaken before the whole unit has to be brought down and recharged, with a safety margin applied to ensure that the yeast doesn’t lose critical vitality mid-way through a batch run (this can be ~ 500K litres or more of product) that would ruin the whole batch. By measuring in real time as our method was designed to permit, the safety margin can be reduced allowing more batches of beer per week to be produced.

With wine, while production quantities tend to be lower, real time monitoring can allow a vintner to better control final quality and again enhance productivity by enhancing the yield of higher margin quality wines.

Finally, this sort of approach to fermentation monitoring has massive potential in the control of pharmaceutical, neutriceutical, and biofuel production where high end bio-products are produced by this route and could even lead to the development of new nano-materials where fermentation control is crucial for economic production methods to be developed.

The idea behind the methods developed are essentially to identify key scattering patterns from the fermentation broth by monitoring light scattering at a large number of different angles (PoC work used 18, Norton has a design for a 360+ LS chip detector) then using multi-variate analysis software routines commonly used for Systems Biology applications to “train” the detection hardware to identify specific scattering signals relating to the yeast cells of interest. The training can be done from either a series of calibration experiments or, if available, using relevant data collected historically in an off-line mode.

This project is also the foundation stone for a broader plan for Norton to develop this approach for monitoring a much wider range of macromolecular (polymer) _ nano-particle production processes using a 15+ year data base of sample analysis work undertaken by my companies over the years to train the process control system to make polymeric materials with specific properties using nanotechnology – this leads to the production of a vast range of “green” high performance materials that have enhanced properties for use in anything from conducting polymers to be used in solar power _ low energy/cost computing systems, through novel microfluidic devices for diagnostics and drug delivery, to advanced composites for use in aerospace _ transport systems.