Principal Component Background Correction (PCBC) exploits the ability of multivariate analyses to reduce the complexity of signal based datasets into the core building block of the dataset. These building blocks are known by a variety of names such as Principal Component, Eigenvector, Loadings, Latent Variables or Latent Factor. PCBC provides unqiue tools that allow the user to maniupulate these building blocks and back transform those manipulations onto original data. Now a user friendly tool has been developed to allow users to exploit the tremendous potential of this approach for themselves
Because it is based on multivariate analysis the approach is a very powerful method that is more statistically robust and more reproducible than estimating the background signal on each individual signal. It can create more accurate as it disentangles each individual source of background signal and also each signal of interest, allowing tailoring of background correction in signals containing complex mixtures of analyte signals and irrelevant background signals. For more scientific detail, read here.
To register your interest in a trial click here
What can I expect from the prototype?
The Prototype is at an early stage of development and will be developed further. At this stage it is already:
A very powerful tool for signal based research
User friendly, many complex algorithms have been automated
Applicable to a wide range of types of digital signal
Integrates with common file formats
Take advantage of the power of PCBC by registering your interest here.
|Home | About Me | Publications | Resources | External Links | Sitemap|