Information page for Principal Components Background Correction


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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

 

Why PCBC?

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


Features:

Automatic fequency analysis to discriminate components with background contributions from those with none, displayed to the user in a colour coded formatLoadings colour coded according to frequency composition
4 different automatic baseline estimation algorithms to handle a wide range of signal challengesPCBC automatically estimates baselines using 4 different algorithms
Simple, easy to use and intuitive optimisation tools to improve fit furtherPCBC allows teh user to optimise the results of the automatic algorithms
Identifies representative spectra to handle the positive and negative parts of the loadings seperately. A future release will allow the user to calculate and recover these representative spectra for improved interpretability of loadings.PCBC seperates out the positive and negative portions of the loadings

Take advantage of the power of PCBC by registering your interest here.

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