Inverse diffusion methods for data peak separation

Previous methods for separation of overlapping data peaks include geometrical assessment and Fourier deconvolution. On the basis of inverse diffusion theory, we present new separation methods suitable for convenient programming and rapid calculation of Gaussian area contributions. Both continuum and discrete inverse diffusion models are described. Example computations are given for biological data: density gradient centrifugation, isoelectric focusing electrophoresis, and high-pressure liquid chromatography.