MOP uses **Maximum Entropy
deconvolution **methods to unscramble electrospray and ionspray m/z data.
MOP treats mass spectroscopy data as if it were a
multiple exposure produced by a defective camera. The result is
the zero-charge, parent mass distribution for the electrospray data.

M.O.P. is an acronym for "Multiple
Overlapping Pictures". MOP is an application of standard
image-processing techniques. The final image (data) is presumed to be a
multiple-exposure; the individual images are obtained from the object by a
series of affine transformations. The Poisson nature of the noise is
rigorously accounted for. MOP is derived using Bayesian probability methods;
the algorithm gives the **most probable object** (parent mass
distribution). The result is a Maximum Entropy result for data with Poisson
(counting) noise.