  A new smoothing method for the improvement on the identification and
quantification of spectral functions based on the previous knowledge of the
signals that are expected to be quantified, is presented. These signals are
used as weighted coefficients in the smoothing algorithm. This smoothing method
was conceived to be applied in atomic and nuclear spectroscopies preferably to
these techniques where net counts are proportional to acquisition time, such as
particle induced X-ray emission (PIXE) and other X-ray fluorescence
spectroscopic methods, etc. This algorithm, when properly applied, does not
distort the form nor the intensity of the signal, so it is well suited for all
kind of spectroscopic techniques. This method is extremely effective at
reducing high-frequency noise in the signal much more efficient than a single
rectangular smooth of the same width. As all of smoothing techniques, the
proposed method improves the precision of the results, but in this case we
found also a systematic improvement on the accuracy of the results. We still
have to evaluate the improvement on the quality of the results when this method
is applied over real experimental results. We expect better characterization of
the net area quantification of the peaks, and smaller Detection and
Quantification Limits.
  We have applied this method to signals that obey Poisson statistics, but with
the same ideas and criteria, it could be applied to time series. In a general
case, when this algorithm is applied over experimental results, also it would
be required that the sought characteristic functions, required for this
weighted smoothing method, should be obtained from a system with strong
stability. If the sought signals are not perfectly clean, this method should be
carefully applied
