  Wavelet-related techniques have proven useful in the processing and analysis
of one and two dimensional data sets (spectra in the former case, images in the
latter). In this work we apply adaptive filters, introduced in a previous work
(Sanz et al. 2001), to optimize the detection and extraction of point sources
from a one-dimensional array of time-ordered data such as the one that will be
produced by the future 30 GHz LFI28 channel of the ESA Planck mission. At a
$4\sigma$ detection level 224 sources over a flux of 0.88 Jy are detected with
a mean relative error (in absolute value) of 21% and a systematic bias of
-7.7%. The position of the sources in the sky is determined with errors
inferior to the size of the pixel. The catalogue of detected sources is
complete at fluxes $\geq$ 4.3 Jy. The number of spurious detections is less
than a 10% of the true detections. We compared the results with the ones
obtained by filtering with a Gaussian filter and a Mexican Hat Wavelet of width
equal to the scale of the sources. The adaptive filter outperforms the other
filters in all considered cases. We conclude that optimal adaptive filters are
well suited to detect and extract sources with a given profile embedded in a
background of known statistical properties. In the Planck case, they could be
useful to obtain a real-time preliminary catalogue of extragalactic sources,
which would have a great scientific interest, e. g. for follow-up observations.
