S430 User Manual
59
In Fig.1,there is a clear alternation of peaks. In Fig.2, the acromion is higher after
derivative. Second order derivative spectrum is clearer. In Fig.3, the original spectrum two
curves are seriously overlapping, but in n=2/4 the peaks are clearer.
Higher order derivative can eliminate the low order background curves. The spectrum
shape is complicated after derivative, but it raises the resolution and sensitivity.
6.4 Smoothing
The basic idea of smoothing is to map a smooth point, then depicte a number of
points around the smooth point to be "fit" or "average" or "sort" in order to obtain the best
estimate of the value of the smooth point to eliminate random noise. With modern
analytical instruments increasing speed and automation, multiple accumulate and
smoothing technology has become a common method of noise reduction.
Prolab provide 3 smoothing methods: Savitsky-Golay, Mean and Median.
6.4.1 Savitzky–Golay
A Savitzky–Golay filter is a digital filter that can be applied to a set of digital data
points for the purpose of smoothing the data, that is, to increase the signal-to-noise ratio
without greatly distorting the signal. This is achieved, in a process known as convolution,
by fitting successive sub-sets of adjacent data points with a low-degree polynomial by
the method of linear least squares. When the data points are equally spaced an
analytical solution to the least-squares equations can be found, in the form of a single set
of "convolution coefficients" that can be applied to all data sub-sets, to give estimates of
the smoothed signal, (or derivatives of the smoothed signal) at the central point of each
sub-set.
6.4.2 Mean smoothing
Median smoothing is to sort the selected data (the number of data points is odd),
then take the intermediate value as the smoothed value.
6.4.3 Median smoothing
Median smoothing is to sort the selected data (the number of data points is odd),
then take the intermediate value as the smoothed value.
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