A signal may be defined as the output of a transducer that
is responding to the chemical system of interest. The signal may be separated
into two parts, one caused by the analyte(s) and the other caused by other
components of the sample matrix and the instrumentation used in the
measurement. This latter part of the signal is known as noise.
Although the
ability to separate significant data-containing signals from meaningless noise
has constantly been a desirable property of any instrument, it has become imperative
with the demand for progressively more sensitive measurements. The amount of
noise present in an instrument system determines the smallest concentration of
analyte that can be accurately measured and also fixes the precision of
measurement at larger concentrations.
Noise reduction (or signal enhancement)
is a primary consideration in obtaining useful data from measurements that
involve either weak signal sources or trace amount of analyte(s).
The two main
methods of enhancing the signal are
(1) the use of electronic hardware devices,
such as filters, or equivalent computer software algorithms to process signals
from the measurement as they pass through the instrument and
(2) post
measurement mathematical treatment of data. Among the more useful post
measurement methods are the statistical techniques.
In addition to signal enhancement, these techniques aid in
identifying sources of error and determining precision, while providing a
method for an objective comparison of results. This module will deal with some
common noise-reduction techniques and briefly review important statistical
methods typically used in the treatment of instrumental data.
After watching this video lecture you will learn about:
· Signal to Noise Ratio
· Sensitivity and detection
limit
· Sources of Noise
· Hardware techniques for
Signal to Noise enhancement
·
Software techniques for Signal to Noise enhancement
· Data treatment by filtering,
Smoothing, and averaging
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