Abstract
Analytical chemists are instilled with the Shannon–Nyquist theorem for measuring practices: the sampling
frequency must be greater than two times the frequency of the signal of interest to avoid misrepresentation.
Furthermore, chemical analysis techniques keep evolving to yield increasing amounts of information
resulting in greater demands in data collection, data analysis and computer memory. This leads to typical
practices of performing software data compression. In addition, techniques that require increases in
spatial frequency of array detectors typically lead to expensive hardware solutions. Over the past ten
years, compressed sensing has presented a sampling paradigm shift. The main idea is to perform
compression during data acquisition which leads to several advantages, including faster analysis time and
availability of cost effective alternatives to array detectors. In this short review, a summary of the main
concepts of compressed sensing are presented. In addition, selected
Original language | English |
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Pages (from-to) | 2165-2174 |
Journal | Journal of Analytical Atomic Spectrometry |
State | Published - Nov 2016 |