Signal Processing in Analytical Chemistry

Signal processing refers to a variety of operations that can be carried out on a continuous (analog) or discrete (digital) sequence of measurements in order to enhance the quality of information it is intended to convey. In the analog domain, electronic signal processing can encompass such operations as amplification, filtering, integration, differentiation, modulation/demodulation, peakdetection, and analog-to-digital (A/D) conversion. Digital signal processing can include a variety of filtering methods (e.g. polynomial least-squares smoothing, differentiation, median smoothing, matched filtering, boxcar averaging, interpolation, decimation, and Kalman filtering) and domain transformations (e.g. Fourier transform (FT), Hadamard transform (HT), and wavelet transform (WT)). Generally the objective is to separate the useful part of the signal from the part that contains no useful information (the noise) using either explicit or implicit models that distinguish these two components. Signal processing at various stages has become an integral part of most modern analytical measurement systems and plays a critical role in ensuring the quality of those measurements.

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