Chemometrics is the application of mathematics and statistics to chemical data in order to design or select optimal experimental procedures, to provide maximum relevant information, and to obtain knowledge about systems under study. This chemical discipline has constantly developed to become a mature field of Analytical Chemistry after its inception in the 1970s. The utility and versatility of chemometric techniques enable spectroscopists to perform multidimensional calibration of spectral data that make identification and quantification of analytes in complex mixtures possible.
Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They are now being adapted for a vast number of signal processing due to their unprecedented success in terms of asymptotic optimality, spatial adaptivity and computational efficiency. In analytical chemistry, they have increasingly shown great applicability and have been preferred over existing signal processing algorithms in noise removal, resolution enhancement, data compression and chemometrics modeling in chemical studies.
We encourage spectroscopic studies focused on the application of chemometrics, in particular wavelet transform, for pharmaceutical and biomedical analysis. The submission of all article types is welcomed in the specialty section Analytical Chemistry.
Chemometrics is the application of mathematics and statistics to chemical data in order to design or select optimal experimental procedures, to provide maximum relevant information, and to obtain knowledge about systems under study. This chemical discipline has constantly developed to become a mature field of Analytical Chemistry after its inception in the 1970s. The utility and versatility of chemometric techniques enable spectroscopists to perform multidimensional calibration of spectral data that make identification and quantification of analytes in complex mixtures possible.
Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They are now being adapted for a vast number of signal processing due to their unprecedented success in terms of asymptotic optimality, spatial adaptivity and computational efficiency. In analytical chemistry, they have increasingly shown great applicability and have been preferred over existing signal processing algorithms in noise removal, resolution enhancement, data compression and chemometrics modeling in chemical studies.
We encourage spectroscopic studies focused on the application of chemometrics, in particular wavelet transform, for pharmaceutical and biomedical analysis. The submission of all article types is welcomed in the specialty section Analytical Chemistry.