Fourier Transforms in Spectroscopy

Fourier Transforms in Spectroscopy

by Jyrki Kauppinen, Jari Partanen


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This modern approach to the subject is clearly and logically structured, and gives readers an understanding of the essence of Fourier transforms and their applications. All important aspects are included with respect to their use with optical spectroscopic data.
Based on popular lectures, the authors provide the mathematical fundamentals and numerical applications which are essential in practical use. The main part of the book is dedicated to applications of FT in signal processing and spectroscopy, with IR and NIR, NMR and mass spectrometry dealt with both from a theoretical and practical point of view. Some aspects, linear prediction for example, are explained here thoroughly for the first time.

Product Details

ISBN-13: 9783527402892
Publisher: Wiley
Publication date: 04/28/2001
Edition description: 1ST
Pages: 271
Product dimensions: 7.07(w) x 9.78(h) x 0.71(d)

About the Author

Professor Dr. Jyrki Kauppinen, born 1944, Dept. of Applied Physics, University of Turku, Finland
Dr. Jari Partanen, Technical Officer, Dept. of Applied Physics, University of Turku, Finland

Prof. Kauppinen is one of the world specialists of Fourier transform applied to spectra analysis. He is an excellent lecturer, his courses are very popular with the students. Has also given lectures in various industry laboratories.
His group works closely with some manufacturers who are converting laboratory interferometers and spectrometers into commercial instruments.
Well known both from the community of chemists and physicists.

Table of Contents

Basic definitions
General properties of Fourier transforms
Discrete Fourier transforms
Fast Fourier transforms
Laplace and other integral transforms
Fourier transform spectroscopy
Fourier transform nuclear magnetic resonance (NMR) spectrometry
FT ion cyclotron resonance mass spectrometry
Diffraction and Fourier transform
Uncertainty principle
Signal processing
Fourier self-deconvolution
Linear Prediction

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