Application of the fast fourier transform method for compression of spectral data obtained from a photodiode array spectrophotometer

doi:10.1016/0097-8485(94)80018-9

Computers & Chemistry

Volume 18, Issue 1, March 1994, Pages 13-20

https://doi.org/10.1016/0097-8485(94)80018-9 Get rights and content

Abstract

Data compression using the Fourier transform method was applied to data generated from an ultraviolet-visible photodiode array spectrophotometer. The spectroscopic data were converted into the Fourier domain by using the fast Fourier transform technique. Four different methods were utilized to reduce the number of Fourier coefficients required for accurate reproductions of the original spectra. The performance of these methods in data compression was evaluated by using synthetic spectra as well as experimental absorption spectra. It was found that the storage space of the spectral data can be reduced by more than 90% by using three of the suggested methods.

References (35)

M.S. Caceci
Comput. Chem.
(1989)
M.D. Barners et al.
J. Am. Chem. Soc.
(1942)
D.P. Binkley et al.
Anal. Chem.
(1980)
C.W. Brown et al.
Appl. Spectrosc.
(1986)
C.W. Brown et al.
Appl. Spectrosc.
(1986)
C.A. Bush
Anal. Chem.
(1974)
J.T. Clerc et al.
Storage and Retrieval of Spectroscopic Data.
S.M. Donahue et al.
Appl. Spectrosc.
(1988)
S.M. Donahue et al.
Appl. Spectrosc.
(1990)
A. Felinger et al.
Anal. Chem.
(1990)

A. Felinger et al.

Anal. Chem.

(1990)

A. Felinger et al.

Anal. Chem.

(1991)

W.I. Friesen et al.

Appl. Spectrosc.

(1985)

P.J. Gemperline et al.

Appl. Spectrosc.

(1987)

P.J. Gemperline et al.

J. Chemomet.

(1989)

P.J. Gemperline et al.

Factor Analysis of Spectro-chromatographic Data.

L. Glasser

J. Chem. Educ.

(1987)

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Application of the fast fourier transform method for compression of spectral data obtained from a photodiode array spectrophotometer

Abstract

Comput. Chem.

J. Am. Chem. Soc.

Anal. Chem.

Appl. Spectrosc.

Appl. Spectrosc.

Anal. Chem.

Storage and Retrieval of Spectroscopic Data.

Appl. Spectrosc.

Appl. Spectrosc.

Anal. Chem.

Anal. Chem.

Anal. Chem.

Appl. Spectrosc.

Appl. Spectrosc.

J. Chemomet.

Factor Analysis of Spectro-chromatographic Data.

J. Chem. Educ.