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## Abstract Principal component analysis (PCA) and principal component regression (PCR) are routinely used for calibration of measurement devices and for data evaluation. However, their use is hindered in some applications, e.g. hyperspectral imaging, by excessive data sets that imply unacceptable calculation time. This paper discusses a fast PCA achieved by a combination of data compression based on a wavelet transformation and a spectrum selection method prior to the PCA itself. The spectrum selection step can also be applied without previous data compression. The calculation speed increase is investigated based on original and compressed data sets, both simulated and measured. Two different data sets are used for assessment of the new approach. One set contains 65 536 synthetically generated spectra at four different noise levels with 256 measurement points each. Compared with the conventional PCA approach, these examples can be accelerated 20 times. Evaluation errors of the fast method were calculated and found to be comparable with those of the conventional approach. Four experimental spectra sets of similar size are also investigated. The novel method outperforms PCA in speed

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Author

F. Vogt; M. Tacke

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Ltd.; Wiley (ISSN 0886-9383)

Published

2002

Language

EN

Field

Chemistry (Physical Sciences)