9 November 2005 Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content
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Proceedings Volume 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality; 599619 (2005); doi: 10.1117/12.630424
Event: Optics East 2005, 2005, Boston, MA, United States
Abstract
A new method is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of Fourier transform near infrared (FT-NIR) spectral signals. An ideal spectrum signal prototype was constructed based on the FT-NIR spectrum of fruit sugar content measurement. The performances of wavelet based threshold de-noising approaches via different combinations of wavelet base functions were compared. Three families of wavelet base function (Daubechies, Symlets and Coiflets) were applied to estimate the performance of those wavelet bases and threshold selection rules by a series of experiments. The experimental results show that the best de-noising performance is reached via the combinations of Daubechies 4 or Symlet 4 wavelet base function. Based on the optimization parameter, wavelet regression models for sugar content of pear were also developed and result in a smaller prediction error than a traditional Partial Least Squares Regression (PLSR) mode.
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Yande Liu, Yibin Ying, Huishan Lu, Xiaping Fu, "Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content", Proc. SPIE 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality, 599619 (9 November 2005); doi: 10.1117/12.630424; https://doi.org/10.1117/12.630424
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KEYWORDS
Wavelets

Calibration

Interference (communication)

Near infrared

Wavelet transforms

Signal to noise ratio

Data modeling

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