Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content

Yande Liu; Yibin Ying; Huishan Lu; Xiaping Fu

doi:10.1117/12.630424

8 November 2005 Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content

Yande Liu, Yibin Ying, Huishan Lu, Xiaping Fu

Author Affiliations +

Proceedings Volume 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality; 599619 (2005) https://doi.org/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.

Citation Download Citation

Yande Liu, Yibin Ying, Huishan Lu, and 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 (8 November 2005); https://doi.org/10.1117/12.630424

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available