Study on Denoising Near Infrared Spectra of Wood Based on Wavelet Transform

Near infrared (NIR) spectra of wood samples are often confused by a series of noise, which greatly influences accurate analytical result. In order to improve analytical precision, the authors need to pretreat the spectrum data. Derivative can correct baseline and background effects, increasing the resolution ratio of the spectra. However, it also increases the noise at the same time. The present paper aims at using wavelet transform to eliminate the noise of the near infrared first derivative spectrum of wood with the methods of 9 point smoothing spectrum, 25 point smoothing spectrum, the nonlinear wavelet hard-threshold spectrum, the nonlinear wavelet soft-threshold spectrum, 9 point smoothing+wavelet transform and 25 point smoothing spectrum+wavelet transform. The results show that the wavelet transform has particular advantage on noise elimination of the near infrared spectra while reserving the useful information of spectrum. It can also improve the signal to noise ratio of spectrum, promising the prospect of a wide application in the wood near infrared spectroscopic analysis.