[Application of wavelet threshold denoising model to infrared spectral signal processing].
Aimed at noise interference of infrared spectra, an example of using infrared spectra to detect fat content value on the surface of cashmere was applied to evaluate the effect of wavelet threshold denoising. The denoising capabilities of three wavelet threshold denoising models (penalty threshold denoising model, Brige-Massart threshold denoising model and default threshold denoising model) were compared and analyzed. Denoised spectra and measured cashmere fat content values were used for calibration and validation with multivariate analysis (partial least squares combined with support vector machine). The authors analyzed and evaluated denoising effects of these three wavelet threshold denoising models by comparing parameters (R2, RMSEC and RMSEP) obtained through calibration and validation of denoised spectra with these three wavelet threshold denoising models respectively. The results show that the three wavelet threshold denoising models all can denoise the infrared spectral signal, increase signal to noise ratio and improve precision of prediction model to some extent; Among these three wavelet threshold denoising models, the denoising effect of Brige-Massart threshold denoising model and default threshold denoising model were significantly better than that of default threshold denoising model; Compared with the prediction precision (R2 = 0.793, RMSEC = 0.233, RMSEP = 0.225) of multivariate analysis model established with original spectra, the prediction precision (R2 = 0.882, RMSEC = 0.144, RMSEP = 0.136) of multivariate analysis model established with spectra denoised by Brige-Massart threshold denoising model and the prediction precision (R2 = 0.876, RMSEC = 0.151, RMSEP = 0.142) both had much more improvements. All the above illustrates that wavelet threshold denoising models can denoise infrared spectral signal effectively, make multivariate analysis model of spectral data and measured cashmere fat values more representative and robust, and so it can improve detection precision of infrared spectral technique.
Wu
Gui-Fang
GF
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China. wgfsara@126.com
He
Yong
Y
D000465:Algorithms; D002138:Calibration; D016018:Least-Squares Analysis; D008055:Lipids / Q000032:analysis*; D008962:Models, Theoretical; D013055:Spectrophotometry, Infrared; D013784:Textiles / Q000032:analysis*; D058067:Wavelet Analysis
D008055:Lipids