Aimed atnoise interference of infrared spectra,an example of using infrared spectra to detect fat content value on the surface of cashmere was applied to evalua 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.Denoisedspectra andmeas-ured cashmere fat cont entvalues 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 modelsres pectively.Ther esults show that the three wavelet threshold denoising models all candenoise the infrared spectral signal,increase signal to noise ratio and improv eprecision 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 de noising models can denoise infrared spectral signal effectively, make multivariate analysis model of spectral data and measured cashmere fat values morerep resentative and robust, and so it can improve detection precision of infrared spectral tchnique.