[Sugar content prediction of apple using near-infrared spectroscopy treated by wavelet transform]

Based on wavelet transform (WT) by using the difference in wavelet modulus maxima evolution behaviors between singular signals and random noises in multi-scale space, the near infrared spectroscopic signals of 90 fruit samples were denoised by wavelet transform. The sugar content in intact apple was calculated by stepwise regression method. The result of calibration model after noise filtering was satisfactory. The relative standard error of prediction is reduced to 6.0% from 6.1% of original spectra. It is concluded that wavelet transform is an useful method to eliminate noise of NIR signals, as it makes the final calibration model more representative and stable and robust.