Study on noise filtering

In the process of collecting spectrum using near-infrared instrument, the final spectrum signal detected by detector contains not only sample related information but also the noise signal because of influence from various aspects of interference factors. In order to eliminate interference resulted by drift and shift of baseline, random noise and stray light of instrument, and to improve signal-to-noise ratio (SNR) of spectrum, various baseline correction methods and spectral smoothing methods were used to process rice spectrums. And then eight models were established. The model which used first derivative and Savizky-Golay convolution had the best performance with comparison of various performance indicators including correlation coefficient(R), root mean square error of calibration(RMSEC), root mean square error of cross validation(RMSECV).