Application of Wavelet Transform and Partial Least Square in Prediction of Common Chemical Compositions in Tobacco Samples

In order to analyze tobacco samples with near infrared spectrum quickly, the data was compressed by wavelet transform (WT) and the model on the compressed data was developed by partial least square (PLS).In comparison with PLS algorithm, the WT-PLS effectively compressed the original spectra data, removed the interference of noise and background, and reduced the randomness of the developed model.Therefore, the computation speed was significantly improved and the precision of prediction was increased.