Influence of Data Pre-processing on NIR Prediction Model for Tobacco Chemical Composition

The data of NIR spectra, total sugar, reducing sugar and total nicotine from 198 tobacco samples were processed by four different data pre-processing approaches, baseline correction, decongvolution, first differential and second differential, and two different statistic methods, principle component regression (PCR) and partial least square (PLS).The respective NIR calibration models for total sugar, reducing sugar and total nicotine in tobacco were developed and the regressive parameters of those models were compared.The results indicated that:1) the model developed by NIR spectra processed with second differential approach had the highest correlation coefficient, and the lowest relative deviation;and 2) the quantitative model established with PLS was better than that with PCR.