Influence of different pre-processing methods in predicting sugarcane quality from near-infrared (NIR) spectral data

The influence of different data pre-processing methods (smoothing by moving average (MA), multiplicative scatter correction (MSC), Savitzky-Golay (SG), standard normal variate (SNV) and mean normalization (MN) on the prediction of sugar content from sugarcane samples was investigated. The performance of these pre-processing methods was evaluated using spectral data collected from 292 sugarcane internode samples using a visible-shortwave near infrared spectroradiometer (VNIRS). Partial least square (PLS) method was applied to develop both calibration and prediction models for the samples. If no pre-processing method was applied, the coefficient of determination (R2) values for both reflectance and absorbance data were 0.81 and 0.86 respectively. The highest prediction accuracy values were obtained when the data was treated with MSC method, where the R2 values for reflectance and absorbance being 0.85 and 0.87, respectively. From this study, it was concluded that pre-processing can improve the model performances where MSC method was found to give the highest prediction accuracy value.