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.