Model fusion for prediction of apple firmness using hyperspectral scattering image
Hyperspectral scattering image is an advanced technology widely used in non-destructive measurement of fruit quality. To develop a better prediction model for apple firmness, the present study investigates a model fusion method coupled with wavelength selection algorithms. The current paper first discusses two wavelength selection algorithms, namely, uninformative variable elimination (UVE) and supervised affinity propagation (SAP). The selected effective wavelengths are then set as input to the partial least square (PLS) model. Six hundred Golden Delicious apples were analyzed. The first 450 apples were used as sample for the calibration model, whereas the remaining 150 were used for the prediction model. Compared with full wavelengths, the number of effective wavelengths based on the UVE and SAP algorithms decreased to 34% and 35%, but the correlation coefficient of prediction (Rp) increased from 0.791 to 0.805 and 0.814, whereas the root mean-square error of prediction (RMSEP) decreased from 6.00 to 5.73 and 5.71N, respectively. A fusion model was then developed using UVE-PLS and SAP-PLS models coupled with backpropagation neural network. A better prediction accuracy was achieved from the fusion model (Rp=0.828 and RMSEP=5.53N). The model fusion provides an effective modeling method for apple firmness prediction using hyperspectral scattering image technique.
model fusion hyperspectral scattering image wavelength selection uninformative variable elimination supervised affinity propagations partial least squares