Application of Radial Bases Function Network and Response Surface Method to Quantify Compositions of Raw Goat Milk with Visible/Near Infrared Spectroscopy

Raw goat milk pricing is based on the milk quality especially on fat, solid not fat (SNF) and density. Therefore, there is a need of approach for composition quantization. This study applied radial basis function network (RBFN) to calibrate fat, SNF, and density with visible and near infrared spectra (400~2500 nm). To find the optimal parameters of goal error and spread used in RBFN, a response surface method (RSM) was employed. Results showed that with the optimal parameters suggested by RSM analysis, R difference for training and testing data set was the smallest which indicated the model was less possible of overtraining or undertraining. The R for testing set was 0.9569, 0.8420 and 0.8743 for fat, SNF and density, respectively, when optimal parameters were used in RBFN.