Quantitative inversion model of protein and fat content in milk based on hyperspectral techniques
2022
Jin, Xu | Xiao, Zhi-yun | Xiao, Dou-xin | Dong, Alideertu | Nie, Qi-xin | Wang, Yi-ning | Wang, Lifang
Traditional chemical methods for detecting milk composition suffer from many disadvantages, such as low efficiency and complicated operations. We propose a novel method based on hyperspectral inverse modelling method that combined Savitzky–Golay and first differentiation (SG_FD) to process the spectral data, coupled with an innovative application of improved spatial frog-hopping algorithm (IVRF_CA) to filter the feature wavebands, followed by a voting regressor (VR) to predict the fat and protein content in milk. The results demonstrated that the SG_FD algorithm is a hyperspectral preprocessing method that effectively improves the modelling accuracy, and the IVRF_CA algorithm reduced model complexity while ensuring the accuracy of the model. The test set coefficients of determination (R²) for the fat and protein partial least squares regression (PLSR) models built using feature wavebands filtered by the IVRF_CA were 0.9608 and 0.8623, respectively, while the corresponding test set R² for the VR model were 0.9834 and 09607, respectively.
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