Quantitative inversion model of protein and fat content in milk based on hyperspectral techniques

文献类型： 外文期刊

第一作者： Jin, Xu

作者： Jin, Xu;Xiao, Zhi-yun;Nie, Qi-xin;Wang, Yi-ning;Jin, Xu;Xiao, Zhi-yun;Nie, Qi-xin;Wang, Yi-ning;Xiao, Dou-xin;Dong, Alideertu;Xiao, Dou-xin;Dong, Alideertu;Wang, Li -fang;Wang, Li -fang

作者机构：

期刊名称：INTERNATIONAL DAIRY JOURNAL （ 影响因子：3.572； 五年影响因子：3.89 ）

ISSN： 0958-6946

年卷期： 2022 年 134 卷

页码：

收录情况： SCI

摘要： 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 pre-processing 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 (R-2) 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 R(2 )for the VR model were 0.9834 and 09607, respectively. (C) 2022 Elsevier Ltd. All rights reserved.

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