A prediction model for soybean meal protein content was developed using low-field NMR and near-infrared spectral data fusion for rapid protein content detection during soybean meal production.Firstly,the low-field NMR and near-infrared spectral data were collected from test samples.Secondly,the two collected signals were preprocessed and the Successive Projections Algorithm(SPA)was used to extract the characteristic variables of the low-field NMR and near-infrared spectra.The partial least squares method,BP(Back Propagation)neural network and Sparrow Search Algorithm(SSA)were employed to optimize the BP neural network(SSA-BP).The selected characteristic variables were fused to establish a prediction model for soybean meal protein content.The SSA-BP model,constructed by fusing low-field NMR and near-infrared feature layer data,showed the best performance,with a calibration set determination coefficient of 0.983 0,RMSE of 0.127 3,validation set determination coefficient of 0.956 4,and RMSE of 0.203 9.In summary,this method enables achieve rapid,non-destructive and accurate quantitative detection of soybean meal protein content while verifying,feasibility and effectiveness of low-field NMR and near-infrared data fusion.
关键词
蛋白质/低场核磁共振/近红外/特征层融合/豆粕蛋白质
Key words
protein/low-field nuclear magnetism resonance/near-infrared/feature layer fusion/soybean meal protein
维普
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