Prediction Model of Soybean Meal Protein Content Based on Low-field Nuclear Magnetic Resonance and Near-infrared Data Fusion
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...
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| Format: | Article |
| Language: | English |
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Academy of National Food and Strategic Reserves Administration
2025-01-01
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| Series: | Liang you shipin ke-ji |
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| Online Access: | http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250117 |
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| author | REN Guo-wei ZHENG Sheng-guo LU Bing LU Dao-li CHEN Bin |
| author_facet | REN Guo-wei ZHENG Sheng-guo LU Bing LU Dao-li CHEN Bin |
| author_sort | REN Guo-wei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ef152606e8e842c79b866dae3b8b84fa |
| institution | Kabale University |
| issn | 1007-7561 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Academy of National Food and Strategic Reserves Administration |
| record_format | Article |
| series | Liang you shipin ke-ji |
| spelling | doaj-art-ef152606e8e842c79b866dae3b8b84fa2025-01-23T14:41:45ZengAcademy of National Food and Strategic Reserves AdministrationLiang you shipin ke-ji1007-75612025-01-0133115616310.16210/j.cnki.1007-7561.2025.01.016Prediction Model of Soybean Meal Protein Content Based on Low-field Nuclear Magnetic Resonance and Near-infrared Data FusionREN Guo-wei0ZHENG Sheng-guo1LU Bing2LU Dao-li3CHEN Bin4School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, ChinaSuzhou Niumag Analytical Instrument Corporation, Suzhou, Jiangsu 215163, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, ChinaA 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.http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250117proteinlow-field nuclear magnetism resonancenear-infraredfeature layer fusionsoybean meal protein |
| spellingShingle | REN Guo-wei ZHENG Sheng-guo LU Bing LU Dao-li CHEN Bin Prediction Model of Soybean Meal Protein Content Based on Low-field Nuclear Magnetic Resonance and Near-infrared Data Fusion Liang you shipin ke-ji protein low-field nuclear magnetism resonance near-infrared feature layer fusion soybean meal protein |
| title | Prediction Model of Soybean Meal Protein Content Based on Low-field Nuclear Magnetic Resonance and Near-infrared Data Fusion |
| title_full | Prediction Model of Soybean Meal Protein Content Based on Low-field Nuclear Magnetic Resonance and Near-infrared Data Fusion |
| title_fullStr | Prediction Model of Soybean Meal Protein Content Based on Low-field Nuclear Magnetic Resonance and Near-infrared Data Fusion |
| title_full_unstemmed | Prediction Model of Soybean Meal Protein Content Based on Low-field Nuclear Magnetic Resonance and Near-infrared Data Fusion |
| title_short | Prediction Model of Soybean Meal Protein Content Based on Low-field Nuclear Magnetic Resonance and Near-infrared Data Fusion |
| title_sort | prediction model of soybean meal protein content based on low field nuclear magnetic resonance and near infrared data fusion |
| topic | protein low-field nuclear magnetism resonance near-infrared feature layer fusion soybean meal protein |
| url | http://lyspkj.ijournal.cn/lyspkj/article/abstract/20250117 |
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