| 刊名 | Agricultural Biotechnology |
| 作者 | Shengye WANG1, Siting WU1, Jinming LIU1*, Chunqi WANG2, Zhijiang LI2 |
| 作者单位 | 1. College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China; 2. College of Food Science, Heilongjiang Bayi Agricultural University, Daqing163319, China |
| DOI | DOI:10.19759/j.cnki.2164-4993.2026.01.010 |
| 年份 | 2026 |
| 刊期 | 1 |
| 页码 | 42-46 |
| 关键词 | Rice protein; Near-infrared spectroscopy; Stacking ensemble learning; Multi-model fusion; Integer encoding |
| 摘要 | [Objectives] This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm. [Methods] A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning. A base learner pool was constructed, containing Partial Least Squares (PLS), Support Vector Machine (SVM), Deep Extreme Learning Machine (DELM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Multilayer Perceptron (MLP). PLS, DELM, and Linear Regression (LR) were used as meta-learner candidates. Employing integer coding technology, systematic dynamic combinations of base learners and meta-learners were generated, resulting in a total of 40 non-repetitive fusion models. The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators. [Results] The combination "PLS-DELM-MLP-LR" (code 1367) achieved coefficients of determination of 0.973 2 and 0.978 0 on the validation set and independent test set, respectively, with relative root mean square errors of 2.35% and 2.36%, and residual predictive deviations of 6.107 5 and 6.747 9, respectively. [Conclusions] The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis, providing an efficient and feasible solution for modeling complex agricultural product spectral data. |
