| 刊名 | Agricultural Biotechnology |
| 作者 | Baihong TONG, Jinming LIU, Jianfei SHI* |
| 作者单位 | College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163000, China |
| DOI | DOI:10.19759/j.cnki.2164-4993.2024.05.018 |
| 年份 | 2024 |
| 刊期 | 5 |
| 页码 | 83-85,92 |
| 关键词 | Hemicellulose; Near-infrared spectrum; Characteristic wavelength selection; Intelligent optimization algorithm; Dung beetle algorithm |
| 摘要 | Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China, as well as an important source of modern biomass energy and new materials. Hemicellulose is an important component in corn stalks, and it is very important to determine its content in corn stalks. In this paper, the feasibility of near-infrared spectroscopy (NIRS) combined with chemometrics for rapid detection of hemicellulose content in corn stalks was studied. In order to improve the accuracy of NIRS detection, a new intelligent optimization algorithm, dung beetle optimizer (DBO), was applied to select characteristic wavelengths of NIRS. Its modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm (GA) and binary particle swarm optimization (BPSO), and it was found that the characteristic wavelength selection performance of DBO was excellent, and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms. |
