| 基于dbiPLS-SPA变量筛选的固态发酵湿度近红外光谱检测 |
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| 引用本文： | 刘国海,江 辉,梅从立.基于dbiPLS-SPA变量筛选的固态发酵湿度近红外光谱检测[J].农业工程学报,2013,29(25):218-222. |
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| 作者姓名： | 刘国海 江 辉 梅从立 |
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| 作者单位： | 江苏大学电气信息工程学院，镇江 212013;江苏大学电气信息工程学院，镇江 212013;江苏大学电气信息工程学院，镇江 212013 |
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| 基金项目： | 国家中小型企业创新基金项目（12C26213202207）；江苏高校优势学科建设工程项目（PAPD[2011]6）；镇江市农业科技支撑项目（NY2010017） |
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| 摘 要： | 为了提高基于近红外光谱技术的固态发酵关键过程参数——湿度快速检测的精度和稳定性，研究采用动态反向区间偏最小二乘（dbiPLS）法结合连续投影算法（SPA）进行最佳光谱子区间和特征组合变量的筛选，通过交互验证法确定偏最小二乘（PLS）模型的主成分因子数，并以预测均方根误差（RMSEP）和相关系数（Rp）作为模型的评价标准。试验结果显示，最佳dbiPLS-SPA模型筛选的组合变量个数为8，其RMSEP和Rp分别为1.1795%（质量分数）和0.9430。试验结果表明，dbiPLS-SPA是一个有效的波长组合变量筛选方法，可简化模型结构、增强模型精度和稳健性。
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| 关 键 词： | 发酵，湿度检测，近红外光谱，动态反向区间偏最小二乘，连续投影算法 |
| 收稿时间： | 2012/9/11 0:00:00 |
| 修稿时间： | 2013/4/19 0:00:00 |
Rapid detection of moisture content in solid-state fermentation by near-infrared spectroscopy combined with dbiPLS-SPA |
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| Liu Guohai,Jiang Hui and Mei Congli.Rapid detection of moisture content in solid-state fermentation by near-infrared spectroscopy combined with dbiPLS-SPA[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(25):218-222. |
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| Authors: | Liu Guohai Jiang Hui and Mei Congli |
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| Abstract: | Near-infrared spectroscopy (NIR) as an ideal tool was applied to measure moisture content in solid-state fermentation (SSF) of protein feed. To improve the detection precision and stability in determination of the moisture content in SSF by use of the NIR technique. Firstly, the raw spectra of all fermented samples obtained were preprocessed by use of the first derivative (1st Der). Secondly, the several efficient spectral subintervals were selected by use of dynamic backward interval partial least squares (dbiPLS). Thereafter, the feature combination variables were further extracted by successive projections algorithm (SPA) from the several spectral subintervals selected. Lastly, the partial least squares (PLS) model was developed by use of the feature combination variables selected for the measurement of moisture content of SSF of protein feed. In model calibration, the PLS factors were determined by a cross-validation, and The performance of the final model was evaluated according to the root mean square error of prediction (RMSEP) and correlation coefficient (Rp) in the validation set. The experimental results showed that the optimal model was obtained with 8 combined variables included, and these efficient variables corresponded to 7312.75 cm-1, 5850.97 cm-1, 5893.40 cm-1, 8527.68 cm-1, 5634.98 cm-1, 9538.20 cm-1, 9634.62 cm-1 and 9515.06 cm-1, respectively. The result of the RMSEP and Rp were 1.1795% (w/w) and 0.9430 in the validation set, respectively. Finally, the superior performance of the dbiPLS-SPA model was demonstrated by comparison with four other PLS models. The results indicate that NIR spectroscopy can be successfully used for measurement of moisture content in solid-state fermentation. Additionally, it is necessary to select characteristic wavelength variables of near-infrared spectra in model calibration. The dbiPLS-SPA is an effective method of combined variable selection. It can effectively reduce the complexity and improve generalization performance of the detection model when NIRS technique is used for on-line detection of the process parameters of SSF. |
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| Keywords: | fermentation moisture determination near-infrared spectroscopy dynamic backward interval partial least squares successive projections algorithm |
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