Liu Yan, Lu Chengyu, Meng Qingfan, Lu Jiahui, Fu Yao, Liu Botong, Zhou Yongcan, Guo Weiliang, Teng Lesheng
College of Life Science, Jilin University, Jilin, Changchun 130012, China.
Ocean College, Hainan University, Hainan, Haikou 570228, China.
Saudi J Biol Sci. 2016 Jan;23(1):S106-12. doi: 10.1016/j.sjbs.2015.06.023. Epub 2015 Jul 10.
In our previous work, partial least squares (PLSs) were employed to develop the near infrared spectroscopy (NIRs) models for at-line (fast off-line) monitoring key parameters of Lactococcus lactis subsp. fermentation. In this study, radial basis function neural network (RBFNN) as a non-linear modeling method was investigated to develop NIRs models instead of PLS. A method named moving window radial basis function neural network (MWRBFNN) was applied to select the characteristic wavelength variables by using the degree approximation (Da) as criterion. Next, the RBFNN models with selected wavelength variables were optimized by selecting a suitable constant spread. Finally, the effective spectra pretreatment methods were selected by comparing the robustness of the optimum RBFNN models developed with pretreated spectra. The results demonstrated that the robustness of the optimal RBFNN models were better than the PLS models for at-line monitoring of glucose and pH of L. lactis subsp. fermentation.
在我们之前的工作中，采用偏最小二乘法（PLSs）建立了用于在线（快速离线）监测乳酸乳球菌发酵关键参数的近红外光谱（NIRs）模型。在本研究中，研究了采用径向基函数神经网络（RBFNN）作为非线性建模方法来建立NIRs模型，以替代PLS。应用一种名为移动窗口径向基函数神经网络（MWRBFNN）的方法，以近似度（Da）为准则选择特征波长变量。接下来，通过选择合适的常数展宽对具有选定波长变量的RBFNN模型进行优化。最后，通过比较用预处理光谱建立的最优RBFNN模型的稳健性，选择有效的光谱预处理方法。结果表明，最优RBFNN模型在在线监测乳酸乳球菌发酵葡萄糖和pH值方面的稳健性优于PLS模型。
