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作 者:郭伟良[1,2] 王丹[1] 宋佳[1] 逯家辉[1] 杜林娜[1] 滕利荣[1]
机构地区:[1]吉林大学生命科学学院,吉林长春130012 [2]海南大学海洋学院,海南海口570228
出 处:《光学学报》2011年第2期274-281,共8页Acta Optica Sinica
基 金:中国医学基金会新药发展基金(20061108)资助课题
摘 要:建立了同时快速测定蛹虫草菌丝体中腺苷、蛋白质、多糖和虫草酸含量的近红外(NIR)光谱定量分析校正模型。收集了来自不同蛹虫草突变株在不同的发酵条件下发酵获得菌丝体并采集其NIR光谱,采用径向基神经网络(RBFNN)建立样品的NIR光谱与其腺苷、蛋白质、多糖和虫草酸含量间的相关模型。RBFNN模型经过采用可移动窗口法提取各组分的NIR光谱特征波长变量、选择有效的光谱预处理方法、隐含节点数和扩展常数进行优化,获得测定腺苷、蛋白质、多糖和虫草酸含量的最优模型,其训练集样本的参考值与模型预测值间的相关系数(Rc)分别达到0.9436,0.9884,0.9018和0.8848,表明模型的拟合度很好,测试集均方根误差(RMSEP)RMSEP分别为0.6225 mg/g,0.0179 g/g,0.0115 g/g和0.0102 g/g,表明模型的预测性能很好。Near infrared (NIR) spectroscopy calibration models have been developed for simultaneous and rapid determining the adenosine, protein, polysaccharide and cordyceps acid contents in cordyceps militaris mycelium powder. Cordyceps militaris mycelium powder samples are collected by culturing different cordyceps militaris mutant strains under various cultured conditions and the NIR spectra are acquired. Radial basis function neural network (RBFNN) is used for modeling the relation between the NIR spectra and the adenosine, protein, polysaccharide and cordyceps acid contents. The RBFNN models have been optimized. Firstly, movable window method is employed to select characteristic wavelength variables. And then, spectra preprocessing methods, the number of hidden nodes and the spread constant are selected. The optimum RBFNN models for determining the adenosine, protein polysaccharide and cordyceps acid contents are obtained. The correlation coefficients (Re) between reference values of calibration set and the predictive values are 0. 9436, 0. 9884, 0. 9018 and 0. 8848, respectively, which indicate that the fit of these models is satisfying. The root mean square error of prediction set (RMSEP) RMSEP are 0. 6225 mg/g, 0. 0179 g/g, 0. 0115 g/g, and 0. 0102 g/g, respectively, which indicate that the predictive capabilities of the models are fine.
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