虫草氨基酸的人工神经网络-近红外光谱快速测定方法

提出了用近红外漫反射光谱技术快速检测发酵冬虫夏草中氨基酸含量的新方法。采用比色法测定虫草菌粉中氨基酸含量。用BP神经网络建立了近红外光谱数据与氨基酸、精氨酸和总氨酸含量间的定量关联模型。通过比较不同的光谱预处理方法及光谱范围 ,得到最优模型 ,即在 75 0 1 7～ 6 0 97 8,5 4 5 3 7～ 42 4 6 5cm- 1 区域内 ,近红外光谱的一阶微分光谱与其氨基酸含量之间建立模型。甘氨酸、精氨酸和总氨基酸的预测标准偏差分别为 0 0 8,0 0 7和 0 36 ,均优于主成分回归 (PCR)和偏最小二乘回归 (PLS)等线性模型的处理结果。结果表明 ,该方法是一种有效实用的非线性校正方法。为近红外光谱快速测定中药组分含量提供了一条新途径

A New Approach to the Fast Measurement of Content of Amino Acids in Cordyceps sinensis by ANN-NIR

A new method for fast determining the content of amino acid in Cordyceps sinensis by means of near infrared (NIR) spectroscopy was developed. Colorimetry was first employed to measure the actual content of amino acid in Cordyceps sinensis. BP neural network was introduced to model the quantitative correlations between the NIR spectra and the contents of glycine, arginine and total amino acid. By comparing several preprocessing procedures and wavelength ranges, the optimal models could be obtained in the range of 7 501.7-6 097.8 cm -1 and 5 453.7-4 246.5 cm -1 with first derivative NIR spectra. Standard errors of prediction set (RMSEP) for glycine, arginine and total amino acid were 0.08, 0.07 and 0.36, respectively. The ultimate experimental results indicated that the proposed artificial neural network model was far superior to those of partial least square regression(PLS) and principal component regression(PCR). As an effective nonlinear multivariate calibration strategy, this paper could offer a new approach to the fast measurement of content of chemical components in traditional Chinese medicine by NIR spectroscopy.