Abstract：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.
赵 琛，瞿海斌，程翼宇* . 虫草氨基酸的人工神经网络-近红外光谱快速测定方法 [J]. 光谱学与光谱分析, 2004, 24(01): 50-53.
ZHAO Chen, QU Hai-bin, CHENG Yi-yu . A New Approach to the Fast Measurement of Content of Amino Acids in Cordyceps Sinensis by ANN-NIR . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2004, 24(01): 50-53.
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