应用荧光相关光谱测定L-色氨酸的浓度

doi:10.3964/j.issn.1000-0593(2018)08-2451-05

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Abstract The three-dimensional fluorescence spectrum of the L-tryptophan solution was measured in a fluorescence spectrometer. The result showed that the fluorescence peak of L-tryptophan located at 270 nm/350 nm (excitation wavelength/emission wavelength). As the emission wavelength was fixed at 350 nm, an excitation spectrum can be measured. It can be found from the excitation spectrum that the curve has a high slope and good linearity in the range of 250~260 nm. Thus, the excitation wavelengths of 250, 255 and 260 nm were chosen to excite the sample, and three fluorescence emission spectra were measured. Based on the three spectra, the auto correlation spectra with disturbance variable of wavelength were constructed. In addition, the emission spectra of the ultrapure water under different excitation wavelength were measured and averaged to be reference spectrum. The auto-correlation spectra with disturbance variable of concentration were constructed by correlation calculation between the reference spectrum and the averaged spectrum of the samples. Combined the correlation spectral data with partial least squares regression (PLSR) and radial basis function neural network (RBFNN), the prediction models of the concentration of L-tryptophan were measured, respectively. The prediction results showed that the correlation spectrum constructed with disturbance variable of concentration had a better signal-to-noise ratio and better prediction performance. Furthermore, with the same disturbance variable, the model based on RBFNN was more precise than that based on PLSR. Among all the models, the model based on RBFNN with disturbance variable of concentration had the best prediction performance with correlation coefficient of 99.91% and root-mean-square error of 0.033 μg·mL^-1. This method can provide helps in the food safety supervision for the accurate determination of substances.

Key words：L-tryptophan; Fluorescence correlation spectrum; Partial least squares regression; Radial basis function neural network

Received: 2016-12-20 Accepted: 2017-06-22

ZTFLH:

O657.3

Corresponding Authors: CHEN Guo-qing E-mail: cgq2098@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)08-2451-05 OR https://www.gpxygpfx.com/EN/Y2018/V38/I08/2451