1. College of Science, Jiangnan University，Wuxi 214000，China
2. Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology，Wuxi 214000，China
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
[1] Yang Renjie, Liu Rong, XU Kexin. Proceedings of SPIE, 2012, 8229: 822918.
[2] Liu Xin, Lü Hua, Xu HuaChen. Chinese Chemical Letters, 2015, 26(2): 205.
[3] BO Gui-jun, YU Jing, DI Hui-hui, et al(卜贵军, 于 静, 邸慧慧，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(2): 362.
[4] Dong Yongjiang, Lu Hao, Yong Zhengdong, et al. Applied Optics, 2015, 54(23): 7032.
[5] LIU Rong, YANG Ren-jie, XU Ke-xin(刘 蓉, 杨仁杰, 徐可欣). The Journal of Light Scattering(光散射学报), 2013, 5(1): 92.
[6] SUN Yan-hui, CAI Hua-zhen, JIA Xiao-li, et al(孙艳辉, 蔡华珍, 贾小丽，等). Chinese Journal of Analytical Chemistry(分析化学), 2013, 41(9): 1373.
[7] Feng Juan, Han Ting, Zhang Miqing, et al. Chinese Chemical Letters, 2015, 26(2): 210.
[8] HE Jing-wei, ZHAO Xiu-ying, ZHENG Wei, et al(贺经纬, 赵秀英, 郑 玮, 等). Acta Polymerica Sinica(高分子学报), 2016, 2(2): 234.
[9] LI Dan, YANG Zhong-qiang, XI Long, et al(李 丹, 杨中强, 奚 龙，等). Acta Polymerica Sinica(高分子学报), 2016, 2(2): 164.
[10] NI Li-jun, ZHANG Li-guo(倪力军, 张立国). Basic Chemometrics and Its Application(基础化学计量学及其应用). Shanghai: East China University of Science and Technology Press(上海：华东理工大学出版社), 2011. 56.
[11] WANG Xu-dong, SHAO Hui-he(王旭东, 邵惠鹤). Control Theory and Applications(控制理论与应用), 1997, 14(1): 59.
