[Study on saltation of fluorescence excitation spectra of erythrosine].

The fluorescence excitation spectra and absorption spectra of six kinds of erythrosine solutions with concentrations of 10, 20, 30, 40, 50 and 60 microg x mL(-1) were experimentally measured. It was found that the fluorescence excitation peaks are both located at 530 nm significantly when the concentrations of erythrosine solutions are 10 and 20 microg x mL(-1). However, the linetype saltation of fluorescence excitation spectrum occurs as the concentration of erythrosine solution is above 30 microg x mL(-1). The valley is located at 530 nm and two new peaks appear at both flanks of the valley. Compared with fluorescence excitation spectra, the absorption spectra of erythrosine solutions are without saltation and the peaks are all located at 530 nm. According to calculations and a series of contrast experiments, it was demonstrated that the absorption characteristic of erythrosine and the spectral measurement mode conspire to cause the saltation of fluorescence excitation spectra. The results can provide guidance for further research on physical and chemical properties of erythrosine, and offer help and reference for study on saltation behavior in fluorescence excitation spectra and improvement in spectral measuring mode.

[Quantitative determination of melamine by fluorescence spectroscopy and radial basis function neural networks].

Based on the experimental study, it was found that melamine solution excited by UV light can generate a strong fluorescence. The fluorescence spectrum is within a range from 310 to 600 nm, the peak wavelength of the fluorescence is about 420 nm, and the relationship between fluorescence intensity and melamine solution concentration is nonlinear. A method for the determination of melamine solution concentration was presented, which was based on fluorescence spectroscopy and radial basis function neural networks. For each sample, 30 emission wavelength values were selected, the fluorescence intensity corresponding to the selected wavelength was used as the network data, and a radial basis function neural network was trained and constructed. The trained radial basis function neural network was employed to predict the melamine solution concentration in five kinds of samples, and the relative errors of the results were 0.93%, 0.09%, 0.31%, 1.55% and 4.61%, respectively. The results show that this method can determine the content of melamine quickly and accurately. The whole research outcomes will provide a new method for determining the content of melamine and food safety supervision.

[Determination and identification of synthetic food colors based on fluorescence spectroscopy and radial basis function neural networks].

Taking ponceau 4R and amaranth as an example, concentration prediction and kind identification of synthetic food colors by fluorescence spectroscopy and radial basis function neural networks are introduced. By using SP-2558 multifunctional spectral measuring system, the fluorescence spectra were measured for solution of ponceau 4R and amaranth excited respectively by the light with the wavelength of 300 and 400 nm. For each sample solution of ponceau 4R, 15 emission wavelength values were selected. The fluorescence intensity corresponding to the selected wavelength was used as the network characteristic parameters, and a radial basis function neural network for concentration prediction was trained and constructed. It was employed to predict ponceau 4R solution concentration of the three kinds of samples, and the relative errors of prediction were 1.42%, 1.44% and 3.93% respectively. In addition, for solution of ponceau 4R and amaranth, the fluorescence intensity corresponding to the fluorescence wavelength was used as the network characteristic parameters, and a radial basis function neural network for kind identification was trained and constructed. It was employed to identify the kind of food colors, and the accuracy is 100%. These results show that the method is convenient, fast, and highly accurate, and can be used for the detection of synthetic food color in food safety supervision and management.