Investigation On Fluorescence Spectra Of Synthetic Food Dyes Sunset Yellow And Tartrazine

Food pigment, one of the most important food additives and also called food dyes or colorant, has been widely applied to food, medicine and cosmetic industry. Food pigment can be classified as natural pigment and synthetic food dyes according to their sources. Synthetic food dyes contain some compounds in which have the structures of R-N=N-R bond, benzene ring or xanthene, and so on. These compounds may be converted to carcinogen in human body, and cause damage. Therefore, the varieties, the number and the ADI of synthetic food dyes must be controlled strictly. Moreover, fast and efficient inspection must be performed.In recent years, many food safety issues occurred frequently such as sudan incident, pesticide incident, melamine incident, and so on. Many nations have regarded the food safety as a national public safety and have strengthened the management system. It is important to improve the detection technique of food pigment. At present, many methods such as UV spectrophotometry, high performance liquid chromatography, derivative adsorptive voltammetry, have researched extensively.Few researches about the synthetic food dyes qualitative characterization and quantitative by the fluorescence spectroscopy analysis were reported no matter domestically and overseas. While the properties of measurement precision, sample less, high-resolution, make molecular fluorescence spectrometry attrictive for many applications in residual content and specise identification, they pose unique challenges to meet the shortage of the tradtional methods. Unfortunately, the synthetic food dyes contain a large number of fluorophores and its fluorescence spetra law is complex. In the fluorescence spetra, the non-linear relationship between fluorescence intensity and concentration could not be qualitative and quantitative analysed by the methods such as PLS, multiple linear regression and so on.In this paper, 0.100mg/ml sunset yellow and tartrazine solution fluorescence spectra are detected by the Roper Scientific SP-2558 multi-function spectrometer. The results show that their best excitation wavelength is 370nm and 350nm respectively, their fluorescence peak is respectively at 576nm and 569nm in the fluorescence spectra induced by 310nm to 400nm UV light. According to the principle of molecular spectroscopy, the mechanism on sunset yellow and artrazine emit fluorescence is discussed. The reason why sunset yellow and artrazine can emit fluorescence is discussed. There are some fluorescent chromophoric and auxochrome groups existed in molecular, such as =C=O, benzene ring, naphthalene ring,–OH, -SO3Na, etc. They are connected together through azo bond, lead to the formation of conjugated double bond system. Electronic existed in it can easily absorb photons by the transition forms ofπ→π*and emit fluorescence. Then, the fluorescence spectra characteristic at different concentration of sunset yellow and tartrazine were detected and discussed.Based on the experiment, derivative spectrophotometry is employed to deal with fluorescence spectra data of sunset yellow and tartrazine. There are obvious difference between the sunset yellow and the tartrazine fluorescence spectra. The recognition ability is enhanced by using fluorescence spectroscopy. Some intelligent algorithms, such as BP neural network, radial basis function neural network, GA-BP neural network, are used to detect sunset yellow and tartrazine quantitatively. Complex nonlinear classification could be completed by using radial basis function neural network and BP neural network in treatment process, without considering systematic error and mathematical model of objects. Furthermore, when compared to BP neural network, the results are more accurate and more stable, the analysis time is more shorten by using radial basis function neural network. GA-BP neural network, combined the advantages of neural network and GA, could predict the exact concentration of sunset yellow. In this paper, wavelet transform was used to compress fluorescence spectra data. It maintained the original characteristic peak. Meanwhile, it reduced amount of data and neural network processing time, and enhanced the measuring accuracy in the program of network training and prediction. These methods not only utilize their own strongpoints, but also combined the advantages of fluorescence spectroscopy such as high sensitivity, small quantity of sampling, speediness and convenience. The average error and RSD were below 5%.Although we take sunset yellow and tartrazine as the research objects, but the content has universality. Our research findings also can be used to recognize and predict the concentration of other synthetic food dyes and additives. They not only provide theoretical support for further studying the molecular structure and the mechanism of toxicity of synthetic food dyes, but also present a new idea and a new way for food safety inspection.