应用导数荧光光谱和概率神经网络鉴别合成色素

实验测量了食品色素胭脂红、苋菜红、诱惑红和工业色素苏丹红Ⅳ溶液分别在波长为300,400,440和380 nm的光激发下产生的荧光光谱.对这4种红色素的各8个溶液样本选取60个发射波长值所对应的荧光强度作为网络特征参数,训练、建立概率神经网络.据此,对32个色素溶液样本进行种类识别.为解决原始荧光光谱重叠造成识别准确率不高的问题,应用导数荧光光谱,将二阶导数光谱数据作为网络特征参数,建立网络,进行识别,识别准确率达100%.由此,提出了应用二阶导数荧光光谱结合概率神经网络对合成色素方便、快捷、准确地进行种

Identification of synthetic colors using derivative fluorescence spectroscopy and probabilistic neural networks

Excited respectively by the light with wavelengths of 300, 400, 440 and 380 nm, the fluorescence spectra of synthetic food color ponceau 4R, amaranth, allurea red and industrial dye Sudan Ⅳ have been measured. For each sample, 60 emission wavelength values were selected. The fluorescence intensity corresponding to the selected wavelength was used as the network characteristic parameters, a probabilistic neural network for kind identification was trained and constructed. It was employed to identify the 32 kinds of color solution samples. Because the fluorescence spectra of these colors overlap, the identification rate is low. In order to solve this problem, a derivative fluorescence spectroscopy was introduced. The derivative fluorescence data was used as the network characteristic parameters, a probabilistic neural network was constructed and was employed to identify colors. The identification rate is up to 100%. Based on this, a new method is presented, which combines the derivative fluorescence spectroscopy and probabilistic neural network, and can identify synthetic colors easily, quickly and accurately. This method can provide support for food safety supervision and management.