摘要
为了测定混合色素溶液中胭脂红的浓度,采用归一化的方法对荧光光谱进行数据预处理,将处理后的光谱数据结合径向基神经网络,建立了对胭脂红含量的预测模型。结果表明,3维同步荧光光谱、普通3维荧光光谱预测结果的平均相对误差分别为2.86%,11.12%;对于混合色素溶液中单个色素浓度的测定,3维同步荧光光谱结合径向基神经网络效果较好。该研究为预测混合色素溶液中各色素浓度提供了帮助。
In order to determine carmine concentration in the mixed pigment solution,normalization method was used to preprocess the fluorescence spectra. The processed data were combined with radial basis function neural network to establish the prediction model of carmine content. The average relative error of the prediction results of 3-D synchronous fluorescence spectrometry and 3-D ordinary fluorescence spectrometry were 2. 86% and 11. 12% respectively. The results showed that the 3-D synchronous fluorescence spectrometry was superior for the determination of the mixed pigment solution. The research provides the help for the prediction of the pigment concentration in the mixed pigment solution.
出处
《激光技术》
CAS
CSCD
北大核心
2017年第4期503-506,共4页
Laser Technology
基金
国家自然科学基金资助项目(61378037
61178032)
中央高校基本科研业务费专项资金资助项目(JUSRP51517)
关键词
光谱学
3维同步荧光光谱
径向基神经网络
胭脂红
spectroscopy
3-D synchronous fluorescence spectrometry
radial basis function neural network
carmine