Water-source characterization and classification with fluorescence EEM spectroscopy: PARAFAC analysis

Water-quality protection and environmental forensics require rapid water monitoring and source identification. In this paper, parallel factor analysis (PARAFAC) of fluorescence excitation-emission matrix spectra (EEMS) was used to characterize and classify water samples from landfills, wastewater treatment plants, lakes, and rivers. The study showed that the optimal number of components was four to represent the data set. The fluorescence fingerprints for water samples from different sources were sufficiently different, so qualitative water classification could be achieved. Specifically, Component 1 was the major fluorescing centre in river waters, with characteristics consistent with humic-like fluorophores; Component 2 was the dominant fluorophore in the treated wastewaters; Component 3 was the characteristic fluorophore in landfill leachates; and Components 1, 3, and 4 existed in lake waters at comparable weight, among which Component 4 may be considered as a protein- or amino acid-like fluorophore.     

三维荧光光谱结合平行因子算法测定两种食品色素的含量

应用英国Edinburgh FLS920P稳态和时间分辨荧光光谱仪测量了12个混合色素样本的三维荧光光谱,将得到的三维荧光光谱数据矩阵(EEMS)与平行因子算法相结合,来建立一种检测食品色素的方法。实验中,使用核一致诊断法(CORCONDIA)确定了混合溶液的组分数为3,然后利用平行因子分析法(PARAFAC),得到胭脂红、诱惑红的平均回收率分别为(99.3%±5.0%)和(102.2%±5.6%),且预测均方根误差(RESEP)分别为(0.054)和(0.205)。结果表明,该法可在干扰物质苋菜红存在的情况下,对混合溶液中胭脂红、诱惑红二种色素同时定量测定,具有简便、快速等优点,可为合成食品色素的检测提供借鉴。