摘要
将三维荧光光谱技术与秩消失因子分析、广义秩消失因子分析和交替三线性分解3种二阶校正方法相结合,建立了测定未知混合物中苯酚含量的三维荧光二阶校正新方法。设定在激发波长240~280 nm和发射波长280~360 nm范围内测定未知混合物中苯酚的三维荧光光谱,构建三维响应数据阵,运用基于三线性分解的二阶校正算法进行解析。结果表明,当模拟样品的组分数为2时,秩消失因子分析、广义秩消失因子分析和交替三线性分解3种方法测定苯酚的预测均方根误差分别为0.33,1.18和0.15,平均回收率分别为101.6%,115.6%和101.9%;当组分数为3时,3种方法的预测均方根误差则分别为1.61,1.80和0.51,平均回收率分别为134.2%,133.9%和107.1%;将其分别应用于实际样品中苯酚的测定,结果满意,且交替三线性分解法的测定结果优于秩消失因子分析法和广义秩消失因子分析法。
A new approach was proposed for the determination of phenol in an unknown mixture by using three-dimension fluorescence coupled with second-order calibration methods based on rank annihilation factor analysis（ RAFA）,generalized rank annihilation factor analysis（ GRAFA） and alternating trilinear decomposition（ ATLD） algorithms,respectively. Fluorescence spectra were detected at consecutive time points in excitation range of 240- 280 nm and emission range of 280- 360 nm for each sample. Therefore,a three-way response data array was established and analyzed by second-order calibration method of trilinear decomposition. When the component number of sample was set to2,the obtained average recoveries（ AR） of phenol were 101. 6%,115. 6%,101. 9%, and the root- mean- squared errors of prediction（ RMSEP） for RAFA,GRAFA and ATLD were 0. 33,1. 18 and 0. 15,respectively. When the component number of sample was set to 3,the obtained average recoveries of phenol were 134. 2%,133. 9%,107. 1%,and the root- mean- squared errors of prediction for RAFA,GRAFA and ATLD were 1. 61,1. 80 and 0. 51,respectively. The proposed method was applied in the determination of phenol in actual water samples with satisfactory results,and the associated verified experiments showed that ATLD is superior to RAFA and GRAFA.
出处
《分析测试学报》
CAS
CSCD
北大核心
2016年第6期654-661,共8页
Journal of Instrumental Analysis
基金
山西省自然科学基金项目资助(2011011010-1)
关键词
苯酚
三维荧光
二阶校正法
秩消失因子分析法
广义秩消失因子分析法
交替三线性分解
phenol
three-dimensional fluorescence
second-order calibration
rank annihilation factor analysis（RAFA）
generalized rank annihilation factor analysis（GRAFA）
alternating trilinear decomposition（ATLD）
