论文部分内容阅读
通过三维荧光光谱技术和平行因子分析法相结合,提出了一种石油类污染物的识别和检测方法。以97#汽油、0#柴油和普通煤油的不同浓度CCl4溶液为测量样品,不考虑每种油的具体成分,仅将其视为一个整体作为一种组分来研究,通过汽油、柴油不同比例的混合以及存在煤油作为干扰物的情况下,利用FLS920全功能型荧光光谱仪测量得到样品的三维荧光光谱数据。经过激发与发射校正以及空白扣除,去除了仪器误差和散射的影响并得到了样品的真实光谱。实验采用基于平行因子的二阶校正算法分析测得的光谱数据,体现了算法的二阶优势,验证了在未知干扰存在的情况下依然能够对混合样品各成分进行准确的识别和浓度测量,并得到满意的回收率。
Through the combination of three-dimensional fluorescence spectroscopy and parallel factor analysis, a method for identifying and detecting petroleum pollutants was proposed. Taking the different concentrations of CCl4 solution of 97 # gasoline, 0 # diesel oil and ordinary kerosene as the measurement samples, the specific components of each oil are not considered and are only considered as a whole to be studied as one component. Through different proportions of gasoline and diesel oil, And the presence of kerosene as an interfering substance, the three-dimensional fluorescence spectrum data of the sample was measured by using a FLS920 full-function fluorescence spectrometer. After excitation and emission calibration and blank subtraction, the effects of instrumental errors and scattering are removed and the true spectrum of the sample is obtained. The second-order calibration algorithm based on parallel factor is used in the experiment to analyze the measured spectral data, which shows the second-order advantage of the algorithm and verifies that the components of the mixed sample can be accurately identified and measured in the presence of unknown interference Get a satisfactory recovery rate.