基于三维荧光光谱结合自加权交替三线性分解算法的石油污染物识别与测定

Q Physics and Astronomy
Pengfei Cheng, Yutian Wang, Zhi-kun Chen, Zhe Yang
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引用次数: 6

摘要

石油污染严重危害生物环境和人类健康。由于油类的多样性和油类成分的复杂性,对油类污染物的识别具有重要意义。采用三维荧光光谱结合二阶校正算法对具有重叠荧光光谱的混合油进行了测量。自加权交替三线性分解(SWATLD)是近年来发展迅速的一种二阶校正方法。用不同浓度的0号柴油、93号汽油和普通煤油配制胶束溶液。用FLS920荧光光谱仪测量了混合油溶液的三维荧光光谱。采用SWATLD算法对光谱数据进行分解。实验结果表明,SWATLD算法对混合油的预测浓度和回收率具有对组分数不敏感和分辨率高的优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification and Determination of Oil Pollutants Based on 3-D Fluorescence Spectrum Combined with Self-weighted Alternating Trilinear Decomposition Algorithm
Oil pollution seriously endangers the biological environment and human health. Due to the diversity of oils and the complexity of oil composition, it is of great significance to identify the oil contaminants. The 3-D fluorescence spectrum combined with a second order correction algorithm was adopted to measure an oil mixture with overlapped fluorescence spectra. The self-weighted alternating trilinear decomposition (SWATLD) is a kind of second order correction, which has developed rapidly in recent years. Micellar solutions of #0 diesel, #93 gasoline and ordinary kerosene in different concentrations were made up. The 3-D fluorescence spectra of the mixed oil solutions were measured by a FLS920 fluorescence spectrometer. The SWATLD algorithm was applied to decompose the spectrum data. The predict concentration and recovery rate obtained by the experiment show that the SWATLD algorithm has advantages of insensitivity to component number and high resolution for mixed oils.
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CiteScore
0.70
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0.00%
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审稿时长
2.3 months
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