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基于离散三维荧光光谱法的水中藻类在线分析

On-line analysis of algae in water by discrete three-dimensional fluorescence spectroscopy.

作者信息

Zhao Nanjing, Zhang Xiaoling, Yin Gaofang, Yang Ruifang, Hu Li, Chen Shuang, Liu Jianguo, Liu Wenqing

出版信息

Opt Express. 2018 Mar 19;26(6):A251-A259. doi: 10.1364/OE.26.00A251.

Abstract

In view of the problem of the on-line measurement of algae classification, a method of algae classification and concentration determination based on the discrete three-dimensional fluorescence spectra was studied in this work. The discrete three-dimensional fluorescence spectra of twelve common species of algae belonging to five categories were analyzed, the discrete three-dimensional standard spectra of five categories were built, and the recognition, classification and concentration prediction of algae categories were realized by the discrete three-dimensional fluorescence spectra coupled with non-negative weighted least squares linear regression analysis. The results show that similarities between discrete three-dimensional standard spectra of different categories were reduced and the accuracies of recognition, classification and concentration prediction of the algae categories were significantly improved. By comparing with that of the chlorophyll a fluorescence excitation spectra method, the recognition accuracy rate in pure samples by discrete three-dimensional fluorescence spectra is improved 1.38%, and the recovery rate and classification accuracy in pure diatom samples 34.1% and 46.8%, respectively; the recognition accuracy rate of mixed samples by discrete-three dimensional fluorescence spectra is enhanced by 26.1%, the recovery rate of mixed samples with Chlorophyta 37.8%, and the classification accuracy of mixed samples with diatoms 54.6%.

摘要

针对藻类分类在线测量的问题,本文研究了一种基于离散三维荧光光谱的藻类分类及浓度测定方法。分析了五类十二种常见藻类的离散三维荧光光谱,建立了五类离散三维标准光谱,并通过离散三维荧光光谱结合非负加权最小二乘线性回归分析实现了藻类类别的识别、分类及浓度预测。结果表明,不同类别离散三维标准光谱之间的相似度降低,藻类类别识别、分类及浓度预测的准确率显著提高。与叶绿素a荧光激发光谱法相比,离散三维荧光光谱法对纯样品的识别准确率提高了1.38%,对纯硅藻样品的回收率和分类准确率分别提高了34.1%和46.8%;离散三维荧光光谱法对混合样品的识别准确率提高了26.1%,对含绿藻混合样品的回收率提高了37.8%,对含硅藻混合样品的分类准确率提高了54.6%。

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