摘要
针对水体藻类群落离散三维荧光光谱的识别,以5种常见门类藻种(铜绿微囊藻、斜生栅藻、菱形藻、楯形多甲藻和隐藻)为对象,研究了基于自加权交替三线性分解(SWTATLD)算法的藻类离散三维荧光光谱识别方法,并将识别结果与平行因子(PARAFAC)算法的解析结果进行了对比分析。结果表明:PARAFAC算法解析得到的铜绿微囊藻的平均回收率为92.73%±13.99%,斜生栅藻的平均回收率为105.51%±11.58%,菱形藻的平均回收率为89.25%±13.68%,楯形多甲藻的平均回收率为109.48%±13.47%,隐藻的平均回收率为88.76%±13.60%;SWTATLD算法解析得到的铜绿微囊藻的平均回收率为96.70%±3.94%,斜生栅藻的平均回收率为98.07%±4.48%,菱形藻的平均回收率为101.71%±3.97%,楯形多甲藻的平均回收率为97.26%±4.11%,隐藻的平均回收率为103.57%±4.34%;相比于PARAFAC算法,SWTATLD算法的解析结果更接近于真实浓度且偏差更小。研究结果为水体浮游藻类群落的有效识别及定量分析提供了一种良好的方法。
To identify the phytoplankton community by discrete three-dimensional fluorescence spectra, we studied a method based on a self-weighted alternating trilinear decomposition(SWTATLD) algorithm for five common alga species(Microcystis aeruginosa, Scenedesmus obliquus, Nitzschia sp., Peridinium umbonatum var. inaequale, and Cryptomonas obovata.). Then, the results were compared with those of the parallel factor(PARAFAC) algorithm. The recovery of the PARAFAC algorithm is 92.73%±13.99% for Microcystis aeruginosa, 105.51%±11.58% for Scenedesmus obliquus, 89.25%±13.68% for Nitzschia sp., 109.48%±13.47% for Peridinium umbonatum var. inaequale and 88.76%±13.60% for Cryptomonas obovata. The recovery of the SWTATLD algorithm is 96.70%±3.94% for Microcystis aeruginosa, 98.07%±4.48% for Scenedesmus obliquus, 101.71%±3.97% for Nitzschia sp., 97.26%±4.11% for Peridinium umbonatum var. inaequale, and 103.57%±4.34% for Cryptomonas obovata. The recovery results based on the SWTATLD algorithm were closer to the real concentration and have smaller deviations than those based on the PARAFAC algorithm. Our results provide a method for the effective identification and quantitative analysis of phytoplankton communities.
作者
程钊
赵南京
殷高方
张小玲
刘建国
刘文清
Cheng Zhao;Zhao Nanjing;Yin Gaofang;Zhang Xiaoling;Li Jianguo;Liu Wenqing(Key Laboratory of Environmental Optics and Technology,Anhui Institute of Optics and Fine Mechanics,Chinese Academu of Sciences,Hefei,Anhui 230031,China;University of Science and Technology of China,Hefei,Anhui 230026,China;Key Labomtory of Environmental Optical Monitoring Technology of Anhui Province,Hefei,Anhui 230031,China;Anhui University,Hefei,Anhui 230601,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2021年第14期222-229,共8页
Acta Optica Sinica
基金
国家重点研发计划(2016YFC1400600)
安徽省重点研发计划(1804a0802192)
安徽省自然科学基金(2008085QF316)。
关键词
光谱学
光谱识别
平行因子算法
自加权交替三线性分解算法
浮游藻类群落
离散三维荧光光谱
spectroscopy
spectral identification
parallel factor algorithm
self-weighted alternating trilinear decomposition algorithm
phytoplankton community
discrete three-dimensional fluorescence spectrum
