溶解有机物三维荧光光谱结合多变量分析在赤潮藻识别中的应用
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摘要
有害赤潮肆虐对我国沿海的海洋生态、资源、环境造成了严重的破坏和重大的经济损失。要预防和减少有害赤潮灾害,首先要能够准确、实时地对发生赤潮的肇事藻种进行识别测定,荧光技术因具有仪器简单、灵敏度高和易于实现现场实时检测等优点而受到广泛的关注。
目前根据浮游植物活体三维荧光光谱对浮游植物进行种类识别的研究已经取得了一定的成果,而利用浮游植物生长过程中藻滤液的三维荧光光谱进行赤潮藻种类识别的研究还未见报道,本文从赤潮藻滤液的三维荧光光谱着手,选择了我国沿海常见的10种赤潮藻,在实验室条件下进行培养,分别利用平行因子分析、主成分分析和小波分析对赤潮藻生长过程中藻滤液的三维荧光光谱进行特征提取,并用多元线性回归对赤潮藻进行识别测定。
(1)应用平行因子分析对赤潮藻滤液三维荧光光谱进行分析,得到了各赤潮藻的特征光谱,不同赤潮藻的特征光谱之间存在差异,聚类结果显示同属赤潮藻特征光谱相似性较高。赤潮藻各组分最大荧光峰强度与生长阶段显示出一定的关系：在指数生长期,藻滤液中类蛋白和类腐殖质荧光物质不断积累,稳定期滤液中类色氨酸和类腐殖质荧光物质快速增加,主要来自衰老和死亡细胞的破碎释放,以及细菌降解作用。这和本实验室以往的研究结果一致,说明平行因子分析可以有效的提取赤潮藻荧光特征,并考察赤潮藻各组分荧光峰强度和生长阶段的关系。
(2)主成分分析在赤潮藻识别中的应用。应用主成分分析对赤潮藻滤液的三维荧光光谱数据进行特征提取,根据Bayes判别的结果选取第一主成分载荷谱作为识别特征谱,利用聚类分析法得到识别标准谱,用多元线性回归(以非负最小二乘法解析)对赤潮藻进行识别。原甲藻属的东海原甲藻和海洋原甲藻的正确识别率较低,其他3种甲藻识别率≧95%,对于硅藻,中肋骨条藻和柔弱角毛藻正确识别率较低,其他3种硅藻识别率≧83%,在属水平上,角毛藻属的正确识别率为96%。
(3)小波分析在赤潮藻识别中的应用。分别用3种小波函数coif2、db-3和db-7对赤潮藻滤液的三维荧光光谱进行特征提取,根据Bayes判别的结果分别选择第三层尺度分量作为识别特征谱。利用聚类分析方法得到了每种小波函数的识别标准谱。分别用这3种标准谱以多元线性回归法对赤潮藻进行识别,3种标准谱对东海原甲藻、中肋骨条藻和柔弱角毛藻的正确识别率较低,对其他藻种识别率≧80%。在属水平上,3种标准谱对角毛藻属的三种赤潮藻的识别率都为96%。
(4)4种标准谱对中肋骨条藻和柔弱角毛藻的识别率很低,其他相对较高,其中db-3标准谱的识别结果最好。为进一步提高正确识别率乃至实现现场数据的识别,还要进一步对不同温度、不同光照组合条件下培养的赤潮藻样品进行特征提取,并对混合藻的光谱特征提取及拆分进行研究。
Harmful red tides occur frequently in the China Sea, which have caused severe damage to the ocean ecology, resources and environment. The key to predicting the red tides and reducing the influence is to identify the red tide algae real time and precisely. The fluorescence methods can satisfy the requirements of identifying the red tide algae rapidly and real time due to the high sensitivity and simple equipments, which have attracted more and more attention.
Some identification methods based on the Excitation-Emission matrix spectroscopy (EEMs) of phytoplankton have been reported while the methods based on the EEMs of the filtrate of phytoplankton culture have never been seen. In this paper, ten algae species which belong to Dinophyta and Bacillariophyta were chosen to culture under laboratory conditions. The Excitation-Emission matrix spectroscopy (EEMs) of the filtrate of each alga culture during its growth process was obtained. Parallel factor analysis (PARAFAC), principal component analysis (PCA) and wavelet methods were chosen to extract the features of the Excitation-Emission matrix spectroscopy. Then multivariate linear regression was used to identify the algae species.
(1) The EEMs of each alga are combined to form three-dimensional data matrix and analyzed using PARAFAC. The characteristic spectra of each alga are obtained. The characteristic spectra of each alga have dissimilarities. The cluster analysis results show that the characteristic spectra of algae belonging to the same genus are similar. The relationships between the fluorescence intensities and algae growth phases were investigated. In the exponential phase, the protein-like and humic-like substances accumulate in the filtrate of algae culture. In the stationary phase, the protein-like and humic-like fluorescence intensities increased quickly, which suggest that the two kinds of fluorescent substances were produced by the broken algal cells and the degradation by marine bacteria. These results are consistent with our previous research, which imply that the PARAFAC model can be successfully used to extract the features of EEMs and investigate the relationship between fluorescence intensities and algae growth phase.
(2) The application of PCA in the identification of algae species. The PCA is used to extract features of EEMs. The first principal component loading of EEMs is chosen as the identification characteristic spectra according to the results of Bayes discriminate analysis. The identification feature spectra are established using the cluster analysis. The ten algae are tested using multivariate linear regression (using non-negative least square method) based on the feature spectra. Among the five Dinophyta species, the correct identification rates(CIR) of Prococentrum marinum and Prorocentrum donghaiense are relatively low compared to the other 3 Dinophyta species(≧ 95%). Among the five Bacillariophyta species, CIR of Skeletonema costatum and Chaetoceros debilis are low compared to the other 3 Bacillariophyta species(≧ 83%). In the level of genus, CIR of Chaetoceros can reach 96%.
(3) The application of wavelet analysis in the identification of algae species. Three wavelet functions coiflet2 (coif2), daubechies-3(db-3) and daubechies-7(db-7) are used to extract the features of the EEMs respectively. The third scale vectors are selected respectively as the identification characteristic spectra for the three wavelet functions according to the results of Bayes discriminate analysis. The identification feature spectra of three wavelet functions are obtained respectively using cluster analysis. The ten algae are tested using multivariate linear regression(using non-negative least square method) based on the three types of identification feature spectra. The results show that the CIR of Prorocentrum donghaiense, Skeletonema costatum and Chaetoceros debilis are relatively low compared the the other 7 species(≧ 80%). In the level of genus, CIR of Chaetoceros are 96% for all the three wavelet function.
(4) The CIR of Skeletonema costatum and Chaetoceros debilis are relatively low for all 4 types of identification feature spectra. Among the 4 types of identification feature spectra, db-3 provides the best CIR. In order to improve CIR, more work needs to be done such as feature extraction of EEMs of the red tide algae cultured under different temperature & illumination conditions and the research on the EEMs of mixtures of red tide algae.
目前根据浮游植物活体三维荧光光谱对浮游植物进行种类识别的研究已经取得了一定的成果,而利用浮游植物生长过程中藻滤液的三维荧光光谱进行赤潮藻种类识别的研究还未见报道,本文从赤潮藻滤液的三维荧光光谱着手,选择了我国沿海常见的10种赤潮藻,在实验室条件下进行培养,分别利用平行因子分析、主成分分析和小波分析对赤潮藻生长过程中藻滤液的三维荧光光谱进行特征提取,并用多元线性回归对赤潮藻进行识别测定。
(1)应用平行因子分析对赤潮藻滤液三维荧光光谱进行分析,得到了各赤潮藻的特征光谱,不同赤潮藻的特征光谱之间存在差异,聚类结果显示同属赤潮藻特征光谱相似性较高。赤潮藻各组分最大荧光峰强度与生长阶段显示出一定的关系：在指数生长期,藻滤液中类蛋白和类腐殖质荧光物质不断积累,稳定期滤液中类色氨酸和类腐殖质荧光物质快速增加,主要来自衰老和死亡细胞的破碎释放,以及细菌降解作用。这和本实验室以往的研究结果一致,说明平行因子分析可以有效的提取赤潮藻荧光特征,并考察赤潮藻各组分荧光峰强度和生长阶段的关系。
(2)主成分分析在赤潮藻识别中的应用。应用主成分分析对赤潮藻滤液的三维荧光光谱数据进行特征提取,根据Bayes判别的结果选取第一主成分载荷谱作为识别特征谱,利用聚类分析法得到识别标准谱,用多元线性回归(以非负最小二乘法解析)对赤潮藻进行识别。原甲藻属的东海原甲藻和海洋原甲藻的正确识别率较低,其他3种甲藻识别率≧95%,对于硅藻,中肋骨条藻和柔弱角毛藻正确识别率较低,其他3种硅藻识别率≧83%,在属水平上,角毛藻属的正确识别率为96%。
(3)小波分析在赤潮藻识别中的应用。分别用3种小波函数coif2、db-3和db-7对赤潮藻滤液的三维荧光光谱进行特征提取,根据Bayes判别的结果分别选择第三层尺度分量作为识别特征谱。利用聚类分析方法得到了每种小波函数的识别标准谱。分别用这3种标准谱以多元线性回归法对赤潮藻进行识别,3种标准谱对东海原甲藻、中肋骨条藻和柔弱角毛藻的正确识别率较低,对其他藻种识别率≧80%。在属水平上,3种标准谱对角毛藻属的三种赤潮藻的识别率都为96%。
(4)4种标准谱对中肋骨条藻和柔弱角毛藻的识别率很低,其他相对较高,其中db-3标准谱的识别结果最好。为进一步提高正确识别率乃至实现现场数据的识别,还要进一步对不同温度、不同光照组合条件下培养的赤潮藻样品进行特征提取,并对混合藻的光谱特征提取及拆分进行研究。
Harmful red tides occur frequently in the China Sea, which have caused severe damage to the ocean ecology, resources and environment. The key to predicting the red tides and reducing the influence is to identify the red tide algae real time and precisely. The fluorescence methods can satisfy the requirements of identifying the red tide algae rapidly and real time due to the high sensitivity and simple equipments, which have attracted more and more attention.
Some identification methods based on the Excitation-Emission matrix spectroscopy (EEMs) of phytoplankton have been reported while the methods based on the EEMs of the filtrate of phytoplankton culture have never been seen. In this paper, ten algae species which belong to Dinophyta and Bacillariophyta were chosen to culture under laboratory conditions. The Excitation-Emission matrix spectroscopy (EEMs) of the filtrate of each alga culture during its growth process was obtained. Parallel factor analysis (PARAFAC), principal component analysis (PCA) and wavelet methods were chosen to extract the features of the Excitation-Emission matrix spectroscopy. Then multivariate linear regression was used to identify the algae species.
(1) The EEMs of each alga are combined to form three-dimensional data matrix and analyzed using PARAFAC. The characteristic spectra of each alga are obtained. The characteristic spectra of each alga have dissimilarities. The cluster analysis results show that the characteristic spectra of algae belonging to the same genus are similar. The relationships between the fluorescence intensities and algae growth phases were investigated. In the exponential phase, the protein-like and humic-like substances accumulate in the filtrate of algae culture. In the stationary phase, the protein-like and humic-like fluorescence intensities increased quickly, which suggest that the two kinds of fluorescent substances were produced by the broken algal cells and the degradation by marine bacteria. These results are consistent with our previous research, which imply that the PARAFAC model can be successfully used to extract the features of EEMs and investigate the relationship between fluorescence intensities and algae growth phase.
(2) The application of PCA in the identification of algae species. The PCA is used to extract features of EEMs. The first principal component loading of EEMs is chosen as the identification characteristic spectra according to the results of Bayes discriminate analysis. The identification feature spectra are established using the cluster analysis. The ten algae are tested using multivariate linear regression (using non-negative least square method) based on the feature spectra. Among the five Dinophyta species, the correct identification rates(CIR) of Prococentrum marinum and Prorocentrum donghaiense are relatively low compared to the other 3 Dinophyta species(≧ 95%). Among the five Bacillariophyta species, CIR of Skeletonema costatum and Chaetoceros debilis are low compared to the other 3 Bacillariophyta species(≧ 83%). In the level of genus, CIR of Chaetoceros can reach 96%.
(3) The application of wavelet analysis in the identification of algae species. Three wavelet functions coiflet2 (coif2), daubechies-3(db-3) and daubechies-7(db-7) are used to extract the features of the EEMs respectively. The third scale vectors are selected respectively as the identification characteristic spectra for the three wavelet functions according to the results of Bayes discriminate analysis. The identification feature spectra of three wavelet functions are obtained respectively using cluster analysis. The ten algae are tested using multivariate linear regression(using non-negative least square method) based on the three types of identification feature spectra. The results show that the CIR of Prorocentrum donghaiense, Skeletonema costatum and Chaetoceros debilis are relatively low compared the the other 7 species(≧ 80%). In the level of genus, CIR of Chaetoceros are 96% for all the three wavelet function.
(4) The CIR of Skeletonema costatum and Chaetoceros debilis are relatively low for all 4 types of identification feature spectra. Among the 4 types of identification feature spectra, db-3 provides the best CIR. In order to improve CIR, more work needs to be done such as feature extraction of EEMs of the red tide algae cultured under different temperature & illumination conditions and the research on the EEMs of mixtures of red tide algae.
引文
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