基于色素荧光的浮游藻识别测定技术研究
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摘要
近年来，我国海域赤潮频发，对海洋渔业、养殖业、水产资源造成极大的损害，对海洋生物和人类健康的发展构成威胁，迫切需要建立一种快速、有效地监测浮游藻群落组成的方法，以实现赤潮的应急、常规监测。浮游藻三维荧光光谱能给出激发-发射范围的全部荧光指纹信息，对浮游藻的分类识别有很大的应用潜力，是近年来备受关注的浮游藻群落测定方法之一。目前的研究主要针对浮游藻活体荧光展开，对于远海海域及海洋调查采集的大量浮游藻膜样品无法进行测定，迫切需要建立针对膜样品分析的技术，从而实现对浮游藻群落组成的识别测定。同时针对本课题组前期基于小波分析建立的浮游藻活体荧光识别技术对某些赤潮藻的识别率较低的问题，本文采用多种技术提取光谱特征，着力建立各种识别技术之间的互补联立谱库，完善现有的浮游藻活体荧光识别测定技术。
第一部分：在对我国近海海域赤潮与非赤潮期间浮游藻群落组成结构特点总结的基础上，选择39种浮游藻（优势藻和赤潮藻）进行研究，分别测得目标藻种在不同生长条件下的色素萃取液三维荧光光谱，综合利用小波、小波包、二维小波变换及Bayes判别分析、多元线性回归等方法，分别建立浮游藻色素萃取液的三种荧光识别测定技术，基本上实现了浮游藻群落组成门类水平和属水平（发生赤潮时）上的识别分析，具体研究成果如下：
1以相对标准偏差（RSD）为标准，检验了浮游藻色素萃取液荧光光谱的稳定性；通过组间组内方差分析，得到不同门、属间的浮游藻色素萃取液荧光光谱具有较明显的差异性。
2分别采用小波（db7，coif2小波）、小波包、二维小波变换对浮游藻色素萃取液荧光光谱进行特征提取，通过Bayesian判别分析法选择分类判别能力最佳的浮游藻识别特征谱，并构建相应的浮游藻色素萃取液荧光识别特征谱库；在此基础上，结合非负最小二乘法解析的多元线性回归分别建立相应的浮游藻色素萃取液荧光识别测定技术。对各种技术进行测试：对于单种藻样品，得到门水平上的平均识别正确率分别为95.5%，95.4%，95.5%，94.3%，平均识别相对含量分别为89.4%，87.6%，88.8%，87.3%；属水平上的平均识别正确率分别为89.3%，89.1%，90.1%，87.8%。对于模拟混合藻样品，当门上比例达50%时，小波、小波包技术能对除黄藻门以外的其它6个门类达90%以上的识别正确率，二维小波技术对黄藻门、蓝藻门识别效果不好；当混合比例达60%时，三种技术能对除黄藻门以外的其它6个门类藻均达85%以上的识别正确率。在属水平上，当浮游藻优势度达80%时，能对优势种达75%以上的识别正确率。当浮游优势度达90%时，能对优势种达80%以上的识别正确率。
3将db7小波识别技术用于现场浮游藻膜样品的识别测定，结果显示对硅藻的识别和HPLC-CHEMTAX结果一致，隐藻不能被正确识别。将HPLC-CHEMTAX显示相对含量较高的现场样品谱加入到所建荧光识别特征谱库进行识别，整体识别情况有所改善，除硅藻能被正确识别外，隐藻的识别正确率明显提高。表明所建技术用于浮游藻样品浮游藻群落组成的识别分析是可行的。如果通过现场培养浮游藻或是现场采集浮游藻样品（尤其是赤潮期间采样）获得现场谱更新识别特征谱库，识别结果将会得以改善。
第二部分：选择近海海域常见的53种浮游藻（优势藻和赤潮藻）进行研究，分别测得目标藻种在不同生长条件下的活体三维荧光光谱，综合利用小波、小波包、二维小波变换及Bayesian判别、系统聚类分析及多元线性回归等方法，分别建立小波、小波包、二维小波的浮游藻活体荧光识别测定技术，并研究各种技术对光谱特征的提取能力及相互补充作用，最终建立互补联立谱库的识别测定技术，实现浮游藻群落组成门类水平和属类水平（发生赤潮时）的识别分析，具体研究成果如下：
1.分别采用小波（db7，coif2小波）、小波包、二维小波变换提取浮游藻活体荧光光谱特征，结合多元线性回归法分别建立3种浮游藻活体荧光识别测定技术。将各种识别技术用于对浮游藻单种样品、混合样品进行测试，并研究不同技术对浮游藻特征谱的提取能力及相互补充作用，最终建立了以db7-ca3小波标准谱库为一级谱库，其它特征谱库作为二级谱库进行补充的联立识别测定技术。
2.将联立谱库识别技术用于浮游藻样品的识别测定，对于浮游藻单种样品，得到门水平上的平均识别正确率为96.0%，属水平上的平均识别正确率为87.4%，其中Oc，Db，Ld，Rh，Ks，Pl，As的识别率分别提高16.7，10，6.8，11.7，14.2，16.7，33.3个百分点。对于不同比例（60%，70%，80%，90%）的浮游藻模拟混合样品，在门水平上的平均识别正确率分别为89.3%，93.8%，96.1%和96.9%；平均识别相对含量分别为58.4%，68.7%，77.5%和86.1%；在属水平上的平均识别正确率分别为64.7%，92.6%，93.8%和93.9%。其中，Cf（60%优势度）和Db（60%的优势度）的识别率分别提高31.2和35.2个百分点。
3.将联立谱库识别技术用于胶州湾和围隔实验采集的24个水样分析：23个水样门类识别结果与镜检结果一致，属水平上对于优势度大于80%（藻细胞丰度）的8个水样，5个水样的优势藻能被正确识别。
4.将联立谱库识别技术用于渤海、胶州湾水体分析，得到海域的浮游藻群落组成识别情况与历史资料研究较好地保持一致。
本文的创新之处在于基于浮游藻色素萃取液三维荧光光谱，综合利用小波、小波包及二维小波分析技术建立浮游藻色素萃取液荧光识别测定技术，用于满足大量浮游藻膜样品的快速分析需要。同时基于浮游藻活体三维荧光光谱，综合利用小波、小波包及二维小波分析技术建立浮游藻活体荧光识别测定技术，并研究各种技术对光谱特征的提取能力及相互补充作用，建立互补联用谱库，提高了某些浮游藻尤其是某些赤潮藻的识别正确率。实现了在海域正常情况下能够在门类水平上测定浮游藻群落组成，赤潮发生时在属水平上对引发赤潮的肇事藻进行快速识别。
Harmful algal blooms are ubiquitous natural phenomena caused by the excessive growthof phytoplankton. In recent years, more and more red tides have occurred in the marine areasof China and the key bio-species here have rapidly increased. This has led to severe economicinjury, serious risks to the local marine culture and fishing industry, and increased potentialhuman health risks. These problems have drawn significant attention from the scientificcommunity, and call for a rapid and effective method for emergency monitoring ofphytoplankton communities. The three-dimensional fluorescence spectra can give all the‘fingerprint’ information of the algae within the wavelength coverage and is a potentialtechnique for the algae identification, which has drawn more attention for determining thephytoplankton community. Current researches conducted aiming at in vivo fluorescence ofphytoplankton, and large amounts of membrane samples can’t be measured by thesetechniques. It urgently needs to establish the method for the distant waters and analyzing thefilter samples and discriminate the algae population composition on the basis of it. Also, thefluorescence discrimination technique was established based on wavelet in our researchgroup, and there existed some problems such as the poor discrimination of some algae(especially some red tide algae). Here, some techniques were used to extract the featurespectra, and the complementary simultaneous feature spectra database was to be establishedand to perfect the in vivo fluorescence discrimination technique
Part Ι: Based on the study of the composition of phytoplankton population in coastalarea of China,39algae species(most were dominant species or red tide algae) were selectedfor the experiment and cultured in different conditions for the3-D fluorescencemeasurements. Three kinds of mathematical method of wavelet, wavelet packet,2-D waveletanalysis, and some chemometrics methods such as Bayesian analysis, MLR(Multiple Linear Regression) were used together. The pigment extract fluorescence discrimination techniquewas established finally and it had better capabilities of yielding differentiated assessments ofalgae population distributions at the division level and the genus level(when the HABsbroken). Specific research results were as follows:
1. The relative standard deviation (RSD) was used as a criteria to analysis the stability of thefluorescence spectra, and the variance (between-column variance and interclassvariance)was used to analyze the otherness between different division and genus level.
2. Wavelets analysis(db7, coif2), wavelet packet analysis,2-D wavelet analysis were used toextract the feature spectra of algae species, and the optimal feature spectra were selectedby Bayesian analysis to compose the reference spectra database. Based on the database,three different discrimination fluorescence techniques were established by non-negativeleast squares. Above all techniques were tested: For single samples, the correctlydiscriminating ratios(CDRs) were95.5%，95.4%，95.5%and94.3%，with the relativecontent were89.4%，87.6%，88.8%，87.3%at the division level, respectively；And theCDRs were89.3%，89.1%，90.1%and87.8%at the genus level, respectively. Forsimulative mixtures, when the mixed proportion was50%, the CDRs could be greaterthan90%except Xanthophyta by the wavelet and the wavelet packet techniques; and thediscrimination was a little poor by the2-D technique for the Xanthophyta andCyanophyta. When the mixed proportion was60%, the CDRs could be greater than85%except Xanthophyta by the three techniques. For the discrimination at the genus level,when the proportion of the dominant species reached80%, the dominant algae speciescould be recognized and the CDRs was greater than75%. When the proportion of thedominant species reached90%, the CDRs was greater than80%and20red tide algaecould be recognized correctly with the CDRs of90%.
3. The db7fluorescence technique was used to analyse the filter samples, the results wereconsistent with the results of HPLC-CHEMTAX for the discrimination of Diatom.However, it couldn’t discriminate the Cryptomonas correctly. When the feature spectra database was expanded by adding the field spectra and to analyze the sample, thediscrimination results were improved especially for the discrimination of Cryptomonas. Itclearly seen that the fluorescence technique could be used to analyze the filter samples ofthe phytoplankton, and if more and more field sample spectra were obtained to update thefeature spectra database, the technique would be gain better discrimination results.
Part Π:53algae species(most were dominant species or red tide algae) were selected forthe experiment and cultured in different conditions for the3-D fluorescence measurements.Three kinds of mathematical methods of wavelet, wavelet packet,2-D wavelet analysis, andsome chemometrics methods such as Bayesian analysis, Cluster analysis and MLR(MultipleLinear Regression) were used together, three in vivo fluorescence discrimination techniquewere established, and the complementarities between the different fluorescence techniquewas discussed to establish the simultaneous discrimination technique which had bettercapabilities of yielding differentiated assessments of algae population distributions at thedivision level and the genus level(when the HABs broken). Specific research results were asfollows:
1. Wavelets analysis(db7, coif2), wavelet packet analysis,2-D wavelet analysis were usedto extract the feature spectra of algae species, and three different discriminationfluorescence techniques were established by non-negative least squares. And thecomplementarities were discussed. And the simultaneous discrimination technique wasestablished finally and the db7norm spectra database was the first database of thediscrimination and other feature spectra were construct the second data database.
2. The simultaneousness spectra database technique was tested: for the single samples, theaverage CDRs was96.0%at the division level, the CDRs was87.4at the genus level.The algae species such as Oc，Db，Ld，Rh，Ks，Pl，As, the CDRs of which were increase16.7,10，6.8，11.7，14.2，16.7，33.3percentage point. For the simulative samples whenthe algae dominance were60%,70%,80%,90%, the CDRs were89.3%,93.8%,96.1%and96.9%at the division level, with the average relative contents of58.4%,68.7%, 77.5%, and86.1%, respectively; the CDRs were64.7%,92.6%,93.8%and93.9%,respectively at the genus level. In which, Cf (60%dominance) and (60%dominance) Dbwere increase31.2and35.2percentage point, respectively.
3. For24particular field samples from Jiaozhou Bay and Enclosure experiment, the resultsof23samples were consistent with the microscopic examination at the division level;and5samples from8samples were successfully recognized at the genus level which thealgae dominance achieved80%of the total biomass.
4. For particular applications in Bohai sea and Jiaozhou Bay, it was possible to estimate thephytoplankton community composition and relative abundance of different classes, andthe whole results agree with that of published papers.The innovation of this paper is: Based on3-D fluorescence spectra of the phytoplankton
pigment extract, three kinds of mathematical method of wavelet, wavelet packet and2-Dwavelet analysis were synthetically applied to establish the pigment extract fluorescencediscrimination technique. The technique could meet the demand of the distant waters andanalyzing large amounts of membrane samples. Another innovation was based on the in vivofluorescence spectra of phytoplankton, three kinds of mathematical method of wavelet,wavelet packet and2-D wavelet analysis were synthetically applied to extract the featurespectra and the complementarities were studied. The complementary simultaneousdiscrimination technique was established finally and the CDRs were improved for somespecies especially for some red tide algae. The technique not only can discriminate algaepopulations at division level, but also can identify the algae species causing harmful algaeblooms(HAB) at genus level when HAB happens.
第一部分：在对我国近海海域赤潮与非赤潮期间浮游藻群落组成结构特点总结的基础上，选择39种浮游藻（优势藻和赤潮藻）进行研究，分别测得目标藻种在不同生长条件下的色素萃取液三维荧光光谱，综合利用小波、小波包、二维小波变换及Bayes判别分析、多元线性回归等方法，分别建立浮游藻色素萃取液的三种荧光识别测定技术，基本上实现了浮游藻群落组成门类水平和属水平（发生赤潮时）上的识别分析，具体研究成果如下：
1以相对标准偏差（RSD）为标准，检验了浮游藻色素萃取液荧光光谱的稳定性；通过组间组内方差分析，得到不同门、属间的浮游藻色素萃取液荧光光谱具有较明显的差异性。
2分别采用小波（db7，coif2小波）、小波包、二维小波变换对浮游藻色素萃取液荧光光谱进行特征提取，通过Bayesian判别分析法选择分类判别能力最佳的浮游藻识别特征谱，并构建相应的浮游藻色素萃取液荧光识别特征谱库；在此基础上，结合非负最小二乘法解析的多元线性回归分别建立相应的浮游藻色素萃取液荧光识别测定技术。对各种技术进行测试：对于单种藻样品，得到门水平上的平均识别正确率分别为95.5%，95.4%，95.5%，94.3%，平均识别相对含量分别为89.4%，87.6%，88.8%，87.3%；属水平上的平均识别正确率分别为89.3%，89.1%，90.1%，87.8%。对于模拟混合藻样品，当门上比例达50%时，小波、小波包技术能对除黄藻门以外的其它6个门类达90%以上的识别正确率，二维小波技术对黄藻门、蓝藻门识别效果不好；当混合比例达60%时，三种技术能对除黄藻门以外的其它6个门类藻均达85%以上的识别正确率。在属水平上，当浮游藻优势度达80%时，能对优势种达75%以上的识别正确率。当浮游优势度达90%时，能对优势种达80%以上的识别正确率。
3将db7小波识别技术用于现场浮游藻膜样品的识别测定，结果显示对硅藻的识别和HPLC-CHEMTAX结果一致，隐藻不能被正确识别。将HPLC-CHEMTAX显示相对含量较高的现场样品谱加入到所建荧光识别特征谱库进行识别，整体识别情况有所改善，除硅藻能被正确识别外，隐藻的识别正确率明显提高。表明所建技术用于浮游藻样品浮游藻群落组成的识别分析是可行的。如果通过现场培养浮游藻或是现场采集浮游藻样品（尤其是赤潮期间采样）获得现场谱更新识别特征谱库，识别结果将会得以改善。
第二部分：选择近海海域常见的53种浮游藻（优势藻和赤潮藻）进行研究，分别测得目标藻种在不同生长条件下的活体三维荧光光谱，综合利用小波、小波包、二维小波变换及Bayesian判别、系统聚类分析及多元线性回归等方法，分别建立小波、小波包、二维小波的浮游藻活体荧光识别测定技术，并研究各种技术对光谱特征的提取能力及相互补充作用，最终建立互补联立谱库的识别测定技术，实现浮游藻群落组成门类水平和属类水平（发生赤潮时）的识别分析，具体研究成果如下：
1.分别采用小波（db7，coif2小波）、小波包、二维小波变换提取浮游藻活体荧光光谱特征，结合多元线性回归法分别建立3种浮游藻活体荧光识别测定技术。将各种识别技术用于对浮游藻单种样品、混合样品进行测试，并研究不同技术对浮游藻特征谱的提取能力及相互补充作用，最终建立了以db7-ca3小波标准谱库为一级谱库，其它特征谱库作为二级谱库进行补充的联立识别测定技术。
2.将联立谱库识别技术用于浮游藻样品的识别测定，对于浮游藻单种样品，得到门水平上的平均识别正确率为96.0%，属水平上的平均识别正确率为87.4%，其中Oc，Db，Ld，Rh，Ks，Pl，As的识别率分别提高16.7，10，6.8，11.7，14.2，16.7，33.3个百分点。对于不同比例（60%，70%，80%，90%）的浮游藻模拟混合样品，在门水平上的平均识别正确率分别为89.3%，93.8%，96.1%和96.9%；平均识别相对含量分别为58.4%，68.7%，77.5%和86.1%；在属水平上的平均识别正确率分别为64.7%，92.6%，93.8%和93.9%。其中，Cf（60%优势度）和Db（60%的优势度）的识别率分别提高31.2和35.2个百分点。
3.将联立谱库识别技术用于胶州湾和围隔实验采集的24个水样分析：23个水样门类识别结果与镜检结果一致，属水平上对于优势度大于80%（藻细胞丰度）的8个水样，5个水样的优势藻能被正确识别。
4.将联立谱库识别技术用于渤海、胶州湾水体分析，得到海域的浮游藻群落组成识别情况与历史资料研究较好地保持一致。
本文的创新之处在于基于浮游藻色素萃取液三维荧光光谱，综合利用小波、小波包及二维小波分析技术建立浮游藻色素萃取液荧光识别测定技术，用于满足大量浮游藻膜样品的快速分析需要。同时基于浮游藻活体三维荧光光谱，综合利用小波、小波包及二维小波分析技术建立浮游藻活体荧光识别测定技术，并研究各种技术对光谱特征的提取能力及相互补充作用，建立互补联用谱库，提高了某些浮游藻尤其是某些赤潮藻的识别正确率。实现了在海域正常情况下能够在门类水平上测定浮游藻群落组成，赤潮发生时在属水平上对引发赤潮的肇事藻进行快速识别。
Harmful algal blooms are ubiquitous natural phenomena caused by the excessive growthof phytoplankton. In recent years, more and more red tides have occurred in the marine areasof China and the key bio-species here have rapidly increased. This has led to severe economicinjury, serious risks to the local marine culture and fishing industry, and increased potentialhuman health risks. These problems have drawn significant attention from the scientificcommunity, and call for a rapid and effective method for emergency monitoring ofphytoplankton communities. The three-dimensional fluorescence spectra can give all the‘fingerprint’ information of the algae within the wavelength coverage and is a potentialtechnique for the algae identification, which has drawn more attention for determining thephytoplankton community. Current researches conducted aiming at in vivo fluorescence ofphytoplankton, and large amounts of membrane samples can’t be measured by thesetechniques. It urgently needs to establish the method for the distant waters and analyzing thefilter samples and discriminate the algae population composition on the basis of it. Also, thefluorescence discrimination technique was established based on wavelet in our researchgroup, and there existed some problems such as the poor discrimination of some algae(especially some red tide algae). Here, some techniques were used to extract the featurespectra, and the complementary simultaneous feature spectra database was to be establishedand to perfect the in vivo fluorescence discrimination technique
Part Ι: Based on the study of the composition of phytoplankton population in coastalarea of China,39algae species(most were dominant species or red tide algae) were selectedfor the experiment and cultured in different conditions for the3-D fluorescencemeasurements. Three kinds of mathematical method of wavelet, wavelet packet,2-D waveletanalysis, and some chemometrics methods such as Bayesian analysis, MLR(Multiple Linear Regression) were used together. The pigment extract fluorescence discrimination techniquewas established finally and it had better capabilities of yielding differentiated assessments ofalgae population distributions at the division level and the genus level(when the HABsbroken). Specific research results were as follows:
1. The relative standard deviation (RSD) was used as a criteria to analysis the stability of thefluorescence spectra, and the variance (between-column variance and interclassvariance)was used to analyze the otherness between different division and genus level.
2. Wavelets analysis(db7, coif2), wavelet packet analysis,2-D wavelet analysis were used toextract the feature spectra of algae species, and the optimal feature spectra were selectedby Bayesian analysis to compose the reference spectra database. Based on the database,three different discrimination fluorescence techniques were established by non-negativeleast squares. Above all techniques were tested: For single samples, the correctlydiscriminating ratios(CDRs) were95.5%，95.4%，95.5%and94.3%，with the relativecontent were89.4%，87.6%，88.8%，87.3%at the division level, respectively；And theCDRs were89.3%，89.1%，90.1%and87.8%at the genus level, respectively. Forsimulative mixtures, when the mixed proportion was50%, the CDRs could be greaterthan90%except Xanthophyta by the wavelet and the wavelet packet techniques; and thediscrimination was a little poor by the2-D technique for the Xanthophyta andCyanophyta. When the mixed proportion was60%, the CDRs could be greater than85%except Xanthophyta by the three techniques. For the discrimination at the genus level,when the proportion of the dominant species reached80%, the dominant algae speciescould be recognized and the CDRs was greater than75%. When the proportion of thedominant species reached90%, the CDRs was greater than80%and20red tide algaecould be recognized correctly with the CDRs of90%.
3. The db7fluorescence technique was used to analyse the filter samples, the results wereconsistent with the results of HPLC-CHEMTAX for the discrimination of Diatom.However, it couldn’t discriminate the Cryptomonas correctly. When the feature spectra database was expanded by adding the field spectra and to analyze the sample, thediscrimination results were improved especially for the discrimination of Cryptomonas. Itclearly seen that the fluorescence technique could be used to analyze the filter samples ofthe phytoplankton, and if more and more field sample spectra were obtained to update thefeature spectra database, the technique would be gain better discrimination results.
Part Π:53algae species(most were dominant species or red tide algae) were selected forthe experiment and cultured in different conditions for the3-D fluorescence measurements.Three kinds of mathematical methods of wavelet, wavelet packet,2-D wavelet analysis, andsome chemometrics methods such as Bayesian analysis, Cluster analysis and MLR(MultipleLinear Regression) were used together, three in vivo fluorescence discrimination techniquewere established, and the complementarities between the different fluorescence techniquewas discussed to establish the simultaneous discrimination technique which had bettercapabilities of yielding differentiated assessments of algae population distributions at thedivision level and the genus level(when the HABs broken). Specific research results were asfollows:
1. Wavelets analysis(db7, coif2), wavelet packet analysis,2-D wavelet analysis were usedto extract the feature spectra of algae species, and three different discriminationfluorescence techniques were established by non-negative least squares. And thecomplementarities were discussed. And the simultaneous discrimination technique wasestablished finally and the db7norm spectra database was the first database of thediscrimination and other feature spectra were construct the second data database.
2. The simultaneousness spectra database technique was tested: for the single samples, theaverage CDRs was96.0%at the division level, the CDRs was87.4at the genus level.The algae species such as Oc，Db，Ld，Rh，Ks，Pl，As, the CDRs of which were increase16.7,10，6.8，11.7，14.2，16.7，33.3percentage point. For the simulative samples whenthe algae dominance were60%,70%,80%,90%, the CDRs were89.3%,93.8%,96.1%and96.9%at the division level, with the average relative contents of58.4%,68.7%, 77.5%, and86.1%, respectively; the CDRs were64.7%,92.6%,93.8%and93.9%,respectively at the genus level. In which, Cf (60%dominance) and (60%dominance) Dbwere increase31.2and35.2percentage point, respectively.
3. For24particular field samples from Jiaozhou Bay and Enclosure experiment, the resultsof23samples were consistent with the microscopic examination at the division level;and5samples from8samples were successfully recognized at the genus level which thealgae dominance achieved80%of the total biomass.
4. For particular applications in Bohai sea and Jiaozhou Bay, it was possible to estimate thephytoplankton community composition and relative abundance of different classes, andthe whole results agree with that of published papers.The innovation of this paper is: Based on3-D fluorescence spectra of the phytoplankton
pigment extract, three kinds of mathematical method of wavelet, wavelet packet and2-Dwavelet analysis were synthetically applied to establish the pigment extract fluorescencediscrimination technique. The technique could meet the demand of the distant waters andanalyzing large amounts of membrane samples. Another innovation was based on the in vivofluorescence spectra of phytoplankton, three kinds of mathematical method of wavelet,wavelet packet and2-D wavelet analysis were synthetically applied to extract the featurespectra and the complementarities were studied. The complementary simultaneousdiscrimination technique was established finally and the CDRs were improved for somespecies especially for some red tide algae. The technique not only can discriminate algaepopulations at division level, but also can identify the algae species causing harmful algaeblooms(HAB) at genus level when HAB happens.
引文
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