{"title":"Differentiation of phytoplankton populations by in vivo fluorescence","authors":"Wang Xiu-lin","doi":"10.3788/CJL201239.0715003","DOIUrl":null,"url":null,"abstract":"In order to develop an in situ algae fluorescence auto-analyzer,the discriminating technology for phytoplankton populations was established by in vivo chlorophyll fluorescence excitation spectra composed of 12 excitation wavelengths(400,430,450,460,470,480,490,510,525,550,570,and 590 nm) of the algae occurring frequently in the coastal waters of China and a multivariate linear regression model.The linear regression model was solved by non-negative least squares.Some samples were tested.For 79 single division algal samples,96% samples were accurately discriminated,with recovery efficiency above 68%,and the recovery efficiency of 85% samples was above 80%.For 17 mixed division algal samples,76% samples were accurately determinated,with recovery efficiency above 74%.","PeriodicalId":258987,"journal":{"name":"Journal of Tropical Oceanography","volume":"197 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Tropical Oceanography","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3788/CJL201239.0715003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In order to develop an in situ algae fluorescence auto-analyzer,the discriminating technology for phytoplankton populations was established by in vivo chlorophyll fluorescence excitation spectra composed of 12 excitation wavelengths(400,430,450,460,470,480,490,510,525,550,570,and 590 nm) of the algae occurring frequently in the coastal waters of China and a multivariate linear regression model.The linear regression model was solved by non-negative least squares.Some samples were tested.For 79 single division algal samples,96% samples were accurately discriminated,with recovery efficiency above 68%,and the recovery efficiency of 85% samples was above 80%.For 17 mixed division algal samples,76% samples were accurately determinated,with recovery efficiency above 74%.