Fluorometric discrimination technique of phytoplankton population based on wavelet analysis

Zhang, Shanshan; Su, Rongguo; Duan, Ying; Zhang, Cui; Song, Zhijie; Wang, Xiulin

doi:10.1007/s11802-012-1890-1

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three-dimensional (3D) fluorescence spectra are often referred to as the fingerprint of target species because of their unique excitation and emission wavelengths. Three dimensional fluorescence was successfully utilized for identifying microalgae at the phyla level under variable circumstances [16]. Chlorophyta, Cyanobacteria, Heterokontophyta, Haptophyta, Dinophyta, and Cryptophyta were identified by the first submersible fluorometer, using multiple linear regression as calculation and calibration method [17].…”

Section: Introductionmentioning

confidence: 99%

Rapid Algae Identification and Concentration Prediction Based on Discrete Excitation Fluorescence Spectra

Shan

Wang

et al. 2021

Chemosensors

View full text Add to dashboard Cite

In this paper, an algal identification and concentration determination method based on discrete excitation fluorescence spectra is proposed for online algae identification and concentration prediction. The discrete excitation fluorescence spectra of eight species of harmful algae from four algal categories were assessed. After determining typical excitation wavelengths according to the distribution of photosynthetic pigments and eliminating strongly correlated wavelengths by applying the hierarchical clustering, seven characteristic excitation wavelengths (405, 435, 470, 490, 535, 555, and 590 nm) were selected. By adding the ratios between feature points (435 and 470 nm, 470 and 490 nm, as well as 535 and 555 nm), standard feature spectra were established for classification. The classification accuracy in pure samples exceeded 95%, and that of dominant algae species in a mixed sample was 77.4%. Prediction of algae concentration was achieved by establishing linear regression models between fluorescence intensity at seven characteristic excitation wavelengths and concentrations. All models performed better at low concentrations, not exceeding the threshold concentration of red tide algae outbreak, which provides a proximate cell density of dominant algal species.

show abstract

Section: Introductionmentioning

confidence: 99%