Abstract
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.
Highlights
In recent decades, occurrences of harmful algae blooms (HABs) have increased dramatically, causing serious ecological damage and economic loss
Fluorometers are commonly used in many fields of detection, and photosynthetic pigments are one of the major molecules that can be found in the ocean
These pigments have different fluorescence efficiencies when excited by light of different wavelengths, which is indirectly reflected in the fluctuations in excitation fluorescence spectra
Summary
Occurrences of harmful algae blooms (HABs) have increased dramatically, causing serious ecological damage and economic loss. The monitoring of HABs has become an environmental concern worldwide [1]. More than 300 species have been reported to cause HABs, approximately 80 of which are toxic [2]. The rapid and accurate identification of causative species, toxic species, is of great importance for better management and controlling HABs. Today, many techniques are in use for monitoring HABs, classified as imagebased [5], fluorescence-based [6,7,8] and molecular-based technologies [9]. Image recognition technology is the most commonly used technology since the invention of the first microscope.
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