Identification of microalgae by hyperspectral microscopic imaging system

Harmful algal blooms can lead to serious environment problems, and thus monitoring and classifying microalgae have received increasing attention. Microcystis aeruginosa, Chlorella pyrenoidosa, and Nannochloris oculata all are small in size and have a similar morphology, which lead to discriminant difficulties using a traditional optical microscope. In this experiment, an independently developed hyperspectral microscopic imaging system was used to obtain the hyperspectral images of microalgae samples, and a hyperspectral dataset was developed through pretreated steps. Then the Fisher algorithm was employed to identify the species of the microalgae, and its sensitivity and specificity were found to be high. The result demonstrated that this method can classify the microalgae in a quick and convenient manner; in addition, the quantity and spatial information of the samples can be acquired.