Abstract
In this paper, a rapid methodology to elucidate microalgae species in suspensions has been developed and validated. To do this, microalgae spectral signatures from light absorption measurements of different algal species were analysed through an artificial neural network (ANN) in order to describe and classify them. Four important species were used: Nostoc sp., Scenedesmus almeriensis, Spirulina platensis and Chorella vulgaris. Absorbance from monoalgal and mixed algal cultures was the input data for training, testing and validating the ANN. The results show that the ANN was capable of distinguishing between monoalgal and mixed algal cultures, identifying the microalgae species in the monoalgal cultures and providing the approximate composition of mixed algal cultures. These results confirm that the application of spectral signatures with ANN is a suitable method for approximating the biological composition of microalgae cultures.
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