Identification of heavy metal by testing microalgae using confocal Raman microspectroscopy technology.

Abstract

Five copper concentrations (0, 0.5, 1, 2, and 4 mg/l) were used to stress C. pyrenoidosa continuously for five days. The biomass, chlorophyll, and carotenoids of microalgae were measured, and Raman mapping spectral data and Raman single-point spectral data of microalgae were acquired. Principal component-linear discriminant analysis, back propagation-artificial neural network (BP-ANN), and sensitive wavelengths-linear discriminant analysis were used to build models to identify different copper concentrations using the spectral data after pretreatment. The results showed that the BP-ANN model was optimal to identify copper concentrations with prediction accuracy of 92% on day 4.