Rapid in situ measurements of algal cell concentrations using an artificial neural network and single-excitation fluorescence spectrometry

Abstract

Accurate measurements of the algal cell concentration are very important in microalgae culturing and ecological monitoring. To realize an automatic, in situ measurement of the cell concentration of microalgae and to reduce the measurement cost, a detection method combining single-excitation fluorescence spectroscopy and an artificial neural network (ANN) was developed to monitor the cell concentrations of Chlamydomonas reinhardtii in the range of 2 × 105 to 6.4 × 106 mL−1 cells mL−1. Using a 470 nm wavelength light emitting diode (LED) as a light source, samples with different concentrations of Chlamydomonas reinhardtii were electronically excited. The measured fluorescence emission spectra were used as input, and the algal cell concentration was the output. Because there is a nonlinear relationship between the input and the output, a Back Propagation Neural Network Model Optimized by Genetic Algorithms (GA-BP) was established to predict the cell concentration. Then the model was validated by using samples from different growth batches. In addition, the GA-BP model was compared with the existing algae cell concentration detection methods (Back Propagation Artificial Neural Network), and it was found that the GA-BP model was more accurate. Moreover, the equipment used for this method is simple and easy to carry and install. The combination of single-excitation fluorescence spectrometry and an artificial neural network provides a feasible and cost-effective tool for algal cell concentration monitoring.