Model performance of partial least squares in utilizing the visible spectroscopy data for estimation of algal biomass in a photobioreactor

Abstract Spectroscopy technology and statistical methods (Partial Least Squares) have been integrated to develop a model that allows estimating the microalgal biomass in a photobioreactor. The model employing PLS combines the absorption spectrum measurements in the visible range (400–750 nm) with a microalgae cell density in a water sample. First, a calibration model was constructed using a calibration data set, and then, the predictive capacity of the model was determined by cross validation. Finally, an external validation of the predictive performance of the model was carried out with an independent data set. To test the accuracy of the model it was applied to different culture conditions yielding a predictive capacity of 96.7%. The results achieved are highly satisfactory due to the good lineal adjustment between observed cell densities vs. predicted ones obtained. According to the results obtained, the application of the model is a useful tool for the management and the decision-making process when operating a photobioreactor. Moreover, this model may boost the real-time measurements and may represent a previous step for further technical development in the “internet of things” applied to the management of the photobioreactor.