Integration of spectroscopic techniques and machine learning for optimizing Phaeodactylum tricornutum cell and fucoxanthin productivity

[Display omitted] •Media formulations impacted Phaeodactylum tricornutum productivity.•Absorbance and 2D-fluorescence captured biological variance.•A comprehensive dataset was generated and machine learning models applied.•Spectroscopy-based models for monitoring P. tricornutum cultures were developed.•Robust and sensitive monitoring of P. tricornutum fucoxanthin and cell productivity. The development of sustainable and controlled microalgae bioprocesses relies on robust and rapid monitoring tools that facilitate continuous process optimization, ensuring high productivity and minimizing response times. In this work, we analyse the influence of medium formulation on the growth and productivity of axenic Phaeodactylum tricornutumcultures and use the resulting data to develop machine learning (ML) models based on spectroscopy. Our culture assays produced a comprehensive dataset of 255 observations, enabling us to train 55 (24+31) robust models that predict cells or fucoxanthin directly from either absorbance or 2D-fluorescence spectroscopy. We demonstrate that medium formulation significantly affects cell and fucoxanthin concentrations, and that these effects can be effectively monitored using the developed models, free of overfitting. On a separate data subset, the models demonstratedhigh accuracy (cell: R2 = 0.98, RMSEP = 2.41x106 cells/mL; fucoxanthin: R2 = 0.91 and RMSEP = 0.65 ppm), providing a practical, cost-effective, and environmentally friendly alternative to standard analytical methods.