Free-scale quantification of biomass and fucoxanthin in mass culture of Phaeodactylum tricornutum using Raman spectroscopy coupled with machine learning and transfer learning

Rapid, non-invasive monitoring technologies represent an advancement beyond labor-intensive assays for assessing biomass and bioactives in microalgal production. This study established an integrated Raman spectroscopy and machine learning framework for quantification of Phaeodactylum tricornutum biomass and fucoxanthin yield. Workflow combined spectral processing with multiple feature selection strategies and machine learning algorithms. Deep networks demonstrated superior biomass prediction accuracy on full-spectrum (coefficient of determination R 2 = 0.968; root mean squared error RMSE = 0.045 g L −1 ; relative prediction deviation RPD = 5.598), while kernel methods achieved robust fucoxanthin quantification through sparse, regularized features (R 2 = 0.949, RMSE = 1.112 mg L −1 , RPD = 4.408). Direct application of laboratory-trained models to pilot-scale photobioreactors was inaccurate due to domain shifts. Transfer learning effectively mitigated these shifts, restoring accuracy with minimal calibration efforts (10–20%). This work presents an industrially viable analytical tool for quality control and process optimization of microalgae-derived bioproducts.