Raman spectroscopic quantification of phycocyanin and process metadata-based data fusion for dry cell weight in...

Rapid and non-destructive quantification of intracellular target products is crucial for rapid at-line process control in modern microalgae biorefineries. This study established a small-sample (50 independent biological samples) quantitative monitoring framework based on Raman spectra for live Spirulina platensis cells. For intracellular phycocyanin (PC) content quantification, a linear combination of raw full-range spectra, regression coefficients (RC) feature selection, and partial least squares regression (PLSR) delivered the optimal performance, achieving a prediction coefficient of determination (R²) of 0.944 and a relative prediction deviation (RPD) of 4.22. To further extend this monitoring framework to the macro-biomass indicator, dry cell weight (DCW) quantification was simultaneously evaluated. Pure Raman spectral signals, however, encountered non-linear matrix interferences for DCW (RPD < 2.00). To overcome this bottleneck, a multi-source information fusion strategy combining process metadata (strain, light, and cultivation time) side-by-side with optimized spectral features into a fused input matrix was developed. The resulting non-linear Savitzky-Golay (SG) smoothing, successive projections algorithm (SPA) feature selection, and support vector regression (SVR) framework significantly improved the R² and RPD for DCW to 0.937 and 3.98, respectively. Furthermore, Shapley Additive exPlanations (SHAP) analysis successfully demystified the model's black-box nature, revealing that cultivation time exerted a dominant contribution (mean absolute SHAP value = 0.855) to DCW accumulation. This strategy reduces the reliance on massive databases, offering a highly transparent, low-sample-cost, and lightweight novel solution for implementing intelligent microalgae process analytical technology.