Deep learning with multimodal Raman spectral fusion framework: An analytical approach for microalgal lipid quantification.

As a key feedstock for sustainable bioenergy, microalgae require precise regulation of lipid synthesis and accurate detection methods. To efficiently induce lipid accumulation while minimizing chemical intervention, this study employed multi-frequency ultrasound as a stressor. Results demonstrated that the 28 kHz treatment significantly enhanced lipid content by 16.45% compared to the control group, confirming that cavitation effects can optimize lipid synthesis flux. For precise, green, and dynamic regulation of single-cell lipid accumulation, and elucidation of the mechanism underlying ultrasound-induced lipid synthesis in microalgae, this study innovatively proposes a strategy integrating deep learning with multimodal Raman mapping. Specifically, a Dual-Branch Attention-based Convolutional Neural Network (DBACNN) was constructed to fuse Raman spectral data with microalgal cell RGB images. Utilizing Competitive Adaptive Reweighted Sampling (CARS) to select 262 characteristic bands, the DBACNN model exhibited outstanding performance on the prediction set. It achieved a coefficient of determination (R2) of 0.9548, representing an improvement of 5.78% to 10.73% over traditional Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models. Furthermore, the root mean square error (RMSE) was reduced to 0.0052, which was significantly outperforming the conventional models. This study establishes a closed-loop "stress-detection-feedback" system, facilitating real-time the adjustment of ultrasonic parameters based on acquired data during industrial-scale production. This enables intelligent and precise regulation of lipid synthesis, offering significant advantages in enhancing lipid yield, ensuring process sustainability, and meeting industrial requirements. Collectively, this work provides an innovative technological pathway for the large-scale production of microalgal biofuels and high-value lipids.