High-yield astaxanthin production process development and scale-up validation from wild-type Phaffia rhodozyma via parameter optimization and LSTM modeling

Abstract

Introduction: This study developed an integrated strategy to significantly enhance astaxanthin production from wild-type Phaffia rhodozyma GDMCC 2.218, addressing the need for improved natural astaxanthin yields through non-genetically modified approaches. Methods: The research combined traditional parameter optimization with LSTM (Long Short-Term Memory) intelligent modeling. Systematic optimization of fermentation conditions was conducted in 500 mL bioreactors, followed by scale-up to 5 L systems. An innovative LSTM prediction model was constructed to predict astaxanthin concentration throughout the fermentation process. Results: Optimal fermentation conditions were determined as temperature 20°C, pH 4.5, and dissolved oxygen 20%, achieving an astaxanthin yield of 387.32 mg/L within 144 hours in 500 mL bioreactors. Upon scale-up to 5 L, the yield improved to 400.62 mg/L within 165 hours, demonstrating process robustness. The LSTM prediction model showed excellent performance with R2 = 0.978. The achieved yields represented a 10- to 20-fold improvement over previously reported wild-type strain levels and reached or surpassed the production levels of most engineered strains. Discussion: This research confirms the feasibility of achieving commercial-scale production of high-value natural astaxanthin through non-genetically modified approaches. The resulting product combines high productivity, safety, and regulatory advantages, providing an innovative solution for industrial-scale natural astaxanthin production that offers significant commercial potential.