Machine Learning Modeling for Ultrasonication-Mediated Fermentation of Penicillium brevicompactum to Enhance the Release of Mycophenolic Acid

Described here is the modeling used to improve the mycophenolic acid (MPA) titer from Penicillium brevicompactum using central composite design and a comparatively newer, data-centric approach method k-nearest-neighbor algorithm. The two models for enhancing MPA production using P. brevicompactum were compared with respect to ultrasonic stimulation. During the ultrasonic treatment, we studied different independent factors such as ultrasound power, irradiation duration, treatment frequency and duty cycle to determine their ability to enhance the MPA titer value. The optimized factors such as a treatment time of 10 min (50% duty cycles) with a 12-h interlude at fixed ultrasonic power and frequency (200 W, 40 kHz) were used for ultrasonic treatment of a mycelial culture from the 2nd to 10th day of fermentation. Thus the production of MPA was improved 1.64-fold under the optimized sonication conditions compared with the non-sonicated batch fermentation (non-optimized conditions).