A kinetic-assisted growth curve prediction method for Chlamydomonas reinhardtii incorporating transfer learning.

Traditional predictions of microalgal growth states rely on empirical or easily implementable kinetic models, leading to significant biases and elevated cost. This study proposes a kinetic-assisted machine learning method for predicting the growth curve of microalgal biomass under small sample conditions. Firstly, a microalgae growth kinetic model is constructed based on the logistic model. A two-stage kinetic fitting strategy is specified to account for the light-dark ratio. The Box-Behnken method is employed for experimental design. Then, using Two-stage TrAdaboost.R2 algorithm, the kinetic model is utilized as the source domain, and the experimental design data serves as the target domain for training machine learning models. The results indicate that the proposed method outperforms a single machine learning model in terms of prediction and has the potential to rapidly estimate microalgal growth trends under different conditions and accurately predict harvested biomass, potentially reducing the need for laborious, expensive, and time-consuming laboratory trials.