Multiple bioanalytical method based residual biomass prediction in microbial culture using multivariate regression and artificial neural network

Optical density (OD) based measurements cannot be a reliable proxy for residual biomass concentration in polyhydroxyalkanoate (PHA) accumulating microbial culture, as cell size and morphology change during growth. In this study, four independent analytical methods i.e., OD measurement at 540 nm and 600 nm, tetrazolium reduction assay, and intracellular protein estimation were adopted to model residual biomass growth in Cupriavidus necator. The inter-day variation of calibration slope for residual biomass was significant (p < 0.001), and the regression coefficient (R2) of composite samples across the methods varied between 0.74 and 0.96. A reduced quadratic polynomial model (R2, 0.996; adjusted R2, 0.995, cross-validation R2 0.987) was chosen to predict residual biomass using multi-analytical measurements. Partial least square regression and variable selection suggested the inclusion of OD 540 nm and protein measurement into two retained latent variables, with R2 of 0.986, and adjusted R2 of 0.972. ANN model offered good predictability for residual biomass, showing close agreement of experimental and modelled datasets for training, validation, and test subsets with R2 of 0.998, 0.994, and 0.943, respectively. Analytical sensitivity of quadratic and PLS regression models were 0.058 and 0.164 respectively. A comparison of the proposed methods suggests that all three can be used as an alternative to dry-cell-weight-based residual biomass estimation, avoiding lengthy drying time and PHA extraction and measurement steps.

Automated summary

This study demonstrates that multiple analytical methods can be used to accurately predict residual biomass in Cupriavidus necator, overcoming the limitations of optical density measurements when cell size and morphology change.