Bioprocess optimization and purification of DLA produced using food and fishery waste

Food waste is a sustainable and attractive waste biomass which can be utilized as a substrate for the production of value-added bioproducts. In this study, Y. lipolytica engineered for d-lactic acid (DLA) production was optimised for food waste hydrolysate (FWH) and fish protein hydrolysate (FPH). The substrate inhibition studies using FWH showed that after a 50 (g L^-1) concentration of glucose, there was a decline in both specific growth rate and DLA yield, and the Luong model with an R ² of 0.933 gave a better model fit. Nitrogen screening studies revealed that fish protein hydrolysate (FPH) could be an economical replacement for yeast extract. The Placket-Burman (PB) screening evaluation revealed that FWH, pH, and KH₂PO₄ were the key factors affecting DLA production. Furthermore, in central composite design (CCD) studies with optimal parameter levels, an 8.7% increase in DLA production was observed. Additionally, using an Artificial Neural Network (ANN)-linked Genetic Algorithm (GA), optimised parameter levels of FWH 49.98 (g L^-1), pH 8.52, and KH₂PO₄ 3.93 (g L^-1) were obtained, enhancing DLA production by 12.6% compared with the Plackett-Burman studies. Bioreactor studies with FPH as the only nitrogen source and FWH as the carbon source exhibited 0.94 (g g^-1) DLA yields, which were similar to the GA prediction. Kinetic modelling studies using MATLAB/Simulink showed that DLA production followed a mixed growth-dependent product-formation pattern. DLA purification using a butanol and ammonium sulfate solvent system yielded 92.7% recovery efficiency with glucose as the carbon source, and 83.51% recovery efficiency with FWH as the carbon source. Furthermore, characterization with HPLC, FTIR, and NMR reiterated the presence of DLA.