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

Abstract

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 R² 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.