Abstract
Although synthetic colorants are widely used in many industries due to their high stability at different conditions in industrial processes, evidence of its negative impact on health and the environment is undeniable. Filamentous fungi are well known for their use as alternative sources to produce natural pigments. However, an adequate comparison of the productivity parameters between the fermentation systems could be limited to their heterogeneous conditions. Even though Solid-State Fermentations (SSF) on natural substrates are widely used for pigments production, complex media, and non-controlled variables (T, pH, medium composition), these systems could not only hamper the finding of accurate productivity parameters, but also mathematical modeling and genomics-based optimization. In this context, the present study screened five pigment-producing fungi by comparing Submerged (SmF) and Surface Adhesion Fermentation [biofilm (BF) and Solid-State (SSF)] with defined media and controlled variables. For this purpose, we used the same defined media with sucrose as the carbon source for pigment production on SmF, BF, and SSF, and BF and SSF were carried out on inert supports. Five molecularly identified Penicillium and Talaromyces strains isolated from the Peruvian rainforest were selected for their ability to produce yellowish-orange colorants. Highest productivities were obtained from T. brunneus LMB-HP43 in SmF (0.18 AU/L/h) and SSF (0.17 AU/L/h), and P. mallochii LMB-HP37 in SSF (0.18 AU/L/h). Both strains also exhibited the highest yields (AU/g biomass) in the three fermentation systems, reaching values greater than 18-folds in SSF compared to the other strains. Conversely, T. wortmannii LMB-HP14 and P. maximae LMB-HP33 showed no ability to produce pigments in the SSF system. The performed experiments accurately compared the effect of the fermentation system on yield and productivity. From this, further genomics approaches can be considered for an extensive analysis of pigment synthesis pathways and a genomics-driven optimization in the best fermentation system.