Title: Process parameter optimization for enhancing pigment productivity in chlorella vulgaris
Abstract:
Microalgae are recognized as promising biofactories for producing a range of industrially valuable and economically significant products. Microalgal cultivation for high-value pigments often suffers from economic bottlenecks due to low productivity under standard conditions. This study focuses on Chlorella vulgaris, as it is one of the most promising industrial microalgal species, because it has high photosynthetic efficiency, robust cellular makeup, and produces high-value tetraterpenoids, primarily lutein, β-carotene, violaxanthin, and zeaxanthin. The present study aims to improve pigment productivity of the wild-type microalga C. vulgaris via optimization of the growth parameters. The impact of independent parameters like culture medium pH, salinity, light intensity, and photoperiod regimes was screened. The results indicate that precise optimization of these single parameters can produce a large metabolic shift in the wild-type strain. Upon this single-factor optimization of C. vulgaris, an increase of 1.4-fold was observed in the pigment productivity compared to the un-optimized control. This result provides evidence that large productivity improvements can be achieved through optimizing a few physical parameters. These single-parameter changes are a great first step to maximizing natural output, but there is a limit to process optimization. The future work focuses on the genetic engineering of this C. vulgaris strain to overcome these biological limits. This study will allow us to target specific metabolic pathways to engineer regulatory bottlenecks that will help increase pigment productivity for industrial and commercial applications. In addition to the genetic modifications, process parameter optimization needs to be performed to achieve improved carotenoid accumulation without significantly hampering the biomass concentration.
Keywords: Biomass; Chlorella vulgaris; Genetic modification; Pigment productivity; Single-parameter optimization.
