Advanced Statistical Optimization for Enhanced Medium-Chain Fatty Acid Production

Abstract

In recent years, the development of feed ingredients with natural additives has gained significant importance in increasing the health and quality of animal products, as well as in promoting weight gain in animals. Since Salmonella infection is a significant disease that transmits from animals to humans, the inhibition of Salmonella species can be achieved particularly through the improvement of gastrointestinal metabolism in chickens. At this point, the effectiveness of using MCFA (Medium Chain Fatty Acids) as a feed additive has been proven. MCFA are composed of a mixture of various fatty acids, including acetic acid, butyric acid, hexanoic acid, etc. Highest portion of MCFA are hexanoic acid. Besides feed additives hexanoic acid play a crucial role as primary resources in various industries, including the chemical, food, agricultural, and biofuel sectors. It is typically obtained from petrochemical-based solutions but there has been a growing focus on biotechnological production and natural sources in recent years. One of the mostly known bioprocess to produce MCFA is chain elongation (conversion of acetate and ethanol into MCFA by β oxidation reaction) by Clostridium kluyveri. However, as in most biotechnological processes, there are low yields and high costs in these reactions as well. In this study, Box-Behnken Design, a statistical experimental design method, was used to optimize the concentrations of acetate, ethanol (the two primary components of chain elongation reactions) and pH for MCFA production via chain elongation reactions with Clostridium kluyveri. Batch experiments were performed at 30°C and 37°C to also see the effect of temperature. Higher values of hexanoic acid and bacterial growth were observed at 37°C. From an economic perspective, a 14% reduction in costs has been observed with optimized components.