Mead fermentation parameters: Optimization by response surface methodology

Abstract

Introduction. This article presents the development of mathematical models related to the effect of the initial content of dry matter, yeast, and yeast energizer on the fermentation rate, the alcohol content, and the dry matter content in the finished product – mead.
 Study objects and methods. The mathematical models were developed by using the response surface methodology (RSM). The effect of yeast, dry matter, and yeast energizer contents were tested in concentration ranges of 150–600 mg/L, 16.3–24.4%, and 140–500 mg/L, respectively. The starting substrates used were honeydew honey and 10% apple juice. Yeast was rehydrated and added in different amounts to obtain required concentrations. Initial dry matter concentrations were measured by a refractometer. At the end of fermentation, oenological parameters of mead, namely dry matter content, pH, and ethanol yield, were determined according to standard methods.
 Results and discussion. The statistical estimation of the developed models and the individual model parameters showed that the initial dry matter content had a significant effect on the content of alcohol and dry matter in the final product. While, the initial content of yeast and yeast energizer did not have a significant effect in the tested concentration ranges. In addition, it was proved that the initial content of dry matter and yeast energizer had a significant effect on the fermentation rate, i.e. on the course of fermentation, which was described by a second-degree polynomial.
 Conclusion. We determined the optimum content of dry matter (24.4%), amount of yeast (150 mg/L), and concentration of yeast energizer (140 mg/L) in the initial raw material which provided the maximum alcohol yield at a consistent fermentation rate.

Systematic Approach to Optimize Technological and Economical Aspects of Atmospheric Plasma Sprayed Thermal Barrier Coatings

“…[ 23 ] For this reason, it is used to investigate and optimize processes in various fields of production, research, and engineering. [ 24 ] RSM is a collection of statistical techniques that can be used to achieve different objectives. These include 1) setting up a series of experiments (design) for the determination of the system response, 2) fitting a hypothetical model to experimentally received data, and 3) determining ideal combinations of the model's input variables, to optimize the system response.…”