The Response Surface Methodology (RSM) is a statistical/mathematical approach widely used to determine the impact of experimental parameters in the optimization of biotechnological processes. This technique allows to obtain level curves on the basis of linear, quadratic and interactional effects generated by two or more parameters in order to calculate the optimal response of the system. Within fermentation processes using microorganisms, the RSM explores the relationships between experimental variables (independent variables: physico-chemical parameters) and one or more response variables (dependent variable: products of the microbial metabolism), through the definition of a polynomial, not linear, model.

In order to develop new processes aimed at the production of new molecules from yeasts, the DBVPG Collection used the RSM for the definition and validation of the best combination of physico-chemical parameters capable of optimizing: (i) the production of volatile compounds, (ii) the composition of carbohydrates present in substrates obtained from waste biomass for the production of lipids, and (iii) the qualitative parameters of low carb and low alcoholic beer.

G. Tasselli, S. Filippucci, S.  D’Antonio, G.   Cavalaglio, B. Turchetti, F. Cotana , P. Buzzini (2019) Optimization of   enzymatic   hydrolysis   of   cellulosic   fraction   obtained   from stranded driftwood feedstocks for lipid production by Solicoccozyma terricola. Biotechnology Reports, 7;24:e00367; DOI: 10.1016/j.btre.2019.e00367.

A. Troilo, G. De Francesco, O. Marconi, V. Sileoni, B. Turchetti, G. Perretti (2019) Low Carbohydrate Beers Produced by a Selected Yeast Strain from an Alternative Source. Journal of the American Society of Brewing Chemists, 78(1):80; DOI: 10.1080/03610470.2019.1682887.