Statistical Approaches for the Optimization of Parameters for Biotechnological Production of Xylitol

Abstract

Statistics is a fundamental tool in the analysis of any process data where there is variability. There are many ways to approach the problem of optimization and design of a process, which can be handled quickly using a number of statistical techniques. Statistical design of experiments is a mechanism of data collection appropriate to study the biotechnological process, like xylitol production. Several fermentation processes have been optimized using response surface methodology. However, one of the major problems to the researcher is identifying the independent variables that influence the study in order to explain the model which best represents the process. The upstream independent variables studied in the statistical design for fermentation processes are aeration rate, temperature, phosphate level, back pressure, carbon source, pH, power input, agitation rate, carbon/nitrogen ratio, nitrogen source and dissolved oxygen level. The statistical approach for biotechnological production of xylitol from lignocellulosic materials also could be helpful to optimize pretreatment of lignocellulosic biomass, conditioning of hemicellulosic hydrolysates and xylitol recovery from fermented hydrolysates. This chapter will provide an overview on the state of knowledge in these areas focus on statistical approaches.