Improved calibration of a solid substrate fermentation model

Background: Calibration of dynamic models in biotechnology is challenging. Kinetic models are usually complex and differential equations are highly coupled involving a large number of parameters. In addition, available measurements are scarce and infrequent, and some key variables are often non-measurable. Therefore, effective optimization and statistical analysis methods are crucial to achieve meaningful results. In this research, we apply a metaheuristic scatter search algorithm to calibrate a solid substrate cultivation model. Results: Even though scatter search has shown to be effective for calibrating difficult nonlinear models, we show here that a posteriori analysis can significantly improve the accuracy and reliability of the estimation. Conclusions: Sensibility and correlation analysis helped us detect reliability problems and provided suggestions to improve the design of future experiments.

Comparison of experimental methods for measuring infinite dilution volatilities of aroma compounds in water/ethanol mixtures.

Several experimental methodologies exist for measuring volatilities; however, results show great dispersion and sometimes lack of agreement between different methods. The aim of our study was to compare the performance of three static headspace methods (vapor phase calibration, VPC; phase ratio variation, PRV; and liquid calibration static headspace, LC-SH) for determining gas/liquid partition coefficients of two aroma compounds in hydroalcoholic multicomponent solutions at infinite dilution. Comparison with literature data based on static and dynamic methods showed that PRV is simpler than VPC and LC-SH and that VPC and PRV are more accurate than LC-SH, which presented a significant bias (50% lower values).