Chemical vs. biotechnological synthesis of C13-apocarotenoids: current methods, applications and perspectives.

Apocarotenoids are natural compounds derived from the oxidative cleavage of carotenoids. Particularly, C13-apocarotenoids are volatile compounds that contribute to the aromas of different flowers and fruits and are highly valued by the Flavor and Fragrance industry. So far, the chemical synthesis of these terpenoids has dominated the industry. Nonetheless, the increasing consumer demand for more natural and sustainable processes raises an interesting opportunity for bio-production alternatives. In this regard, enzymatic biocatalysis and metabolically engineered microorganisms emerge as attractive biotechnological options. The present review summarizes promising bioengineering approaches with regard to chemical production methods for the synthesis of two families of C13-apocarotenoids: ionones/dihydroionones and damascones/damascenone. We discuss each method and its applicability, with a thorough comparative analysis for ionones, focusing on the production process, regulatory aspects, and sustainability.

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.