Raw material quality assessment approaches comparison in pectin production.

Different opportunities are explored to evaluate quality variation in raw materials from biological origin. Assessment of raw materials attributes is an important step in a bio-based production as fluctuations in quality are a major source of process disturbance. This can be due to a variety of biological, seasonal, and supply scarcity reasons. The final properties of a product are invariably linked with the initial properties of the raw material. Thus, the operational conditions of a process can be tuned to drive the product to the required specification based on the quality assessment of the raw material being processed. Process analytical technology tools which enable this assessment in a far more informative and rapid manner than current industrial practices that rely on rule-of-thumb decisions are assessed. An example with citrus peels is used to demonstrate the conceptual and performance differences of distinct quality assessment approaches. The analysis demonstrates the advantage of characterization through multivariate data analysis coupled with a complementary spectroscopic technique, near-infrared spectroscopy. The quantitative comparative analysis of three different approaches, discriminant classification based on expert-knowledge, unsupervised classification, and spectroscopic correlation with reference physicochemical variables, is performed in the same dataset context. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2762, 2019.

A robust methodology for kinetic model parameter estimation for biocatalytic reactions.

Effective estimation of parameters in biocatalytic reaction kinetic expressions are very important when building process models to enable evaluation of process technology options and alternative biocatalysts. The kinetic models used to describe enzyme-catalyzed reactions generally include several parameters, which are strongly correlated with each other. State-of-the-art methodologies such as nonlinear regression (using progress curves) or graphical analysis (using initial rate data, for example, the Lineweaver-Burke plot, Hanes plot or Dixon plot) often incorporate errors in the estimates and rarely lead to globally optimized parameter values. In this article, a robust methodology to estimate parameters for biocatalytic reaction kinetic expressions is proposed. The methodology determines the parameters in a systematic manner by exploiting the best features of several of the current approaches. The parameter estimation problem is decomposed into five hierarchical steps, where the solution of each of the steps becomes the input for the subsequent step to achieve the final model with the corresponding regressed parameters. The model is further used for validating its performance and determining the correlation of the parameters. The final model with the fitted parameters is able to describe both initial rate and dynamic experiments. Application of the methodology is illustrated with a case study using the ω-transaminase catalyzed synthesis of 1-phenylethylamine from acetophenone and 2-propylamine.