Co-immobilized enzymes in magnetic chitosan beads for improved hydrolysis of macromolecular substrates under a time-varying magnetic field.

Glucoamylase and alpha-amylase co-immobilized with gamma ferric oxide powders in chitosan beads for consecutive starch liquefaction and saccharification under different magnetic fields was investigated. The chitosan concentration in the beads was found to greatly affect the immobilized enzyme performance. Superior immobilization efficiency and enzyme stability were noted when 2% instead of 4% chitosan was utilized. Using confocal microscopy and scanning electron microscopy, the beads with 2% chitosan were seen to exhibit a more rugged surface topology with more macropores and accommodate more protein near the external surface than with the 4% chitosan beads. An optimum loading ratio between alpha-amylase and glucoamylase exists that gives the highest glucose production, and this ratio varies with the size of the beads. The inclusion of the gamma ferric oxide powders renders the beads magnetically anisotropic and causes them to tumble under a single-phase alternating magnetic field, resulted in increased overall reaction rates. When exposed to a three-phase alternating magnetic field, these beads were stirred vigorously, also leading to enhanced reaction rates. The use of multi-enzyme co-immobilization in magnetic anisotropic chitosan beads may be extended to other practical applications that involve coordinated enzymatic reactions of macromolecular substrates.

Observer-based online compensation of inner filter effect in monitoring fluorescence of GFP-expressing plant cell cultures.

The green fluorescent protein (GFP) isolated from the jellyfish Aequorea victoria is a very useful reporter for real-time bioprocess sensing. GFP culture fluorescence is a composite signal that can be influenced by factors such as culture autofluorescence, inner filter effect (IFE), and photobleaching. These factors complicate accurate estimation of GFP concentrations from the culture fluorescence. IFE is especially problematic when using GFP in monitoring transgenic plant cell suspension cultures, due to the aggregated nature of the cells and the high biomass concentration in these culture systems. Reported approaches for online compensation of IFE in monitoring culture NADH fluorescence or bioluminescence require online measurement of biomass density or culture turbidity/optical density, in addition to fluorescence/bioluminescence measurement. In this study, culture GFP fluorescence was used successfully to estimate GFP concentration and other important states in bioreactor culture of transgenic tobacco cells, while the influences of IFE and culture autofluorescence were rectified without the need for an additional biomass sensor. This was achieved by setting up a novel model-based state observer. First, we developed an improved model for a backscatter fluorescence probe that takes into account the influence of IFE and autofluorescence on reporting culture GFP concentration from online fluorescence. The state observer was then established using the extended Kalman filter (EKF), based on the fluorescence probe model, a dynamic state model of the plant cell bioreactor, and online GFP fluorescence measurement. Several versions of the observer were introduced to address practical requirements associated with monitoring GFP fluorescence of plant cell cultures. The proposed approach offers an effective means for online compensation of IFE to enable quantitative interpretation of the culture fluorescence signals for accurate reporting of GFP or GFP-fusion protein expression.

State and parameter estimation of microalgal photobioreactor cultures based on local irradiance measurement.

Local photosynthetic photon flux fluence rate (PPFFR) determined by a submersible 4pi quantum micro-sensor was used in developing a versatile on-line state estimator for stirred-tank microalgal photobioreactor cultures. A marine micro-alga Dunaliella salina was used as a model organism in this study. On-line state estimation was realized using the extended Kalman filter (EKF), based on a state model of the photobioreactor and on-line local PPFFR measurement. The dynamic state model for the photobioreactor was derived based on mass-balance equations of the relevant states. The measurement equation was established based on an empirical correlation between the microalgal biomass concentration and the local PPFFR measured at a fixed point inside the photobioreactor. An internal model approach was used to estimate the specific growth rate without the need of state-based kinetic expression. The estimator was proven to be capable of estimating biomass concentration and specific growth rate, as well as phosphate and dissolved oxygen concentrations in a photobioreactor illuminated with either fixed or time-varying incident radiation. The quantum sensor was shown to be robust and able to quickly respond to dynamic changes in local PPFFR. In addition, the quantum sensor outputs were not affected by bubble aeration or agitation within the typical operating range. The strong filtering capacity of EKF gives the state estimator superior performance compared to direct calculation from the empirical biomass/local PPFFR correlation. This state estimation system makes use of inexpensive and reliable sensor hardware to report key process dynamics of microalgal photobioreactor cultures on-line, enabling improved operation of such a process.

A novel high-order associative memory system via discrete Taylor series.

This paper proposes a novel high-order associative memory system (AMS) based on the discrete Taylor series (DTS). The mathematical foundation for the new AMS scheme is derived, three training algorithms are proposed, and the convergence of learning is proved. The DTS-AMS thus developed is capable of implementing error-free approximation to multivariable polynomial functions of arbitrary order. Compared with cerebellar model articulation controllers and radial basis function neural networks, it provides higher learning precision and less memory request. Furthermore, it offers less training computation and faster convergence rate than that attainable by multilayer perceptron. Numerical simulations show that the proposed DTS-AMS is effective in higher order function approximation and has potential in practical applications.